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Tenology in Physical Activity and Health Promotion
As tenology becomes an ever more prevalent part of everyday life and population-based physical activity programmes seek new ways to increase lifelong engagement with physical activity, so the two have become increasingly linked. is book offers a thorough, critical examination of emerging tenologies in physical activity and health, considering tenological interventions within the dominant theoretical frameworks, exploring the allenges of integrating tenology into physical activity promotion and offering solutions for its implementation. Technology in Physical Activity and Health Promotion occupies a broadly positive stance toward interactive tenology initiatives and, while discussing some negative implications of an increased use of tenology, offers practical recommendations for promoting physical activity through a range of media, including: social media mobile apps global positioning and geographic information systems wearables active video games (exergaming) virtual reality seings. Offering a logical and clear critique of tenology in physical activity and health promotion, this book will serve as an essential reference for upperlevel undergraduates, postgraduate students and solars working in public
health, physical activity and health and kinesiology, and healthcare professionals. Zan Gao is a faculty member at the Sool of Kinesiology in the University of Minnesota-Twin Cities, USA, specializing in physical activity and health. His resear has primarily focused on promoting health through populationbased physical activity interventions with emerging tenologies, su as active video games, online social media and mobile device apps.
Routledge Resear in Physical Activity and Health
e Routledge Research in Physical Activity and Health series offers a multidisciplinary forum for cuing- edge resear in the broad area of physical activity, exercise and health. Showcasing the work of emerging and established solars working in areas ranging from physiology and ronic disease, psyology and mental health to physical activity and health promotion and socio- economic and cultural aspects of physical activity participation, the series is an important annel for ground-breaking resear in physical activity and health. Physical Activity and the Gastro- Intestinal Tract Responses in Health and Disease Roy J. Shephard
Tenology in Physical Activity and Health Promotion Edited by Zan Gao
Tenology in Physical Activity and Health Promotion
Edited by Zan Gao
First published 2017 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 711 ird Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2017 selection and editorial maer, Zan Gao; individual apters, the contributors e right of Zan Gao to be identified as the author of the editorial maer, and of the authors for their individual apters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, meanical, or other means, now known or hereaer invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice:
Product or corporate names may be trademarks or registered trademarks,
and are used only for identification and explanation without intent to infringe. British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data
A catalog record for this book has been requested ISBN: 978-1-138-69576-4 (hbk) ISBN: 978-1-315-52617-1 (ebk) Typeset in Times New Roman by Wearset Ltd, Boldon, Tyne and Wear
Contents
List of illustrations Notes on contributors Preface
PART I Introduction to emerging tenology and physical activity health 1 Foundations of tenology and health effects of physical activity ZAN GAO, ZACHARY POPE, AND NAN ZENG Understanding the foundations of technology Understanding the underlying science behind health benefits of PA Application of emerging technologies in promoting PA and health
2 Overview: promoting physical activity and health through emerging tenology HAICHUN SUN, NAN ZENG, AND ZAN GAO Traditional technology in PA promotion Emerging technology and its applications in PA promotion Opportunities and challenges
3 Social and behavioral theories in promoting physical activity ZAN GAO AND JUNG EUN LEE Personal-level theoretical perspectives and PA promotion Behavior micro-environment theoretical perspectives and PA promotion Behavior macro-environment theoretical perspectives and PA promotion
Application of behavior theories using emerging technologies Opportunities and challenges moving forward
PART II Emerging tenologies in physical activity and health 4 Computer and Internet use in enhancing physical activity JUNG EUN LEE AND ZAN GAO Components of Internet-based PA interventions Effectiveness of Internet-based PA interventions Behaviors targeted and theories used Assessment of PA in Internet-based interventions Internet-based PA interventions in the healthcare field Internet-based PA interventions among various populations Opportunities and challenges Practical implications
5 Online social media and physical activity promotion JUNG EUN LEE AND ZAN GAO Implementation of social media-based interventions on behavior change Social media-based PA interventions in the healthcare field Effectiveness of social media-based PA interventions Negative aspects of online social media Practical implications Directions for future studies
6 Mobile device apps in enhancing physical activity ZACHARY POPE AND ZAN GAO mHealth apps and health promotion in various populations Application of mHealth apps in various contexts Application of mHealth apps in the healthcare field Use of mHealth apps in Big Data analysis Application of mHealth apps Practical implications
7 Global positioning systems and geographic information systems and physical activity ZACHARY POPE AND ZAN GAO GPS/GIS and health promotion in various populations GPS/GIS and different intensity levels and types of PA Application of GPS/GIS in different contexts Application of GPS/GIS in the healthcare field Use of GPS/GIS in developing effective PA interventions Practical implications of GPS/GIS use in promoting health
8 Health wearable devices and physical activity promotion NAN ZENG AND ZAN GAO Wearable devices in assessing and promoting PA Health wearable devices and different PA intensities Using wearable devices to collect data, process data, and interpret feedback Application of wearable devices in developing PA interventions Practical implications
9 Active video games and physical activity ZAN GAO, NAN ZENG, AND ZACHARY POPE Active video games and health promotion in various populations Effects of active video games on physical activity and health-related outcomes Application of active video games in different contexts Application of active video games in the healthcare field Augmented reality games and active video games Practical implications
10 Virtual reality in physical activity promotion NAN ZENG AND ZAN GAO Application of virtual reality in different PA settings
Application of virtual reality technology in rehabilitation Practical implications
PART III Applications for physical activity and health promotion 11 Negative aspects of emerging tenologies in physical activity promotion ZACHARY POPE AND ZAN GAO Negative aspects and limitations of emerging technologies Challenges for researchers and clinicians
12 Emerging tenologies in promoting physical activity and health ZAN GAO Adoption of social and behavioral theories in promoting PA Emerging technologies for PA assessment and intervention The Internet of health things Cross-technology issues Promote and model digital citizenship Index
Illustrations
Figures 1.1 Two kids playing active video games 1.2 Smart wates validation test 1.3 Use of a smart wat in health promotion 1.4 A kid playing soccer video games 1.5 Kids playing Pokémon Go 1.6 A group of kids playing active video games 1.7 Application of virtual reality CAVE 2.1 Young kids playing Kinect Just Dance 2.2 Testing smart wates 2.3 Facebook social media 2.4 A kid playing Pokémon Go 2.5 Young adults playing active video games 2.6 Active bike game 2.7 A young adult playing virtual reality games 3.1 Kids playing active video games 3.2 eory of Planned Behavior 3.3 Social Cognitive eory 3.4 Elite athletes playing active video games 4.1 World Wide Web 4.2 Internet use in physical activity promotion 5.1 Social network on mobile devices 5.2 Online social media on a mobile device 5.3 Online social media on a computer
6.1 Big Data in GPS and GIS 6.2 GPS traing in biking 7.1 Application of a GPS-embedded physical activity app in hiking 7.2 Pokémon Go app outdoors 7.3 Traing physical activity with a mobile phone app 7.4 A researer teaing a patient to use a smartphone physical activity app 8.1 e use of a smart wat in traing 8.2 Synronizing a smart wat with a smartphone 8.3 Testing smart wates 8.4 Validation of smart wates 9.1 Elite athletes playing active video games 9.2 Young adults playing active video games 9.3 Kids playing active video games 9.4 Kids playing active video games 9.5 Kids playing active video games 9.6 Kids playing active video games 9.7 Kids playing an active video game 9.8 Elite athletes playing active video games 9.9 Kids playing active video games 9.10 Young adults playing active video games 9.11 Young adults playing active video games 9.12 A kid playing Pokémon Go 9.13 Elite athletes playing active video games 9.14 Kids playing active video games 9.15 Kids playing active video games 10.1 An adult playing a virtual reality active game 10.2 From the lens of a virtual reality game 10.3 Experiencing virtual reality in CAVE 10.4 Playing an augmented reality game––Pokémon Go 11.1 Designing websites for Internet-based intervention 11.2 Playing video games 11.3 Application of a smart wat in health promotion 11.4 Combination of a mobile device and a computer
12.1 Robot, the future direction in tenology in promoting health 12.2 Young ildren playing active video games 12.3 Young ildren playing active video games 12.4 A kid playing Pokémon Go
Table 8.1 Selected devices and descriptions
Contributors
Zan Gao is a faculty member at the Sool of Kinesiology in the University of Minnesota-Twin Cities, USA, specializing in physical activity and health. His resear has primarily focused on promoting health through population-based physical activity interventions with emerging tenologies su as active video games, online social media and mobile device apps. Gao has been the principal investigator of National Institute of Health resear grants, and of the Robert Wood Johnson Foundation Grant. He has published more than 100 resear articles in peerreviewed journals, and book apters. Jung Eun Lee is an Assistant Professor at the University of Minnesota at Duluth, USA. Her resear interests are the psyological correlates of physical activity, tenology-based physical activity promotion, and motor skill enhancement. Zaary Pope is a researer focused on the use of smartphone applications, wearable te, GPS/GIS, and active video games in health promotion across diverse populations. Other interests include epidemiological studies of the antecedents/correlates of physical activity participation. Haiun Sun is an Associate Professor at the University of South Florida, USA. Her resear focuses on ildren and adolescent physical activity program design and evaluation as related to the physical, cognitive, and motivational domains.
Nan Zeng is a researer focused on the behavior aspects of physical activity, su as physical activity intervention and health promotion in a diverse array of populations using modern tenology, including but not limited to active video games, fitness wristband, and smartphone applications.
Preface
In the past decades, resear has documented the numerous benefits of regular physical activity participation, su as preventing obesity and diabetes, treating mental problems, preventing falls among the elderly, and decreasing ronic heart diseases. However, nowadays, most young people and adults are not active regularly, and in many countries they tend to discontinue structured physical activity programs, especially those underserved populations with limited resources in the developed countries. Given the fact that many physical activity interventions were not as effective as expected in the past, population-based physical activity programs (i.e., working site, aer-sool programs) have taken an approa more aligned with public health in recent years. With this public health emphasis has come the sear for activity modalities whi can impact individuals’ lifelong physical activity participation and physical activity determinants, su as self-efficacy and social support. As tenology continues to become ever more pervasive in our daily lives, and is of great interest to this generation, professionals have begun to integrate emerging tenology not only into population-based physical activity programs to promote health, but also into assessment methods of individuals’ physical activity, fitness, and health. Early integration of tenology within the population-based physical activity programs involved video cameras and audio players, but the advent of new interactive tenology including new generations of active video gaming (a.k.a., exergaming; e.g., Wii, Xbox), smartphone/tablet-based applications (a.k.a., apps), global positioning systems (GPS), among others, has offered new opportunities for studying and applying this tenology to
the field of physical activity and health. For example, new generations of active video games have seen a rapid rate of growth in physical activity seings over the past decade. We currently live in a digital world— encompassed by tenology in a diverse array of seings. In fact, the current youth generation’s leisure time is increasingly spent on mobile devices, using apps, playing video games, texting, and perusing social media. It is almost as if the majority of our leisure time is consumed interacting with screen-based tenology. erefore, it is only logical to ask how the omnipresence of screen-based tenology impacts our lifestyles, health, and fitness. Further, what does this tenology-laden environment mean with regard to exercising in a digital world? In response, this book has been wrien to help the audience exercise regularly with the use of emerging tenologies, in addition to exploring the allenges of integrating new interactive and emerging tenology in promoting physical activity and health—addressing core problems and offering practical and meaningful implications for health professionals. Developing emerging tenology to promote physical activity participation, while also studying human-tenology interaction in physical activity and health, has the potential to have a clear and important impact on improving the effectiveness of physical activity and healthcare programs—ultimately improving the health status and quality of life among various populations. In summary, Technology in Physical Activity and Health Promotion offers novel contributions in the following ways: 1. is book will critically scrutinize the latest development of emerging tenologies in the past decade, and will demonstrate the important role that emerging tenologies play in a grand societal allenge toward improved health and wellbeing. 2. Instead of concentrating on the potentially negative health impact and risks a digital world poses, this book will counter with information on how to integrate apps, active video games, social media, and other tenologies in a manner capable of promoting physical activity and health. is book will provide individualized
physical activity prescriptions capable of being adopted in and integrated with a digital world—steering away from prescribing a blanket solution to the integration of emerging tenology in promoting health and physically active lifestyles. 3. is book features a unique assemblage of several widely used theoretical frameworks in promoting physical activity and behavioral anges relevant to tenology interventions, including the Self-efficacy eory, the Self-determination eory, the Transtheoretical Model, Social Cognitive eory, and the Social Ecologic Model. 4. Although academic in nature, this book will still be wrien in an inclusive manner for use by solars, graduate/undergraduate students, and healthcare professionals.
Part I Introduction to emerging tenology and physical activity health
1 Foundations of tenology and health effects of physical activity Zan Gao, Zachary Pope, and Nan Zeng
Take a look around the room in whi you are currently siing. What do you see? Most likely, you see a smartphone, maybe a tablet and/or laptop, and a television. Maybe you are a fan of video games and have a gaming console in the room as well. Regardless of the presence of one or more of these items in your room, however, all of these tenologies serve various purposes in your life. Your smartphone and tablet/laptop allow you to connect with friends and do work. Conversely, your television and gaming console allow for entertainment and relaxation. Indeed, these tenologies, together with hundreds of thousands of other tenological innovations, provide you with unprecedented access to the world around you and make life easier in innumerable ways. Notably, the aforementioned tenologies, as well as several others, are currently being used to promote physical activity (PA) participation and improved health outcomes in numerous populations and seings. roughout this book, readers will be treated to a comprehensive discussion of emerging tenologies currently being used in PA and health promotion. Some of these tenologies one might have guessed would be used for health promotion (e.g., a health wearable like a Fitbit) while other tenologies might be novel to the reader in their use as public health tools
(e.g., using virtual tenology to create an artificial world in whi the player can be more active). Whether you are a student, health professional, or solar, it is hoped that the content of this book will inform your future career direction, current practice, or future resear, respectively. at said, it is important for readers to understand the foundations of tenology and how some of the most frequently used emerging tenologies in the promotion of PA and health were developed. As su, the first section of this apter will devote itself to a brief review of the history of these tenologies. Following the preceding discussion, the remainder of this apter will concentrate on the physiological and psyological effects of PA before ending with an overview of the importance of mastering the effective implementation of these tenologies in the community health and medical fields (Figure 1.1). erefore, sit ba and wat as tenology meets health!
Figure 1.1
Two kids playing active video games.
Source: Photo by Zan Gao.
Understanding the foundations of tenology A remarkable evolution in twenty-first-century tenology is currently being observed. Notably, however, the eighteenth through the twentieth centuries also experienced rapid tenological advances. Indeed, the Industrial Revolution from the mid-eighteenth century to the start of the twentieth century necessitated advances in tenology as business and commerce were beginning to increase within and between more developed countries—resulting in the need for beer communication, transportation, and power sources, among other tenologies (Buanan, 2016). Yet, as Buanan (2016) highlights, it was nearer 1900 when tenology started to become fully realized as a crucial part of civilized life in developed countries. As a result of this realization, the 20th century up to the present day has been filled with tenological milestones—fueled in large part by the increasing globalization of our world (i.e., the need to do business and connect with individuals around the globe) and the desire to improve the health and well-being of the world’s population. While many tenological milestones during the twentieth century could be highlighted, for the purposes of this book, only a select group will be concentrated on below given these tenologies’ current use in PA and health promotion.
e computer, the Internet, social media and pertinent products Given the great integration of computers in our daily life, it is easy to forget just how recent an innovation computers are. From traditional personal
computers/laptops to computers’ placement within our smartphones and even many household appliances, computers have revolutionized the way we interact and obtain information in addition to how we carry out our daily tasks. In the mid- to late-1940s, Alan Turing was the first to develop the concept of the modern digital computer, later joining the National Physical Laboratory where he began to develop the prototype for this computer (Turing, 1937, 1946). It was not until several years later in 1952, however, with the first description of the integrated circuit by Geoffrey Dummer (Dummer, 1978), that the age of the digital computer really began to progress. Briefly, the integrated circuit Dummer proposed and developed made the microips in present-day computers and other electronic devices (e.g., smartphones, video game consoles) possible. By the 1980s, computers as we know them today were becoming more common with companies like Microso and Apple developing and selling progressively smaller computers to the general consumer (Computer History Museum, 2016). However, the computers of the 1980s were used mainly for text and data-processing purposes. It was not until nearly 1990 with advent of the World Wide Web, whi allowed individuals to connect on what is more commonly known as the Internet, that computers began to gain capabilities whi have forever anged our world (Pew Resear Center, 2014). In the early 1990s, the Internet was used mainly for communication, with companies like AOL creating the first forms of email and messenger services. It was not until the mid- to late-1990s that large sear engines su as Yahoo and Google became available—adding another dimension to the Internet as users could now sear and find any information they desired with a few keystrokes (Pew Resear Center, 2014). Towards the turn of the century, however, computer and Internet tenology were becoming advanced enough to allow for the streaming of music and videos, offering users more than just text-based mediums by whi to gather information and interact with one another. Indeed, in 2003, MySpace, the first widely used social media site launed. By 2005, Facebook and Twier had launed as well (Terrel, 2015). Ea of these sites had features whi allowed users to post text, audio forms, and video-based forms of media,
read national and world news, and interact with friends via embedded groups and messaging services. Notably, Facebook is currently the largest site of this type with 1.71 billion users as of mid-2016 (Statista, 2016b). Other interactive platforms also emerged with the advent of the Internet. For example, blogs, akin to online diaries, have become one of the most popular tenologies among users, allowing for the sharing of personal reflections on specific topics su as barriers to participation in PA and the enjoyment of sports events. Further, the development of Wikis, a type of server soware, allows users to freely create and edit web content simultaneously online. Wikis are interactive in nature in that they allow users to contribute to certain content while communicating with other users who share common interests. Using a similar platform, Google has developed Google Drive (formerly known as Google Docs). is platform acts as a file storage and synronization service provided by Google, allowing users to store files in the cloud and share and edit files simultaneously with other collaborators (Mossberg, 2012). In addition, Google has also developed Google Hangouts—a communication platform whi includes instant messaging, video at, Short Message Service and Voice over Internet Protocol features. Collectively, these are interactive means of communicating via the Internet. Indeed, computers, the Internet, and social media have become nearly inseparable aspects of our daily lives. With computing tenology continuing to advance at an exponential pace, researers/health professionals have been increasingly examining the effectiveness of the Internet and social media in the delivery of PA and health interventions over the past decade (Nigg, 2003). Specifically, researers/health professionals can tailor websites to impart health-related knowledge or develop social media groups whi allow the intervention of participants to support one another as they ea try to improve their health through greater participation in exercise and healthy eating behaviors. In detail, the Internet and social media have advantages over traditional face-to-face interventions as interventionists can rea a large and more diverse sample of individuals while also reducing the burdens that might arise during typical face-to-face
interventions (e.g., the need for transportation). ese advantages and the relevant literature will be highlighted in Chapters 4 and 5. Nonetheless, it is safe to say that computers, the Internet, and social media will play a part in PA intervention delivery for many years to come.
Mobile devices Alexander Bell is credited with inventing and patenting the first modernday telephone in 1876 (Borth, 2015). Bell’s phone was far from portable, however. In fact, it was not until just over 100 years aer Bell’s patent in 1983 that the first commercial, mobile phone became available (Borth, 2016). is phone was large and bulky with the ability to make calls only. Yet, as time went on, mobile phones began to gain new capabilities, su as the ability to send and receive text messages and, later, to send pictures, videos, and access the Internet via 4G and WiFi connectivity—resulting in terming these mobile phones “smartphones.” Today, there are very few individuals without a smartphone. e most recent Pew Resear Group report states that 64% of adults now own a smartphone, with this number continuing to increase steadily (Pew Resear Center, 2014). Notably, however, smartphones represent just one type of popular mobile device. Tablet computers (a.k.a., tablets) are also popular mobile devices seen nowadays. Tablets offer capabilities akin to that of a laptop computer, but are smaller and typically bigger than smartphones. In 1987, the modern-day tablet was introduced to the market in the form of Cambridge Researer’s Z88 and Linus Tenologies’ Write top (Gregersen, 2016). Although, as Gregersen (2016) notes, while the aforementioned tablets sparked a number of other companies’ endeavors to break into the tablet market, the popularity of the tablet was low until the introduction of Apple’s iPad in 2010. Indeed, the introduction of the iPad to the market has resulted in a booming present-day tablet industry, with most of the major tenology companies marketing tablets to consumers. Since 2012, the number of tablet shipments has
consistently been between 40 and 80 million (Statista, 2016c)—indicating the popularity of these powerful mobile devices among consumers. Nonetheless, whether a user prefers to use a smartphone or tablet, these mobile devices offer researers/health professionals the capability to use mobile device applications (a.k.a., apps) to promote health and wellness. As of 2015, Google Play and the Apple Store offered 1.6 and 1.5 million apps, respectively, for users to download to their mobile device (Statista, 2016a). Moreover, 165,000 of these apps were considered mobile health (a.k.a., mHealth) apps—providing the user with the ability to tra or manage their PA routines, healthy eating habits, and stress levels, among other health behaviors (Intercontinental Marketing Services [IMS] Institute of Health Informatics, 2015). As su, researers/health professionals have recently capitalized on the use of mHealth apps in the promotion of health and wellness in numerous general and clinical populations using researerdeveloped or commercially available mHealth apps. Given the fact that intervention participants may find the need to carry around a laptop computer to participate in an intervention to be burdensome, the small and portable nature of smartphones and tablets in the promotion of PA and health is a promising field of resear in a generation in whi individuals are increasingly using mobile device apps to assist in daily activities. at said, the use of mHealth apps in the promotion of PA and health will be reviewed in Chapter 6.
Global positioning systems (GPS) and geographic information systems (GIS) While researers and health professionals appreciate the provision of PA intensity data through accelerometers and other devices equipped with accelerometry (e.g., Fitbit), these devices cannot provide health professionals with information regarding the location of PA in one’s environment. is inability to tra PA location is unfortunate, as researers over the past 15
years have made clear the negative impact that the built environment (e.g., sidewalk construction, land use, trail systems) can have on PA (Handy, Boarnet, Ewing, & Killingsworth, 2002; Krenn et al., 2011). Global positioning systems (GPS) or geographic information systems (GIS), however, have shown great promise in providing researers/health professionals information regarding PA location, aer whi detailed analyses of the relationship of PA and the built environment can be explored. e tenology behind GPS was developed approximately 40 years ago for exclusive use by the military (Maddison & Mhuru, 2009). In 2000, however, President Clinton made GPS tenology available for civilian use— citing the incredible potential of this tenology to provide highly accurate positioning data for everyone (U.S. Government Information, 2000). Highte GIS soware was then marketed, allowing for the upload, storage, and analysis of the GPS data (National Geographic Society, 2016). In brief, GIS soware gives researers the power to construct highly specific maps whi allow these investigators to correlate individuals’ GPS-measured PA data with different locations to assess where anges in the built environment might be needed to facilitate greater PA participation.
Figure 1.2
Smart wates validation test.
Source: Photo by Zan Gao.
As a result of the commercialization of GPS, GPS units have become widely available over the last 15 years in the form of GPS wates and other units whi can be aaed around the waist or carried like a bapa (Maddison & Mhuru, 2009) (Figure 1.2). Researers/health professionals can provide individuals with these small GPS devices whi serve to assist these professionals in conducting PA environment audits whi, again, through later analysis via GIS, can allow these professionals to advocate for anges in the built environment (e.g., increased sidewalk connectivity, greater access to trails and parks) that facilitate PA. Furthermore, recent resear using minimalistic GPS devices (e.g., the Spi Elite™) has been conducted to investigate athlete velocity, anges in direction, and training load—all with the aim of reducing injury and improving performance (MacLeod, Morris, Nevill, & Sunderland, 2009; Malone et al., 2015). erefore, it should be clear that GPS/GIS tenology offers a unique and needed solution regarding how to evaluate and assess PA location and
intensity in order to promote health and wellness among the general population and safety/performance in athletes. e aforementioned benefits and more will be discussed in Chapter 7.
Health wearables
Figure 1.3
Use of a smart wat in health promotion.
Source: pixabay.com.
Activity traers (a.k.a., health wearables) are likely the newest type of emerging tenology used by researers and health professionals to promote PA and health in various populations. Although pedometers and accelerometers have been available for some time, these tenologies were either limited in their ability to tra multiple types of PA (e.g., pedometers are poor at assessing activities other than walking or running) or, like accelerometers, were expensive and primarily only used in resear seings. In 2007, however, the company Fitbit was founded—selling the first of its kind health wearable devices to consumers in order to give health-
conscience individuals the ability to tra and self-regulate health behaviors (e.g., steps taken per day) (Fitbit, 2016). While the first Fitbit was aaed to the waist and had limited capabilities other than traing PA, subsequent iterations of this device have been wrist-mounted and equipped to tra a number of health metrics (Figure 1.3). Regardless of the limited functionality of the first Fitbit, at the time of its release the Fitbit inspired a whole market of health wearables (e.g., the Jawbone) and pushed these devices from relative obscurity into the mainstream. Although the style and placement (e.g., wrist, belt, ankle) may differ for various health wearables, the majority of these devices sync wirelessly with mobile devices via associated mHealth apps and are now equipped to evaluate a number of different health metrics su as steps per day, sleep duration/quality, steps climbed, and heart rate, among other indices. Yet, while the popularity of health wearables like the Fitbit continues to be high, the introduction of e Pebble smartwat in 2012 anged the landscape of health wearables once again. Indeed, smart wates are poised to be the next generation of common health wearable tenology—with major tenology companies su as Apple capitalizing on the current popularity of smart wates with devices like the Apple Wat. While functions vary, depending on the smart wat in question, these devices offer a number of advantages over other, more traditional health wearable tenologies. As Rawassizadeh and colleagues (2015) state, the two biggest advantages to smart wates is these devices’ consistent wear location at the wrist and the continual contact these devices have with the user’s skin. Specifically, the consistent wear location is notable, given the fact it can be assumed the user is wearing the device in the same location over multiple days—increasing the validity of the health data collected—while the continual contact that smart wates have with users’ skin allows for the measurement of a number of health indices including, but not limited to: heart rate, galvanic skin response, blood oxygen levels, and heart rate variability (Rawassizadeh, Price, & Petre, 2015). Aside from the aforementioned advantages, smart wates also allow the user to engage in tasks su as reading texts/emails, playing music, and eing the weather—functions that most traditional health wearables have
not yet gained the ability to complete. It should also be noted, however, that some resear-grade accelerometry companies have noted the trends in health wearables and have begun developing their own “smart” products. For example, ActiGraph Co. recently released a smart wat-like wearable device called the ActiGraph GT9X Link, whi provides data via a sleek and high resolution liquid crystal display screen for optional real-time feedba. e GT9X Link also offers a variety of wireless features, including proximity detection, heart rate monitoring, and communication with its mobile app through Bluetooth®Smart tenology. Regardless of whether researers and/or health professionals use a more traditional health wearable, su as a Fitbit or Jawbone or ActiGraph Link, or a smart wat, su as e Pebble or Apple Wat, it is clear these devices hold promise in the traing and self-regulation of PA and other health behaviors. In Chapter 8, readers will be treated to a discussion of the extant literature on the use of health wearables in the promotion of PA and health.
Active video gaming and virtual reality Home video games are a relatively recent innovation whi certainly have benefited from the tenological advances made in the personal computer industry over the last half of the twentieth century up to the present day. In 1972, an engineer by the name of Ralph Baer patented what was then termed the “television gaming apparatus” for a video game console called the Magnavox Odyssey whi allowed users to play a game called Fox and Hounds (Lowood, 2016). Not long aer the release of the Magnavox Odyssey Atari released their first video gaming consoles—most notably, the Atari 2600 Video Computer System. e first active video game, however, was not released until 1988 with Atari’s Foot Craz, requiring players to run in place on a tra displayed on a television screen while trying to beat a virtual opponent (Montfort & Bogost, 2009). However, Foot Craz and other early active video games aieved only limited success. It was not until 1998, with
the release of Dance Revolution on the Play-Station, that active video gaming really started to aract consumer interest (Chamberlin & Maloney, 2013). Moreover, as tenology advanced, so did the active video gaming consoles. During the mid- to late-2000s PlayStation’s EyeToy, Nintendo’s Wii, and Microso’s Kinect were all released, with the Wii experiencing enormous success. As Chamberlin and Maloney (2013) state, it did not take long for the Wii and other similar consoles to gain the interest of researers/health professionals regarding their use in PA and health promotion. Notably, active video games are also considered a form of virtual reality. Specifically, active video games are considered a form of nonimmersive virtual reality as these games typically do not fully immerse players in a virtual environment—using only television screens and associated gamepads or motion-sensing cameras during user gameplay (Pasco, 2013). Virtual reality, as most have come to know it, is immersive, oen employing headset displays, body motion sensors worn by the user, and/or helmets to completely simulate an environment (Figure 1.4). Virtual reality has a similar timeline to active video gaming, given this tenology’s similar dependence on advances in the computer industry. Modern-day virtual reality was first conceived in the 1960s and was used almost exclusively in the job training of individuals su as pilots in a controlled and low-risk manner (Lowood, 2015). However, as Lowood (2015) states, it was not until the 1990s that virtual reality companies began to aggressively develop virtual reality systems for the general consumer for entertainment purposes. Today, personal virtual reality systems su as the Ocular Ri, Samsung Gear VR, PlayStation VR, HTC Vive dominate the market—providing relatively inexpensive virtual reality experiences (Lamkin, 2016). Indeed, researers/health professionals can use these virtual reality systems to create virtual worlds of PA for intervention participants wherein the movements required to participate in certain activities can be mimied (e.g., a golf swing or free throw in basketball), whi will hopefully lead to increased confidence in participating in these sports in the real world.
Finally, augmented reality, a tenology format combining the physical and virtual worlds—most commonly using a smartphone app—has also become popular in recent years with games like Zombies, Run! and the recently released Pokémon Go among the most popular (Figure 1.5). Of interest to researers/health professionals is the health benefits whi might arise from playing these augmented reality games as the games require players to move to different locations in the environment to engage in gameplay (Baranowski, 2016). Chapters 9 and 10 will cover the extant literature to date regarding active video gaming and various types of virtual reality, respectively.
Figure 1.4
A kid playing soccer video games.
Source: Photo by Zan Gao.
Figure 1.5
Kids playing Pokémon Go.
Source: Photo by Zan Gao.
roughout the remainder of this apter, the readers will be treated to a review of the physiological and psyological benefits related to PA— providing the basis for why the preceding tenologies might prove vital to public health initiatives.
Understanding the underlying science behind the health benefits of PA Physical activity (PA) refers to any bodily movement produced by the skeletal muscles that results in a substantial increase in energy expenditure above resting levels (Claude, Steven, & William, 2012). Regular PA promotes health and fitness. More specifically, moderate-to-vigorous-intensity aerobic PA helps build and maintain healthy bones and muscles; reduces the risk of developing obesity and ronic diseases, su as diabetes and cardiovascular disease; and reduces symptoms of depression and anxiety, thereby promoting cardiorespiratory fitness (CRF) and psyological well-being (U.S. Department of Health and Human Services [USDHHS], 2008). However, only 21% American adults meet the 2008 PA Guidelines, and less than three in ten (29%) high sool students get at least 60 minutes of PA every day (Centers for Disease Control and Prevention [CDC], 2014). Even more unfortunate is that nearly half of presool ildren do not meet the recommended 60 daily minutes of PA for this age group (Tuer, 2008) (Figure 1.6). Indeed, globally, 81% of adolescents aged 11–17 years and approximately 23% of adults aged 18 and over were insufficiently physically active in 2010 (World Health Organization [WHO], 2016). In fact, physical inactivity is one of the ten leading risk factors for global mortality, whi is on the rise in many countries, adding to the burden of non-communicable diseases (e.g., cardiovascular diseases, cancer and diabetes) and affecting general health worldwide (WHO, 2016). As global inactivity has rapidly increased due to individuals spending less time performing PA, this section provides an overview of the scientific evidence linking PA to physiological and psyological health—furthering the rationale behind the use of emerging tenology in PA promotion.
Cardiorespiratory health Since the late 1950s, numerous scientific studies have examined the relationships between PA and cardiovascular health. Expert panels, convened by national organizations and other international organizations, along with the 1996 U.S. Surgeon General’s Report on PA and Health, have reinforced scientific evidence regarding the ability of regular PA to positively impact various measures of cardiovascular health. Based upon more than five decades of epidemiological studies, it is now widely accepted that physically active individuals tend to develop less coronary heart disease (CHD) than their sedentary peers, with higher PA paerns and CRF levels associated with beer health outcomes (Myers et al., 2015).
Figure 1.6
A group of kids playing active video games.
Source: Photo by Zan Gao.
e human body responds to PA through a series of integrated anges in function that involve most, if not all, of its physiological systems. For example, movement requires activation and control of the musculoskeletal system, with the cardiovascular and respiratory systems providing the ability to sustain this movement over extended periods of time. If an individual engages in PA several times a week, ea of the aforementioned physiological systems undergoes specific adaptations that increase the body’s efficiency and capacity. Generally, regular PA participation improves cardiovascular function by lowering resting heart rate and increasing stroke volume at submaximal workloads (Vella & Robergs, 2005). Additionally, the muscles used during aerobic activities show improvements in strength and
endurance while the muscles used for breathing (i.e., the intercostals and abdominals) become more resistant to fatigue. Finally, individuals who regularly participate in endurance exercises develop the ability to sustain a higher fractional utilization of their maximal oxygen uptake—becoming more physiologically efficient during various aerobic activities (Ghosh, 2004). e preceding result is almost entirely a result of increased maximal cardiorespiratory endurance (VO2 max) increases along with the ability of the muscle to extract oxygen to power PA. Given the preceding physiological adaptations, fier individuals can more easily participate in PA at a given workload as compared to less fit individuals. Regular participation in PA is also associated with an improved atherogenic dyslipidemia profile, resulting in increased high-density lipoprotein (HDL) olesterol, lower total olesterol, and lower triglyceride levels. Additionally, improvements in autonomic nerve function, increases in aerobic and anaerobic enzymes, increased lactate threshold (max), improved glucose homeostasis, improved endothelial function, and reduced inflammation related to oxidative stress are also common physiological adaptations observed regular PA participation (Kohl & Murray, 2012). Notably, high-intensity PA results in increases of both glycolytic and oxidative enzymes (Bogdanis, 2012). Last but not least, increased total energy expenditure and reduced body fat percentage and waist circumference have been seen over time among various populations when regularly performing PA at a moderate- or vigorous-intensity level.
Metabolic health e Physical Activity Guidelines Advisory Commiee (PAGAC, 2008) reported clear and strong scientific evidence indicating regular PA improves the metabolic health of individuals who are at least moderately active by 30–40% compared to sedentary populations. e benefits of PA on metabolic health apply equally for a variety of populations. Generally, PA-related
adaptations that positively influence metabolic health are the same as those seen in conjunction with cardiorespiratory adaptations to PA. Explicitly, regular PA participation results in increased HDL levels and reduced lowdensity lipoprotein (LDL) levels (Kokkinos & Fernhall, 1999), as well as reduced triglyceride levels (LeBlanc & Janssen, 2010). Further, both aerobic and resistance exercises increase glucose transporter type 4 (GLUT4) abundance and blood glucose (BG) uptake (Colberg et al., 2010). As GLUT4 levels increase with PA, insulin resistance is prevented (Ross, 2003) while improvements in insulin sensitivity are observed (Duncan et al., 2003)—a key factor in diabetes prevention and treatment. Existing evidence also indicates regular PA to be highly related to improved glucose tolerance (Aller & van Baak, 2013) in addition to an improved protein synthesis rate and amino acid uptake into skeletal muscle (van Loon, 2014). Overall, significant metabolic adaptations occur in response to PA and exercise. Indeed, by improving metabolic health, individuals decrease the likelihood of ronic disease and improve their quality of life.
Musculoskeletal health Participating in activities su as strength and resistance training has been proven effective in improving musculoskeletal function (Baele & Earle, 2008). Even with moderate-intensity PA (e.g., carrying heavy loads, climbing stairs, etc.), improvements in muscular strength and endurance are observed in most healthy individuals. Increases in VO2 max (5% ange) have been observed in those who participated in interval training combining resistance training and aerobic exercise, while sedentary individuals have demonstrated VO2 max improvements of 3% with resistance training alone (Geman & Pollo, 1981). Increases in strength and power have also been observed over time when PA regimens incorporate resistance training (Bogdanis, 2012; Hong, Hong, & Shin, 2014). e preceding musculoskeletal strengthening is
facilitated by the body’s ability to recruit more motor units (i.e., the motor neuron and the muscle fibers it innervates), the increased size of individual muscle fibers, the increased numbers of anaerobic enzymes, and the higher amounts of anaerobic energy store su a phosphocreatine and glycogen (Kohl & Murray, 2012). Notably, all of the aforementioned health benefits are critical for performing short-duration and high-intensity musculoskeletal activity. Of clinical importance, increased ligament and tendon strength and increased collagen content are the results of connective tissues anges caused by the stress placed on specific muscles during musculoskeletal strengthening activities—whi may reduce the risk of injury for those of all ages. Further, resistance training activities can trigger positive hormonal anges that allow for the remodeling of bone, whi can improve bone mineral density (BMD) in some people, while delaying bone mass loss for individuals at risk of osteoporosis—particularly females. Finally, exercise physiology resear has clearly demonstrated that regular PA and musculoskeletal strengthening exercise participation increase lean muscle mass by way of improved muscle quality and quantity while also reducing body fat and aiding in maintenance or loss of weight (Chaput et al., 2011). Of note, the amount of physiological adaptation associated with ea of the benefits of PA and exercise reviewed to this point are dose-dependent— indicating that lower doses of PA have smaller physiological effects and anges than performing higher doses (Kohl & Murray, 2012). is doseresponse relationship must be kept in mind when critiquing literature regarding PA interventions or developing an intervention of your own.
Mental health Mental disorders are of major public health significance. Specifically, mental disorders have a long-lasting impact on an individual’s mood or affect, personality, cognition, and perception (USDHHS, 1996). e Physical
Activity Guidelines Advisory Commiee (PAGAC; 2008) listed common mental disorders or problems that have been investigated in relation to PA, whi included mood disorders, anxiety disorders, psyological distress, low self-esteem, age-related decline in cognitive function, and eating or exercise-related disorders. Of the most frequently examined outcomes of mental health examined in relation to PA, investigations have included mood (e.g., anxiety, depression, positive affect, and negative affect), selfefficacy, self-esteem, and cognitive functioning. Among these outcomes, affective (mood) and anxiety disorders are the most frequently reported. Current scientific evidence supports the assertion that regular PA and exercise lower the risk/symptoms of depression (Mammen & Faulkner, 2013), anxiety (Stonero, Hoffman, Smith, & Blumenthal, 2015), psyological distress (Matzka et al., 2016), and age-related decline in cognitive function (Chang et al., 2010). Furthermore, improvements in positive affect, general psyological well-being (Brown et al., 2013), and self-esteem (Awi et al., 2016) were also observed. Overall, physically inactive populations are twice as likely to have symptoms of depression and anxiety compared to those who are more active. e general consensus is that people who are physically active or have higher levels of cardiorespiratory fitness trend to have enhanced mood (greater positive affect and less negative affect), higher self-esteem, greater confidence in their ability to perform tasks requiring PA (i.e., greater self-efficacy), and beer cognitive functioning than those who are physically inactive or live a sedentary lifestyle. Notably, it has been claimed that light PA (e.g., performing activities of daily living, for example) is likely not intense enough to elicit significant physiological responses. As su, it is still recommended that PA must be at a moderate or vigorous intensity to generate the physiological stimulus necessary to promote mental health (Kohl & Murray, 2012). Despite substantial resear in the area, however, PA has not been shown to be effective in the treatment of mental health disorders. at said, only a paucity of resear has been conducted on the role that PA might play in the prevention of mental health disorders. Indeed, the extant literature studying individuals with mental health disorders, su as depression and anxiety,
represents the best current evidence available regarding aempts to promote PA in the prevention of mental health disorders. Unfortunately, the literature is less clear regarding the psyological effects of regular PA for individuals who have relatively good health status, or those with other mental disorders, including sleep and eating disorders, sizophrenia, dementia, personality disorders, and substance-related disorders. erefore, more studies are warranted to further explore the effects of PA on mental health. In summary, PA has numerous beneficial and well-documented physiological effects. Most widely appreciated are its effects on the cardiorespiratory and musculoskeletal systems, but benefits for the functioning of the metabolic systems of the body are also considerable. ese beneficial physiological effects have been observed in individuals of all race/ethnicity, gender, and ages. Moreover, emerging positive effects of PA on relieving symptoms of anxiety and depression, and improving mood, self-esteem, cognitive functioning, and general psyological well-being are promising. Nevertheless, most physiological and psyological health benefits from PA are dose-dependent. at is, regular PA must be performed at moderate-to-vigorous intensity and in episodes of at least 10 minutes in duration to generate the necessary physiological stimulus to promote overall health.
Application of emerging tenologies in promoting PA and health e physiological and psyological benefits of regular PA participation have been well documented and include improved cardiorespiratory fitness, enhanced self-efficacy, and reduced risk of non-communicable ronic diseases su as obesity, type 2 diabetes, and cardiovascular diseases, among numerous other positive outcomes. Despite the recognized benefits, the majority of Americans do not meet the recommended levels of PA on a daily basis. As known, tenology is embedded in our society and is anging our lives in positive and negative ways. Traditionally, tenology-based media su as television, video games, and computer games have been blamed for their negative effects on individuals’ health, as these media usually result in increased screen time and reduced PA levels in various populations, contributing to public health concerns su as obesity and type 2 diabetes. Fortunately, just like the famous Chinese saying “fight fire with fire,” the present-day health professionals are intentionally using emerging tenology to promote PA participation through the provision of tenologies su as health wearable devices and active video games that are now available on the market. In fact, these emerging tenologies have entered into homes, sools, communities, and other places, and have been very popular among various populations. For example, nowadays smartphones not only allow us to regularly communicate with others through phone calls and text messages, but also let us monitor our health status and monitor PA behaviors via multiple health-oriented apps. Health professionals can play a valuable role in the selection and integration of emerging tenology. is section will equip the reader with the requisite knowledge base regarding the use of emerging tenology for PA assessment and promotion. It is hoped that su knowledge will promote
further integration of emerging tenology into PA and health by researers and health professionals alike.
Emerging tenology for PA assessment Tenology is constantly anging our living environment and lifestyles. Approximately a decade ago, electronic devices that monitor PA by detecting and processing human movement made an impact with regard to assessing habitual PA levels due to their accuracy and ease of use—replacing the widely used PA questionnaires. Particularly, the most popular electronic devices, su as pedometers, accelerometers, and heart rate monitors, provide details of PA measures, including but not limited to: steps, estimated distance, energy expenditure, time in different intensity levels of PA, and heart rate. Beyond their measurement aributes, these devices also play an important role in providing feedba to clients whi can act to motivate these individuals to participate in more PA. With the rapid development of tenology, accelerometers capable of assessing sedentary behavior, light PA, and moderate-to-vigorous PA are now appearing in smartphones and GPS devices. While tenology advancement offers opportunities for the public to engage in sedentary behavior, the integration of accelerometers into a greater number of electronic devices is providing opportunities for health professionals to objectively assess PA and sedentary behavior to address various health issues. In fact, the extant electronic devices are aractive for health professional in that they have aieved greater ease of use, greater precision, and greater scope (i.e., data from various sources in one device) by integrating innovative emerging tenologies su as mobile device apps, GPS units, and health wearables. is is imperative for health professionals implementing PA interventions, because more precise assessments allow for a beer understanding of when, where, and how PA participation occurs— improving subsequent behavioral ange strategies.
Currently many mobile devices (smartphones and tablets) and fashion accessories (belt, nelace, bracelet, and rings) have embedded GPS and accelerometer tenologies whi are being used to identify and promote PA participation as well as understand naturalistically occurring activities (Graham & Hipp, 2014). Some smart devices are even taking advantage of the camera function of mobile devices. For example, SenseCam, a wearable digital camera that takes photos automatically without user intervention, along with mobile apps and other visual devices can provide details of PA contexts and an individual’s lifestyle. Furthermore, tenology advancement has included the development of multiple sensor systems, enabling beer identification of previously nonidentifiable physical activities (e.g., ben press, stair climbing) through pedometers or accelerometers alone. Presently, the vast majority of monitors (i.e., Actigraph), devices (i.e., Polar M400 Sports Wat), or apps (i.e., MapMyFitness) are able to send data to an individual’s account and relevant communities via remote servers. is invites the further embrace of online tenology (e.g., adopting online social-networks like MapMyFitness Friends as a source of extrinsic motivation to aieve predetermined personal health goals), and PA promotion websites su as Stik.com that let individuals make public contracts visible to other users and also use monetary incentives to promote PA behavioral anges. Additionally, tenological advances like crowdsourcing (i.e., applies to the masses, or crowds, of individuals using the Internet, social media, and smartphone apps) allows for input from a large base of diverse users that can help to identify and improve the infrastructure for PA. For example, professionals can identify environmental features that are inhibiting or facilitating PA behavior on a regional or even national scale, based upon the input from large groups. Overall, although validity testing is warranted when applying emerging tenologies in assessing and evaluating PA behavior, it is critical and noteworthy that researers and practitioners should embrace new tenologies give the fact that: (1) emerging tenologies can greatly increase our capability to analyze PA behavior paerns; (2) the ease of use and transferability can significantly impact large populations from a
longitudinal perspective; (3) emerging tenologies can improve the ongoing, systematic process evaluation of any intervention program, given their real-time data sharing capabilities; (4) researers are calling for new strategies to deal with big data through data mining or data texting (PA data, posts on social media, GPS and/or GIS data, etc.). With the improvements in mathematical models and computer algorithms, researers will empower themselves to allow greater capacity for assessing and evaluating PA data; and (5) increasingly blurred boundaries between PA assessment and intervention (e.g., self-traing PA) may require a reevaluation of the traditional scientific model to design and evaluate these types of studies (Graham & Hipp, 2014).
Emerging tenology in PA promotion As previously stated, researers and health professionals have taken advantage of the emerging tenology that is available in PA promotion. However, it should be noted that some emerging tenologies su as GPS/GIS have only been used to measure and tra PA behavior, with few interventions of this type having the aim of anging an individual’s PA behaviors—the core element of PA promotion. Fortunately, the introduction of persuasive tenology shows great potential for emerging tenology application in PA promotion. More specifically, persuasive tenology refers to a tenology that is designed to ange individuals’ aitudes or behaviors through persuasion and social influence but not through coercion (Fogg, 2003). Resear involving persuasive tenology focuses primarily on interactive and computational tenologies su as video games, desktop computers, the Internet, and mobile devices, yet it also incorporates behavioral theories and human-computer interactions (Dominic, Hounkponou, Doh, Ansong, & Brighter, 2013) (Figure 1.7). Indeed, persuasive tenology is designed with the goal of anging a particular
aspect of human behavior, including PA behavior, in a predefined way in non-commercial domains. Some emerging tenologies play key roles in persuading individuals to engage in greater PA participation in that: (1) they serve as the tool (e.g., recording heart rates and calories with the Apple wat); (2) they are linked to social media (e.g., synronize exercise app data to Facebook or Twier); and (3) they offer social interaction (e.g., Kinect Just Dance 5 allow for dancing with another person from the online community). e applications of su tenology have been increasingly seen in our daily life. For example, MapMyFitness, a popular smartphone exercise app, is a good intervention strategy in PA promotion, as it integrates with the music on the smartphone and provides instant feedba from a bout of exercise to users. Users can also share their accomplishments with their friends using this app or can synronize data to Facebook. Further, this app once even partnered with Sears soon aer the app’s release to offer monetary incentives to users who engaged in exercise. Another example is Just Dance, a type of active video game with dance movement. Just Dance provides scores and instant feedba to users for ea of their movements while allowing users to compete with others simultaneously on site or online. Most importantly, this tenology’s interactive feature captures users’ aention so that they play the dance game without knowing that they are actually exercising.
Figure 1.7
Application of virtual reality CAVE.
Source: Photo by Zan Gao.
According to Dominic et al. (2013), emerging tenology is generally deemed as a tool thus far, and the tenology use alone may not lead to ideal persuasion and expected intervention outcomes. Changing behavior is never an easy task. erefore, it is critical to integrate emerging tenology and behavioral theories to promote PA among various target populations. In fact, a number of behavioral theories have been widely used in some PA interventions involving emerging tenology. e reader will be treated to details concerning theory application in PA interventions using emerging tenology in Chapter 3. Finally, health professionals should keep in mind several tenologies have been integrated into singular applications over the past several years. By embracing the strengths of ea tenology, an integrated tenology application may be very powerful and convenient for users. In fact, a number of products are now available on the market to combine PA assessment and intervention delivery in a single application. Presently, the
aforementioned MapMyFitness, XBox Kinect, and the Apple wat are su applications. us, more resear will be warranted on using these multitenologies devices. Overall, tenology is continuously evolving and constantly anging our lives. On the horizon are novel and exciting cuing-edge tenologies that have great potential for PA promotion. Indeed, human beings have applied tenology in promoting PA for some time. Yet, in this day and age, emerging tenologies and relevant behavioral theories are providing us with needed and exciting opportunities to assess and promote PA on a larger scale—particularly when discussing novel tenologies and methodologies su as augmented reality games, crowdsourcing, and online active gaming. Undoubtedly, the tenology era appears to be a prime time for researers and health professionals in PA and health.
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2 Overview Promoting physical activity and health through emerging tenology Haichun Sun, Nan Zeng, and Zan Gao
Physical inactivity has been identified as one of the main causes of premature death and many ronic diseases su as cancer, cardiovascular disease, stroke, type 2 diabetes, some cancers, and obesity. Tenology plays a complicated role in physical inactivity—mu like a double-edged sword. On one hand, some tenologies, su as sedentary video games, TV and computers, have contributed to the epidemic of physical inactivity and sedentary leisure-time behavior (e.g., playing sedentary video games). Conversely, some newly emerged tenologies have been increasingly used to assess and promote physical activity (PA) and health. For example, pedometers and accelerometers have been extensively used as tools in assessing and promoting PA among various populations. Within the past decade, emerging tenology including online social media, mobile devices applications, health wearable devices, GPS, GIS, and AVGs have been used to promote PA participation. erefore, although tenology has potential negative effects on a healthy and active lifestyle, tenology does also have great possibilities to facilitate PA promotion. Notably, this apter does not include an overaring review of all the tenologies used in the PA and health field, su as the metabolic
assessment devices used in highly advanced laboratory seings. Instead, this apter begins by providing a broad picture of the traditional and current emerging tenologies that have been employed in promoting PA and health on a large scale in population-based seings, including communities, sools (Figure 2.1), and homes, as well as findings regarding the application of ea tenology in PA contexts. e following section describes the opportunities and allenges concerning the emerging tenology integration in the promotion of PA and health.
Traditional tenology in PA promotion Tenology has long been used as a means for PA promotion. Traditionally, many tenologies for PA promotion were categorized into four groups: (1) telephone; (2) mass media; (3) computer/Internet; and (4) electronic devices. We describe ea of these groups in the following subsections.
Figure 2.1
Young kids playing Kinect Just Dance.
Source: Photo by Zan Gao.
Telephone Telephone-based intervention/program delivery is one of the most accessible approaes to PA promotion and has been used by major health agencies and organizations to conduct national surveillance studies whi survey population-level rates of PA participation. As a result of telephone-based surveillance, previous resear has provided strong evidence supporting telephone-based tenologies as an effective method to promote PA behavior
anges, with the ability to scale telephone-based interventions to the population level. is represents a major advantage of this tenology (Goode, Reeves, & Eakin, 2012). Given the strength of evidence regarding telephone-based PA interventions, Goode et al. (2012) stated that the evaluation of the longer-term effects aer cessation of this type of intervention as well as greater emphasis on the dissemination of the study methodology are warranted. As mobile phones have become more ubiquitous over the last two decades, text messaging has also emerged as a telephone-based method to communicate with and promote individuals’ participation in health-related behaviors. However, a recent systematic review identified that only a small group of researers had conducted studies with regard to text messagebased PA promotion (Buholz et al., 2013). at said, these limited text message-based PA intervention studies did show promising results for increasing PA. Buholz et al., therefore, argued that further resear in this area is imperative.
Mass media e mass media have diversified and continues to diversify communication methods, allowing for qui and efficient dissemination of information to large groups of people. Traditional mass media include radio, television, and print media (e.g., newspapers and magazines). A meta-analysis regarding the use of mass media for PA promotion indicated mixed results (Abioye, Hajifathalian, & Danaei, 2013). Nonetheless, Abioye et al. (2013) concluded that the mass media might promote greater walking participation, but may not be effective in reducing sedentary behavior or helping individuals participate in the recommended levels of PA. As su, the researers stated that further resear is needed to examine the effects of the mass media on outcomes su as objectively assessed PA and the necessary intensity and frequency of health messages delivered during mass media PA interventions.
Further, more studies are warranted in lowand middle-income countries and in a sample of more diverse race/ethnicity. Studies addressing the aforementioned limitations are vital, given the mass media’s ability to deliver content to large populations at low cost.
Computer and Internet use e Internet and the World Wide Web represent information tenology inventions and applications that have remarkably altered our lifestyles. Hundreds of thousands of websites have been developed to promote PA via information dissemination or direct PA intervention purposes. Resear on Internet-based PA interventions, however, is still a relatively new area of inquiry. Previous Internet-based PA intervention studies have focused on developing programs tailored to participants’ specific aracteristics using interactive self-monitoring and feedba tools. As a result of the Internetbased PA interventions that use complex computer algorithms to offer tailored feedba to participants, it is not surprising that a meta-analysis recently reported that the delivery of Internet-based interventions is effective in producing positive PA behavioral anges (Davies, Spence, Vandelanoe, Caperione, & Mummery, 2012). However, the authors indicated that the effect sizes of Internet-based interventions were relatively small. Similarly, Hamel, Robbins, and Wilbur (2011) reviewed 14 randomized controlled trials or quasi-experimental studies, concluding that while most Internet-based PA interventions have promoted significantly increased PA or positive PA-related behavioral anges, these findings were small and, notably, short-lived. Future resear will be required to discover how to more effectively increase PA participation, as well as how to ensure the sustainability of PA behavioral ange in the long term. With the preceding limitations of past resear in mind, researers have stated that future Internet-based PA interventions should include the use of objective PA measures, more programs tailored toward males, the involvement of greater
social support, the integration into sool curricula, the implementation of sound theoretical frameworks for intervention development, and individually tailored interventions (Hamel et al., 2011).
Electronic devices for PA assessment Electronic devices have also been used to detect and process human body movement—subsequently providing quantifiable data on PA levels. e most popular electronic devices for this purpose are pedometers, accelerometers, and heart rate monitors. We discuss ea of these devices in the following paragraphs. A pedometer is a device that counts and monitors the number of steps taken during PA. Specifically, pedometers count the number of steps an individual takes by liing an internal lever arm meanically, electrically, or using a piezoelectric strain gauge (e.g., New Lifestyles NL-1000). Pedometers can be aaed to a firm waistband, carried in a shirt/pants poet, in a bag held close to the body, or worn on the ankle or in a shoe (Tudor-Loe et al., 2011)—with resear indicating the positioning of the pedometer in a pants poet or bapa might decrease accuracy (Hasson, Haller, Pober, Staudenmayer, & Freedson, 2009). Nevertheless, resear has shown pedometers to be effective as both motivational tools and PA measurement devices. Bravata and colleagues (2007) reported that pedometer users increased their PA by 27% over baseline, whi significantly decreased their body mass index, body weight, and systolic blood pressure. Importantly, researers have developed health benefit thresholds for walking using pedometers (Tudor-Loe et al., 2011). Further, using criterion-referenced approaes, researers can identify youth-specific walking thresholds for good health with these devices as well. In the future, it will also be critical to establish health risk thresholds for steps per day related to cardiovascular diseases (CVD), obesity, and osteoporosis—a task in whi pedometers may play a major role.
Similar to pedometers, accelerometers are small portable electronic devices that record minute-by-minute information about the frequency, duration, intensity, and paerns of body movement. In detail, accelerometers are designed to assess total ambulatory activity levels via measurement of tri-axial accelerations and are, therefore, capable of providing an estimate of energy expenditure. Accelerometers have shown acceptable validity and good reliability and have been anowledged as an accurate objective field measure for PA (Freedson & Miller, 2000). Accelerometers have also been used in national surveillance studies to objectively measure and monitor PA levels among the U.S. population. However, large-scale PA interventions using accelerometers are scarce due to the relatively high cost of accelerometers. Future development of lowercost accelerometers are warranted in order to facilitate greater use of this device in national surveillance studies and smaller community-based interventions. Motion sensors, su as pedometers and accelerometers, are considered practical, objective, and valid assessments of PA (Sirard & Pate, 2001). ese instruments have advantages, su as minimal participant and experimenter burden, being unobtrusive to participants, and being comfortable and acceptable for the subjects to wear. Notably, pedometers are less expensive (approximately $20–$50) than accelerometers (e.g., approximately $100– $500), and are ideal for measuring the number of steps during a designated time period, recording distances, and providing estimated calories. However, accelerometers can provide very detailed information regarding the PA intensity (sedentary, light, moderate, and vigorous), the time spent at different PA intensity levels, and provide more accurate estimations of energy expenditure as compared to pedometers. Nonetheless, both types of motion sensors can accurately measure PA among ildren in real-world seings (e.g., physical education). at said, these devices are not yet equipped to adequately capture PA levels during su activities as stationary cycling and weightliing (Freedson & Miller, 2000; Sirard & Pate, 2001). Finally, heart rate monitors are devices that monitor individuals’ PA intensity and can be used to estimate the individual’s energy expenditure.
Heart rate monitors have been widely used to estimate calories burned during PA due to the linear relationship between heart rate and energy expenditure during steady-state exercise. However, heart rate monitors are unable to accurately distinguish between light- and moderate-intensity activities or record the frequency of activity within a limited time frame (Hands, Parker, & Larkin, 2006). Moreover, the accuracy of heart rate monitors can also be affected by environmental factors, su as temperature, heat, and humidity. erefore, future resear is warranted to improve the measurement capabilities of this tenology.
Emerging tenology and its applications in PA promotion e world is going through a cultural tenological revolution. Over the course of the past several years, “Fourth screen” tenology—smartphones, tablets, etc.—has occupied substantial amounts of personal time and anged the way we interact with one another. Indeed, smartphones and tablets have become broadly accessible and major annels for accessing social media or various types of apps. Wearable devices are also increasing in popularity and ubiquity (Figure 2.2). is section reviews these tenologies and their application in PA interventions.
Online social media Over the past two decades, the rapid rise of online social media su as Facebook, Twier, and YouTube, among others, has resulted in these social media formats being investigated as intervention modalities, given their great potential to rea large groups of participants at a lower cost. A recent survey reported that in the U.S., 91% of young adults aged 18–29 years used online social media sites su as Facebook and Twier. Different from traditional Web-based interventions, interventions using online social media allow participants to obtain health-related information, form social connections, and log health behaviors and activities any time without limited access (Zhang, Brabill, Yang, & Centola, 2015). As a global phenomenon—particularly among young adults—online social media have gained popularity among PA researers. Resear findings are promising, yet mixed. Some studies have suggested that online social media could be effective tools for both PA participation and weight loss. For
example, Hales, Davidson, and Turner-McGrievy (2014) found engagement in Facebook support groups was significantly associated with weight loss during the 4-month maintenance period of the intervention, even aer adjusting for face-to-face meeting aendance. Other studies, however, did not suggest su positive results. Cavallo et al. (2012) conducted a randomized controlled trial among female undergraduate students to promote PA via a Facebook page. Researers observed no differences between the online social media/Facebook PA page intervention group and the control group without access to a Facebook PA page. Taken together, more studies are needed to determine the effectiveness of online social media on PA and PA-related outcomes (Figure 2.3).
Figure 2.2
Testing smart wates.
Source: Photo by Zan Gao.
Recently, researers have started to explore the effects of different types of posts on individuals’ engagement with online social media. Strekalova and Krieger (2015) examined the impact of post type on audience
engagement in association with the classification of a social media post as a photo, status update, video, or link. Findings indicated that photos have significantly greater impact on audience engagement than other media post types. Although this study was not completed within the context of PA, the findings certainly provide researers with an idea of how to increase the effectiveness of their online social media-based PA interventions via experimentation with post type.
Figure 2.3
Facebook social media.
Source: Photo by Zan Gao.
Another popular function of the Internet is electronic publishing. Selfvideo publishing, in particular, may serve as a new dimension of Internetbased PA interventions. YouTube is the most popular video-sharing website —allowing users to upload, view, and share video clips. Like other online social media, self-video publishing sites, su as YouTube, can rea a large group of people rapidly. As su, these sites have a great potential to deliver health-related PA information and facilitate PA behavior anges. Clearly, there is a vital need to explore effective ways to use these new Internet opportunities to promote PA.
Mobile devices and apps e mobile phone has also been widely anowledged as a viable intervention delivery method that is easily accessible by a large number of individuals. Over the past several decades, mobile phones have developed dramatically in both design and function. Newly developed smartphones can be as sophisticated as tablets or a mini-personal computer, in addition to the capability to call and text. According to the Pew Resear Center (Perrin, 2015), 64% of American adults own a smartphone, increasing the rationale for implementation of mobile phone-based PA interventions. Indeed, mobile phones and smartphones have unique features and components, including the ability to aid in self-monitoring and goal-seing, whi can be utilized in PA interventions to promote behavior ange (Duncan et al., 2014). In recent years, mobile/smart phones have been implemented in PA interventions in addition to being integrated into existing online Internetbased programs. For example, Kirwan et al. (2012) examined the effectiveness of a smartphone application, the iStepLog, on PA behaviors. Specifically, the application was designed to allow participants to record their daily PA levels on their smartphones. rough the built-in traing soware, researers were able to monitor the time participants spent using the application, the frequency they logged their steps, and the most popular functions of the application. eir findings revealed that the application helped participants to maintain PA participation as compared to participants in the control group who showed a significant decline in the frequency and numbers of steps logged during the intervention period. Although mobiles/smartphones have been observed to provide users more convenient access to the resear-designed materials or information than print-based mediums, mobiles-/smartphone-based PA interventions might not be superior to print-based interventions in their effectiveness at promoting or anging PA behaviors. Duncan et al. (2014) compared the effectiveness of a 9-month Internet and mobile phone-based intervention to a print-based intervention on middle-aged males’ PA, dietary behaviors, and
health-related literacy. ey found that participants in both groups had significant improvements in self-reported minutes and sessions of PA and overall dietary behaviors over time. However, there were no significant differences between the Internet/mobile phone-based group and the printbased group, suggesting that both methods could effectively deliver PArelated interventions. More importantly, Duncan et al. (2014) provided empirical evidence for future mobile phone/smart phone-based interventions. Specifically, these researers revealed that participants are more likely to initiate PA allenges than the healthy eating allenges— further suggesting that light-strength PA allenges were the ones most frequently selected and completed by middle-aged males. In general, mobile-/smartphone-based interventions allow participants to receive automated feedba, whi provides these individuals with the additional ability to self-monitor their progress toward meeting their PA goals. Indeed, resear has shown positive effects of mobile-/smartphonebased PA interventions. More studies are needed, however, exploring the long-term impact of mobile-/smartphone-based PA interventions on individuals’ PA behavior.
Health wearable devices In recent years, PA monitoring devices have gained popularity, with commercially available wearable activity monitors and traers—health wearables (i.e., fitness bands and sports wates)—representing a rapidly growing health-focused industry. Health wearables use sensors to help users automatically tra and set goals regarding PA (e.g., calories burned, step counts, distance traveled, etc.), sleep, diet, and other behaviors so as to optimize health behaviors (Almalki, Gray, & Sanez, 2015). Over the last five years, health wearable tenology has fast become a popular accessory. ese health wearables can aid in the design and implementation of personal PA plans. Frequently, health wearables are used to motivate people
to engage in more PA by viewing data and the results captured in real time, aer whi structured PA plans can be developed. Rapid developments in tenology have encouraged the use of health wearable devices (e.g., Fitbit, Apple Wat, TomTom sports wat, Microso Band, etc.) in PA resear and have generated new opportunities for promoting healthy lifestyles across diverse populations. Advances in the tenology of health wearable devices also provide professionals and researers a variety of PA measurement options to assess multiple health-related outcomes. orndike and colleagues (2014) demonstrated that utilizing health wearable tenology (Fitbit) may have potential for promoting healthier lifestyles among clinical populations. Further, Cadmus-Bertram et al. (2015) found that employing the Fitbit associated Internet-based app can promote postmenopausal women’s moderate-to-vigorous PA (MVPA) and steps. Similarly, Wang and colleagues (2015) combined the Fitbit and short message service (SMS) text-messaging prompts to increase PA in overweight and obese adults. ese researers found the Fitbit One is capable of promoting a small increase in MVPA. Conversely, Melton and colleagues (2016) found that health wearables (Jawbone) are not effective in improving PA participation or sleep quality among African American college women. Moreover, while Jakicic and colleagues (2016) found individuals using health wearable devices that monitor and provide feedba on PA did have significant improvements in body composition, fitness, PA, and diet, these health wearable devices did not offer an advantage over standard behavioral weight loss approaes. Although evidence regarding the effectiveness of health wearable devices in promoting PA and health is inconclusive, studies have demonstrated that these devices hold great promise for promoting healthier lifestyles among a diverse array of populations. Overall, the market for health wearable devices continues to grow in popularity, and the potential usefulness of using these devices to promote health is considerable.
GPS/GIS As one of the most sophisticated modern tenologies to be used in PA and health promotion, GPS and GIS—two related tenologies, only recently popularized for health and fitness—show great promise in the public health field. Briefly, GPS calculates geographic locations and accurately tras activity using 24 satellites and ground stations as reference points, whi can be used in conjunction with accelerometers to assess and monitor PA parameters, su as activity time, distance, altitude, and average velocity (Rodriguez, Brown, & Troped, 2005; Troped et al., 2008). GIS is a computer system that caes information in relation to location and the surrounding environment (Heyward & Gibson, 2014). PA data captured by GPS can then be analyzed by GIS in relation to the locations where these activities took place—facilitating researers’ ability to discern associations between locations where the PA participation occurred and features of the built environment (e.g., sidewalk construction, land use, and trail systems) whi may inhibit or facilitate PA participation. As more sophisticated GPS tenology has been developed within the past several years, resear has shied concentration from testing the reliability and validity of GPS and GIS to using these tenologies to promote PA participation and health promotion. Indeed, the majority of the literature to date on the topic of GPS/GIS and PA and health focuses on the assessment of PA paerns instead of on PA promotion interventions. Furthermore, researers using GPS/GIS are investigating how PA participation and promotion can be improved, implemented, and aieved by modifying the built environment to facilitate PA behavior ange among youth and adults (Maddison et al., 2010; Wheeler, Cooper, Page, & Jago, 2010) (Figure 2.4). In addition to the built environment, GPS tenology has also been employed in professional sports to assess PA paerns and examine position differences in intensity during the game su as: overall time in light, moderate, and vigorous exercise; distance traveled; and average/peak velocities (Kempton,
Sullivan, Bilsborough, Cordy, & Cous, 2015; Macutkiewicz & Sunderland, 2011).
Figure 2.4
A kid playing Pokémon Go.
Source: Photo by Zan Gao.
Overall, GPS and GIS systems show promise as a way of improving our understanding of the relationship between environmental aributes and PA behavior at the population level, despite the relative paucity of literature using these tenologies in the field of PA intervention and health promotion. at said, with the use of GPS/GIS for PA and health promotion among the general population continuing to increase, it is clear that the potential of GPS/GIS tenology in PA and health promotion is considerable.
Active video games (AVGs)
Figure 2.5
Young adults playing active video games.
Source: Photo by Zan Gao.
AVGs are active video games that require players’ bodily movement during gameplay. AVGs were originally designed to engage players in a more active mode of video game play, with resear indicating AVGs have great potential to serve as a motivational source to initially engage players in PA (Sun, 2015). For example, Sun (2012) compared students’ perceived situational interest between an AVG unit and a cardiovascular fitnesseducation unit. Findings indicated that students had significantly higher levels of situational interest in the AVG unit than they did in the fitnesseducation unit. Specifically, researers found that increased situational interest resulted from the fact that AVGs provided a unique opportunity for players to experience new features, to use high levels of aention throughout gameplay, to have opportunities to explore different game situations, and be offered both cognitive and physical allenges, and be able
to aieve a high level of instant enjoyment (Figure 2.5). Nonetheless, although AVGs demonstrate an ability to motivate individuals during gameplay, the sustainability of this motivational function is questionable. How to aieve sustainable motivation as well as motivate sustainable PA behaviors outside of AVG play should be the focus of future AVG development and AVG-based interventions.
Figure 2.6
Active bike game.
Source: Photo by Denis Pasco.
In addition to their motivational function, previous studies have demonstrated AVGs’ ability to increase energy expenditure and produce positive health benefits while also examining the influence of gender,
intensity, and active body mass on the overall energy cost of AVGs (Bailey & McInnis 2011) (Figure 2.6). Moreover, while participants generally enjoyed the AVGs, perceived enjoyment varied by gender and BMI classifications. Notably, ildren with higher BMIs enjoyed AVGs with greater social interaction offering intermient high-intensity gameplay than ildren with lower BMIs. is finding is significant in that it provides empirical data for future AVG design and interventions that specifically target adolescents “atrisk” for overweight. Recently, Sun (2015) further indicated the great potential of AVGs to facilitate players’ cognitive development. For instance, Gao and colleagues (2013) examined the impact of Dance Revolution (DDR) on Latino ildren’s physical fitness and academic aievement. ey found that ildren in the intervention group displayed greater improvement on math test scores than ildren in the comparison group. e results are of great importance for future practice. More studies are needed to identify the role of AVGs in aiding players in learning PA-related knowledge, concepts, or strategies. While the great majority of AVG studies have focused on ildren, this tenology also holds promise for adults and seniors with respect to balance improvement, fall prevention, reduction of premature disability, increasing functional independence, and maintaining health (deJong, 2010). e reader is referred to Chapter 9 to obtain a deep understanding of AVGs on PA and health.
Virtual reality Among emerging tenologies poised to aid in the assessment and promotion of PA and health, virtual reality and augmented reality (a.k.a., simulation tenology) are arguably the most exciting and tenologically advanced. Virtual reality is a digital tenology that replicates a real or imagined environment, and simulates a user’s physical presence in this environment, allowing for user interaction (Figure 2.7). e latest
commercially available virtual reality headsets, su as the Oculus Ri, PlayStation VR, Samsung Gear VR, or HTC Vive artificially generate sensory experiences, whi can include visual, auditory, tou, and scent stimuli, while allowing a user to manipulate objects within the virtual environment (Isaac, 2016). By contrast, augmented reality is a direct or indirect live view of a physical, real-world environment whose elements are supplemented by computer-generated sensory input su as sound, video, graphics or GPS data (Graham, Zook, & Boulton, 2013). Augmented virtual reality games, like Pokémon Go, Zombies, Run!, and Geocaing are unique in that they overlay aspects from the physical and virtual worlds into one cohesive experience. To date, virtual reality has been extensively used in many health domains, with most virtual reality systems used for the purpose of rehabilitation. In fact, the potential of virtual reality in PA and health promotion has been explored, with improved motor function (McEwen, Taillon-Hobson, Bilodeau, Sveistrup, & Finestone, 2014; Saposnik et al., 2016), cognitive learning (Man, Chung, & Lee, 2012), and psyological well-being (Gaggioli et al., 2014) observed among various populations using this tenology. Yet, while considered a cuing-edge tenology possessing great potential in promoting PA, the use of virtual reality tenology to promote PA is still in its infancy (Pasco, 2013). Nonetheless, the preceding literature review regarding the use of virtual reality in various PA seings indicates virtual reality tenologies can have current applications in physical education and sports seings to promote PA and athletic performance. However, few largescale, methodologically rigorous studies have been conducted in the literature, with no high-quality study completed in a physical education class. erefore, researers and practitioners should continue to explore the benefits of virtual reality and harness its potential in the promotion of PA and health among a variety of populations.
Figure 2.7
A young adult playing virtual reality games.
Source: Photo by Zan Gao.
Opportunities and allenges Tenology has anged our lives tremendously over the past several decades and played a paramount role in shaping our lifestyles and health status. Emerging tenologies bring exciting or even stunning opportunities for the promotion of PA and health. Yet, not all contributions from emerging tenologies have been positive. e emerging field of PA and health, coupled with the rapid development of tenology, has presented both allenges and opportunities that deserve further considerations for researers. According to King and colleagues (2015), allenges and opportunities can be classified into four categories: (1) data collection and data expansion; (2) tenical considerations; (3) areas for “bridging the gap”; and (4) privacy protection.
Data collection and data expansion While processing data gathered from emerging tenologies, a number of allenges have emerged. First, there has been a la of Big Data analysis, despite the potential large amount of PA data that can be retrieved from multiple emerging tenologies (e.g., PA data, posts on social media, GPS/GIS data, etc.) to improve the population’s health. us, great opportunities exist to deal with Big Data via improved mathematical models and computer algorithms. Undoubtedly, researers will find it vital to empower themselves to assess and analyze PA data through data and text mining within the next decade. Indeed, the ability to conduct Big Data analysis will be paramount in offering users constant personalized exercise prescriptions through mobile device apps in the future. Second, there has been lile use of crowdsourcing data in the PA field. Further, many
emerging tenologies are not yet capable of capitalizing on the ubiquity and heterogeneity of potential environmental data sources or of employing crowd-sourcing to evaluate and manage large datasets in an effort to improve public health. As su, crowdsourcing data in PA and health is an emerging field. For example, researers have started to use crowdsourcing to complete PA evaluation and surveillance (Amazon Meanic Turk; www.mturk.com). It appears promising to take advantage of crowdsourcing data in promoting PA in the future. ird, there is a la of GIS infrastructure in many regional and local areas. Indeed, discrepancies exist for GIS infrastructure between major metropolitan areas and small towns. us, it is recommended that local municipalities and/or companies share GIS resources. Fourth, there is still a la of sufficient PA data. Recruiting and training more researers to study PA and emerging tenologies are strongly encouraged. Finally, lile understanding of person–environment interactions in studies using emerging tenologies has been seen. It is, therefore, warranted to increase the number of studies that investigate different dimensions of an individual’s personal aributes (e.g., self-esteem, aitudes, cognition, weight status) and environments (e.g., social environment, physical environment).
Tenical considerations Tenology is rapidly developing at an amazing speed, thus offering numerous allenges and opportunities. First, a allenge exists in keeping up to date with the latest tenological advances. Nowadays it is not uncommon for researers to find that their recently purased products have become outdated aer a short period of time. King et al. (2015) suggested the centralization of tenological resources for researers with links, critiques, etc. It is also recommended to partner with companies in the public sector to begin developing and testing emerging tenologies. Second, it is noteworthy that a virtual exercise advisor has now been applied in PA
promotion. For example, a culturally and linguistically adapted virtual exercise advisor (i.e., Carmen) was used to provide tailor-based PA advice and support to the elderly. Given the major potential of this type of interactive tenology, it is advisable for health professionals and researers to solicit small business resear grants to develop cuing-edge tenologies for PA promotion. ird, augmented reality games (e.g., Pokémon Go) have successfully gained aention from users in recent years. ese reality games are aractive as they integrate the physical and virtual worlds into one interface on mobile devices, particularly the apps of smartphone devices. Of note for health professionals, augmented reality games require users to walk around and explore their local surroundings; hence increasing PA participation. However, physical harm may occur, su as playing these games while walking or driving. Playing the games also increases the economic burden as a result of in-app purasing and heavy data usage, and potentially leads the users to enter inappropriate or dangerous areas. In addition, the goe-locating feature embedded within some games can result in crime (e.g., criminals using the “lure” function of Pokémon Go). It is, therefore, imperative to offer safety guidelines for users of su games. Fourth, the cost of recent tenology and relevant equipment and supplies can be a problem. In many cases, researers provide subjects with the required equipment and supplies, assuming the investment will increase consistency in intervention across participants and decrease the potential barriers to participation and adherence. Yet, it can be quite expensive to offer lots of devices to a large population at one time. To deal with this issue, researers can solicit resear funds and industry donations for tenology-based PA promotion programs. Researers can also enroll participants in cohorts to decrease the number of devices needed. For some programs, it is possible to request participants cover the costs (e.g., when using smartphone and its apps as intervention strategies)—potentially providing a more contextually relevant evaluation of the intervention program. Additionally, it is allenging to secure funds for longitudinal data collection. Indeed, with longitudinal or prospective resear studies, external funds may be necessary to fully integrate emerging tenologies and
examine their effectiveness. In these circumstances, researers can again use established relationships with stakeholders to cover implementation costs. Finally, allenges exist due to the discrepancies in access to emerging tenology among individuals from different socioeconomic status. It is suggested to locate and use publicly available tenological resources or to employ widely used low-fee mobile devices, su as smartphones for PA promotion.
Areas for “bridging the gap” To bridge the gap between resear on the use of emerging tenologies to promote PA and the real world, researers face a number of allenges. First, tenology is continuously becoming more sophisticated, and experts in various disciplines are needed to develop and implement tenology applications to improve health in field-/clinic-based seings. Many times, some well-functioning applications are designed by computer scientists while neglecting the needs of clients, whi leads to lile or no intervention impact. Hence, to design and implement an effective tenology application for PA promotion, it is critical to recruit researers and practitioners from different disciplines, including PA specialists, computer scientists, and health practitioners as well as consulting the end users. Once more, forming institutional collaborations with initiatives and monetary incentives is encouraged. Notably, with the increasing application of the Social Ecological Model, the built environment inevitably becomes an indispensable component of PA promotion. As su, partnering with experts across disciplines (e.g., PA, planning, transportation, tenology) is also encouraged. Second, with the integration of electronic devices, mobile devices, and apps, bridging the gap between PA assessment and intervention becomes a allenge for researers. us, working with all stakeholders, including clients, to identify the leverage points for behavioral ange and integrating everyday PA data seamlessly into the lives of participants to
promote behavioral ange strategies will be important in the years to come. Finally, to effectively implement tenology-based PA promotion, it is imperative to understand the community and organizational systems, as well as all the other stakeholders’ needs and interests. For example, in sool-based PA programs, tenology can offer more oice to students. Children may be able to oose from different active video games, su as Wii Fit, Kinect Just Dance, and Dance Revolution and other games. is increased autonomy acts as a motivational tool to make ildren be active and improve class participation. Yet, it is quite allenging to implement su programs in sools due to la of support from the sool administration, space limitations, curricular conflicts, and budget shortages. As a result, King et al. (2015) recommended teaming up with organizational personnel from different community sectors, engaging with supportive decision-makers, and collaborating with industry to assess tenology applications in PA and health field.
Privacy protection ere are substantial concerns regarding how the digital world has compromised and is compromising anonymity. Briefly, emerging tenologies that continuously collect data regarding PA behavior as well as other social and environmental aspects related to PA invite several important ethical considerations for researers in the areas of anonymity, privacy, and participants’ informed consent. erefore, researers should develop standardized protocols for privacy protection. More details will be discussed in Chapter 12.
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3 Social and behavioral theories in promoting physical activity Zan Gao and Jung Eun Lee
e health benefits of regular physical activity (PA) participation among various populations has been well documented for several decades. However, the proportions of different age groups meeting the current PA recommendations are relatively low (Blair & Morris, 2009). For example, approximately 21% of adults meet the PA Guidelines of the Centers for Disease Control and Prevention (CDC) of 150 minutes/week moderateintensity aerobic activity, and less than 30% of high sool students have at least 60 minutes of PA every day. Unfortunately, it has been evident that anging individuals’ PA behaviors is rather complicated due to the fact that individuals usually encounter considerable barriers and allenges from personal and environmental factors during the behavior ange process (Sallis, Cervero, Aser, Henderson, Kra, & Kerr, 2006). Oentimes, however, the barriers and allenges that individuals face when seeking to ange PA behavior can be minimized through the application of sound social and behavioral theory (also termed “models”). Notably, early studies intervening to promote PA did not typically embrace social and behavioral theoretical frameworks. Later, as the field developed, it became necessary to employ appropriate theoretical frameworks to beer implement effective PA interventions and allow for subsequent intervention replication. As a result of these needs, solars started to apply social,
psyological, behavioral, and even environmental theories to beer understand the determinants and correlates of PA behavior (Suon, 2008). According to King et al. (2015), there are three classifications and seven theories for the types of theories that have been widely applied in the field of PA and health. ese classifications are: 1. Personal-level theories: Self-efficacy eory, Goal Seing eory, Self-determination eory, eory of Planned Behavior, and the Transtheoretical Model. 2. Behavior micro-environmental theories: Social Cognitive eory. 3. Behavior macro-environmental theories: Social Ecologic Model. Indeed, over the past 20 years, a burgeoning of theory-based studies in the PA and health promotion field has been observed, with many studies using one or more of the seven theories listed above. eories and models are important in that: (1) they allow us to beer understand and predict individuals’ PA behavior; (2) they provide a scientifically validated blueprint from whi to formulate effective PA behavioral interventions; and (3) they enable us to organize PA behavior variables in a coherent manner. Simply stated, theories and models can help explain PA behavior, as well as suggest how to develop and implement more effective intervention methodology capable of influencing and anging su behavior to a greater degree. Undoubtedly, the application of the aforementioned theories has tremendously improved our understanding of individuals’ PA correlates/determinants that are associated with or impact their PA behavior. is apter focuses on behavioral theories for PA promotion—particularly those most pertinent to the integration of emerging tenology within PA interventions. First, an overview of widely used behavioral theories for the development, implementation, and evaluation of PA promotion interventions is provided. e following section describes important theories and key concepts/constructs within ea theory that are oen used to promote PA and health. Immediately following will be a brief summary of the empirical evidence concerning the application of theories in emerging tenologybased PA promotion interventions. Finally, a short discussion of the
allenges and future directions for the application of theories in tenologybased practice and empirical resear will be given.
Personal-level theoretical perspectives and PA promotion Self-efficacy eory Self-efficacy eory originated from the Social Cognitive eory and includes self-efficacy and outcome expectancy as the two major constructs (Bandura, 1986, 1997). is intrapersonal-level behavioral theory proposes that an individual’s behavior can be explained and predicted by the positive relationship between self-efficacy and outcome expectancy. Notably, selfefficacy refers to beliefs about one’s capabilities to learn or perform specific behaviors with a certain degree of mastery. Specifically, individuals who feel efficacious are more likely to perform at a higher level, try new behaviors, expend more effort on those behaviors, and persevere longer when they encounter allenges to these behaviors. In PA contexts, evidence has been clear that self-efficacy directly influences or predicts behavior through its effect on goal seing, the capability to persist while facing obstacles, and coping with setbas and stress. Outcome expectancy refers to a person’s beliefs concerning the likely consequences of a behavior. e importance of an outcome and the degree of its influence may have a great deal of variability among individuals. us, it is crucial not to presume that outcomes will always act as incentives for motivated behavior (Rodgers & Brawley, 1991). As a result, Rodgers and Brawley proposed that outcome expectancy is formed by the interaction of two factors: (1) outcome likelihood, whi refers to the probability that a certain action will lead to a certain outcome; and (2) outcome values, whi refer to the values the individual assigns to the possible outcomes of the behavior. However, relative to self-efficacy, less aention has been paid to
outcome expectancy. Of note, in sports and PA contexts, resear has shown small or no associations between outcome expectancy and behavior (e.g., Rovniak, Anderson, Wine, & Stephen, 2002). Resear focusing on the ways that self-efficacy and outcome expectancy might operate together to impact individuals’ behaviors has produced mixed findings. Bandura (1986, 1997) has proposed that, when one is predicting behaviors in whi outcomes are highly contingent on the quality of performance, outcome expectancy does not explain significant variation beyond that explained by self-efficacy. is is evidenced by studies indicating outcome expectancy accounted for lile variance in motivational indices or behavior aer controlling for self-efficacy (e.g., Gao, Lee, Solmon, & Zhang, 2009; Gao, Lee, Kosma, & Solmon, 2010). Other resear work, however, has observed that both self-efficacy and outcome expectancy were independent predictors of behavior in sports and PA (Gao, Xiang, Lee, & Harrison, 2008). Clearly, the predictive aributes of self-efficacy and outcome expectancy require further investigation. Finally, according to Self-efficacy eory, an individual’s self-efficacy may vary, based upon the dimensions of strength, magnitude, and generality. However, the vast majority of previous studies have only assessed the strength of self-efficacy while neglecting the measurement of magnitude and generality. Additionally, some researers only measured outcome likelihood for outcome expectancy while omiing the important role that outcome values played. As su, it is possible that the predictive strengths of selfefficacy and outcome expectancy may have been misinterpreted. Consequently, further resear is warranted, particularly with regard to the assessment of self-efficacy and outcome expectancy. In sum, resear evidence has consistently confirmed self-efficacy to be a reliable and positive predictor of PA behavior. As a result, self-efficacy has been integrated into other social and behavioral theories and models su as the eory of Planned Behavior and the Transtheoretical Model.
Goal Setting eory
Over the past few decades, the Goal Seing eory has been increasingly used as an effective strategy to motivate individuals in many fields, including sports and PA (Loe & Latham, 1990). In particular, goal seing has been proven to be an effective motivational tenique for enhancing an individual’s productivity and performance (Loe & Latham, 1990). In fact, the Social Cognitive eory identified goal seing as an important strategy to understand and facilitate behavior ange at the intrapersonal level. According to Loe’s meanistic goal theory, goal seing refers to a process of seing targets as a means of developing and sustaining effort and persistence, mobilizing energy expenditure, developing self-regulation strategies, and directing appropriate aentional focus. ree major aspects of Loe’s theory focus on the effects of goal specificity, goal difficulty and goal proximity on performance: (1) seing specific goals has superior performance effects over seing “do-your-best” goals or no goals; (2) seing difficult but aievable goals has superior performance effects over easy goals; and (3) goals can be set at a distal (long-term) or proximal (short-term) level whereby distal goals make it easy to postpone effort yet proximal goals motivate immediate effort. In addition to the aforementioned goal aspects, namely, goal specificity, goal difficulty, and goal proximity, feedba (i.e., knowledge of personal status concerning one’s selected goal) and rewards (i.e., a motivator to continue progress toward a goal) have been added to the Goal Seing eory to enhance motivation and task performance. ese goal aspects and added components (feedba and rewards) are imperative to increase individuals’ self-efficacy, motivation, and ultimately PA behavior anges. Resear evidence has strongly supported the effectiveness of these postulations in the industrial and organizational fields. In PA contexts, the majority of resear studies have found goal seing to be associated with enhanced performance (Boyce, Wayda, Johnston, & Bunker, 2001; Mooney & Mutrie, 2000). A few studies, however, failed to substantiate the effects of goal specificity, goal difficulty, and goal proximity on performance (Weinberg, Burton, Yukelson, & Weigand, 1993). e inconclusive results have been aributed to several methodological flaws su as failure to control
spontaneous goal seing in “do-your-best” (control) groups, failure to make specific goals difficult, and failure to control for social comparisons, whi are inherent in sports and PA. Given these methodological considerations, it is imperative to heed Loe and Latham’s suggestions when conducting goal seing resear in PA contexts. One possible way to limit spontaneous goal seing is by considering the oice of task relative to the target population completing this task. Specifically, selection of a novel task and/or participants’ la of experience with a task could limit participants’ ability to spontaneously set goals (Boyce, 1994). For example, Gao and Podlog (2012) adopted a newly emerged PA, Dance Revolution, and examined the effects of different levels of goal specificity and goal difficulty on Latino ildren’s dance performance and PA levels. ey found that easy and difficult goal groups showed significant improvements for dance performance over the “do-your-best” goal group, with the difficult goal group also displaying the highest improvement for PA levels over time. Based upon the findings, Gao and Podlog suggested health professionals should be well versed in appropriate goal seing teniques and implementation in sool-based PA interventions—a suggestion whi could be applied to other contexts as well. erefore, it seems vital to incorporate cognitive-behavioral interventions, like goal seing, into a manageable protocol to be implemented by educators and community health professionals, many of whom may not possess (nor require) psyological training.
Self-determination eory To promote a continued and physically active lifestyle among individuals who do not meet the current PA recommendations, health professionals must aempt to adopt cognitive theories concerning motivation with the goal of promoting long-term behavior ange. Recently, at the intrapersonal level, the application of the Self-determination eory (Ryan & Deci, 2000) has
been fruitful in explaining individuals’ situational/proximal motivation for PA and health behaviors.
Figure 3.1
Kids playing active video games.
Source: Photo by Zan Gao.
According to Vallerand (2001), situational motivation refers to the motivation that individuals experience when they are currently engaging in an activity. Situational motivation is comprised of three main types of motivation: (1) intrinsic motivation; (2) extrinsic motivation; and (3) amotivation. Intrinsic motivation refers to an individual’s participation in an activity for its own sake (e.g., having fun, feelings of accomplishment), and involves the greatest degree of autonomous self-regulation (Figure 3.1). Extrinsic motivation (i.e., comprised of integrated regulation, identified regulation, introjected regulation, and external regulation) refers to activities that are carried out as a “means to an end” and not for their own sake, and involves less autonomy. With integrated regulation, an individual’s behavior has been assimilated into the sense of self by the individual. It represents the
highest degree of self-determined regulation within extrinsic motivation. As for identified regulation, behaviors occur when individuals accept certain activities as important to their personal goals and values (e.g., an individual sees exercise as necessary to rea their weight loss goal). Finally, in introjected regulation, an individual’s participation in a behavior occurs primarily out of a sense of compulsion and duress/guilt, among other external factors, while individuals in external regulation carry out behaviors in order to gain an external reward or avoid punishment. Notably, amotivation refers to a la of intention and a relative absence of motivation (Ryan & Deci, 2000). ese motivation types lie on a self-determination continuum with individuals becoming increasingly self-determined as one moves from amotivation to intrinsic motivation. Along the continuum, intrinsic motivation, integrated regulation, and identified regulation represent higher levels of self-determined motivation and lead to more positive behavioral consequences (Gao, 2012a; Gao, Podlog, & Harrison, 2012). Indeed, the Self-determination eory proposes that an individual’s behavioral regulation of an activity varies to the extent to whi the behavior is self-determined or controlling. Generally, individuals with higher intrinsic motivation, integrated regulation, and identified regulation demonstrate beer effort and engagement in PA contexts. Introjected regulation, external regulation, and amotivation represent lower levels of self-determined motivation and lead, most oen, to negative consequences. For example, individuals with dominant amotivation display boredom and more readily opted out of PA (Gao, 2012a; Gao, Hannon, Newton, & Huang, 2011). Further, the Self-determination eory posits that an individual has three basic psyological needs: (1) autonomy (i.e., beliefs about one’s oice regarding his or her own behavior); (2) competence (i.e., beliefs about one’s capabilities to learn a task or perform a behavior); and (3) relatedness (i.e., feeling connected to others, an organization, or a community). ese psyological needs are deemed to be essential to an individual, as an individual may be increasingly intrinsically motivated to perform a behavior if he or she perceives the behavior as important to satisfy one or more of these needs.
Hence, if these needs are satisfied, an individual will demonstrate optimal motivation, producing positive behavioral outcomes; and vice versa (Ryan & Deci, 2000). In the field of PA and health, researers are seeking to understand the meanism that supports need satisfaction and the subsequent optimal motivation using the Self-determination eory as a framework, and are increasingly applying this theoretical framework in guiding PA interventions (Gao, 2012a; Gao, Hannon, Newton, & Huang, 2011; Gao, Podlog, & Harrison, 2012). Yet, further studies are warranted in applying this theory to guide PA behavior ange as, due to its comprehensive nature, the Self-determination eory has the potential to facilitate further understanding of behavioral meanisms; thus aiding in the design of effective and appropriate interventions targeting PA promotion.
eory of Planned Behavior To increase individuals’ PA levels, identifying factors that influence the decision-making process is critical. A model that has been used to understand this process is the eory of Planned Behavior (Ajzen, 1991) (Figure 3.2). e eory of Planned Behavior was developed on the basis of an earlier work, the eory of Reasoned Action (Ajzen & Fishbein, 1980). e eory of Reasoned Action emphasized intention as the most immediate or proximal factor of the desired behavior ange—a factor influenced by two constructs: aitudes and subjective norms. However, the theory was later expanded to include the construct of perceived behavioral control, in what we now know as the eory of Planned Behavior. Specifically, the eory of Planned Behavior (Ajzen, 1991) postulates that aitudes, subjective norms, and perceived behavioral control jointly influence intention whi still serves as the most proximal determinant of behavior. Notably, perceived behavioral control is also hypothesized to directly affect behavior.
Figure 3.2
eory of Planned Behaviour.
Source: Photo by Zan Gao.
According to the eory of Planned Behavior, PA intention is defined as an individual’s desire and motivation to be active. Aitude refers to the perceived consequences (positive or negative) of PA participation— representing a function of behavioral beliefs (i.e., perceived advantages and disadvantages of PA participation and evaluation of behavioral outcomes). Subjective norms reflect the perceived social pressure from significant others to engage in an active lifestyle and functions on the basis of normative beliefs (i.e., perceptions about whether certain individuals encourage or discourage PA participation). Lastly, perceived behavioral control reflects both selfefficacy (i.e., situational self-confidence for initiating and maintaining physically active behavior) and controllability of the behavior (i.e., perceived ability to overcome PA barriers). As a result of the aforementioned considerations, a basic hypothesis can be postulated for the eory of Planned Behavior. Namely, an individual’s intention to perform a behavior increases if they possess favorable aitudes and subjective norms, with concurrently strong perceived behavioral control. Overall, the predictive strength of eory of Planned Behavior for PA has been supported. Specifically, in two meta-analyses regarding the use of the eory of Planned Behavior (Hagger, Chatzisarantis, & Biddle, 2002; Symons Downs & Hausenblas, 2005), the most important predictor of PA was observed to be intention, while the most significant predictors of intention
were aitude and perceived behavioral control; generally, subjective norms demonstrated small or no significant effects on PA intention. Empirical studies indicate that the eory of Planned Behavior is useful in explaining behavioral intentions in various populations (Chatzisarantis, Frederi, Biddle, Hagger, & Smith, 2007). However, there may be some limitations for the eory of Planned Behavior with regard to explaining PA behaviors. Most notably, results regarding the effect of the time interval between increased intention and subsequent behavior ange is inconclusive. According to one previous meta-analysis (Hausenblas, Carron, & Ma, 1997), the strength of the intention–behavior relationship did not weaken over time, whi contradicts the findings of other literature (Chatzisarantis, Hagger, Biddle, & Smith, 2005). However, Downs and Hausenblas (2005), when examining the predictive utility of intention on behavior ange, suggested that to improve the predictive utility of intention on subsequent behavior ange, it is necessary to assess intention as close as possible to the initiation of required behavior. e preceding temporal consideration needs to be considered by health professionals desiring to implement the eory of Planned Behavior within PA and health promotion interventions. Furthermore, the large amount of unexplained variance between intention and behavior still remains problematic and raises questions regarding whether researers should solely rely on this theory for future interventions. In summary, although the application of the eory of Planned Behavior has strong support for understanding PA behavior, the implementation of the theory in future interventions warrants caution—particularly with respect to the temporal assessment of its construct.
Transtheoretical Model In addition to a number of social cognitive behavioral theories, stage-based behavioral models have also been considered when wishing to understand the adoption of health behaviors. e most popular stage-based approa applied in PA contexts is the Transtheoretical Model (Proaska &
DiClemente, 1982). Within the Transtheoretical Model, individuals are posited to progress through a series of stages, with ea stage consisting of different behavioral and psyological paerns regarding the adoption of a health behavior. Specifically, individuals are classified into one of the following six stages as assessed by their readiness to engage in behavior ange: (1) precontemplation; (2) contemplation; (3) preparation; (4) action; (5) maintenance; and (6) termination (Proaska & DiClemente, 1982). In the precontemplation stage, a person does not intend to make any behavioral ange. is reluctance to make a behavior ange may be due to la of information and/or motivation. Regarding the second stage, contemplation, we see an individual beginning to think about anging their behavior within the next six months—oen weighing the pros and cons of anging the behavior. Of note, individuals in the contemplation stage are not yet ready to take action to ange the behavior. An individual is ready to take action to ange a behavior within the next month aer progression to the preparation stage, and typically have a plan for how they intend to ange the behavior. Action represents the fourth stage of behavior ange and refers to an individual using their previously developed behavior ange plan to begin actively anging a behavior. Notably, the action stage is the most variable, with individuals at greatest risk of regressing to an early stage. If an individual does not regress to an earlier stage when in the action stage and successfully adheres to their behavior ange plan for six months or longer, these individuals will then be classified as being in the maintenance stage. e major focus of the maintenance stage is relapse prevention. Only when and if relapse is successfully prevented and the individual states they have no desire to go ba to their old behavior and are comfortable engaging in their new behavior, is the individual said to be in the termination stage. However, most individuals who have anged their behavior stay somewhere in the maintenance stage (Edberg, 2007). According to Transtheoretical Model, as individuals progress through ea stage, it is important to tailor intervention strategies to address the different psyological and cognitive needs aracterized by ea stage. Notably, however, the model is not linear, meaning individuals do not progress
systematically from one stage to the next. Rather, individuals may enter at any stage but relapse to an earlier stage, beginning the process again. us, designing stage-tailored interventions whi meet the stage-specific needs of individuals will not only increase intervention adherence, but will also alleviate reluctance to initiate participation in the desired behavior(s) (Proaska & Marcus, 1994). Literature regarding the effectiveness of interventions using the Transtheoretical Model have demonstrated mixed findings. In a systematic review by van Sluijs and colleagues (2006), the authors did not find any favorable effects of Trantheoretical Model-based interventions on PA compared to other alternative theories or models. Additionally, Adams and White (2003) found that despite more than two-thirds of short-term (< 6 months) studies indicating positive effects of Transtheoretical Model-based interventions compared to control, the number of long-term studies reporting the positive effect of the Transtheoretical Model-based interventions was small. ere may be two main reasons why Transtheoretical Model-based interventions demonstrated a la in effectiveness over time in previous literature. First, the criteria used for assessing the methodological quality of empirical studies seems to vary from study to study. Indeed, while some empirical studies utilized a randomized controlled trial design, others laed a control group (e.g., cross-sectional design) (West, 2005). Consequently, deciding upon the inclusion and exclusion criteria in reviews could have significantly impacted the analysis and subsequent conclusions regarding the effectiveness of the Transtheoretical Model. Second, the la of strong evidence may be due to the fact that some health behaviors are simply more conducive to Transtheoretical Model-based interventions (Bridle et al., 2005). Undoubtedly, fundamental differences exist between some health behaviors, su as PA, and the addictive behavior upon whi the theory was originally developed (i.e., smoking; Proaska & DiClemente, 1982). Indeed, PA is a multifaceted and complex behavior, with health professionals who favor the model possibly focusing too mu on the individual while underestimating the multiple external factors affecting individuals’ PA behavior.
Behavior micro-environment theoretical perspectives and PA promotion Social Cognitive eory At the behavior micro-environment level, it is assumed that an individual’s beliefs, thoughts, social support, and the external environment influence his or her behavior, with reciprocal effects observed between these factors. e Social Cognitive eory (Bandura, 1986, 1997) is one of the most frequently used theoretical frameworks used at this level of PA and health promotion to explain individuals’ PA correlates and behaviors. is theory proposes that the behavior, the individual’s environment, and the individual continuously ange and simultaneously influence ea other in a relationship termed reciprocal determination (Figure 3.3). Specifically, reciprocal determinism means that an individual can be both an agent for ange and also a responder to ange. erefore, three factors (personal factors, environmental factors, and behavior) must be considered when trying to understand individuals’ PA behaviors (Gao, 2012b).
Figure 3.3
Social Cognitive eory.
Source: Photo by Zan Gao.
According to the Social Cognitive eory (Bandura, 1986), self-efficacy, goals, outcome expectancy, socioeconomic status, age, and gender, represent major constructs of personal factors, among others. For example, in PA contexts, high self-efficacy positively relates to the adoption of and adherence to PA behavior among a wide range of populations (Gao, 2012b; Gao, Lee, & Harrison, 2008; Gao, Xiang, Lee, & Harrison, 2008). As self-efficacy, goals, and outcome expectancy were described in great detail in the previous sections (Gao, 2012b; Gao, Huang, Liu, & Xiong, 2012; Gao, Lobaum, & Podlog, 2011), this terminology and their relationships with PA behavior are not reiterated here. Environmental factors include, but are not limited to, social support and physical/social environmental factors. Social support refers to the physical and emotional comfort given by family, friends, co-workers and others. A number of studies have documented that social support, especially support from family and peers, plays an important role in promoting adolescents’ PA
levels (Davison, 2004; Hohepa, Scragg, Sofield, Kolt, & Saaf, 2007). Particularly, parents have been observed to influence adolescents’ PA participation by providing transportation and encouragement, as well as participating in PA with their ildren (Dowda, Dishman, Pfeiffer, & Pate, 2007). In terms of peer support, it has been evident that the frequency of individuals’ participation in PA with friends, especially in shared PA, has a positive association with their overall frequency of PA (Beets, Pitei, & Forlaw, 2007; Voorhees et al., 2005). Moreover, other resear has also indicated that social support, coupled with self-efficacy, had a significant association with students’ moderate and vigorous PA (Gao et al., 2012; Martin & McCaughtry, 2008a). Physical and social environmental factors, on the other hand, include physical opportunities for PA, access to safe PA environments, and social restrictions. Substantial evidence supports that physical and social environmental factors seems to hold great promise for understanding PA behavior (Sallis, Proaska, & Taylor, 2000). However, more resear is needed. In conclusion, the Social Cognitive eory describes factors that may influence behavior while also identifying the reciprocal meanisms regarding how these factors interactively affect one another to lead to behavior ange. Within this theory, the intrapersonal, interpersonal, sociocultural, and environmental factors function interactively to facilitate or inhibit an individual’s PA behavior. As a result, this theory has emerged as one of the most popular theoretical frameworks used in the field of PA and health.
Behavior macro-environment theoretical perspectives and PA promotion Social Ecologic Model To beer understand PA behavior ange, researers have also incorporated the influence of an individual’s environment at all levels of society in the analysis and promotion of behavior ange—thus adopting the Social Ecological Model in the study of PA behaviors (Addy et al., 2004). e Social Ecological Model is a multilevel theoretical approa that introduces socialenvironmental factors su as social support from friends, and physical environmental factors su as convenient access to PA facilities (Zhang, Solmon, Gao & Kosma, 2012) while also emphasizing the influence of organizations, the community, and public policy as important vehicles in the shaping of behavior. e basic assumption of the Social Ecological Model is that individuals’ behaviors are the results of interactions among individual, interpersonal, organizational, societal, and community-level factors occurring within a specific behavioral seing (Stokols, 2000). Guided by the Social Ecological Model, researers have systematically investigated the individual, social, and physical environmental correlates of PA in a variety of populations (Addy et al., 2004; Sallis et al., 2008). Notably, using the social ecological perspective in this manner to promote PA can provide useful insights concerning effective interventions to increase PA participation at the population-level. e use of this ecological approa has been widely accepted as a method to guide interventions taling public health concerns in the United States (U.S. Department of Health and Human Services, 2010). However, there are
some allenges for researers relying on this ecological approa to inform interventions within the domain of PA behavior. Specifically, while ecological models may have great strength in providing overaring frameworks to guide interventions, these models are sometimes inadequate at discerning specific meanisms by whi one level influences or interacts with other factors at different levels to explain individual behavior. us, it is understandable why researers within the PA domain are still in favor of the previously reviewed cognitive-based theories. Indeed, these cognitivebased theories satisfy the researers’ traditional desire to infer causality when solving problems at the individual level. at said, PA is a complex behavior. Due to the preceding fact, researers are becoming aware that this behavior cannot be explained as a linear process happening exclusively at the individual level. As su, the ecological approaes are becoming more widely accepted and integrated, in order to beer understand how complex interactions between multiple levels of influence may impact PA determinants and the mediating role of these determinants (Bronfenbrenner, 2005; McLaren & Hawe, 2005). Another important aspect to anowledge is that the influence of determinants may not be the same over time and that the determinants may also have direct influence on behavior (Plsek & Wilson, 2001). e traditional resear approa proposes that the measurement of social cognitive factors su as aitudes, efficacy, and beliefs will determine the effectiveness of interventions in improving participation in a specific behavior. is approa may be flawed, however, as it fails to appreciate the complexities involved in human behavior (Resnicow & Page, 2008)—yet another potential advantage of incorporating the Social Ecological Model. In conclusion, the Social Ecological Model recently has been accepted in PA seings due to the appreciation of the multilevel factors that may directly or indirectly impact complex PA behavior. However, despite its success in incorporating various determinants into the explanation of PA behaviors, la of information regarding specific meanisms for the relationship between factors at different levels of this model may be noted as a limitation. Future
endeavors to integrate some of the cognitive theories with this ecological model may be necessary to overcome this shortcoming.
Application of behavior theories using emerging tenologies Knowing the various influences of interventions on individuals’ PA correlates and determinants is an important first step in designing intervention programs to promote PA participation and health. roughout the apter, we have reviewed the details of seven theoretical frameworks that have been widely employed in the field of PA and health: (1) Self-efficacy eory; (2) Goal Seing eory; (3) Self-determination eory; (4) eory of Planned Behavior; (5) Transtheoretical Model; (6) Social Cognitive eory; and (7) the Social Ecological Model. Studies to date using emerging tenologies have typically addressed the feasibility or efficacy of su tenologies, or have simply used these tenologies as motivational tools or incentives for PA behavior ange. However, researers have applied some of the preceding social and behavioral theories in many projects integrating emerging tenologies, su as computers and the Internet, online social media, active video games, and mobile device apps. Some exceptions are studies using newly-available emerging tenologies su as health wearable devices, GPS/GIS, and virtual reality. e aforementioned social and behavioral theories have been extensively used as the theoretical frameworks for numerous observational and experimental studies involving phone-based, computer-based, and Internetbased PA interventions a decade ago (Lewis et al., 2008; Lewis, Marcus, Pate, & Dunn, 2002). For example, Lewis et al. (2008) investigated differences in use paerns and perceived website usefulness between stage-tailored Internet and standard Internet groups using the Transtheoretical Model and Social Cognitive eory as theoretical frameworks, finding that the stage-tailored Internet group logged onto their website significantly more than the standard Internet group. ey also indicated goal seing and self-monitoring to be
most useful for PA behavior ange (Lewis et al., 2008). In another example, Papandonatos and colleagues (2012) adopted the Transtheoretical Model in a 12-month randomized controlled trial using both print-based and phonebased Transtheoretical Model intervention strategies, and found statistically significant anges in the self-efficacy, decisional balance, and cognitive and behavior processes of ange at the 6-month post-intervention assessment. ese authors also suggested that the magnitude of these anges increased during the 6–12-month intervention follow-up in the group receiving print materials. Among ildren, Pope et al. (2016) investigated the effects of an active video game program on ildren’s Transtheoretical Model-based PA determinants and PA levels. At pre-test and post-test, elementary ildren were administered measures regarding stages of ange for PA behavior, decisional balance for PA behaviors, PA self-efficacy, and self-reported PA levels. Following the pretest, a weekly 30-minute, 18-week Dance Revolution intervention was implemented. Children were classified into three groups: progressive ildren (i.e., progressed to a higher stages of ange); stable ildren (i.e., remained at the same stages of ange); and regressive ildren (i.e., regressed to a lower stages of ange). e researers found that progressive ildren had more improvements in self-efficacy, decisional balance, and PA levels than regressive ildren over time. Generally speaking, Social Cognitive eory has been widely applied in studies using emerging tenology su as active video games and online social media. For example, in a longitudinal intervention study with active video games, Gao and associates indicated that an active video game intervention had a positive impact on ildren’s self-efficacy and social support but not outcome expectancy over the course of one year (Gao, Huang, Liu, & Xiong, 2012). Pope and Gao (2017) also used this theory as a guide to examine the effectiveness of a smartphone exercise app (i.e., MapMyFitness) on college students’ sedentary behavior and PA, as well as social cognitive beliefs and identified differences on some social cognitive outcomes. As one major component of this theory, social support has also been studied in numerous projects—particularly those interventions using online
social media and games. Verheijden et al. (2005), for instance, investigated the role of social support in lifestyle-focused weight management interventions using online social media. Social support among ildren and teaers (Fogel et al., 2010) and between player and peers (Paw et al., 2008) have also been investigated. e findings of these studies unanimously support the use of social support in the regulation of individuals’ PA behavior when integrating emerging tenology. Other theories, su as the Self-determination eory and the eory of Planned Behavior, are also used in this area of inquiry. To examine the extent to whi ildren’s self-determined motivation predicted ildren’s moderate-to-vigorous PA when playing active video games, Gao and his resear team conducted a number of observational studies (Gao et al., 2011; Gao, 2012b; Gao, Podlog, & Huang, 2013). ey found that ildren’s intrinsic motivation positively predicted ildren’s moderate-to-vigorous PA while amotivation was a negative predictor; thus recommending professionals offer su games in an interesting and enjoyable way to foster ildren’s intrinsic motivation. Further, in a study by Maddison et al. (2007), the eory of Planned Behavior was utilized as the theoretical framework with researers reporting active video game play significantly affected ildren’s PA aitudes, subjective norms, intention, and strenuous exercise behavior. Finally, the Goal Seing eory has been used as either a stand-alone theory or as a major component incorporated into other theories like the Social Cognitive eory. Recently, Gao and Podlog (2012) used the Goal Seing eory in their active video game study. In particular, they instructed ildren to set different goals—specific goals (difficult goal, easy goal) and a “do-your-best” goal in the intervention—finding ildren who set specific goals had beer outcomes than those who set vague and the “do-your-best goal,” hence recommending specific goals be set to improve ildren’s sustained PA behavior. Notably, the Social Ecological Model has rarely been used in the previously mentioned experimental or observational studies. Yet, with the prevalence of GPS/GIS use in people’s daily life, the application of the Social Ecological Model in PA behavior anges interventions shows promise.
Opportunities and allenges moving forward In this apter, seven social and behavioral theories have been elaborated upon while numerous other theories (e.g., aievement goal theory, motivation protection theory, etc.) were neglected. However, the selection of the theories included in this apter is based upon the popularity and application of these theories in the field of PA and health. Furthermore, it should be reiterated that the aforementioned theories also demonstrate the complex interplay of behavioral determinants of PA behavior at different levels. For example, PA behavior is influenced by a number of personal factors including self-efficacy, motivation, goal seing, intention and behavioral stages at the intrapersonal (individual) level, social support from family and friends at the interpersonal level, and environment, policy, as well as perceptions of crime and safety at the community/regional level. Traditionally, intervention strategies to ange PA behaviors have focused on intrapersonal factors su as beliefs, cognition, knowledge, and movement skills. In doing so, researers and health professionals usually select from intrapersonal-level theories su as the Self-efficacy eory, the Goal Seing eory, the Self-determination eory, the eory of Planned Behavior, and the Transtheoretical Model. Indeed, these theories correctly target behavior ange at the intrapersonal level by manipulating constructs su as: selfefficacy, goal seing, stages of ange, aitudes, outcome expectancy, and intrinsic motivation. For example, enhancing an individual’s self-efficacy has been proposed by many theories to promote PA behavior. at is, health professionals may make deliberate efforts to enhance individuals’ selfefficacy using su effective strategies su as: (1) helping individuals successfully complete the task to foster a sense of accomplishment; (2) providing timely and accurate feedba on a task; (3) seing small,
incremental, and aievable goals; (4) using good role models for behavioral ange; and (5) monitoring and reinforcing behavior by keeping records. Commonly used cost-effective strategies at the interpersonal level include establishing social support programs and building accommodating social environ ments—strategies accomplished more easily using online social media and online communities for active video games and apps. ese behavior ange strategies utilize the Social Cognitive eory whi explains human behavior through a three-way, dynamic, reciprocal model in whi personal factors, environmental factors, and behavior continually interact with one another (Figure 3.4). From a behavior micro-environment theoretical perspective, key constructs of this theory that are relevant to behavior ange interventions include not only intrapersonal factors (e.g., self-efficacy, self-regulation), but also social environmental factors (e.g., social support, social environment, and social networks).
Figure 3.4
Elite athletes playing active video games.
Source: Photo by Zan Gao.
Aside from the above approaes to PA behavioral ange, it has been shown that approximately 50% of individuals who initiate a PA program drop out within the first six months, and without successful intervention at multiple levels of the environment impacting behavioral ange, the retention rate drops dramatically post-intervention (Dishman & Buworth, 1996). us, ecological models have gained credibility over the past decade as there is now consensus that PA behavior ange is a complicated and multifaceted phenomenon with multiple levels of factors affecting intervention outcomes. In response, the Social Ecological Model, a behavior macro-environment theory, has been increasingly used to explain the impact of individuals, social environments, physical environments, and policies on individuals’ PA behavior ange. To aieve enduring anges as a result of PA behavior ange interventions from a multilevel perspective, researers should continue to embrace the use of this theory to inform behavioral interventions (Humpel, Owen, & Leslie, 2002; Stokols, 2000). Notably, however, this shi to the examination of a complex range of factors whi shape PA behaviors might make the selection of behavioral ange intervention strategies daunting. With the preceding point in mind, the fact remains that the oice of effective behavioral intervention strategies depends upon the selection of whi theory is most appropriate under specific circumstances in a target population. Despite the great potential that the Social Ecological Model may hold to ange complex behaviors (su as PA) through multilevel interventions, certain issues exist regarding the extent to whi particular factors from various levels interact and influence PA behavior in a given context. is calls for more resear to investigate the factors that exert the most influence on PA behavior under certain circumstances before an agreed consensus is reaed. Meanwhile, researers should still rely heavily upon other theories, su as Social Cognitive eory and Transtheoretical Model, to make up for the limitations present in the Social Ecological Model and best assess factors related to individuals’ PA behavior ange. Simply stated, different theories are best suited to different problems at various levels of
intervention, su as individuals, groups, and/or communities — a fact that health professionals should keep in mind. eory, resear, and practice are part of a continuum to understand PA behavior correlates and determinants, to test the effectiveness of behavior ange strategies, and to disseminate cost-effective and efficacious interventions. What is clear from this review is that PA behavior ange is a complicated and multifaceted phenomenon that is affected by numerous factors at multiple levels. Simple reliance upon certain intrapersonal or interpersonal theories does not necessarily result in effective behavior ange among target populations. Moreover, although the Social Ecological Model includes factors from different environmental levels in the prediction and influence of an individual’s PA behavior, caution should be noted with regard to the application of this theory at the individual level in specific situations. Meanwhile, emerging tenologies, su as online social media, active video games and health wearable devices, have expanded the range of theory-based strategies available for effective PA behavior ange over the past decade. at said, more resear is needed to develop and test theories (and the integration of multiple theories) to create evidence-based, theory-baed PA intervention guidelines using emerging tenologies.
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Part II Emerging tenologies in physical activity and health
4 Computer and Internet use in enhancing physical activity Jung Eun Lee and Zan Gao
Despite national physical activity (PA) recommendations of at least 150 minutes per week of moderate-to-vigorous PA (MVPA), less than one-third of American adults (21%) meet this recommendation (Centers for Disease Control and Prevention, 2014). As a result, innovative and effective approaes to promote PA and health have been investigated. As of January of 2014, 87% of the U.S. adults (231 million) use the Internet (Perrin & Duggan, 2015), with Internet users similar across age, gender, and ethnicity (Perrin & Duggan, 2015). For example, it has been reported that Internet use is most popular among individuals aged between 18 and 64 years— demonstrating the Internet’s ability to aid a wide range of individuals in accessing information (Carr et al., 2013). Due to the aforementioned wideranging use of the Internet, Internet-based PA interventions are among the most promising annels of health promotion in the present day. Increasing the aractiveness of Internet-based PA interventions is the ability to use this mode of intervention delivery to reduce some of the barriers that traditional face-to-face intervention delivery poses to participants (e.g., transportation, increased time requirements, etc.; Napolitano et al., 2003). Indeed, Internet-based PA interventions can deliver individually tailored PA and health-related messages, enhancing access to
expert feedba regarding numerous PA modalities. Individually tailored interventions are appealing, given the fact that tailored messages are easier to read/remember and are more relevant and effective in promoting health behavior anges than non-tailored interventions (Hamel, Robbins, & Wilbur, 2010). Finally, Internet-based PA interventions offer researers and health professionals the ability to implement interventions any time/any place and oen at a lower cost (Joseph, Durant, Benitez, & Pekmezi, 2014). roughout the remainder of this apter, the reader will be treated to a review of the extant literature regarding Internet-based PA interventions, with the greatest aention given to the health benefits arising from these interventions. Following this discussion, the limitations of Internet-based PA interventions will be reviewed, aer whi, directions for future resear and the proper application of Internet-based interventions will be presented.
Components of Internet-based PA interventions Delivery methods for Internet-based PA interventions vary from study to study. Most frequently, Internet-based PA interventions have been delivered by email-only or website portal-only or via a combined approa (Figure 4.1). Further, some Internet-based PA studies have also included nonInternet components, including face-to-face and/or telephone-based counseling, as well as concurrently using mobile device applications in the traing and uploading of PA statistics (Joseph et al., 2013).
Figure 4.1
World Wide Web.
Source: pixabay.com.
Internet-based PA interventions using website portals to engage participants and deliver intervention content have used message boards/at rooms. For example, in a study by Carr et al. (2013), participants were asked to post any health-related questions to the researers on a message board placed on a study-related website. e message board’s main purpose was to encourage participant’s PA participation. Message boards/at rooms have
also been used to promote social support among study participants. Specifically, in some studies, message boards/at rooms allowed participants to interact with one another—exanging ideas, sharing information, and receiving support for PA participation (Grim, Hortz, & Petosa, 2011; Morgan et al., 2011; Reid et al., 2012; Wadsworth & Hallam, 2010). Another prominent strategy employed via website-based interventions has been the use of educational modules (Benne, Broome, Swab-Pilley, & Gilmore, 2011; Glasgow et al., 2010; Laausse, 2012; Magoc, Tomaka, & Bridges-Arzaga, 2011; Motl, Dlugonski, Wójcii, McAuley, & Mohr, 2011). For example, Magoc and colleagues (2011) used Internet-based educational modules to deliver structured, theory-based health information to promote PA. Over the course of the intervention, these modules asked participants to complete tasks regarding various PA-related topics su as self-monitoring, goal seing, and barriers. Other studies (e.g., Marcus et al., 2007; Slootmaker, Chinapaw, Suit, Seidell, & Van Meelen, 2009; Wanner et al., 2009; Watson, Bimore, Cange, Kulshreshtha, & Kvedar, 2012) used computer-generated, individually tailored feedba to further increase the effectiveness of Internet-based PA interventions. In the Step into Motion trial (Marcus et al., 2007), participants completed monthly online questionnaires throughout the intervention that provided individually tailored feedba to participants regarding increasing their PA participation using computer algorithms. Finally, some Internet-based PA interventions have also incorporated the concurrent use of mobile device applications for goal-seing and selfmonitoring purposes (e.g., Benne et al., 2011; Booth, Nowson, & Maers, 2008; Carr et al., 2013; Glasgow et al., 2010; Magoc et al., 2011; Morgan et al., 2011; Pellegrini et al., 2012; Wadsworth & Hallam, 2010; Watson et al., 2012). For example, participants have been asked to record their daily PA or step counts and upload these results to an online system whi provides timely feedba in relation to the number of steps taken (Booth et al., 2008).
Effectiveness of Internet-based PA interventions According to a meta-analysis regarding the effectiveness of Internet-based PA interventions, a small yet significant short-term effect is present when this intervention modality is implemented (Davies, Spence, Vandelanoe, Caperione, & Mummery, 2012). Notably, this effect was stronger when structured educational PA information was provided to participants (Davies et al., 2012). However, researers indicated a need for future studies to explore the long-term effect of this type of intervention. Another review also discussed factors whi might influence the effectiveness of Internet-based PA interventions (Vandelanoe et al., 2007). Among the most notable factors that could influence the effectiveness of su an intervention, the frequency of contact that participants had over the Internet via emails, education modules, at rooms, or online coa had the highest association with positive outcomes. Indeed, Internet-based PA interventions with five or more routes of communication between study participants and/or researers demonstrated more favorable PA behavior anges than those with five or less (Vandelanoe et al., 2007). Vandelanoe and colleagues also found the use of theory during Internet-based PA interventions to influence study outcomes. In a study by Carr et al. (2013), several components of an Internet-based PA intervention were employed using the Social Cognitive eory as a theoretical framework. Participants in the intervention group were included in a researer-developed “Step into Motion” PA website, while control participants used publicly available PA websites. Findings indicated the intervention group using the researer-developed, “Step into Motion” website, whi was baed by theory, had significantly greater increased PA levels at three months compared to the control group—
demonstrating that Internet-based PA interventions that are baed by theory may be more effective than those devoid of theory. According to Joseph et al. (2013), studies comparing the delivery of PA interventions via Internet-based platforms as compared to in-person (Pellegrini et al., 2012; Touger-Deer, Denmark, Bruno, O’Sullivan-Maillet, & Lasser, 2010) or print-based platforms (Cook, Billings, Hers, Ba, & Hendrison, 2007; Marcus et al., 2007) observed that these delivery methods yielded approximately identical results. ese findings indicate that Internetbased PA interventions may be equally effective in promoting PA behavior ange compared to more traditional PA intervention delivery methods (i.e., in-person or print-based delivery). However, it is noteworthy that Internetbased PA interventions are capable of aieving these results in a more costeffective and accessible manner among a wider range of populations— making Internet-based PA interventions a promising method of intervention delivery (Joseph et al., 2013). Further, pairing Internet-based PA interventions with other emerging tenologies being used more frequently to promote health (e.g., mobile device apps, health wearables) may further enhance the effectiveness of Internet-based PA interventions. More resear is needed to investigate these statements.
Behaviors targeted and theories used Internet-based PA interventions have sought to target a number of specific health outcomes. In a review by Joseph and colleagues (2013) regarding Internet-based PA interventions, it was found that while PA was oen the primary outcome of the interventions, many studies incorporated other health-related outcomes affected by or related to PA su as weight loss and healthy eating indices in addition to risk factors for cardiovascular disease, diabetes, arthritis, or depression. Notably, approximately 70% of the Internetbased PA interventions included in the preceding review used an established theoretical framework. eories are useful while planning, implementing, and evaluating interventions (Glanz, Rimer, & Viswanath, 2008). Indeed, interventionists can use theories to target specific health behavior determinants (e.g., self-efficacy) and develop intervention strategies to improve these determinants. As different theories are best suited to different health behavior determinants, oice of a suitable theory usually begins with identifying the problem, goal, and unit of study (e.g., individual, group, and organization level). For example, when researers aempt to develop smoking cessation interventions at the individual level, the Transtheorectical Model may be useful (Glanz et al., 2008). While select studies may oose to develop and implement interventions using more than one health behavior theory (see Chapter 3), most interventions employ a single theory for this purpose.
Assessment of PA in Internet-based interventions Various measures have been used to assess PA during Internet-based PA interventions. Given the fact that the majority of Internet-based PA interventions do not have a vast number of face-to-face interaction, most studies have used self-reported PA measures to assess this health behavior (Joseph et al., 2013). e three most frequently used subjective measures have been the International PA estionnaire (Craig et al., 2003), the Godin Leisure Time Exercise estionnaire (Godin & Shephard, 1985), and the Seven-Day PA Recall (Sallis et al., 1993). at said, a few studies used either objective measures su as pedometers or accelerometers, or both subjective and objective measures (Joseph et al., 2013).
Internet-based PA interventions in the healthcare field An increased proportion of the population is now using the Internet to acquire health information. Resulting from increased use of the Internet to acquire health information, greater-diversity has been seen in the populations seeking this information. e healthcare field is mirroring these trends, with Internet-based PA interventions implemented among patients with: cardiovascular disease, type 2 diabetes, and other clinical diagnoses su as arthritis/rheumatism, angina, cancer, ronic obstructive pulmonary disease, and depressive symptoms. In this section of the apter, a brief review of the effectiveness of Internet-based PA interventions for these populations will be provided.
Patients with cardiovascular disease A systematic review examining the effect of Internet-based PA interventions in patients with diagnosed cardiovascular disease highlighted five welldesigned randomized controlled trials (Pietrzak et al., 2014). e seings for these interventions varied from an interactive health management program (Southard, Southard, & Nuolls, 2003) to cardiac rehabilitation programs (Devi, Powell, & Singh, 2014; Zutz, Ignaszewski, Bates, & Lear, 2007) or selfmanagement programs (Blasco et al., 2012; Vernooij et al., 2012). Most oen, these Internet-based PA interventions asked participants to self-monitor their disease symptoms and risk factors, engage in online communication with nurses or health experts, set treatment goals, and participate in online educational sessions (Figure 4.2).
e findings from these studies were promising. Southard et al. (2003) observed body mass index in the intervention group to decrease compared to a control group, but no differences were seen between groups on PA, blood pressure, lipid levels, and dietary habits. During two self-management programs, heart aa risk was lower in the intervention group compared to the controls, with individual risk factors favoring the intervention group patients as well (Blasco et al., 2012; Vernooij et al., 2012). Finally, two studies demonstrated that a higher proportion of patients enrolled in an Internetbased PA intervention met treatment goals for blood pressure compared to standard care control groups (Blasco et al., 2012, Goessens et al., 2008). A major limitation of these studies, however, was that only one study (Zutz et al., 2007) included PA as the primary outcome. Given the paucity of literature regarding the effectiveness of Internet-based PA interventions in patients with cardiovascular disease, more studies in this line of inquiry are warranted.
Figure 4.2
Internet use in physical activity promotion.
Source: pixabay.com.
Populations with diagnosed diabetes Internet-based PA interventions have also been used among patients with diabetes, with mostly positive results. Specifically, among diabetes patients, most Internet-based PA interventions were found to improve blood pressure and lipid profiles, with weight loss frequently observed as well. Notably, all studies implemented some type of interactive self-management program with study staff to aid in improving blood pressure, blood glucose, PA participation, weight loss, and medication adherence. In a study by Bond et al. (2007), nurse provided feedba was provided to diabetic patients via instant messaging emails, with online educational discussion sessions also held—ea with the objective to aid diabetic patients’ management of their disease. In two studies by Glasgow et al. (2010, 2012), participants were asked to upload their health goals for medication adherence and exercise/food oices to the program websites aer whi they received feedba on whether they met their goals. Participants also had access to blood test results and educational modules. Findings from both studies indicated improvements in healthy eating and PA at four months, with the intervention group’s improvements being maintained at 12 months—a finding not seen in the control group. Van der Weegen et al. (2015) also observed greater PA post-intervention and at a 3month follow-up in the group receiving the Internet-based PA intervention compared to the partial intervention or usual care groups. Finally, Pietrzak et al. (2014) indicated a majority of studies using Internet-based PA interventions among patients with type 2 diabetes observed decreased blood pressure and improved glycosylated hemoglobin levels.
Other clinical populations Internet-based PA interventions have been used in numerous other clinical populations in the healthcare field. In a study by Bosak, Yates, and Pozehl
(2010), the feasibility of delivering an Internet-based PA intervention was examined in adults with metabolic syndrome. ey found that the website was accessed a total of two hours over six weeks—greater than the time spent delivering usual care—with participants’ evaluations of the intervention being positive. A similar feasibility trial found Internet-based PA interventions more favorable among young adult cancer survivors comparted to Internet-based interventions exclusively reviewing cancerrelated topics (Rabin et al., 2012). Effectiveness resear has also been completed on populations with clinically physical and psyological diagnoses. For example, one study randomly assigned patients with rheumatoid arthritis to an Internet-based PA intervention providing individualized feedba and group contacts or a comparison group receiving an Internet-based PA intervention providing general PA information only (van den Berg et al., 2006). At the 6- and 9month assessments, a greater proportion of intervention participants engaged in requisite amounts of moderate- and vigorous-intensity PA compared to comparison participants, with significantly higher vigorousintensity PA seen in the intervention group at 12 months as well. However, no group differences were observed for objectively measured PA levels, selfreported quality of life, or disease progression. Regarding psyological diagnoses, Strom et al. (2013) found an Internet-based PA intervention to significantly reduce the presence of depressive symptoms among the intervention group as compared to a waitlist control. Unfortunately, no group differences were seen with regard to PA. As mental illnesses affect millions of people daily, more resear into the use of Internet-based PA interventions to improve PA and depression symptoms is required.
Internet-based PA interventions among various populations Children and adolescents Not surprisingly, adolescents spend a great deal of time engaging in computer-based activities (Hamel, Robbins, & Wilbur, 2010). Recent reports state that ildren aged between 8 and 18 years spend approximately an hour a day, on average, engaged in non-work-related computer activity (Rideout, Roberts, & Foehr, 2005). at said, data also shows that adolescents prefer obtaining health information online when compared to printed materials or other traditional modalities (e.g., face-to-face interventions) (Hamel et al., 2010). As su, researers have begun to implement Internetbased PA interventions among youth populations, with the aim of promoting PA and other proper health habits—potentially mitigating poor health outcomes in adolescence and adulthood. ree studies were among the earliest randomized controlled trials completed on the use of Internet-based PA interventions among youth (Jago et al., 2006; Marks et al., 2006; Williamson et al., 2006). Jago et al. (2006) implemented an Internet-based PA intervention in 473 10- to 14-year-old Boy Scouts in Texas, whi used role-modeling, goal seing, and problemsolving to improve ildren’s accelerometer-determined PA levels. Compared to a control group receiving a nutritional intervention, significantly greater increased light PA was observed among the intervention ildren. Conversely, Marks et al. (2006) found increased PA only among adolescents within a control group receiving a print-based PA intervention compared to an intervention group receiving Internet-based interactive games, quizzes, and arts developed with the purpose of
improving adolescent PA levels—perhaps due to the short duration of the intervention. Finally, Williamson et al. (2006) compared an Internet-based PA intervention to an Internet-based health education intervention in African American female adolescents. Aer the first six months, girls in the intervention group lost more mean body fat (1.12%) compared to the control group. However, over the next 18 months, both groups regained all weight lost, with no difference between group at this time point. Unfortunately, no PA data was provided. Other interventions have incorporated parental feedba and have been implemented in sool-based seings. Haerens and colleagues (2007a, 2007b) implemented an 8-month randomized controlled trial among adolescents, wherein the adolescents were placed within an intervention or control group receiving identical Internet-based PA interventions. However, only the intervention group received parental feedba. Researers observed that the intervention adolescents increased moderate-to-vigorous PA by 4 minutes/day while the control group’s moderate-to-vigorous PA decreased 7 minutes/day indicating parental feedba/support may still be needed among younger populations, likely due to the fact PA and eating-related behaviors are not solely at the discretion of youth populations—influenced heavily by their parents. e same researers also compared a shorter sool-based 3-month individually tailored Internet-based PA intervention to a non-individually tailored, paper-based PA intervention (Haerens et al., 2009), with findings indicating no significant differences between groups. e preceding findings are in contrast to the findings of Bourdeaudhuij et al. (2010). ese researers conducted a sool-based randomized-controlled trial in a group of 12–17-year-olds from six European countries investigating the short-term (1 month) and mid-term (3 months) effectiveness of an individually tailored Internet-based PA intervention compared to a non-individually tailored Internet-based PA intervention. As a result of targeting adolescents’ determinants of PA (e.g., self-efficacy, social support), it was observed that the intervention group more frequently cycled for transportation, walked more in leisure time, and participated in greater durations of total moderateto-vigorous PA at three months in comparison to controls. Potential
moderators and limitations of the studies reviewed must be kept in mind, however. Investigations into the moderators whi influence the effectiveness of Internet-based PA interventions in youth indicate Internet-based PA interventions might be more effective in youth when the participant is male, is older, has higher baseline intentions to increase PA, and possesses higher perceived social support for or modeling of PA by siblings. Regarding the limitations of the literature to date on the use of Internet-based PA interventions, it is clear that the majority of the studies regarding the effectiveness of Internet-based PA interventions were designed exclusively for or had a majority sample of girls. According to recent data, as of 2011, only 38.3% of males in grades 9–12 met the national 60 minutes per day recommendation for moderate-to-vigorous PA (Healthy People 2020, 2016). Furthermore, according to Ogden et al. (2014), prevalence of overweight or obese boys aged between 12 years and 19 years are higher (35.1%) than for girls (33.8%). us, developing appropriate PA interventions for boys is imperative. Internet-based PA interventions targeting youth were also found to be most effective when taking a multi-factorial approa—incorporating parent/sibling support and modeling and the use of a theoretical framework to guide the development, implementation, and analysis of the intervention (Cook et al., 2014). However, only a small proportion of the literature reviewed individually tailored the Internet-based PA intervention to the youth. As we know, individually tailored interventions are more productive than non-individually tailored interventions (see Lewis et al., 2006), more Internet-based PA interventions need to strive to individually tailor content and feedba to ea participant. Finally, the seing in whi the Internetbased PA intervention is implemented is important (Hamel et al., 2010). Indeed, according to Hamel and colleagues (2010), sool-based interventions may be more effective than home-based interventions, with sool-based interventions oen demonstrating greater increases in PA and reductions in weight loss than home-based interventions—perhaps due to the integration of these interventions into the sool curriculum and with teaer/peer support. Given the paucity of literature that have implemented
Internet-based PA interventions in the sool seing, a concerted effort is needed to integrate this type of PA intervention in this seing.
Young adults Most studies of Internet-based PA interventions among young adults reported positive results (Joseph et al., 2014; Mailey et al., 2010; Okazaki et al,. 2014; Sriramatr et al., 2014) except for one (Franco et al., 2008). In an earlier study by Mailey et al. (2010), researers examined the effects of an Internet-based PA intervention on self-efficacy, depression, anxiety, and objectively measured PA in college students receiving mental health counseling. At the conclusion of the 10-week intervention, the intervention group showed increased PA, self-efficacy, and improvements in depression and anxiety—suggesting Internet-based PA interventions are an efficacious supplement to face-to-face psyological counseling. Specific populations of young adults have also been examined. Joseph et al. (2014) examined the feasibility of a culturally relevant Internet-based PA intervention predicated upon the Social Cognitive eory in the promotion of PA among overweight/obese African American female college students, observing initially increased PA at the 3-month assessment, but decreased PA ba to baseline levels at the 6-month assessment. In a study among female college students in ailand, Sriramatr et al. (2014) also used the Social Cognitive eory to guide the implementation of an Internet-based PA intervention. Resulting from traing PA and seing PArelated self-efficacy and outcome expectancy goals, findings indicated that the Internet-based PA program was effective at promoting and allowing for the maintenance of increased PA at 3- and 6-month assessments when comparing the intervention group to the control group. Finally, Okazaki et al. (2014) investigated the effectiveness of an Internet-based PA intervention in the promotion of increased PA and energy expenditure among college students. Participants used the online website portal to set weekly PA goals
and were asked to send a weekly email to the researers reviewing their progress toward aievement of these goals in addition to providing a statement of new goals for the coming week. Individually tailored feedba was given regarding their goal aievement. Findings revealed the intervention group to engage in significantly higher PA levels when compared to the control group—highlighting once more the importance of individually tailoring Internet-based PA interventions to participants. However, not all Internet-based PA interventions in young adults have been successful. In 2008, Franco and colleagues conducted a study investigating the effects of an Internet-based PA intervention on college students’ PA and nutrition-related behaviors. Participants were randomly assigned to either two different Internet-based intervention groups or an aention placebo control group. While both intervention groups significantly increased fruit and vegetable intake compared to the control group, no increase in PA levels were seen among intervention groups. Researers commented that the results seen were likely a result of the fact the emphasis on PA was not as high as it could have been and that the baseline PA among the student participants was already high—suggesting again that aention needs to be paid to how to best target certain health behaviors regardless of the population of interest. Mu like studies of Internet-based PA interventions among youth, a major limitation of literature concerning this PA intervention delivery method among young adults was the fact the samples were largely comprised of women. Further, some interventions were offered as part of a college course—perhaps biasing the results in a favorable manner. Finally, conflicting findings were seen regarding whether or not greater baseline PA influences intervention effectiveness (Joseph et al., 2014; Okazaki et al., 2014). us, future studies need to design Internet-based PA interventions that appeal to both genders, are not part of a college course, and are individually tailored to ensure a participant’s baseline PA levels do not bias the results.
Adults and older adults Studies on Internet-based PA interventions among adults and older adults have revealed mixed findings (Broekhuizen et al., 2016; Hargreaves, Mutrie, & Fleming, 2016; Marsaux et al., 2015; Zaaria et al., 2013). Specifically, Broekhuizen et al. (2016) tested the effectiveness of an Internet-based PA intervention in inactive older adults (60–70 years) with the aim of improving participants’ PA levels and quality of life. Participants were placed in an intervention or control group. e intervention group was given DirectLife (Philips, Consumer Lifestyle, Amsterdam, the Netherlands) whi consisted of an accelerometer-based activity monitor, a personal website, and a personal e-coa. Participants wore the activity monitor with data uploaded to an online database. Notably, individually tailored goals were set by the DirectLife program based on participants’ baseline assessment while uploaded data from the activity monitor was used for the provision of regular feedba on their progress toward goals. Findings indicated that 42% of the intervention group participants reaed their PA goals in addition to significantly improving their quality of life compared to the control group. Similarly, positive findings were seen in the StepWise study by Hargreaves et al. (2016), with intervention participants traing and receiving positive and supportive feedba regarding their walking activity, increasing and maintaining their daily step count at 11,000 steps higher than seen at baseline, with concomitant improvements observed for blood pressure, resting heart rate, and motivation to be physically active. Finally, some systematic reviews have suggested Internet-based PA interventions to be effective when implemented among adults in the workplace (Zaaria et al., 2013). Nonetheless, the literature regarding the use of Internet-based PA interventions among older adults is sparse—likely because the current generation of older adults is not as tenologically adept as the current youth generation. at said, concerted efforts must be made to increase the ease of use of Internet-based PA interventions among this population as preliminary findings look promising.
Opportunities and allenges Many PA interventions incorporated theoretical models into their interventions. However, it is hard to conclude whether the interventions whi incorporated theories were more effective in promotion of PA (van den Berg, Soones, Vliet Vlieland, 2007). One of the biggest future allenges, therefore, will be to conduct larger, high-quality randomized controlled trials to discern the increased effects of theory-based Internetbased PA interventions above and beyond that of non-theory-based interventions. Other allenges for future Internet-based PA interventions are related to the limitations seen most frequently in the literature. To begin with, few Internet-based PA interventions included objective PA measurements. To generate strong conclusions, it is important to determine PA in relation to PA levels assessed via objective measures (e.g., accelerometers and pedometers). Second, there needs to be uniformity in measuring PA outcomes. In particular, empirical studies most oen measured PA in time, energy expenditure, and/or proportions of people meeting PA recommendations. Other studies used indirect measures su as weight loss, heart rate, or stages of motivational readiness to measure PA instead of directly measuring anges in PA levels. Consensus measurement protocol will be paramount to the future of this field.
Practical implications To enhance the effectiveness of Internet-based PA intervention, it is crucial to engage participants by introducing them to intervention materials and increasing their Internet use. It may be possible that participants who are motivated to ange their behavior are more likely to use the Internet more than those who la motivation. However, empirical data indicates that participants who logged on more oen had greater exposure to intervention material and demonstrated more positive outcomes. One way to increase the interactivity may be through the use of computer algorithms to provide participants with immediate and tailored feedba about their PA behavior. Providing participants with adapted tips and suggestions may increase their engagement and retention in the program. Researers have also suggested interventions that stimulate active participant interaction and use behavioral modification teniques might be helpful in eliciting more participant engagement and retention. Future resear regarding the use of Internet-based PA interventions should also make the following considerations before implementation. When working with ildren, Internet-based PA interventions should be implemented in the sool seing, given the fact the large majority of ildren can be reaed in this context. Second, using established theory as an intervention framework is also vital in the development and planning of effective programs, given the fact that theory allows researers to more effectively implement methodology capable of targeting certain health behaviors in addition to allowing for beer interpretation of the findings (Brug, Oenema, & Ferreira, 2005).
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5 Online social media and physical activity promotion Jung Eun Lee and Zan Gao
e turn of the century ushered in a new age of Internet connectivity with the advent of social media sites su as MySpace in 2003. Today social media sites abound, with sites su as Weat, Facebook, Twier, Snapat and Instagram among the most popular. Recent data (Perrin & Duggan, 2015) indicated that 78% of adults access the Internet and, of this proportion of adults accessing the Internet, 65% use online social media like Facebook and Twier. Facebook, for example, is the world’s largest and most frequently used social media site, with 1.32 billion users as of June 2014 (Maher et al., 2014). Moreover, a recent poll revealed 50% of the Facebook users visit the website everyday (Statistics Brain, 2013). Indeed, Internet users are spending one-quarter of all time spent on online using social media (Maher et al., 2014). Social media sites are popular as they allow users to create an online profile to connect with friends/family, opine on current events, and access/view information presented in various media formats (i.e., text-, video-, or audio-based content) within their network (Greene, Sas, Piniewski, Kil, & Hahn, 2013). Recently, social media sites have become an avenue through whi researers/health professionals are promoting physical activity (PA) and health.
Social media is being targeted as a PA intervention delivery approa due to these sites’ ability to do the following: (1) rea large and diverse populations; (2) present health messages using video and auditory-based formats of media as opposed to only traditional text-based media formats; and (3) maintain high levels of user engagement and retention (Maher et al., 2015). Indeed, in the past decade, individuals have been seeking easily accessible, low-cost, online tenologies to address their health needs (Fallows, 2005). Notably, social media sites have been shown to be an important component in altering social norms related to certain health outcomes su as obesity—potentially increasing individuals’ awareness of the need to be physically active and, therefore, promoting weight loss (Leahey, Gokee LaRose, Fava, & Wing, 2011). For example, placing intervention participants in a private group on a social media site su as Facebook allows researers/health professionals to deliver empirically valid health information to these individuals in a cost-effective manner. Delivering health information via groups created on social media sites also allows for interventionists and participants to interact with one another— anging PA-related aitudes and social norms and increasing the likelihood of engaging in PA participation by as mu as 45% (Nakhasi, Shen, Passarella, Appel, & Anderson, 2014). Finally, social media-based PA interventions also reduce the participant burden associated with traditional face-to-face wellness counseling (e.g., transportation, missed time at work, etc.). roughout this apter, we will review the current status of social media-based PA interventions, critique the effects of this intervention modality on various health outcomes and discuss the negative issues related to social media, as well as the practical implications and directions for future studies.
Implementation of social media-based interventions on behavior ange According to a review by Maher et al. (2014), social media-based interventions demonstrate small to moderate effects in promoting health behavior (e.g., PA, healthy eating) and related outcomes (e.g., weight loss, more favorable body composition). e demonstrated effectiveness of social media-based interventions is likely due to social media’s ability to address the resear-related issues of rea (the extent to whi the population of interest is exposed to the content), engagement (the degree to whi participants get involved and participate in the interventions), and retention (how many people are retained at the end of intervention). Despite its capability of reaing large and diverse populations, social media-based health interventions do not always aieve high levels of engagement—limiting the effectiveness of the intervention due to the small level of participation by participants following intervention protocols. Aside from one social media-based health intervention whi aieved a decent rate of engagement (see Foster, Linehan, Kirman, Lawson, & James, 2010), most studies have reported engagement levels between 5% and 15% (Maher et al., 2014). e aforementioned engagement rates were in spite of researers’ aempts to continuously interact with participants, provide prompts, and send reminder emails regarding the resear’s protocols. Differences between studies with regard to participant recruitment might also explain some differences in engagement. For example, in the above study reporting higher participant engagement, Foster et al. (2010) recruited participants who already knew ea other and created a friendly, competitive environment with an online ranking board of ea participant’s step counts—contrasting with other studies whi aieved low engagement while striving to use Facebook and Twier to promote PA social support
among individuals with no previous relationship (Napolitano Hayes, Benne, Ives, & Foster, 2013; Valle, Tate, Mayer, Allico, & Cai, 2013). Indeed, it might be argued that the higher engagement rates seen by Foster and colleagues (2010) were aributable to the fact that the researers’ approa was beer aligned with how people use social media (Figure 5.1). Specifically, individuals more commonly use sites like Facebook to interact with people with whom they share an offline connection as well, rather than using Facebook to interact with new people (Ellison, Steinfield, & Lampe, 2007).
Figure 5.1
Social network on mobile devices.
Source: pixabay.com.
Finally, with regard to retention, social media-based health studies have most frequently used Facebook, as this social media site reports 61% of regular users access the site daily (Kaushal, 2013). Researers using Facebook to implement health interventions frequently managed to retain a high proportion of participants across the study period (77–96% of users) (Cavallo et al., 2012; Foster et al., 2010; Napolitano et al., 2013; Valle et al., 2013). High retention is important, given the fact that health behavior
interventions with higher retention rates are vital to the overall effectiveness of the intervention (omas, Nelson, & Silverman, 2011). Stated simply, if individuals cannot sti with an intervention, health behavior ange is not able to take place.
Social media-based PA interventions in the healthcare field Social media-based PA interventions have also been implemented in the healthcare field. is intervention delivery modality is desirable in the healthcare field as it reduces participant burden—an important fact as many patients may have diseases or treatment limitations that make tasks su as transporting themselves to and from a clinic difficult. Below, major studies in the healthcare field whi have used social media-based PA interventions will be introduced, with their findings highlighted.
Individuals with mental disorder Mental disorders have an influence on a person’s thoughts, mood, and feelings. As a result, these conditions can have a major impact on an individual’s quality of life as they affect relationships and the functions of everyday life. Approximately 46% of the U.S. population has a diagnosed mental disorder, with anxiety disorders being the most common (Kessler et al., 2005). Social media-based PA interventions have been implemented in patients with mental disorders. For example, Asbrenner Naslund, Shevenell, Mueser, & Bartels (2015) investigated the feasibility of a weight loss program with peer support mobile tenology and social media interaction for obese individuals with mental illnesses. e 24-week intervention consisted of: (1) once-weekly 90-minute weight management group sessions taught by two lifestyle coaes and supported by a wellness peer; (2) twice-weekly optional 1-hour group exercise sessions led by a certified fitness trainer; and (3) continuous mHealth tenology and social media interaction to increase motivation, facilitate self-monitoring of
diet/PA behavior, and promote peer support outside of treatment sessions. While aendance at the weekly sessions was low (56%), findings indicated that 45% of participants had lost weight at the 6-month follow-up, with the same percentage of individuals demonstrating improved fitness. Notably, participants suggested future interventions should include more active learning, a simplified dietary component, more in-depth tenology training, and greater aention to mental health. As this study represents the one of few exemplar investigations into the delivery of a social media-based PA intervention among individuals with mental disorders, researers should build upon the suggestions made by the participants.
Cancer survivors Social media-based PA interventions have also been used among young adult cancer survivors. In a 12-week feasibility study, Valle et al. (2013) implemented an intervention called FITNET via Facebook with 86 young adult cancer survivors, aimed at increasing moderate-to-vigorous-intensity PA (Figure 5.2). Participants in the FITNET intervention group received PArelated Facebook messages and tips and traed and recorded pedometer steps on the site, aer whi personalized feedba was provided. Additionally, researers facilitated social support via Facebook by posting discussion questions, PA-related videos/articles, and prompts related to the traing of PA and progress toward personal goals. e FITNET intervention group was compared with a self-help comparison (SC) group having all of the preceding intervention components delivered via Facebook but no access to the goal seing and feedba tools. Findings revealed both groups to have increased self-reported weekly minutes of MVPA over the course of the 12week intervention, with no significant difference between groups. However, increases in light PA were greater in the FITNET group relative to the SC group. Researers speculated the la of an intervention effect on MVPA relative to the SC group might have been due to: (1) insufficient power to
detect a significant difference as a result of a small sample size and large within-group variance; and (2) the simultaneous targeting of multiple psyosocial constructs precluded researers from determining the extent to whi specific strategies within the program accounted for group anges in PA. Again, as this study represents one of few exemplar studies using a social media-based PA intervention among cancer survivors, future studies should improve upon the methodology.
Figure 5.2
Online social media on a mobile device.
Source: pixabay.com.
Effectiveness of social media-based PA interventions Studies exploring the feasibility/effectiveness of social media-based PA interventions have also been completed among healthy, but inactive populations. Of these studies, a majority recruited adults, with a paucity of resear targeting adolescents. In this section, these studies and their noteworthy findings, strengths, and limitations will be highlighted. Implications for future resear and practice will also be discussed.
Adolescents Few studies have addressed the effectiveness of social media-based PA interventions in adolescents. Ruotsalainen and colleagues (2015) evaluated the effects of a 12-week, Facebook-delivered intervention on PA and body mass index in over-weight/obese Finnish 13–16-year-olds. Participants were randomly assigned to one of three groups: (1) lifestyle and self-monitoring intervention group: received Facebook-delivered lifestyle counselling and PA self-monitoring tools; (2) lifestyle-only group: received the same Facebookdelivered lifestyle counselling but no PA self-monitoring tools; and (3) control group: received post-intervention feedba on their PA. Findings revealed no significant differences between the two intervention groups or control group over time for PA levels or body mass index. Notably, however, the lifestyle and self-monitoring group demonstrated lower sedentary time on weekdays at post-intervention compared to the control group, with no significant within-group anges seen in any study group. at said, focus group resear among adolescent females found PA intervention components to be most appealing to adolescents when emphasizing sport,
exercise, and fitness, in addition to providing opportunities for socialization with friends and self-improvement strategies, with Facebook representing the most widely desired social media site for this type of intervention. Conversely, cartoon imagery, humor, a sool-based intervention, and a protocol whi only gave advice on increasing walking behaviors were undesirable features of social media-based PA interventions (van Kessel, Kavanagh, & Maher 2016). Researers should take note of features desired most by adolescents to increase the effectiveness of their social media-based PA interventions as this intervention modality holds great promise among this tenologically advanced generation.
Young adults Delivering a social media-based PA intervention in college students has high potential effectiveness beacause of two factors. First, this population demonstrates a high usage rate of social media sites. Second, college students are at great risk of overweight and obesity, given the fact that many college students are making PA and nutritional oices autonomously for the first time. Indeed, high rates of physical inactivity and obesity have been seen among this demographic (Desai, Miller, Staples, & Bravender, 2008; Nelson, Gortmaker, Subramanian, Cheung, & Wesler, 2007; Sira & Pawlak, 2010). erefore, college students are another population in whi su interventions have been implemented—oen with more frequency than other populations. Merant et al. (2014) conducted a randomized controlled trial to examine the effectiveness of a social media-based PA intervention in the promotion of weight loss among overweight/obese college students. Participants used their personal accounts to access a health page moderated by a health coa. Researers concluded that Facebook can be used to remotely deliver health content within a PA intervention to promote weight loss with the help of a health coa who can iteratively tailor content and interact with
participants. Notably, however, engagement with the study’s Facebook page was highly variable and declined over time—an area of concern for future studies. An earlier study by Napolitano et al. (2013) also targeted college students with a social media-based intervention, using different combinations of Facebook and/or text messaging to implement the intervention. At 8 weeks, the Facebook plus text messaging group had significantly greater weight loss than the Facebook-only group and waitlist controls—with a large majority of the participants finding the intervention content helpful and stating they would recommend the intervention to others. Given the results of both of the studies reviewed, it is clear that innovative social media-based PA and weight loss interventions can be effectively implemented via platforms su as Facebook given social media’s integration into college students’ lives.
Adults Social media-based PA intervention studies conducted on adults have explored features of social media that might have the most influence on intervention effectiveness. For example, social media sites can function as support groups, educational modules, and/or a space to keep tra of PA records—all of whi could lead to anges in PA behaviors. us, findings and strengths of the studies that examined these features will be discussed in the following subsections.
Information vs. support
Brindal et al. (2012) compared the effectiveness of a 12-week social mediabased PA intervention to promote weight loss. Eligible individuals were randomly allocated to one of three groups—ea with an associated social media site: (1) an information-based group; (2) a supportive group; or (3) an
individualized-supportive group. Both of the laer two supportive social media sites included weight traing and meal planning tools, in addition to allowing participants to interact and support one another. However, the individualized-supportive site also included a meal planner whi offered individually tailored dietary recommendations using an algorithm based on a user’s preferences for certain foods. Findings demonstrated higher retention for the supportive groups compared to the information-based group at 12 week, but no differences in weight loss between the intervention groups and control group.
Peer encouragement and pedometer use
Maher and colleagues (2015) conducted a study using the Facebook app “Active Team” to implement a social media-based PA intervention. Participants were provided a pedometer and encouraged to aieve the recommended 10,000 steps per day (see Tudor-Loe et al., 2011) using the social media-based Active Team to form groups of between three and eight existing Facebook friends. e Active Team intervention included a calendar to log daily step counts; a dashboard showing step-logging progress, awards, and gis; a team tally board to allow users to monitor their own and their teammates’ progress; a team message board for team members to communicate with one another; daily tips for increasing PA; gamification features, su as awards for individual and team step-logging and step-count aievements; and the ability to send virtual gis to teammates. At the 8week follow-up, the Active Team intervention participants had significantly increased overall PA time by 135 minutes relative to the control group. e main component of the increased PA time was walking time. Researers concluded that a social media-based PA intervention with concurrent pedometer use can produce sizable short-term PA increases.
Promotional messages vs. peer support
A number of features of social media lend themselves well to the enhancement of social support and provision of empirically-based information. To investigate whi features of social media demonstrate the greatest ability to increase PA, Zhang and colleagues (2015) conducted a study to compare the ability of health promotion messages or the creation of peer networks to increase PA in 217 graduate students. A 13-week social media-based PA program was implemented, with participants randomized to two social media-based groups (i.e., a peer support-oriented group or health message-oriented group) or a control group. Findings indicated participants in the peer support group reported exercising moderately for an additional 1.6 days ea week compared to baseline measurements—an increase significantly greater than seen in the control group. Researers concluded that even social support from anonymous individuals was more successful than health messages for improving PA. Explicitly, this study used real-time signals about peers’ exercise behaviors (i.e., number of PA classes within whi a peer is enrolled) as the main form of social support/influence. Zhang and colleagues stated the strength of the effect peer support had on PA in the current study is striking, given the conservative aspect of the peer network.
Education-only vs. self-monitoring on Facebook
In contrast to the other studies of social media-based PA interventions in adults reviewed previously, results in favor of social media-based interventions were not found by Cavello and colleagues (2012). Specifically, these researers randomized female college students into two groups: (1) Facebook intervention group: placed on a Facebook page for social support and given access to a PA-related website; and (2) control group: only received access to the aforementioned PA-related website. Findings indicated
that while intervention participants experienced increased social support and PA over time, no differences in these outcomes were seen between groups over time. Researers concluded that a social media-based PA intervention may not have any greater effects on social support or PA when compared to traditional Internet-based approaes to PA promotion (Figure 5.3).
Figure 5.3
Online social media on a computer.
Source: pixabay.com.
Podcast vs. mobile communication
Turner-McGrievy and Tate (2011) examined whether a combination of podcasts, mobile support communication and diet monitoring can promote weight loss. In this 6-month, minimal-contact intervention, overweight (n = 96) adults were randomly assigned to a podcast and mobile group or podcast-only group. e podcast and mobile group was provided podcast whi reviewed nutrition and PA information while also using a PA/diet monitoring mobile app to tra the health behaviors and communicate with study researers and participants via the social media site, Twier. Only
podcasts were provided to the podcast-only group participants. Findings indicated weight loss and days per week participants engaged in diet monitoring did not differ significantly at 6 months between groups. However, participants in the podcast and mobile group were 3.5 times more likely than participants in the podcast-only group to use an app to monitor dietary intake, with the number of podcasts participants reported downloading over the 6-month period significantly moderately correlated with weight loss in both groups. Finally, significant differences were reported for sources of social support, with the podcast group participants relying more on friends, and the podcast and mobile group participants relying to a greater degree on online resources, however, the PA measured by energy expenditure was not significantly different between groups. Noteworthy is the fact that the numerous components common to face-toface interventions that the researers tried adding to this study (i.e., selfmonitoring, group support, and contact with study counselors) were seldom used by participants and may have detracted from what was already a successful podcast intervention. Moreover, the components may have detracted from the podcast and mobile group participants seeking social support from friends and family—support whi is imperative for behavior ange.
Offline social support
While social media-based PA interventions have oen aempted to encourage use of this platform to facilitate online social support for PA, few aempts have been made using these sites to facilitate offline social support for PA in the real world. Indeed, for participants with lile offline social support, modest online increases in social media-based support social support may not be sufficient to increase PA. To address this issue regarding offline social support for PA, Sneider and colleagues (2015) used the free, commercially available “Meetup,” an
online social media site used commonly to sedule community event locations and times, to examine if creating walking groups for dog owners on the site might facilitate increased offline social support for PA and PA participation. Participants from two cities enrolled in the 6-month study. Findings revealed no differences for PA between the Meetup intervention group and the control condition wherein participants only received emails with content regarding increasing PA. Further, while participants in the Meetup condition reported an increase in the perceived positive outcomes of dog walking compared to the control group, social support, sense of community, and dog walking barriers did not ange appreciably over time or in comparison to the control group. More resear is needed to discern how to promote offline social support via social media-based PA interventions.
Negative aspects of online social media While social media does offer numerous benefits in its ability to rea large and diverse segments of population and deliver content in multiple media formats (e.g., video, audio, and text-based formats), there are negative aspects of social media worth noting that might influence social mediabased PA interventions. While the list below is not exhaustive, the list does discuss the negative aspects most pertinent to social media-based intervention delivery.
Social overload Social media users are asked to respond to increasing numbers of messages and social relationships when using social media sites. Consequently, some users may feel that too mu social support is required for them to engage with other online friends—whi results in driving these individuals away from using the said social media site. is situation has been termed “social overload,” and is influenced by the extent of usage, number of online friends, subjective social norms, and type of relationships (being online-only friends vs. being online and offline friends). An individual experiencing social overload oen reports exhaustion and low user satisfaction whi then increases the intentions of users to reduce or stop social media use (Bouc, 2013). erefore, the allenge for researers/health professionals is how to engage individuals in social media-based PA interventions without contributing to social overload and driving these individuals away from social media and, more specifically, the online intervention.
Online bullying and trolling rough social media, individuals sometimes feel they have the power to post offensive remarks, pictures, or video clips that could potentially cause a great amount of emotional pain for another individual. Online bullying, (i.e., cyber-bullying) occurs relatively oen, with 39% of social media users admiing to being cyber-bullied (Turkle, 2011). Currently, there are few limitations regarding the content individuals can post online. As su, researers/health professionals need to be sure to promote a positive and supportive social media-based intervention environment whi does not condone offensive or judgmental remarks. If a participant is posting disparaging content whi is diminishing the morale/confidence of other participants, the ill-mannered participant should be dropped from the program. e action of actually bullying someone online and continuing to engage in these actions is referred to as trolling. Trolling can take many different forms, including, but not limited to: persistent name-calling, regularly playing online pranks on volatile individuals, and continually posting controversial comments with the intention of causing anger and arguments. As trolling has become commonplace online, it is important for researers/health professionals to find ways in whi they can protect their participants’ identity. On a site like Facebook, an intervention group can be implemented using a “Secret Group.” ese Secret Groups are invitationonly and cannot be seared by anyone on Facebook. is ensures that no one looking to troll can access the group and disrupt the positive, socially supportive environment that researers are striving to create in order to promote PA and health.
Internet addiction
Social networking can also have an impact on individual’s loneliness. Socialnetworking sites like Facebook and MySpace may provide people with a false sense of connection that ultimately increases the sense of loneliness in people who feel alone (Cornbla, 2009). It is also possible that social networking can foster feelings of sensitivity to disconnection, whi also leads to loneliness. Online social networking plays a positive role in subjective well-being when the networking is used to facilitate physical interactions, but networking activities that do not facilitate face-to-face interactions tend to diminish trust, whi ultimately results in negative subjective well-being. As su, it is important for researers/health professionals to find ways to create social media-based PA interventions that do not promote these type of negative psyological effects, but to possibly design interventions whi facilitate online and offline connections.
Privacy issues Sharing of personal identifying information and the control of information posted on social media sites—even aer this type of information has been deleted—have raised privacy concerns among social media site users as this information has in some cases been illegally given to third parties for their use. A good example was when the controversial social media site “eup” gathered users’ social media account information for use in a spamming operation (Barnes, 2006). Further, in spring 2015, a news radio piece entitled “Weighing Privacy Vs. Rewards of Leing Insurers Tra Your Fitness” introduced how life insurance companies are now offering lower insurance premiums to people who are willing to report their activity traer data to the company (Farr, 2015). is may sound like a good deal, but this could be an example of a privacy trade-off between confidentiality and improved personal finances. Finally, another example was observed in the fall of 2015 when Facebook used their newsfeed to do a study on how different types of posts could impact mood without informing its members.
is experiment by Facebook may not have caused any harm to its members, but people were furious when they discovered that they had been experimented on without being informed. us, the fact that social media sites can use personal data in this manner raises red flags. With regard to scientific resear and medical treatments desiring to use social media-based intervention methods, privacy concerns among intervention participants are somewhat mitigated by the fact that information gathering and data collection procedures are heavily scrutinized by institutional or Medical Review Boards. Specifically, these commiees ensure that participants are adequately and fully informed of study procedures and that ea participant knows they have a right to discontinue participation for any reason at any time. at said, enrolling participants in a social media-based intervention presents unique allenges to review boards as social media sites contain a large amount of personal information about participants. Notably, while the information on social media sites is considered public, it is not yet known whether republishing this information in a resear paper is considered an invasion of privacy (Lowry, Cao, & Everard, 2011).
Practical implications Currently available empirical studies have shown that social media-based PA interventions can be useful in promoting health in various populations. Indeed, su interventions have the following benefits with regard to implementation: (1) researers/health professionals can tailor information to individual or groups of participants; (2) social media sites offer intervention participants a source of social support whi can act as a motivation to continue engaging in PA and other health behaviors; and (3) social media sites allow for improved rea, engagement, and retention of participants. To more effectively capitalize on the aforementioned advantages of social media-based PA interventions, the use of theory in interventions is paramount if researers/health professionals wish to develop and implement interventions whi are capable of targeting specific health behaviors (e.g., PA) with a high degree of effectiveness. Lile use of behavioral theory was seen among the social media-based PA interventions. eories, su as the Social Cognitive eory and the Transtheoretical Model, are behavioral theories whi can be used to tailor interventions to participants. ese theories have been used with success in many PA interventions and should be considered for social media-based PA interventions as well—particularly as resear shows tailored interventions to be more effective than non-tailored interventions in the promotion of weight loss, healthy eating, and PA (Khaylis, Yiaslas, Bergstrom, & GoreFelton, 2010). Moreover, as social support is oen a component in PA interventions, resear should also consider grouping individuals in social media-based PA intervention groups with individuals whom they already know. Greater effectiveness of social media-based PA interventions has been demonstrated when participants were grouped with individuals whom they
already knew. Finally, researers need to be vigilant about keeping users engaged, as user engagement can slip during social media-based PA interventions. Oentimes “gamification” (i.e., the act of offering small rewards for meeting intervention goals) of interventions works best to increase user engagement in online forms of intervention delivery (Jepson, Harris, Pla, & Tannahill, 2010). A final area of concern when implementing social media-based PA intervention lies in the need for researers and health professionals to keep in mind how they might need to orient social media-based PA interventions toward specific populations. For example, walking may be the preferred exercise type among cancer survivors (Jones & Courneya, 2002; Karvinen et al., 2006; Vallance, Courneya, Jones, & Reiman, 2006; Valle et al., 2013). Indeed, Valle et al. (2013) reported that cancer survivors managed to increase only their walking time but not any other PA of higher intensity. When implementing weight loss programs among overweight or obese populations, it has also been found that social support might be the most important component (Verheijden, Bakx, van Weel, Koelen, & van Staveren, 2005). Finally, while lile resear has been conducted on the use of social media-based PA interventions among adolescents, researers and health professionals need to be aware that overweight/obese adolescents may have significantly more negative views of PA than their normal weight counterparts (Defore, de Bourdeaudhuij, & Tanghe, 2006). Additionally, as a result of their poorer health, overweight or obese adolescents may have a unique barrier to PA participation (e.g., reduced motor skill) whi need to be kept in mind (Kantomaa, Stamatakis, & Kankaanpaetal, 2013; Zabinski, Saelens, Stein, Hayden-Wade, & Wilfley, 2003). erefore, it is important to encourage these populations to first reduce their daily sedentary behaviors aer whi small, incremental anges can be made to facilitate more PA (Ruotsalainen et al., 2015).
Directions for future studies Although the majority of studies report the positive effects of social mediabased PA interventions to improve health, some limitations of the current literature should be noted. First, given the high number of quasiexperimental and non-experimental study designs used in the literature, the la of a control group in these studies prevents researers from discerning causal (e.g., direct) effects of su interventions on health outcomes. Furthermore, given the fact that social media represent a new field of inquiry with regard to PA intervention, the majority of studies had small sample sizes, resulting in an inability to detect differences between groups due to low study power. Additionally, some studies did not use objective PA measures, limiting the validity of PA outcomes (Valle et al., 2013). According to Maher et al. (2014), social networking-based health interventions may be effective in anging behavior. However, this field of resear is still in its infancy and it is unclear whether social networkingbased interventions are equally useful for all health behaviors or whether they may be more effective for some more than others. Given that many of the health benefits of health behavior are aieved over a long-term period, further resear is needed to examine whether the short-term behavior ange aieved can be sustained over a longer period. More studies are also needed to determine whether sustained interaction with the user interface is required to sustain behavior ange or whether behavior ange may persist aer interaction with intervention materials ceases (Maher et al., 2014). Additionally, existing online social networks su as Facebook and Twier appear to offer promise for sustained engagement, however, whether retention and engagement with specific aspects of these platforms would mat the intervention engagement or retention is unclear. us, innovative
approaes reflecting the way people use online social networks (with existing friends and for entertainment) are warranted. Finally, future work is also needed to determine how to maintain behavior ange in the longer term, how to rea at-need populations, and how to disseminate su interventions on a mass scale. For logistical reasons, researers used self-reported measures of PA whi are typically considered to be susceptible to social desirability bias (Motl, McAuley, & DiStefano, 2005). Interestingly, Crutzen and Goritz (2011) recently examined this issue in over 5000 participants, and found social desirability bias was, in fact, unrelated to web-based self-reported PA, suggesting that web-based selfreports of PA are more trustworthy and useful. An advantage of selfreported PA, as opposed to objectively measured PA, is the considerably lower participant assessment burden, whi arguably enhances the study’s ecological validity (Maher et al., 2015).
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6 Mobile device apps in enhancing physical activity Zachary Pope and Zan Gao
It is a beautiful sunny day—not one you intend to waste. You call your friends and ask them what they are up to—inquiring if they would like to go cat some Pokémon using the Pokémon Go application (a.k.a., app). Armed with nothing more than your smartphones, you and your friends spend the next several hours traversing around town to local landmarks whi also serve as Pokéstops—hotspots where Pokémon are most likely to be located in the augmented reality world created by the Pokémon Go app. Not only do you and your friends enjoy an active day together cating Pokémon, you also manage to make friends with other fellow Pokémon players also playing the game. Notably, mobile device apps like Pokémon Go, whi augment the real world in a virtual reality game-based manner, are starting to become available to the consumer. e objective of these games is to get individuals outside and moving, with the intended outcome being long-term health. Indeed, with the advent of the iPhone 3 and the iPad, along with other subsequent smart mobile devices, computer soware programmers have developed thousands of apps for use by individuals to monitor their physical activity (PA), fitness, health and wellness over the past several years. ese apps include both simple (e.g., providing information on steps and estimated distance) and complex programs (e.g., providing information on vital signs
and physiological indices su as heart rate and energy expenditure). To describe the ever-growing field of health-oriented apps, these mobile device health apps have been termed “mHealth apps.” Nowadays it is uncommon to meet an individual who does not possess a mobile device. e most recent Pew Resear Center report indicated an astounding 64% of Americans now own a smartphone, with 88–97% of respondents using their smartphone for texting, email, voice/video calls, and internet access (Pew Resear Group, 2015). As a result of their deep integration into our lives, smartphones have revolutionized nearly every aspect of our daily lives over the past decade. From education and business to social life and health, the impact of the smartphone can be felt (Sarwar & Soomro, 2013). Yet, smartphones are not the only mobile devices transforming our everyday lives. e rise in popularity of the smartphone has given rise to the tablet market as well. Tablets are typically larger and offer more computing power than smartphones—computing power sometimes equivalent to that of smaller laptop computers—making these devices ideal for those who desire/need more computing capabilities than a smartphone provides, but who also prefer lightweight portability. Furthering the impact of smartphones and tablets are these devices’ ability to provide users access to millions of downloadable apps. In 2015, the number of apps available on Google Play (for Android-based phones/tablets) and the Apple App Store (for iOS-based phones/tablets) totaled 1.6 and 1.5 million, respectively (Statista, 2016). Of these apps, 165,000 were considered mHealth applications—a term used to describe health apps whi, through mobile phones, personal digital assistants (PDAs), patient monitoring devices, and other wireless device platforms (e.g., tablets) aid in medical and public health practices (Intercontinental Marketing Services Institute of Health Informatics, 2015). Presently, the Intercontinental Marketing Services Institute of Health Informatics (2015) indicates 59,400 (36%) of the available “Wellness Management” mHealth apps are geared toward “fitness,” with 28,050 (17%) and 19,800 (12%) mHealth apps created for “lifestyle and stress” and “diet and nutrition” purposes, respectfully. ese apps are timely, given the fact that the most recent estimates state the prevalence of obesity among
individuals 20 years or older in the United States to be approximately 35%, with the combined prevalence of overweight and obesity among American adults at a staggering 67% (Ogden, Carroll, Kit, & Flegal, 2014). As overweight/obesity and physical inactivity contribute to a higher likelihood of ronic diseases (Field et al., 2001), mHealth apps are poised to play a major role in the primary prevention of these conditions via PA promotion. Given the large amount of available mHealth apps currently on the market, many of whi were developed to help consumers improve or maintain their health and wellness, knowledge of these apps’ use in the promotion of PA is vital to help ameliorate and possibly reverse the overweight and obesity trends seen in the United States and around the globe—potentially mitigating the poorer health outcomes resulting from overweight/obesity (Reilly & Kelly, 2011). As su, this apter will provide readers with an overview of the use of mHealth apps for PA promotion in various populations aer whi the use of mHealth apps in different contexts and for Big Data analysis will be reviewed (Figure 6.1). e apter will conclude by discussing the development and implementation of effective mHealth PA interventions and the practical implications the use of mHealth apps have for PA promotion and, more generally, health promotion. Given smartphones’ higher frequency among the mHealth literature to date, the apter’s emphasis will lean more towards a discussion of smartphone apps in the promotion of PA and health. However, where high-quality resear regarding the promotion of PA is available using other mobile devices (e.g., tablet-based apps), this resear will be reviewed as well.
mHealth apps and health promotion in various populations While the widespread presence of smartphones and tablets has been mentioned as contributing to these devices’ ability to impact our daily lives, the fact these devices and their apps are easy to use further contributes to these devices’ popularity. Within the mHealth literature, the ease with whi the apps on these devices can be used has made mHealth interventions for improving PA participation and related behaviors possible among many different populations. Not only have mHealth interventions been implemented among individuals of varying age, these interventions have been used among individuals of different race/ethnicity, socioeconomic status, and disease states (e.g., healthy, non-clinical populations versus clinical populations). In what follows, the reader will be treated to a review of these mHealth interventions by age group with differences in race/ethnicity, socioeconomic status, and disease states highlighted throughout. Notably, two types of mHealth apps exist: (1) mHealth apps designed by researers specifically for resear projects and not for consumer download; and (2) commercially designed mHealth apps designed for consumer download whi may or may not have had researer input in the development of said app. When necessary, differences in the literature between the two preceding app types will be made.
Figure 6.1
Big Data in GPS and GIS.
Source: pixabay.com.
Youth (2–19 years old) PA among U.S. youth is poor, with only 42% of ildren and 8% of adolescents (Troiano et al., 2008) meeting the PA guidelines of 60 minutes of moderate-to-vigorous PA per day (U.S. Department of Health and Human Services, 2008). As the current generation of youth have grown up with ever-expanding tenological power, this generation is very interested in tenology and a prime target for mHealth interventions. Despite this generation’s great interest in tenology, mHealth studies of youth younger than 19 years of age are scarce in the literature and have typically been conducted among adolescents. Toscos, Faber, Connelly, & Upoma (2008) were among the first to investigate the use of app-based PA promotion among adolescents—in this case, teenage girls—finding that over the course of a brief 2-week trial, all participants increased their step counts
while also decreasing perceived PA barriers—with social support provided via the app found to be most important. Notably, more recent, methodologically rigorous studies have been conducted. In a recent 8-week randomized controlled trial by Direito, Jiang, Whiaker, & Maddison (2015), the effects of two commercially available apps (“Zombies, Run! 5K Training” and the “Get Running-Coa to 5K” apps) on European adolescents’ cardiorespiratory fitness were evaluated, with findings indicating that, compared to the control group, the mile times of adolescents randomized into either of the app-based interventions were approximately 25–30 seconds faster at posest, but that no significant differences between the app-based groups and control were seen for the secondary psyosocial outcomes or objectively determined PA—likely due to the study’s la of a theoretical framework (Brug, Oenema, & Ferreira, 2005). Fortunately, a pair of randomized controlled trials among underserved adolescent boys of low socioeconomic status (Lubans, Smith, Skinner, & Morgan, 2014; Smith et al., 2014) was more effective in implementing theory as part of a mHealth app-based PA intervention. Utilizing the SelfDetermination eory and Social Cognitive eory, the app-based Active Teen Leaders Avoiding Screen-time (ATLAS) intervention observed significantly reduced screen-time and sugar-sweetened beverage consumption and significantly improved muscular fitness and resistance training abilities among intervention boys compared to controls. Notably, while improvements in PA and BMI favored boys in the intervention group, no significant intervention effects were seen for either variable—a finding aributed to poor accelerometry compliance by the boys and the use of BMI for body composition (Smith et al., 2014). Nonetheless, the findings of this trial highlight not only the potential efficaciousness of an app-based PA intervention among underserved adolescents of low socioeconomic status, but also the importance of theory in developing an effective intervention. As seen, of the scant high-quality literature available regarding the use of apps in PA promotion among youth, results are mixed regarding the effectiveness of mHealth interventions. Reasons for these mixed findings might be aributable, in part, to: (1) the la of a guiding theoretical
framework within some of the literature; and (2) the moderate intervention compliance among the adolescents in the studies. at said, given the high rates of obesity seen among youth globally, mHealth interventions need to use a theoretical framework to guide the development of multi-component studies, whi use several methods to intervene to promote PA and health (e.g., parent involvement, teaer reinforcement, etc.). Finally, these future studies need to ensure that intervention fidelity procedures are in place to ensure youths’ compliance with data collection protocols.
Young adults and adults (20–39 years old) Contrary to youth aged 2–19 years, numerous literature is present regarding the integration of mHealth apps among young adults and adults (aged 20–39 years) to promote PA. e greater amount of literature is likely aributable to the fact that this age range represents a period during whi individuals are most likely to possess a mobile device of their own—dissimilar to youth who may or may not possess a mobile device like a smartphone. Given the fact many individuals 20–39 years old are mobile device owners and well aware of the benefits of PA, this cohort might represent one of the most receptive populations for mHealth interventions. Early investigations into the use of mHealth apps in individuals aged 20– 39 years focused on participants’ ability to integrate apps into their daily routine to monitor health behaviors, with findings indicating smartphone apps to be easier for participants to use for behavior traing than alternatives su as web applications (Gasser et al., 2006). Tsai et al. (2007) also conducted a preliminary trial among overweight or obese adults whi allowed participants to monitor exercise and caloric balance in real time using a smartphone app, observing good compliance with the app and anges in PA and eating behavior as a result of app use. However, studies on mHealth interventions for the self-regulation of health behavior prior to 2010 were exploratory in nature—investigating mHealth app intervention
feasibility (i.e., whether or not these apps would work in the “real world”), but providing lile data on the effectiveness of mHealth apps for PA and health promotion. Subsequent mHealth studies among young adults and adults have investigated the effectiveness of mHealth app intervention and participants’ ability to self-regulate PA and associated behaviors given the importance of self-regulation in behavior ange (Bandura, 1991). One of the first mHealth app effectiveness trials among the age group was conducted by Maila, Lappalainen, Parkka, Salminen, and Korhonen (2010) using a researer-developed smartphone app, e Wellness Diary, in whi 27 overweight or obese male and females traed their weight and related behaviors (e.g. steps, food intake). Findings indicated a strong correlation between diary use and weight loss over the course of the course of the 12week study. Several studies suggested mHealth app interventions resulted in significantly higher light PA and more steps (Hebden, Cook, van der Ploeg, & Allman-Farinelli, 2012; Hebden et al., 2014; Kirwan, Duncan, Vandelanoe, & Mummery, 2012). Finally, other researers have also evaluated whether social support might also facilitate health behavior ange. During a 6-week randomized controlled trial, Harries, Eslambolilar, Stride, Reie, and Walton (2013) found step counts among the individual app and social feedba app conditions to be 59% and 69% higher, respectively, than controls, with this resear highlighting the potential effect that integrating social feedba into mHealth app interventions may have on desired outcomes. As reviewed, numerous resear has been completed among young adults and adults given this cohort’s high likelihood of mobile device ownership (Pew Resear Center, 2015). Nevertheless, most resear among this age group has been conducted among healthy, but overweight or obese individuals in order to begin to reverse and/or prevent the disease and disability overweight and obesity can lead to in middle age and older adulthood (An & Shi, 2015).
Middle-aged adults (40–65 years old) Middle-aged adults represent one of the most important populations of interest for the implementation of effective mHealth app interventions. Field et al. (2001) state that the risk of developing ronic diseases increases substantially among this population with increased overweight and obesity. Given that fact, calls have been made for innovative and multi-factorial interventions to aenuate and perhaps reverse the overweight/obesity epidemic and reduce long-term health consequences associated with the epidemic (Wya, Winters, & Dubbert, 2006). erefore, many innovative and multi-factorial interventions in middle-aged adults have concentrated on reversing the trends of high physical inactivity in this cohort (Hallal et al., 2012). mHealth interventions have been one su approa, with mHealth app interventions proven to be versatile (e.g., capable of being employed in numerous contexts) and efficacious in middle-aged adults for PA promotion. Early studies examined the effectiveness of mobile device apps in the selfregulation and promotion of PA in healthy, but sedentary, minority populations within the workplace. In a mHealth study of minority healthy, but sedentary, middle-aged women, Fukuoka, Viinghoff, Jong, and Haskell (2010) observed a significant increase of approximately 800 steps per day in the sample—suggesting, again, mHealth apps’ effectiveness across diverse populations. Additionally, as most middle-aged adults have steady careers, Carter, Burley, Nykjaer, and Cade (2013) implemented an mHealth app intervention, My Meal Mate, in the workplace to promote PA and weight loss, finding significantly higher intervention adherence and weight loss among the app-based group compare to groups using a website or paper diary—aributable to the ability to tra PA-related energy expenditure with the app. Yet, this represents one of the few mHealth studies done within the workplace—indicating the need for more investigation of mHealth in promoting middle-aged adults’ PA and health.
Clinical populations are also of concern among this age cohort. In a study of 12 type 2 diabetes patients, the effectiveness of a smartphone app over a 6-month period in the self-regulation of blood glucose, PA, and nutrition habits was examined (Arsand, Tatara, Ostengen, & Hartvigsen, 2010). Aer 6 months, researers observed a mean decrease in blood glucose levels of 2 mg/dL and an increase in steps per day, with 58% of the participants also increasing their fruit and vegetable consumption. Other early studies among diabetic middle-aged patients have found similarly positive results for improvement of BMI, total olesterol, steps per day, diastolic blood pressure, and weight loss (Stuey et al., 2011; omas & Wing, 2013). Primary care patients have also been of interest to researers implementing mHealth app interventions. Findings from mHealth app studies in primary care indicated significantly improved daily step counts among patients with acute psyiatric conditions (Glynn et al., 2014) and significant improvements in daily PA and quality of life in patients with type 2 diabetes and/or cardio-obstructive pulmonary disease (Verwey et al., 2014). Clearly, mHealth interventions have been put forward as an innovative solution to help this cohort become more aware of, and positively ange, their health behaviors. As su, mHealth interventions among middle-aged adults have spanned many different populations and contexts. at said, more resear needs to be invested in workplace interventions, given the fact that many middle-aged adults have careers. Nonetheless, building healthy PA habits in middle-aged adulthood can help mitigate some of the health consequences present in old age.
Older adults (65+ years old) As healthcare has improved, life expectancy has increased and contributed to a greater number of older adults still present in the population. In detail, if this cohort’s growth continues at the current rate, by 2050 the Centers for Disease Control and Prevention (2013) projects the number of older adults to
be 89 million. Notably, the high prevalence of overweight and obesity in older adults is a key factor in the high prevalence of ronic diseases (e.g., diabetes, cardiovascular disease, etc.), with 92% of older adults having at least one ronic disease and 77% having two or more (National Council on Aging, 2015). As a result, functional limitations are frequently observed among older adults whi can include limitations in the ability to get dressed, walk from room to room, get up out of a air, or drive, among other activities (An & Shi, 2015). Lile to no resear on the use of mHealth interventions in the promotion of PA had been completed at the time of the writing of this book. e main reason for the la of studies is likely a result of the fact that older adults represent a segment of the population least likely to own a mobile device capable of running an app. In fact, recent data found only 27% of individuals ≥65 years and older owned a smartphone, with fewer expected to own other mobile devices (e.g., tablets; Pew Resear Center, 2015). at said, studies have indicated the elderly to have the lowest PA levels of any age cohort (Sun, Norman, & While, 2013). As su, more effort might be made among older adult populations to implement mHealth interventions. What is clear from the preceding review of mHealth literature is the fact that mobile devices, most notably smartphones, are currently being used among a diverse array of populations and seings to promote PA and healthier behaviors. at said, few large-scale, methodologically rigorous studies have been conducted among the literature, with no study completed among older adults. e aforementioned limitations will need to be addressed if mHealth apps are to prove effective and be adopted by health professionals and PA specialists.
Application of mHealth apps in various contexts
Figure 6.2
GPS traing in biking.
Source: pixabay.com.
As discussed in the introduction of this apter, mobile devices su as smartphones and tablets are ever present in our lives. From the mobile devices we own personally, to mobile devices placed in hospitals, retailers, and numerous other seings (Figure 6.2), these devices are quily becoming the manner by whi we organize our lives and interact with the world. In the healthcare field, we have already seen the diversity present among the environments in whi mHealth apps are being used to promote PA. While only a small amount of resear is available on the use of mHealth in healthcare to increase PA participation, we have discussed mHealth interventions in weight loss and diabetes treatments as well as the treatment
of cardio-obstructive pulmonary disease and psyiatric conditions. Indeed, the use of mHealth apps in the healthcare field—with special aention paid to barriers and future resear directions of mHealth app use in this environment—will be the topic of the next section of the apter. Moreover, we have highlighted the use of mHealth app interventions in free-living conditions su as college environments and in the workplace. Yet, there remains one environment where, despite the potentially major impact mHealth apps might have on PA promotion, lile study of these apps has taken place: sools. As a result, a call has been made to promote healthy behaviors among ildren at younger ages to mitigate the poor health outcomes (i.e., ronic diseases) in adulthood arising due to ildhood and adolescent overweight/obesity (Reilly & Kelly, 2011). Given the fact that the majority of ildren aend sool, this environment is a prime location for PA and health promotion among youth. As younger ildren are seldom mobile device owners, the paucity of literature to date on the use of mHealth apps for PA promotion has typically been among populations of adolescents. For example, a pair of randomized controlled trials by Lubans et al. (2014) and Smith et al. (2014), successfully increased PA and reduced screen-time using a theory-driven multicomponent, app-based intervention, ATLAS. In fact, at the time of the writing, the aforementioned trial’s success has resulted in a new ATLAS 2.0 protocol—a trial just geing underway (Lubans et al., 2016). Similar positive results have been seen in other mHealth studies implemented in a soolbased environment (Waerson, 2012). Unfortunately, this represents the available high-quality literature on sool-based mHealth interventions to promote PA—with younger ildren understudied. As youth represents a critical period during whi to develop lifelong health habits, it is worthwhile outlining potential future resear directions for sool-based mHealth PA interventions among younger ildren. As younger ildren are unlikely to own a mobile device, mHealth appbased PA interventions need to be implemented at the class level within sools, not the individual level. is puts the responsibility for su interventions on sool administrators and teaers. Indeed, resear reports
that approximately 40% of teaers currently use mobile device apps within the classroom or physical education seings (Kervin, Verenikina, Jones, & Beath, 2013). In physical education seings, apps su as IronKids and Short Sequence: Kids Yoga Journey have been forwarded as potential facilitators of ildren’s motor skill development and excitement with regard to greater PA participation (Martin, Ameluxen-Coleman, & Heinris, 2015). Specifically, mHealth apps like the two previously mentioned, were developed to aid physical educators in engaging all ildren within a class in greater PA participation. Yet, no known study has implemented these apps in a physical education seing. Further, given the fact many classroom teaers are currently using apps to aid teaing their ildren, why not use an mHealth app to tea these subjects while also geing ildren moving? For instance, a math teaer might use an app that helps ildren learn to add and subtract. Say a ild was asked to add 4 + 7. Upon giving the answer, 11, the app might then ask all ildren within the class to do 11 jumping jas, with answers to further questions tasking the ildren with completion of other exercises. is approa to teaing will not only promote greater PA, but might also have benefits to ildren’s behavior. at said, a concerted effort needs to be made on the part of researers to show sool administrators, teaers, and physical educators the effectiveness of this type of intervention —not only with regard to engaging ildren in greater PA, but also showcase the benefits these types of activity breaks might have on classroom conduct. As a final note, augmented reality apps also show promise for ildren’s PA promotion in free-living seings. Indeed, the release of the popular Pokémon Go came on the heels of other augmented reality games su as Zombies, Run! Within days of its release, Pokémon Go was geing millions of ildren out the door, engaging in PA, and socializing with others—oen walking and/or biking with friends to the nearest Pokéstop where the newest Pokémon were likely to be located. While no empirical resear has yet been completed on Pokémon Go, researers have posited that it might be worth examining exactly how mu Pokémon Go is increasing PA levels (Baranowski, 2016). Studies of this type are particularly suited for ildren, given this populations’ low PA levels and the need to prevent ronic disease
progression in later life resulting from high levels of overweight/obesity during youth (Ogden et al., 2014). Youth are our future. Sools are a prime venue to promote PA, health and wellness among youth. e fact that mHealth app-based interventions in ildren’s PA and health promotion are understudied—especially given the current generation of youth’s interest in tenology—is a testament to the need for more push by researers to ange the current policies and regulations that continue to limit sools’ focus on physical education/activity and, therefore, ildren’s overall health and wellness. Moreover, researers need to capitalize on gaming trends su as that currently seen for the augmented reality game Pokémon Go to increase ildren’s free-living PA as well. Failure to use the amazing tenology we have to ensure ildren possess the skills to become “lifelong movers” is a travesty and will only result in higher healthcare costs and lower quality of life for all involved.
Application of mHealth apps in the healthcare field Imagine a world where treatments for diseases or adverse health conditions was more convenient and focused largely on proactively treating these diseases/conditions prior to their onset. Imagine a world where the focus of healthcare was not only to educate the patient about their disease/condition, but to empower the patient with the knowledge necessary to manage their long-term health and wellness. Imagine a world where access to a wellness team ready to help with any health malady an individual might be experiencing was right at your fingertips. While this description of healthcare might sound futuristic, the environment described is already here. Indeed, the present-day swit from reactive healthcare (i.e., treating diseases/conditions aer onset) to proactive/preventive healthcare (i.e., treating diseases/conditions prior to onset) is placing mHealth apps as a major player in the current healthcare revolution. In the previous section of this apter, several examples were discussed regarding the effectiveness of mHealth app use in the healthcare field to promote PA. Specifically, the paucity of literature to date has observed mHealth app-based PA interventions to be effective in the management/treatment of diabetes and/or weight loss (Arsand et al., 2010; Stuey et al., 2011; omas & Wing, 2013), cardio-obstructive pulmonary disease (Verwey et al., 2014), and acute psyiatric conditions (Glynn et al., 2014)—with other mHealth app study protocols showing major promise in healthcare (Maddison et al., 2014). Yet, despite mHealth apps’ initial demonstration of their ability to improve clinical outcomes through PA promotion, the preceding examples only represent literature discussing mHealth app-based interventions for PA promotion. As su, it is important to note other ways in whi mHealth apps are being used in healthcare. is
is best accomplished by discussing why mHealth apps are beneficial within an evolving healthcare field, with further discussion of barriers to mHealth app use in healthcare and how future resear might overcome these barriers. In discussing why mHealth apps will be advantageous in the future of healthcare, one cannot ignore the concept of “systems medicine” (Flores, Glusman, Brogaard, Price, & Hood, 2013; Hood, Balling, & Auffray, 2012). While a fuller explanation of “systems medicine” will be provided in the next section, a brief definition of systems medicine is provided here. Broadly, systems medicine takes a multi-faceted approa to patient treatment by considering multiple aspects of an individual’s wellness—from the individual’s genetics at the micro-level up to the environment in whi the individual lives at the macro-level. rough the examination of ea level of an individual’s health, researers can then surmise factors (and interactions between factors) that contribute to disease progression or aenuation. For example, health professionals could mandate various blood, physiological, and psyological testing for patients, aer whi all results could be uploaded to a secure online portal containing the patient’s entire personal health history. Abnormal results from these tests could then be flagged for physician review, with the patient recommended for treatment and provided aid during this treatment by a dedicated wellness team. Notably, given present-day tenology and the gradual shi to a systemsbased approa to medicine, the majority of the patient’s treatment could occur without the patient having to travel into the clinic. In order to employ this type of healthcare approa, however, a medium by whi patients and healthcare professionals (e.g., physicians and wellness teams) to communicate is needed. Resulting from this need, mHealth apps have shown promise and continue to be at the forefront of the discussion on systems medicine. Aside from being used to promote PA, West (2012) states that mHealth apps are being implemented in the systems medicine structure to increase patient medication adherence, ensure healthcare access to those living in rural areas, help patients effectively manage and improve ronic diseases,
and enhance the efficiency of treatment delivery. is resear also indicated that the use of mobile devices in the treatment and management of disease improved physicians’ response times for medical test results, the error rate for patient disarge and medication prescription, and physician record keeping practices, among other aspects of healthcare. Furthermore, continued implementation and use of mHealth are projected to save the United States approximately $200 billion in healthcare costs by 2036, with projected mHealth revenue for the year 2017 projected to be $23 billion (West, 2012). However, with the plethora of benefits to the integration of mHealth apps in healthcare, what are some of the barriers to the widespread adoption of mobile devices in the field? Further, what future resear/policy directions would prove most useful in reducing these barriers? Confidentiality remains the largest barrier to the adoption of mHealth in healthcare. In a 2-year study of randomly selected U.S. adults, Riardson and Aner (2015) found that while an increasing majority of individuals believed the use of a smartphone to share health data and communicate with physicians would improve healthcare quality, a significant concern regarding the privacy and security of the data was still present. As su, mHealth app developers need to ensure the data that would be shared with these mobile device apps was secure and at least as impenetrable as current electronic medical databases. Resear proving the enhanced security of these apps would then have to be used to inform policies regarding the use of mHealth apps for medical record keeping and healthcare delivery. at said, it will be important for patients to decide upon the trade-off between keeping the health information collected via mHealth apps exceedingly private or giving up some right to this health information—providing some potentially identifiable health data to researers and physicians. is large amount of data can then be analyzed via Big Data analysis teniques (see next section) to improve individual and population-level health policies and practice (Kvedar, 2015). Moreover, given the functional limitations oen presenting among older adults (An & Shi, 2015)—at times, limiting this age cohort’s ability to transport themselves to a health clinic—enhancing the security and confidentiality of mHealth might aid in increasing the
popularity of mHealth app use among older adults as a result of not needing to physically go to a treatment facility. In turn, health professionals implementing interventions among older adults might be able to increase treatment adherence leading to potentially more effective treatment outcomes. Another barrier to mHealth adoption in healthcare is access to mobile devices in developing countries. Indeed, in countries like Africa whi are ravaged by deadly diseases, mHealth has seen the lowest adoption rates (West, 2012). As has been discussed, mHealth has been used to provide quality healthcare access to individuals living in rural areas. erefore, more funding needs to be allocated to resear examining the effectiveness of mHealth app use on individuals’ health outcomes in countries where the rates of deadly disease are high and mHealth adoption is low. A final major barrier to mHealth adoption in healthcare applies not to the patient, but to the physicians. According to West (2012), even among the already established mHealth systems, physicians are rarely paid for the time they spend training to use mHealth apps or the time these physicians spend proving feedba to patients via the apps. Simply put, mHealth adoption will not increase if the doctors are not properly compensated for the time they are investing handling patient health inquiries via mHealth apps. Like patient confidentiality, policies and regulations pertaining to the compensation of physicians for the care they provide using mHealth apps within a systems medicine healthcare structure need to be more thoroughly considered. While it is clear that mHealth apps have found a place within the healthcare field—most notably to help increase individuals’ self-regulation of health behaviors—more can be done to fully adopt the extraordinary power of mHealth apps within this field. As su, it is imperative that a greater number of large-scale and methodologically rigorous studies be completed, showing how to securely provide access to quality healthcare via mHealth apps to individuals in developed and developing countries. Doing so will provide evidence that can later be used to show key stakeholders within the healthcare community the usefulness of mobile devices in the treatment and
prevention of disease—improving mHealth policies and increasing adoption of this type of healthcare delivery method.
Use of mHealth apps in Big Data analysis With the proliferation of mobile devices, it is not surprising to note that mobile device apps are gaining more aention in Big Data analysis. Big Data analysis refers to “large volumes of high velocity, complex, and variable data that require advanced teniques and tenologies to enable the capture, storage, distribution, management, and analysis of the information” (TeAmerica Foundation’s Federal Big Data Commission, 2012, p. 10). In simpler terms, our society is incredibly tenologically advanced and capable of producing and storing large amounts of data in text, audio, and visual formats—seen on sites su as Facebook and YouTube. However, in some ways, the production and storage of data have out-paced society’s ability to analyze this data and form meaningful conclusions regarding the relationships between ourselves and the environment. is ability to discern the relationships between ourselves and the environment around us has major implications for our health. Specifically, evaluation of this individualenvironment relationship could be used in the healthcare system to inform health behavior decisions and facilitate new innovations whi might improve our ability to prevent and/or manage disease and improve quality of life. e use of mHealth in Big Data analysis is scarce, with lile to no resear available. at said, mHealth has great potential to facilitate Big Data analysis within the framework of systems health (Flores et al., 2013; Hood et al., 2012)—the concept alluded to in the previous section of this apter. As stated, systems health is an integrated approa to healthcare whi considers all levels of an individual’s wellness—from the individual’s genetic make-up to cells to organ structure/function to the environment within whi this individual lives. Considering ea bodily process, and how the environment interacts with ea process, allows for an examination of
the factors whi might contribute most to disease (or the absence of disease). Once information regarding these individual-environment interactions is available, interventions can then be developed to aenuate disease progression or even prevent disease prior to onset. As outlined by Hood et al. (2012), the end goal of systems health would be to create a “P4 Medicine” structure whi posits that healthcare would be: (1) predictive (i.e., information will be available on an individual’s health indices—allowing for prediction of an individual’s risk for certain diseases or conditions); (2) preventive (i.e., provision of information regarding an individual’s risk for certain diseases or conditions would facilitate lifestyle modifications to prevent certain maladies before onset); (3) personalized (i.e., lifestyle modifications in the prevention/treatment of diseases and conditions would be personalized to the individual—potentially leading to improved patient adherence and beer treatment outcomes); and (4) participatory (i.e., individuals will be able to take an active and welleducated approa to the prevention or management of diseases/conditions). Nonetheless, the preceding health structure will require a medium by whi patients/clients can provide information regarding their health habits (e.g., sleep, exercise, nutrition, etc.) and/or disease progression. Similarly, health professionals would need a medium by whi to disseminate information regarding lifestyle modifications to promote proper health practices and/or directives for disease management in a cost-effective and accurate manner. is is where mHealth shows promise. rough mHealth apps, a patient’s communication with their physician can be facilitated within this P4 Medicine structure. For example, a male patient may visit his physician for a baery of health tests, su as blood assays as well as cardiorespiratory, strength, body composition, and neurological tests. Results from these tests could then be uploaded to a secure server and made available to both physician and patient via a P4 mobile device app, with results indicating disease or an adverse health condition flagged for physician’s review. Perhaps in this example, the patient’s tests revealed that he is 30 pounds overweight and has mild type II diabetes. Once this patient’s results are reviewed by a physician, an
appointment could be set up explaining treatment options. For cases mirroring our current patient’s diagnosis, these appointments might take place via the P4 mobile device app in a manner similar to Microso’s Skype or Apple’s Facetime—reducing burden on both the physician and patient. Regardless of the meeting’s format, the physician would then refer our patient to a wellness team who specialize in weight loss. is wellness team would hold an initial in-person meeting to explain what lifestyle modifications would be necessary to ensure weight loss and prevention of further diabetes symptoms. For instance, the wellness team might inform our patient that he must start taking Metformin, a diabetes medication, in addition to starting a structured exercise program. In order to ensure our patient’s adherence to his new program, the wellness team would send our patient daily reminders to take his Metformin as well as provide him with a 5-day exercise program ea week. However, in contrast to current medical practice, this treatment would not take place at a clinic, nor would the treatment be explained only once to our patient, aer whi no further contact would be made. Instead, the wellness team would increase our patient’s probability of adhering to treatment via the P4 mobile device app. Explicitly, daily reminders to take Metformin and a detailed outline of the exercise program would be placed on the P4 mobile device app. Further, the medication reminders and exercise program would be personalized to our patient’s preferred daily time to take medication and to our patient’s preferred exercise activities, respectively. As part of the P4 medicine structure, this personalization of our patient’s program would improve our patient’s long-term motivation to engage in the lifestyle anges needed to improve health. Finally, congruent with the P4 Medicine structure, the P4 mobile device app would be optimized to ensure communication between the wellness team and our patient. e app would not only ensure that our patient has the ability to interact via a secure messaging system with the wellness team, but the app would also allow the patient to art his exercise, diet, and sleep, with reminders to do so sent to the patient’s mobile device. In this manner, our patient can ask the team questions and/or receive timely feedba regarding his progression through
the program—feedba whi may make the difference between successful and unsuccessful treatment outcomes. Data from thousands of patients treated in the same manner as our patient could then be analyzed via Big Data analysis to further streamline treatment procedures and improve treatment outcomes. Although a brief example of the role mHealth could play in Big Data analysis and how Big Data analysis might be used in the context of the healthcare field, the tenology infrastructure and buy-in from key stakeholders (i.e., healthcare systems, private hospitals/clinics, etc.) remain a barrier. Indeed, new methods of analysis need to be made available to examine the relationships between an individual’s health and their environment as, currently, the majority of the data available in the world of Big Data is unstructured in the form of text, videos, and pictures—not always in the numerical, spreadsheet-based format whi has traditionally allowed for the analysis of large volumes of data (Gandomi & Haider, 2015). While it appears the day when systems medicine and the P4 Medicine structure are commonplace is drawing nearer, we are not quite there yet. Until then, however, mHealth has continued potential to help individuals self-regulate health behaviors and modify these behaviors in a manner more congruent with good health and be conducive to the Big Data analysis infrastructure.
Application of mHealth apps Previous sections of this apter have made clear the versatility of mHealth apps in the promotion of PA and health. From community, home, sool and clinical seings and among diverse populations, mHealth apps offer a convenient way in whi individuals can self-regulate their PA behavior, whi—through incremental anges in these behaviors—aids in improving health. However, despite the promise of mHealth apps in the promotion of PA, successful PA interventions depend on more than the app itself. Indeed, it is oen other factors, su as the theoretical baing of the intervention, the intervention’s design and quality, and the app-related functionality that determine whether or not the mHealth intervention is effective. To begin, PA interventions must have a theoretical baing. eory allows researers and practitioners to develop and implement an intervention whi has a greater likelihood of being effective. Notably, however, the mere presence of a theory does not increase the probability of an intervention’s success. As Brug, Oenema, and Ferreira (2005) state, theory has an indirect effect on the behavior ange process. Instead, using theory to develop and implement a PA intervention allows interventionists to leverage determinants known to be closely associated with behavior ange. For example, motivation has long been known to highly correlate with PA behavior. As su, a researer might use Social Cognitive eory to guide the construction of an mHealth app-based intervention to more effectively promote increased competence and subsequent PA self-efficacy, aer whi PA stands a greater ance of increasing. It is in this manner that the theory’s indirect effect on an intervention’s success can be seen. As su, theory allows researers and practitioners to more effectively discern the meanisms whi lead to an intervention’s success (or la thereof), to
beer articulate these findings to interested individuals/parties, and to develop more effective interventions thereaer. Once theory is in place, interventionists must then take care of ensuring intervention fidelity—a factor whi is not mutually exclusive from the use of theory in an intervention. Borrelli (2011) states intervention fidelity to be “the ongoing assessment, monitoring, and enhancement of the reliability and internal validity” of an intervention (p. S52), aer whi she outlined five distinct domains related to this study design principle. ese domains are (1) intervention design; (2) provider training; (3) intervention delivery; (4) intervention receipt; and (5) intervention enactment. Looking at ea of these domains in relation to our Social Cognitive eory example will allow for a beer understanding of the importance of intervention fidelity in the implementation of an effective intervention. To begin, intervention design allows an intervention to be constructed whi more readily conforms to the intervention’s osen theory. Further, time spent ensuring the use of an appropriate intervention design will also help interventionists beer investigate links between study variables— confident that the anges seen as a result of the intervention were not due to variables outside the study. As su, our interventionists would start by deciding upon what type of intervention design would be best for discerning whether a mobile device app is effective in increasing an individual’s mastery (i.e., competency) to perform strength exercises, with the objective being to facilitate increased self-efficacy and PA. In this accord, our interventionists might decide to implement a randomized controlled trial where the experimental group uses a mHealth app containing video demonstrations of strength exercises in addition to an 8-week strength training program these individuals can follow. e comparison group would receive identical information, but via a paper-and-pencil paet. is would allow researers to determine the effectiveness of an app-delivered program versus that of a traditional paper-and-pencil program in relation to the variables comprising Social Cognitive eory that these researers are using to frame their study.
e next two domains of intervention fidelity, provider training and intervention delivery, oen go hand in hand. It is imperative that, following the selection of the intervention design, interventionists develop a standardized and highly detailed plan by whi to train the providers responsible for intervention delivery—training that considers everything from the qualifications/aracteristics of the individuals hired to deliver the intervention to how and where these individuals will be trained. Moreover, a detailed provider training plan will result in more consistent intervention delivery (i.e., the third intervention fidelity domain) as less variation/error will be present between providers, and more reliable adherence to the intervention’s theoretical framework will be observed—increasing the likelihood of a successful intervention. Having decided upon the intervention’s design, our interventionists would then go about the process of hiring and training providers to deliver the intervention, with the focus being to standardize the intervention’s delivery as mu as possible. As su, our interventionists would first develop a detailed plan to hire and train providers. In detail, our interventionists might desire to hire individuals with experience implementing theory-baed interventions and/or those with experience using mHealth apps to promote PA behavior ange. Our interventionists would then train the individuals hired to deliver the intervention. is training would include instruction on how the providers will demonstrate use of the mobile device app (the experimental group) or the paper-and-pencil paet (the comparison group), how providers will engage in role playing scenarios to ensure individuals’ understanding of the intervention, and the length of ea training session to ensure equal contact time with both groups, among other plans. is plan would ensure that no maer whi provider is informing an experimental or comparison group member of the different components of the intervention, the intervention’s delivery is consistent. e final two domains related to intervention fidelity shi the focus from the intervention’s development and implementation as it relates to the intervention’s personnel to the individuals participating in the intervention. Intervention receipt assesses whether or not the individual participating in
the intervention understands the intervention’s different components and can demonstrate the skills and knowledge related to the recommendations learned during the treatment. Providers can ensure intervention receipt through pre-post tests and even role-playing scenarios during the intervention sessions, wherein the provider is continually giving the individual feedba on ea intervention participant’s acquisition of the skills and knowledge related to the intervention. To ensure intervention receipt, our interventionists might seek to engage individuals in a roleplaying scenario prior to the intervention wherein the provider can solicit feedba on the individual’s demonstration of strength exercise form and knowledge of what major muscles ea exercise targets. Notably, this roleplaying scenario would be conducted for both the experimental group (i.e., using the mobile device app) and the comparison group (i.e., using the paper-and-pencil paet)—further ensuring that any differences in competency, self-efficacy, and PA between groups at the intervention’s end are aributable to differences in content delivery between groups and not factors outside of the study. Finally, intervention enactment evaluates an individual’s use of the skills and knowledge they gained from intervention participation in the real world. Indeed, while pre-post tests or role playing scenarios may indicate an individual to have the skills and knowledge necessary to participate in an intervention, s/he still might not adhere to the intervention when asked to do so in the real-world. Our interventionists could measure intervention enactment simply by monitoring the average number of times the experimental group uses the mobile device app while completing the same observation on the comparison group’s use of their paper-and-pencil paet. Aside from theory and study design, more practical maers also have to be kept in mind when developing and implementing a mobile app-based intervention. Chief among these considerations is the app’s functionality. To ensure intervention participants will use the mobile device app being used, a user-centered design process is suggested—an approa researers have used to increase the usability of mobile device apps (Kirwan, Duncan, Vandelanoe, & Mummery, 2013; van der Weegen et al., 2013). Kirwan et al.
(2013) make clear the importance of paying aention to many different aspects of the app design if researers and clinicians want to develop an app that will be used regularly by participants and/or patients. ese aspects include, but are not limited to: interface design, feedba, navigation, and terminology. While a number of subfunctions come under the four aforementioned aspects of app design, these researers denote the importance of user input regarding these aspects of app design. ese researers go on to provide a general blueprint of effective user-centered app design to improve later usability of the app during an intervention and in real-world scenarios. Specifically, Kirwan et al. (2012) state that the following steps are needed during the app design process: (1) investigate the most popular commercially available health-related apps, noting features (e.g., color, layout, navigation structure, app feedba, etc.) that most appeal to individuals using the said apps; (2) develop and test an app “in house” among the design team to ensure the interface is ready to be trialed by participants; (3) field test the app with participants and ask for their thoughts—via self-reported questionnaires, interviews, etc.—regarding the app’s functionality and issues or “bugs” whi need modification; and (4) refine the app based on participant feedba, aer whi the apps can be used as part of an intervention and, possibly, put on the market. Development of an app in this manner greatly increases the probability that an individual participating in an intervention or consumers downloading the app will use the app regularly—potentially increasing the effectiveness of the app. Notably, increasing the regularity with whi participants will use an app might also be promoted by providing tailored feedba to participants via the mHealth app. Unfortunately, tailoring of mHealth intervention protocol to individual participants has seldom occurred when using mHealth apps to promote PA/fitness—this is in contrast to recent literature tailoring feedba regarding healthy eating behavior to participants reporting their dietary intake via mHealth apps (Allen, Stephens, Dennison Himmelfarb, Stewart, & Hau, 2013; Martin et al., 2015). Finally, aention to detail during app design will also allow for closer
adherence to the theoretical underpinnings of the intervention whi bodes well for the intervention’s effectiveness. at said, not all researers or clinicians will want—or have the funds necessary—to develop their own app and, instead, might use a free, commercially available app. If this is the case, these researers/clinicians would be wise to ensure that the mobile device app they want to use is compatible with the theoretical framework of the intervention. If the app is not able to promote the constructs within the intervention’s guiding theory, the intervention stands a mu lower likelihood of success. Nonetheless, the functionality of the mobile device app implemented is closely related to future intervention success and warrants as great a degree of aention as the selection of an intervention’s guiding theory and the development of an intervention’s fidelity measures. Overall, effective mHealth app interventions, with the aim of promoting PA, are predicated upon three interrelated factors. First, effective mobile device app interventions need a strong theoretical framework on whi the intervention can be developed. eory allows for more effective intervention development and more accurate interpretations of the intervention’s results. Second, to ensure that the intervention properly uses the selected theory, detailed intervention fidelity measures must be put in place by the resear team. Proper intervention fidelity measures ensure smaller variation and error between providers with regard to intervention implementation— increasing the ability of researers and clinicians to aribute intervention results to the different methodology used to implement the intervention, with lile influence of outside factors. Finally, resear teams developing their own app must consider the app’s functionality. In doing so, interventionists can ensure the app is used with more regularity while still adhering to the theory baing the intervention. If interventionists decide not to develop their own app, opting instead to use a free, commercially available app, close aention needs to be paid to how well the app promotes the constructs related to the intervention’s theory. Being mindful of the preceding information during the development and application of a mHealth
app-based PA intervention should increase the likelihood of positive intervention outcomes.
Practical implications e public health landscape is anging rapidly with increasingly powerful tenology available at our fingertips. With the advent of smartphones and, later, tablets, mHealth apps represent an efficacious manner by whi to promote increased PA participation and improved health at the population level, given mobile devices’ widespread use among nearly all ages, races/ethnicities, and socioeconomic status. Further, not only do mHealth apps represent an effective manner by whi to promote PA, implementation of these apps into fields su as healthcare promises to increase the efficiency with whi treatments/interventions are delivered. However, barriers remain with regard to the widespread integration of mHealth apps into PA interventions in any seing. Among the biggest barriers reviewed were the privacy of individuals’ health information, the disparity in access to live-saving mHealth apps present in lower-income countries fighting to manage and treat deadly diseases, and policy/regulatory practices that may not always compensate healthcare professionals for time spent providing care to patients/clients via an mHealth app-based medium. Finally, as we gaze into the future, the validity and reliability of mHealth apps, as well as apps associated with newer, more fashionable health wearable devices (e.g., nelaces, belts, and upscale smartwates; Charara, 2016) capable of traing PA and physiological variables, must be evaluated further. Researers and clinicians must work together to overcome these barriers through the development of improved mHealth app privacy meanisms, the allocation of funding to resear aimed at developing a methodology by whi to implement mHealth app-based interventions in lower-income countries, and through the refining of current policies and regulations regarding the compensation of healthcare professionals for their provision of care by way of mHealth apps. Failure to improve upon the promising initial
work on the use of mHealth apps in the promotion of PA and other health behaviors will not only limit the adoption of this novel form of healthcare, but will impede the provision of healthcare access to those individuals who might need it the most (e.g., those living in rural areas and lower-income countries).
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7 Global positioning systems and geographic information systems and physical activity Zachary Pope and Zan Gao
Up to this point in the book, several novel ways in whi emerging tenologies are being used to promote physical activity (PA)—ea with the aim of reducing sedentary behavior and preventing the development of ronic diseases. Indeed, many of the previously reviewed tenologies have literature dating from the turn of the twenty-first century to the present day —indicating the use of these complex tenologies to promote PA is at the cuing-edge. One of the most sophisticated modern tenologies to be used in PA and health promotion is Global Positioning Systems (GPS) and Geographic Information Systems (GIS)—two related tenologies that, while only recently popularized for health and fitness, are older than many other tenologies reviewed in this book. Originally developed and used by the military in the late 1970s, GPS was finally made available for civilian use in 2000 (Maddison & Mhuru, 2009). Since being made available for civilian use, numerous GPS devices have been developed to allow individuals to tra all types of movement and activity. Moreover, GPS PA data have more recently been analyzed in relation to the built environment using GIS (Kirtland et al., 2003). In brief, GIS soware allows for PA data captured by GPS to be analyzed in relation
to the locations where these activities took place and, thus, facilitating researers’ ability to discern relationships between locations and features of the built environment (e.g., sidewalk construction, land use, and trail systems) whi may facilitate or inhibit PA participation (Troped et al., 2001). While a fuller explanation of GIS and associated teniques is beyond the scope of this apter, the reader is directed to other sources whi devote themselves to simply and fully explaining this tenology (National Geographic Society, 2016). Nonetheless, the combination of GPS/GIS is among the most promising tenologies available to advocate for anges to the built environment whi may beer promote PA participation in addition to facilitating the development, implementation, and assessment of PA interventions. roughout this apter the reader will be provided an in-depth look into how GPS/GIS is currently being used to promote PA and health. Specifically, the apter will first focus aention on the use of this tenology in different populations aer whi the discussion will swit to an examination of the literature regarding GPS/GIS in the assessment of different intensities and types of PA, as well as the limitations of GPS/GIS use in different contexts. Following a discussion of the preceding topics, the emphasis of the apter will swit to the use of GPS/GIS in healthcare with a further discussion of how health professionals might develop an effective PA interventions using this tenology. Finally, the apter will close with a brief review of the practical implications of the topics discussed.
GPS/GIS and health promotion in various populations
Figure 7.1
Application of a GPS-embedded physical activity app in hiking.
Source: pixabay.com.
Many readers may personally use a GPS device for the purpose of traing activities su as walking, running, biking, and hiking (Figure 7.1), among other activities. Indeed, it is during the aforementioned situations where it was most common to see GPS in use (i.e., sport-related performance traing and assessment) a decade ago. However, noteworthy is the fact that GPS and, now, GIS, are presently being more frequently used by non-athlete populations. Specifically, these tenologies have been used in two broad segments of the population: (1) PA and health promotion during PA interventions and environmental audits among the general population; and (2) sport-related performance traing and assessment among athletes. As
the focus of this book is on PA and health promotion, we will first discuss the extant GPS/GIS literature on this topic among the general population before turning to the laer topic regarding sport-related performance traing and assessment. Emphasis will be placed on the discussion of the populations in whi these GPS/GIS studies took place and the findings of the literature.
Use of GPS/GIS in the general population e use of GPS/GIS in PA and health promotion is still seen less frequently than that of other wearable tenologies whi may or may not have GPS embedded in them (e.g., health wearables su as the Fitbit, Jawbone, etc.). at said, the present literature has looked first to prove the validity and reliability of GPS/GIS tenologies aer whi the effectiveness of these tenologies in the promotion of PA and health has been investigated. In a population of 20 older adults, Webber and Porter (2009) examined the feasibility of traing the mobility paerns of this population. As is common to GPS resear due to GPS’s poorer ability to discern PA type or tra PA well indoors, these older adults were fied with one GPS wat and two accelerometers. Findings indicated that while the older adults had no difficulty using the GPS wates and did not perceive these wates to be bothersome, the data obtained from these devices was significantly impacted by the amount of time the participants spent indoors—highlighting further the inability of GPS to accurately tra indoor movement. In a similar study, Oliver Badland, Mavoa, Duncan, and Duncan (2010) investigated the feasibility of combining GPS, GIS, and accelerometry to assess transportation-related PA, indicating that out of 259 potential trips whi could have been analyzed using the GPS data collected, only 29 were analyzed as other trips had issues related to the GPS devices su as signal loss, device failure, lost devices, and unworn devices. Notably, to reduce measurement error, a more recent study developed and validated a model
mathematic equation allowing researers to use GPS and accelerometry data to accurately predict PA intensity and duration, as well as energy expenditure during controlled and free-living conditions (Nguyen, Lecoultre, Sunami, & Sutz, 2013). However, widespread use of this model has yet to be seen. As a more sophisticated GPS has been developed in the last several years, however, a small body of literature has shied concentration from the validity and reliability of GPS/GIS to these tenologies’ capability to promote individuals’ PA participation. In fact, the majority of the literature to date has focused not on interventions per se, but on the assessment of PA paerns, in addition to the built environment, and how the promotion of PA might be aieved in these environments through later behavioral interventions or initiatives whi seek to modify the built environment to be more conducive to PA participation. Moreover, mu of the resear to date has focused on youth populations. For example, several large-scale projects have been initiated using GPS/GIS, in combination with accelerometry, to examine and facilitate youth PA promotion by investigating the locations in whi youth PA takes place. e UK-based Personal and Environmental Associations with Children’s Health (PEACH) project (Wheeler, Cooper, Page, & Jago, 2010) is one su large-scale study whi gathered GPS/GIS data from 1307 10-year-old and 11-year-old ildren, finding ildren spent an average 13% of aer-sool time outdoors, with further findings indicating that outdoor time contributed to 30% of ildren’s daily PA participation and 35% of their daily MVPA. Similar results were seen in the Teen Environment and Neighborhood (TEAN) study, with Carlson et al. (2016) indicating the highest proportion of adolescent PA (42%) to occur at sool, with approximately 19% and 21% of PA occurring at home or near home/sool, respectively. ese findings mirrored that of a smaller, earlier study indicating the majority of adolescent participation in PA takes place near or at sool/home (Maddison et al., 2010). As a result, it is clear that increasing PA among youth must consider the impact of the built environment on PA behavior.
Despite the fact that the built environment plays a role in influencing youth PA behavior, modifying the built environment through reconstruction is allenging. As su, recent studies have focused on educating youth regarding approaes in whi they can use their current built environment to engage in more PA. e 6-month Children’s Use of the Built Environment (CUBE) study among youth aged 10–16-years-old used GPS/GIS, along with accelerometry, to observe moderate-to-vigorous PA (MVPA) in different locations of their environment. Following baseline measurement, the intervention youth and their parents met with researers who counseled the youth on how to use their most frequently traversed built environments (data provided by GIS analysis of ea ild’s GPS data) to increase PA (Oreskovic, Goodman, Park, Robinson, & Winioff, 2015; Oreskovic, Winioff, Perrin, Robinson, & Goodman, 2016). Control group youth received standard information regarding PA participation and healthier living. Researers found that, on average, the intervention youth receiving counseling had just over 9 more minutes of daily MVPA than controls at post-intervention. Similarly, researers in New Zealand used GPS/GIS to discern if playground upgrades resulted in greater PA (PA) among 5–10year-olds, observing significant higher PA over time as a result of these upgrades (igg, Reeder, Gray, Waters, & Holt, 2013). Findings from the preceding studies indicate the usefulness of examining the built environment when investigating how to best intervene and increase ildren’s PA levels. e built environment has also been the largest area of inquiry among adults in the general population. Among the earliest studies in this line of resear was a pilot study by Rodriguez, Brown and Troped (2005) during whi GPS/GIS was used to investigate the location of MVPA in 35 adults, finding 46% of MVPA occurred within the participant’s neighborhood— congruent with the later results of Troped and associates (2010). Notably, both studies found environmental variables su as land use, housing unit/population density, intersection density, and vegetation index had an effect on PA. Nonetheless, GPS/GIS resear among adults has also found that as mu as 60% of PA occurs further than 800 meters from individuals’
homes (Hillsdon, Coombes, Griew, & Jones, 2015)—necessitating the investigation of PA participation in other locations within communities solely built to promote PA participation. Indeed, other studies have investigated how the provision of biking/running trails and parks within a community can influence PA. Troped et al. (2001) used GPS/GIS to assess the biking/running trail use of 413 adults, observing significantly decreased trail use the farther the participant lived from the trail, when the study participants were older, and when the study participant was female. Interestingly, recent multi-site resear using GPS/GIS to assess adult use of 31 parks in five U.S. states found that adults who visit a park at least once weekly had significantly higher MVPA compared to adults who did not visit a park (Evenson, Wen, Hiller, & Cohen, 2013). Finally, GPS/GIS data have even shown that the implementation of public transit can promote greater PA among adults in the surrounding community, and this does not necessarily displace other active forms of transportation su as walking or biking (Miller et al., 2015). While the aforementioned studies are not meant to be exhaustive, they are representative of the high-quality literature available regarding the use of GPS/GIS to aid in the promotion of PA and health among the general population. What should be clear in the discussion above is the usefulness of GPS/GIS in studying the influence of the built environment on the PA participation of youth and adults. Specifically, information gleaned from studies using GPS/GIS to study the PA paerns of youth and adults can be used to advocate for modifications and policy anges governing community land use, in addition to aiding in the development, implementation, and assessment of PA interventions. For instance, modifications could be made to a local park (e.g., updated playground equipment) or community governing bodies could implement policies whi designate certain areas of land to be only for recreational use —all anges whi would increase the likelihood of greater PA participation. Moreover, it might be worth examining how new augmented reality games su as Pokémon Go—whi use GPS-enabled devices to display on the screensof these devices a virtual world overlaid onto real-
world environments (Figure 7.2)—might be used to increase PA at the population level. Indeed, researers have postulated the possibility of using games su as Pokémon Go to increase PA at the population level (Baranowski, 2016). Finally, with 64% of adults currently owning a smartphone (Pew Resear Center, 2015), it would be advantageous for researers to use GPS-enabled smartphones in future studies, given the breadth of contextual information these devices might provide regarding PA environments. For example, Hirs and colleagues (2014) described how researers might use commercially available GPS-enabled smartphone apps (e.g., MapMyFitness) to tra where PA occurs in a given environment (e.g., an urban environment) and what type of PA is most likely to occur at a given location. Indeed, participants of a study could send the GPS coordinates of locations where PA is impeded due to broken sidewalks or poorly maintained trails to researers interested in how to modify the built environment to promote PA. ese coordinates could then be used to promote anges to the built environment at the government level. Overall, it is clear the promise of GPS/GIS in PA and health promotion remains great, with the use of GPS/GIS for health promotion among the general population continuing to grow. at said, sport-related performance has also been another area of resear wherein GPS and/or GIS have been used.
Figure 7.2
Pokémon Go app outdoors.
Source: pixabay.com.
Sport-related performance traing and assessment among athletes Although sport-related performance traing and assessment are not the main focus of this book, it is noteworthy to briefly discuss the use of GPS and/or GIS during sport participation, given the breadth of literature available on this topic. Moreover, participation in recreational sports throughout the lifespan has been shown to lead to beer health outcomes in late life among recreationally active individuals in the general population (Hirvensalo & Lintunen, 2011). As su, the development of accurate sportrelated performance traing and assessment tools, su as GPS/GIS, is paramount if recreationally active individuals are going to make informed oices about the sport activities they participate in. Notably, lile use of GIS is seen in studies of sport performance as researers typically assess the
competitor’s performance on the field of play and do not try to discern barriers in the built environment inhibiting gameplay. Both team and individual sports have been of interest to researers using GPS to tra and assess sport performance. MacLeod Morris, Nevill, and Sunderland (2009) investigated the validity of an advanced GPS system, the Spi Elite™, as worn by nine recreational trained field hoey players during a circuit requiring the athletes to run and ange direction. Findings indicated that the GPS system was highly accurate, with GPS-recorded distance off by an average of only three meters for the pre-measured course and average GPS speed measurements being identical to that provided by the timing gates. However, Johnston et al. (2012) conducted a similar study, suggesting that while GPS was accurate in traing distance covered and peak speed, the error associated with these measures increased as athlete velocity increased. e results of the Johnston et al. (2012) study are congruent with earlier findings by Cous and Duffield (2010) and Jennings and colleagues (2010), whi found distance and peak velocity measures become less reliable as athlete training intensity increased. e use of GPS in the assessment of PA paerns among professional sports has also been investigated to beer inform athlete training practices. Two studies have used GPS to examine between-position differences in gameplay intensity as measured by parameters su as overall time in light, moderate, and vigorous exercise; distance traveled during the game; and average/peak velocities (Kempton, Sullivan, Bilsborough, Cordy, & Cous, 2015; Macutkiewicz & Sunderland, 2011). Findings indicated that, generally, high-speed running, distance covered, and player load varied by position and was higher during and was oen higher late in the season. Data from studies like the preceding two investigations can be used to beer inform trainers and coaes of the volume of activity ea player is engaging in— allowing the trainers and coaes to manipulate training practices to optimize performance/recovery and reduce likelihood of injury. Notably, optimizing performance/recovery and reducing injury hae been the focus of a growing body of GPS literature in recent years whi has examined athlete
training load and volume (Figure 7.3) (Malone et al., 2015; Nielsen et al., 2013; Smith, Moran, & Foley, 2013). e aforementioned literature, however, is limite in that few studies have investigated recreationally active athletes or sports in whi recreationally active athletes are likely to participate—again, a drawba given sport participation continued throughout the lifespan can result in beer health long-term health outcomes (Hirvensalo & Lintunen, 2011). Geocaing, a sport whi functions mu like a real-world treasure hunting game, requires participants with GPS-enabled devices to walk, bike, hike, and climb their way to a specific location denoted by GPS coordinates (Groundspeak, 2016). Upon reaing the GPS coordinates, the players find containers (termed “geocaes” or “caes”) at whi point they are able to take and keep the contents of the cae. Per the rules of geocaing, however, players must log their find within the cae logbook and leave within the cae an item of at least the same value as the item they took (Groundspeak, 2016). While some solars have stated geocaing to be a twenty-first-century form of outdoor hide-and-seek (Slaer & Hurd, 2005), scarce literature is available whi has tested the effectiveness of a geocaing program in the promotion of PA. Indeed, while researers have posited that geocaing might be a sport capable of increasing PA across adults, ildren, and families, concerns about the safety of geocaing have likely inhibited the implementation of geocaing-based PA interventions (Fle, Moore, Pfeiffer, Belonga, & Navarre, 2010). erefore, future resear might consider how to minimize safety concerns (e.g., the need to, at times, travel to obscure locations to rea a geocae) before implementing a geocaing-based PA intervention.
Figure 7.3
Traing physical activity with a mobile phone app.
Source: pixabay.com.
As can be seen, GPS and GIS demonstrate great utility among both the general population for PA and health promotion and populations of professional and some recreational athletes for sport performance traing and assessment. However, some issues remain. For instance, how do studies decrease the signal loss common when using GPS? Further, despite GPS devices’ ability to tra the speed and distance of PA, how do researers improve the ability of GPS to tra PA intensity and type so that further GPS studies do not have to rely on the concurrent use of GPS and accelerometer devices to tra PA? Finally, how might researers beer integrate GPS devices into the natural behaviors of individuals? Presently this integration is difficult as it might interfere with certain activities. For example, it would be difficult to wear a GPS device during a football game. In subsequent sections of this apter, these questions will be addressed.
GPS/GIS and different intensity levels and types of PA As stated in the previous section of this apter, it is common to have participants in GPS and GIS studies wear accelerometers in addition to the GPS devices provided by the resear teams. While some of the rationale behind wearing accelerometers arises due to the fact GPS devices have difficulty accessing and keeping a signal when traing PA indoors, wearing accelerometers also addresses the limitation of GPS devices to assess PA intensity and type. With regard to PA intensity, previous studies have indicated that, as PA intensity increases, the error associated with measurements made by GPS also increases (Cous & Duffield, 2010; Jennings et al., 2010; Johnston et al., 2012). While some researers are developing mathematical models to help correct for error in measures of PA intensity made by GPS (Nguyen et al., 2013), widespread use of these models has not taken place. Inaccurate measures of PA intensity have major implications when designing and implementing PA interventions. Specifically, if inaccurate GPS measures of PA intensity lead to the design and implementation of a PA intervention that is too intense, intervention participants may lose motivation to participate in PA—decreasing the effectiveness of the intervention and the ability to expand the intervention to the larger population. e assessment of PA type is also an important consideration as it pertains to the limitations of GPS. Maddison and Mhuru (2009) stated that, while GPS is able to provide good information on the distance, velocity, and location of PA participation, the inability of GPS to provide data whi can help differentiate different activities done at the same velocity—for example, slow walking versus slow biking—the usefulness of this tenology in PA traing and assessment is limited. Unfortunately, few investigations into
increasing GPS devices’ capability to accurately assess PA type have been completed, with studies having this objective finding the use of accelerometers is still necessary for the most valid discernment of PA type (Troped et al., 2008). Unfortunately, the necessity of wearing both a GPS device and an accelerometer increases participant burden and subsequently reduces intervention compliance—both outcomes whi, again, limit the researers’ ability to generalize the findings of these investigations to subsequent large-scale intervention development and implementation. Given the previously described limitations of GPS in the measurement of PA intensity and type, future studies must look at ways in whi to improve the capability of GPS devices to provide these measurements concurrently and in a manner that does not result in high participant burden—meaning GPS and accelerometry need to be integrated in a single device. However, of the most recent studies seeking to measure both PA intensity and type concurrently and accurately, it was found that one GPS device and two accelerometer are still needed for the most accurate measurements (Nguyen et al., 2013). Furthermore, while integration of GPS and accelerometry into one device has taken place with the proliferation of smartphones, the validity and reliability of these devices in the systematic traing and assessment of PA intensity and type—not to mention the ability of smartphones to accurately tra PA distance, velocity, and location—remain to be seen. Moreover, while vests and clothing are available whi integrate GPS and accelerometry—su as the Catapult vest (Catapult, 2016; www.catapultsports.com) and the Hexoskin Biometric Shirt (Hexoskin, 2016; www.hexoskin.com)—these articles of clothing may still be uncomfortable and burdensome for participants to wear. It is suggested, therefore, that researers and companies engage in greater collaboration to develop products that have the capability to provide accurate GPS and accelerometer data concurrently and meet the style and comfort preferences of users interested in tenology of this type. Nonetheless, researers and health professionals should still strive to integrate GPS into the traing and assessment of PA intensity among the general population as well as among athletes—particularly recreational
athletes. Indeed, despite the limitations of GPS in the assessment of PA intensity and type, this tenology is still powerful and has the ability to impact public health policy and initiatives—promoting health at both the individual and population levels.
Application of GPS/GIS in different contexts As has been addressed in the previous two sections of this apter, GPS and GIS are used across a diverse array of populations to assess a number of activities. As a result of the aforementioned diversity, these tenologies are used in numerous contexts. Broadly, the contexts could be reduced to two main environments: (1) PA and health promotion in relation to the built environment; and (2) sport-related performance traing and assessment on various fields of sport play. While mu of the literature within these categories has been toued upon, it is important to note where improvements to the use of GPS and/or GIS can be made within ea context to offer greater benefit to clients and consumers who benefit from the data these two related tenologies provide. Within the context of PA and health promotion, we have discussed the use of GPS/GIS to discern the relationship between an individual’s built environment and their PA participation. Examining this relationship has become paramount, given the increasingly obesogenic environment of the present-day wherein tenology continues to make our lives easier, with less need for PA (Jones, Bentham, Foster, Hillsdon, & Panter, 2007). at said, improvements to a couple major aspects of GPS/GIS studies for PA and health promotion are needed to overcome the aforementioned allenges. First, despite the utility of GIS soware in the analysis of participant GPS location data in relation to PA, the privacy of this soware has been questioned for nearly a decade (Brownstein, Cassa, Kohane, & Mandl, 2006; Maddison & Mhuru, 2009). It has been observed that reverse identification of participants’ residencies is possible nearly 80% of the time using publication quality GIS maps (Brownstein et al., 2006). Not only does the possibility of discerning an individual’s residence pose a danger to these
individuals, the potential brea in confidentiality may deter individuals from future GPS/GIS study participation—limiting the investigation of the built environment’s influence on PA and slowing advantageous PA-related modifications to the built environment. Fortunately, recent work has begun to investigate how researers can “obfuscate,” or mask, data points when analyzing GPS data with GIS soware (Seidl, Jankowski, & Tsou, 2016). is work is preliminary, however, and further resear into the masking of participant GPS data without compromising researers’ ability to accurately analyze these data is needed. While minor in relation to questions regarding privacy of GPS/GIS data, a second improvement with regard to the use of GPS/GIS data in studies on PA and health promotion concerns the need for participants to wear both a GPS device and an accelerometer. e previous section of this apter offered a fuller discussion of why this concurrent measurement by two devices is necessary; namely, GPS devices have poor ability to discern PA intensity and type while also having limited use indoors due to signal loss. Although GPS and clothing companies are working to integrate GPS and accelerometry, the fact that two devices are necessary in GPS/GIS studies is noteworthy as it increases participant burden. Increased participant burden can lower compliance to the study protocol—negatively impacting study outcomes and the conclusions whi can be drawn as a result of a given study (Mahews, Hagstromer, Pober, & Bowles, 2012). As stated previously, increased collaboration will be needed between researers and companies to develop products that not only collect GPS and accelerometer data concurrently, but that also meet the style and comfort preferences of users (Figure 7.4).
Figure 7.4
A researer teaing a patient to use a smartphone physical activity app.
Source: Photo by Zan Gao.
Among professional and recreational athletes on the field of play, two major improvements to the use of GPS could also be made. To begin with, as addressed above, GPS has demonstrated higher error as athlete velocity increases (Cous & Duffield, 2010; Jennings et al., 2010; Johnston et al., 2012). Training load is also of serious concern to athletes and trainers, given the fact that too high or too low a training load can have a negative impact on player performance (Manzi, Bovenzi, Impellizzeri, Carminati, & Castagna, 2013). erefore, resear into the development of more-sensitive GPS devices—whi might also be integrated with accelerometry—is necessary to ensure the most accurate traing and assessment of athlete performance, with an additional objective being to reduce injury. Second, if GPS is going to be fully employed in a wide variety of sports, the devices need to be minimally intrusive. While GPS-enabled devices su as the Catapult (Catapult, 2016) are available, oen the devices are still too large and temperamental to be used in sports where large amounts of contact are
to be expected (e.g., football). Researers might look further at how to integrate small GPS units into currently available biometric clothing lines, su as the Hexoskin Biometric Shirt (Hexoskin, 2016), as this type of integration would be less intrusive to the overall performance of the athletes wearing the tenology. Despite the need for improvements in GPS in different contexts, GPS data is still anging the way in whi researers/health professionals improve the ability of the general population to participate in PA, in addition to aiding coaes/trainers to prepare their athletes to compete at the highest level of sport. Given the fast pace at whi tenology continues to advance, it is likely that many of the major improvements desired within the context of PA and health promotion as well as sport-related performance traing and assessment will be partially or fully solved in the next decade. ese solutions will facilitate improved health and performance among community members and athletic populations, respectively.
Application of GPS/GIS in the healthcare field e use of GPS/GIS in healthcare differs slightly from that seen using other forms of tenology. Specifically, while GPS/GIS can and has been implemented in clinical seings, the literature is scarce. Indeed, while these tenologies lend themselves well to the assessment of PA among healthy individuals in addition to athletes and other recreationally active populations, GPS/GIS is less useful in clinical populations where treatment oen takes place indoors—making acquisition of the satellite signal needed for GPS devices difficult and inhibiting further analysis via GIS teniques. Given these limitations, as well as the extant literature reviewed previously, it might be stated that GPS/GIS is of greatest use in healthcare in the public health field—particularly as it pertains to assessing and advocating for anges in the built environment whi would beer facilitate PA participation by individuals in poorer health. One way in whi to harness the power of GPS/GIS among clinical populations is to use this tenology to complete environmental assessments of the individuals for a set period of time, aer whi the individuals can be brought ba to the health clinic and counseled on how to take advantage of the environment around them to increase PA. For example, a male named John who is currently 40 pounds overweight and in the early stages of type 2 diabetes could be provided a GPS device by a wellness team at the weight management clinic where he is currently enrolled. e wellness team would then request that John wear the GPS device over the course of the next month. Following a month of GPS wear, John would then bring the GPS device ba to the weight management clinic, at whi point the wellness team would use GIS soware to analyze the GPS data and create maps of the locations where John engages in the greatest amount of PA. An
appointment could then be set up with John, when these maps would be provided to him. Using these maps, the wellness team could highlight locations within the environment whi John interacts with or frequently passes, where greater PA participation is possible (e.g., nearby walking paths, parks, fitness facilities, etc.). John could then set goals with the wellness team regarding increasing his PA participation (e.g., seing a goal to use the walking path near his house at least three times per week). Periodic calls by wellness team personnel could then be made to John, assessing his adherence to his goals and discussing any new barriers to PA whi have arisen since his previous meeting with the wellness team. While the preceding example is hypothetical in nature, the same design has been used among overweight youth 10–16 years of age within the CUBE study—implemented via an outpatient community health clinic. Specifically, Oreskovic et al. (2016) asked youth to wear GPS devices during three different time periods over the course of one month, before taking part in a counseling session reviewing their GPS data and how to use the built environment they most frequently traversed to increase PA. At the study’s end, researers observed daily MVPA to be approximately 9 minutes higher among the intervention group compared to controls. Although the usefulness of GPS/GIS for healthcare delivery was first addressed nearly a decade ago (see Hanjagi, Srihari, & Rayamane, 2007), a study in healthcare akin to that of the CUBE study has yet to take place among adults. us, more aention needs to be paid to this line of investigation as findings regarding the built environment from GPS data may have wide implications as these pertain to advocating for public policy anges, affecting how communities use land and the provision of recreational facilities by these same communities. ese same policy anges could also complement healthcare delivery approaes whi seek to use the built environment to increase PA participation and promote health—akin to John’s example above. In this manner, we can ange a normally obesogenic built environment into a place where the unique aspects of the built environment can be a catalyst for improved PA and health.
Finally, crowdsourcing might also be an innovative way in whi healthcare can use GPS/GIS to improve health at the population level. Briefly, crowdsourcing references large numbers of public citizens being given the opportunity to complete tasks for companies, organizations, or institutions, ranging from the categorization of galaxies in space to solving complex engineering problems (Ranard et al., 2014). Within the healthcare field, crowdsourcing has been used in the surveillance and monitoring of malaria symptoms in India (Chunara et al., 2012) in addition to being used for data processing purposes to identify blood smears containing malariainfected parasites (Mavandadi et al., 2012). GPS has also been used for crowdsourcing within the healthcare field to allow users equipped with a smartphone application to document—using GPS coordinates—where they or others were experiencing flu-like symptoms as well as constructing maps of automated external defibrillators around a city. Indeed, given the high smartphone ownership among individuals in developed countries (Pew Resear Center, 2015), use of GPS-enabled smartphones via a crowdsourcing manner akin to the two examples just mentioned might go a long way to improving population health. For instance, smartphones could be used by patients enrolled in a health management program to send GPS coordinates of places in their environment that are impeding their PA. is information could then be used by researers and health professionals to advocate for anges in the built environment to promote population-level increases in PA participation—mu like the strategy mentioned in the first section of this apter. Nonetheless, with a concerted effort by researers and health professionals, GPS/GIS could have major benefits in the healthcare field via public health policy.
Use of GPS/GIS in developing effective PA interventions GPS/GIS are different from other emerging tenologies in that GPS/GIS tenologies are typically used to inform subsequent interventions or describe PA paerns and are not typically the medium by whi researers and health professionals intervene. Nonetheless, when using GPS/GIS to complement the PA intervention development, implementation, and development process, a few different considerations must be made to ensure an effective PA intervention. As is recommended for all interventions, a theoretical baing in studies looking to use GPS/GIS to develop, implement, and assess an intervention is necessary. eory allows for leveraging the indirect effects of certain determinants of PA engagement (e.g., self-efficacy, motivation, etc.) while also facilitating external and accurate interpretations of study findings (Paen, 2014). With regard to GPS/GIS, theoretical foundations are paramount in discerning how these two emerging tenologies may best benefit the PA intervention. Second, effective use of GPS/GIS in PA and health promotion studies needs to consider intervention fidelity. While a fuller description of intervention fidelity is available in Chapter 6, the term refers to how researers go about protecting the reliability and internal validity of their study (Borrelli, 2011). In studies using GPS/GIS to tra and assess PA levels of participants, the greatest threat to intervention fidelity is signal loss and, as a related metric, participant adherence. One review found that out of 17 studies reporting the amount of GPS data loss, data loss rates were 2.5%–92% (Krenn et al., 2011). erefore, researers must provide thorough information to participants regarding GPS device wear whi outlines, among many possible instructions, the proper wear location, the required
amount of wear time per day, the GPS syncing procedures to allow sufficient satellite signal/reduction of signal loss, and the arging procedures given oen limited baery life of GPS devices. Further, researers should periodically e up on the participants to ensure adherence to these procedures. ese fidelity measures will ensure low loss of GPS data—oen stated to be the largest limitation of PA resear using GPS devices (Krenn et al., 2011)—and facilitate subsequent analysis of this GPS data via GIS soware. Finally, like most new resear fields, future studies must increase the representativeness of the study participants as it pertains to the demographics of the general population (Krenn et al., 2011). is will allow for the findings of said studies to be more effective in influencing the design and implementation of PA interventions across a wide array of populations in the academic and healthcare fields. Not only will this increased representativeness come in the form of more diverse race/ethnicity make-up of studies using GPS/GIS, it must also arise from a more evenly distributed age make-up of these studies. As was evident in the first section of this apter, a great deal of resear has been done on ildren, adults, and athletes, but far less resear is available regarding middle-aged and older adults and their activity paerns. Investigating middle-aged and older adults’ PA paerns—and ways the built environment might be modified to promote PA participation in these older populations—is paramount, given the fact that functional limitations brought about by decreased PA and increased disease commonly observed with aging are currently a public health concern (An & Shi, 2015).
Practical implications of GPS/GIS use in promoting health Aside from the considerations that future resear must take into account when using GPS/GIS to design, implement, and assess PA interventions to ensure these tenologies’ limitations do not bias results and/or limit the use/generalization of the data gathered, GPS/GIS holds major promise. In particular, these two related tenologies have a bright future related to examining the relationships between individuals’ PA paerns and objects within the built environment. Indeed, through the examination of the built environment via this tenology, researers can advocate for anges at the city, state, and national government levels, influencing policies regarding the assignment of land use while also integrating this tenology further into healthcare—teaing clinical populations how to use the built environment around them to increase PA levels. at said, over the next decade, researers and industry members must work together: (1) to integrate GPS and accelerometry into single devices whi appeal to and reduce the burden of study participants, in addition to being capable of providing PA type and intensity information—with the capability of these devices to also measure physiological variables, su as heart rate and energy expenditure also needed; (2) to develop GPS/accelerometry devices whi can be worn by athletes in all types of professional and recreational sports, to effectively manage training load to improve performance and reduce injury; and (3) to learn how to effectively mask the maps published using GIS soware to protect study participant confidentiality and facilitate further desire by community members to participate in studies of this type. Nonetheless, GPS/GIS promises to play a major role in the future of PA and health promotion. Indeed, as GPS/GIS has demonstrated tremendous growth and use in the tenologies’ first 15 years
of civilian use, it will be amazing to see where another 15 years of improvements will take this tenology.
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8 Health wearable devices and physical activity promotion Nan Zeng and Zan Gao
Nowadays, physical activity (PA) monitoring devices are increasingly popular, with commercially available wearable activity monitors and traers —hereaer referred to as health wearables—representing a rapidly growing health-focused industry. Health wearables typically cost US$100–250, making these devices relatively eap compared to resear-grade accelerometers (oen ≥US$250). In fact, approximately 3.3 million health wearable devices were sold between April 2013 and Mar 2014 in the United States— representing a sales growth of over 500% as compared to the previous year (Danova, 2014). By 2018, almost 60 million health wearables will be in use worldwide (Sullivan, 2014). e popularity of su devices has risen as they are affordable, stylish, and useful. Generally speaking, health wearables (e.g., fitness wristbands and sport wates) use sensors to help users automatically tra and set PA-related goals (e.g., calories burned, step counts, distance traveled, etc.), sleep, diet, and other behaviors so as to optimize health (Almalki, Gray, & Sanez, 2015). In early 2016, Samsung showcased an innovative tenology called WELT, a smart wearable healthcare belt offering consumers a more discrete way of using smart sensor tenology to monitor health. WELT is capable of recording the user’s waist size, eating habits, step counts, and inactive time (e.g., siing). A similar smart belt, introduced by Belty, contains integrated
artificial intelligence to promote a healthy lifestyle. For example, while walking, Belty can increase your walking pace through rhythmic sounds. Currently, an interactive helmet, the Open Heart Helmet, is being developed by the Exertion Games Laboratory. is helmet provides a visual of real-time heart rate data—an effective approa for cyclists to monitor their PA intensity. To facilitate traing, many health wearables wirelessly connect to free mobile- and Internet-based applications whi provide users immediate feedba and the ability to store long-term PA information (e.g., stairs climbed, heart rate, active time, etc., see Table 8.1 and Figure 8.1; CadmusBertram, Marcus, Paerson, Parker, & Morey, 2015; Lyons, Lewis, Mayrsohn, & Rowland, 2014). e capability of health wearables to upload health statistics to mobile and Internet-based applications allows health wearables to promote self-regulation via the traing of health metrics (e.g. steps taken, calories burned, etc.) in addition to facilitating social support among connected users of these applications—aiding wearers to aieve their fitness and health goals (Evenson, Goto, & Furberg, 2015; Lyons et al., 2014).
Figure 8.1
e use of a smart wat in traing.
Source: pixabay.com.
To date, health wearable devices have been widely employed to improve overall well-being in a variety of ways. Health wearable-based interventions (e.g., studies employing Fitbits, Apple Wates, Microso Bands, etc.) are used for self-evaluation, self-monitoring, self-reinforcement, and goal seing to increase PA with the aim of promoting health/wellness among various populations including ildren and adolescents, young adults, older adults, and patients (Cadmus-Bertram et al., 2015; Miie et al., 2011; Wang et al., 2015). ese increasingly advanced devices enable the wearers to improve their health outcomes by constantly monitoring their bodily responses to PA, with most users believing health wearable tenology has enhanced their lives (Miie et al., 2011). A national telephone survey conducted by the Pew Resear Center’s Internet and American Life Project in 2012 found that 69% of U.S. adults keep tra of at least one health indicator su as weight, diet, PA participation, or disease symptoms for themselves, family members, or friends using journals, spreadsheets, and/or mobile devices. Notably, among those who traed at least one health behavior or condition, 21% reported they used modern tenology, su as health wearables, to tra their health data. Finally, in a study by Fox and Duggan (2013), 46% of health wearable users indicated that the use of traers had anged their overall approa to maintaining their health or the health Table 8.1
Selected devices and descriptions
of people for whom they provide care, and 34% declared that traing activity has affected decisions about how to treat an illness or condition. Stated simply, health wearable devices have potential benefits for positive health behavior ange. erefore, it comes as no surprise that health wearable devices are a major wellness trend. is apter will review the literature
regarding health wearables in evaluating PA behavior as well as implementing PA plans. Furthermore, data collection, processing, and reporting will be discussed. Finally, evidence concerning the application of health wearables in PA and health promotion in various populations and seings will be presented.
Wearable devices in assessing and promoting PA Current teniques to assess PA and sedentary behavior can be classified as direct observation, self-reported instruments, and motion sensors. Although direct observation is regarded as the gold standard in measuring PA behavior (Sirard & Pate, 2001), this tenique may not be feasible in many situations because of expectancy bias, observation effect, and even participant privacy concerns (Ligge, Gray, Parnell, McGee, & McKenzie, 2012). Additionally, self-reported instruments have some weaknesses including measurement error and recall bias, failure to assess one or more dimensions of behavior, and inability to assess some activities (Prince et al., 2008). Moreover, some questionnaires are not recommended to be used with ildren 10 or 11 years of age or younger as ildren oen la the required cognitive skills needed to complete the questionnaires, leading to inaccurate reporting of PA behaviors (Sallis, 2010). Consequently, objective assessment tools su as health wearable devices with built-in motion sensors are increasingly being used to evaluate PA behaviors among various populations. In recent years, using health wearables to evaluate PA has become increasingly accepted in observational and experimental resear. Advances in the tenology of health wearable devices provide professionals and researers a variety of PA measurement options to assess multiple healthrelated outcomes. For instance, pairing devices with their associated mobile applications could help tra physiological responses to PA su as heart rate, energy expenditure, and other health-related indicators like food consumption or sleep quality indices. Indeed, self-monitoring with health wearables permits individuals to evaluate and regulate their health behaviors and evaluate their progress toward individual wellness goals. In a recent study by Case et al. (2015), researers gauged the accuracy of smartphone
applications and wearable devices compared with direct observation of step counts. Eighteen healthy adults were required to walk on a treadmill set at 3.0 mph for 500 and 1500 steps while wearing the Flex (Fitbit), the UP24 (Jawbone), the Fuelband (Nike), and a Digi-Walker SW-200 pedometer (Yamax). Meanwhile, ea participant carried an iPhone 5s (Apple) simultaneously running 3 iOS applications: Fitbit (Fitbit), Health Mate (Withings), and Moves (ProtoGeo Oy); and a Galaxy S4 (Samsung Electronics) running 1 Android application: Moves (ProtoGeo Oy). Overall, most smartphone applications and health wearable devices were accurate for traing step counts. e findings may help reinforce individuals’ trust in using smartphone applications and health wearable devices to tra health behaviors since step counts are oen used to derive other measures of PA, su as distance or calories burned. Clinicians have also begun employing health wearables to measure patients’ PA. For instance, Alharbi and associates (2016) conducted resear involving 48 cardiac patients and family members who wore Fitbit-Flex and Actigraph simultaneously over four days to monitor daily step counts and minutes of moderate-to-vigorous PA (MVPA). Researers found Fitbit-Flex had high sensitivity in classifying participants who aieved the recommended PA guidelines—seen as relatively accurate in assessing the aievement of PA recommendations and as useful for monitoring PA in cardiac patients. It should also be noted that researers are increasingly interested in the accuracy of using health wearables to tra sleep. Baroni and colleagues (2015) utilized the Fitbit Flex to measure sleep parameters longitudinally in a sample of college students for one week at three time points—baseline, posttest, and 3-month follow-up. Unfortunately, the devices did not capture a significant amount of sleep data at baseline, only about 14% of the Fitbit Flexes recorded sleep six or seven nights, and nearly 35% failed to record any sleep data. Similarly, Lillehei, Halcón, Savik, and Reis (2015) reported an unacceptably high volume of missing data (86%) with the use of Fitbit One. As sleep is vital to health, improvements in health wearables’ capability to tra sleep quality need to be made.
In summary, using health wearable devices to evaluate PA behavior is possible as most health wearables are deemed valid and reliable in monitoring PA—despite these devices’ sometimes insufficient ability to tra other health metrics su as sleep. at said, some caution must be taken when using these devices for resear purposes as some devices may slightly overestimate or underestimate PA behavior. Nonetheless, health wearables are capable of continuously monitoring free-living conditions and providing valuable PA data for all stakeholders to tra PA levels. As a result of their validity and reliability, health wearable devices may aid in the design and implementation of personal PA plans. Frequently, health wearables are used to motivate people to engage in more PA by viewing data and results captured in real time, aer whi structured PA plans can be developed. Specifically, once an individual has established long-term health goals, they may then consider the various features that different health wearables offer, whi are most congruent with their goals. For example, if an individual sets a daily goal of 8000 steps, and only 7000 were aieved by 7:00 p.m., the user might want a health wearable on whi they can set reminders for themselves to go for a walk or at least stand up aer siing for a long period. Aside from simply traing data, the mobile and Internet-based applications associated with health wearable devices can also be used as an easy and practical way to increase PA participation by encouraging peer interaction and motivation through PA contests and games. For instance, the first person to rea a PA milestone (e.g., 10,000 daily steps) every day for one week will be awarded a certain prize via the application. In this manner, wearers can compete with and support friends, family, colleagues, and even strangers as they work toward a common PA goal and share their accomplishments—motivating one another to engage in greater PA. In fact, many mobile and Internet-based apps are specially designed to enable users to easily post PA outcomes su as exercise time, distance and calories on social media platforms like Facebook and Twier. Health wearable devices are also a convenient tool to improve overall wellbeing in the workplace—encouraging employees to plan and participate in PA at work, whi can positively impact productivity (Brown, 2014). For
example, health wearables can remind employees to go for a “walk meeting” to log extra steps or to plan to hit the gym for 30 minutes to avoid fatigue caused by spending most of the day siing in front of the screen. In this situation, the health wearables make workplace wellness more efficient and practical for full-time employees by eliminating the need to tra activity manually. Moreover, health wearables, used in conjunction with wellness allenge platforms, can bring employees together to increase PA engagement and boost morale by encouraging staff to support ea other and make more connections on individual and team PA goals (Brown, 2014). For an international organization, these health wearable devices have the added benefit of engaging and connecting staff all over the world—helping a large number of individuals live more active, healthier lives. As mentioned previously, selection of a health wearable is crucial. While some health wearables are more basic, measuring metrics su as step counts, calories burned, and distance traveled, others employ more advanced options, giving the user crucial feedba on training progress when assessing physiological variables su as heart rate and allowing logging of food (Figure 8.2). For instance, if an individual is hoping to qualify for a marathon, this individual may want to oose a health wearable that measures heart rate (e.g., Fitbit Surge and TomTom) allowing them to plan training runs to be completed within certain heart rate ranges. Conversely, an individual desiring to improve their golf skill should seek out health wearables capable of conducting golf swing analytics (e.g., Microso Band). Nonetheless, if an individual is simply striving to increase daily PA participation, a health wearable with only step counts and distance traveled will likely suffice (e.g., Misfit Xiaomi Band) (Havey, 2015). Whatever the PA goal, however, health wearables offer numerous features —akin to those reviewed above—whi can aid individuals in the development and implementation of a PA plan capable of aiding progress toward personal PA goals. In fact, resear has demonstrated health wearable use is effective in motivating participants to implement exercise regimens and not simply as a device used to just set goals. Wearers suggested that they oen relied on the data (e.g., elists and exercise history) offered by health
wearables to keep them motivated during training, and that reviewing the device’s PA feedba engendered a sense of satisfaction and motivation (Baker & Mutrie, 2005). In this manner, health wearables reduce the day-today variation in PA participation and aid individuals in implementing plans to aieve long-term health goals. Moreover, the mobile and Internet-based applications associated with health wearables allow for the traing of sleep quantity and quality—promoting more restful sleep habits—and food logs to facilitate weight loss (Havey, 2015). Finally, statistics regarding PA, diet, and sleep can then be uploaded onto social media platforms where friends and family can support these healthy anges, thereby increasing the likelihood an individual will stay commied to their PA plan (Havey, 2015). erefore, health wearables users should take advantage of these devices to help design and implement their PA plans.
Figure 8.2
Synronizing a smart wat with a smartphone.
Source: pixabay.com.
Health wearable devices and different PA intensities Generally, PA can be quantified according to intensity (how hard), duration (how long), frequency (how oen), and type (e.g., walking, running, swimming, etc.) (Hills, Mokhtar, & Byrne, 2014). Presently, resear-grade accelerometer-based activity monitors, su as the ActiGraph and Actical, are the most common devices used to objectively measure free-living PA and sedentary behavior among various populations. ere is, however, a paucity of evidence regarding the accuracy and precision of commercially available health wearable devices (i.e., Fitbit, Jawbone Up, TomTom Sport Wat, etc.) in free-living seings, with most of these studies being conducted in highly controlled laboratory seings. For example, one study examined the accuracy of the Fitbit classic wireless activity traer in assessing energy expenditure during a treadmill test and during a simulated free-living PA routine of nine activities. Findings suggested that, when worn on the hip, the Fitbit significantly underestimated energy expenditure for most activities, indicating the variability in this device’s energy expenditure measurements may be problematic for weight management programs as accurate energy expenditure estimates are crucial for implementing and traing the energy deficit necessary to promote weight loss (Sasaki et al., 2015). A similar underestimation was observed for step counts when the Fitbit was employed in older adults completing two walking trials (Phillips, Petroski, & Markis, 2015). Other studies involving a greater number of commercially available health wearables in both free-living and laboratory conditions have been conducted (Figure 8.3).
Figure 8.3
Testing smart wates.
Source: Photo by Zan Gao.
Ferguson and colleagues (2015) compared the performance of seven commercial health wearables (Fitbit One, Fitbit Zip, Jawbone Up, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer, and Withings Pulse) and two resear-grade accelerometers (BodyMedia SenseWear, and ActiGraph GT3X+) during 48 hours of free-living conditions. Findings indicated that most commercially available health wearables showed strong validity for step counts and sleep time measurements and moderate validity for energy expenditure measurements and time spent in MVPA. However, large differences were observed across devices, with the Fitbit One, Fitbit Zip, and Withings Pulse providing the most accurate intensity readings. Similar results were seen in an earlier study where the Body-Media Fit armband and the Fitbit Zip were observed to be promising tools in measuring energy expenditure (Lee, Kim, & Welk, 2014). Finally, the most recent study in the literature compared nine health wearable devices (ActiGraph GT3X+, activPAL, Fitbit One, GENEactiv, Jawbone Up, LUMOba, Nike Fuelband,
Omron pedometer, and Z-Maine) for accuracy during a 24-hour activity measurement. e results showed the resear-grade ActiGraph GT3X+ had the closest measurement for sleep and steps, while the most accurate measures of sedentary behavior, light-intensity PA, and MVPA were completed by the LUMOba, GENEactiv, and Fitbit One, respectively (Rosenberger, Buman, Haskell, McConnell, & Carstensen, 2016). Currently, long-term activity measurement is only possible with researgrade devices, su as the ActiGraph GT3X+ and GT9X. Most of the commercially available health wearable devices are unable to measure the long-term performance of a wide array of health-related outcomes. As evidenced above, there is still a marked la of measurement standards among commercial health wearable devices for PA measurements as measurement of these PA outcomes is highly varied among devices when tested outside the laboratory (Rosenberger et al., 2016). For instance, light PA is usually defined as 1.1–2.9 METs (i.e., walking less than three miles per hour, bicycling less than five miles per hour, performing light housework, etc.), but not all devices identify these activities as light PA. Indeed, time spent at different PA intensities is highly related to optimal health. Unfortunately, many health wearable devices were not designed with the explicit purpose of measuring PA intensity. erefore, individuals oosing a health wearable device should consider the PA outcome measures of greatest importance to their health goals, while researers and health professionals should give thought to the health outcome most important for the purposes of their intervention before selecting a health wearable to implement. Nonetheless, as rapid anges in health wearable tenology continue to take place, evaluations of these devices’ measurement with more robust criterion measures in free-living seings are warranted as future iterations of health wearables must demonstrate high accuracy and precision in order to be used to guide exercise and improve overall health.
Using wearable devices to collect data, process data, and interpret feedba Health wearable devices are normally designed to be worn on the body. ese devices are typically small and light, allowing users to wear them on the wrist as a wristband or wat (Barcena, Wueest, & Lau, 2014), although some health wearables can also be aaed to sports equipment like running shoes, clothes, bikes, etc. Today, commercially available health wearables are equipped with a 3-axis accelerometer that enables movement traing in every direction, orientation, and rotation. Furthermore, these health wearables usually contain gyroscopic sensors whi are responsible for generating the data (Nield, 2016). By reading the real-time data from these sensors and then applying the data to process algorithms, the devices can recognize paerns and identify user’s current PA type and intensity (Barcena et al., 2014). Simply put, health wearable devices measure motion, and then transform everyday activities into discrete data that can be stored, analyzed, and used to guide a process of PA behavior ange and the promotion of improved health. Typically, three steps are required when using health wearables to tra PA. First, researers collect PA data. is step requires participants to adhere to proper wear procedures whi facilitate the traing of acceleration and movement frequency, duration, intensity and paerns by health wearables whi can then be used to discern PA outcomes, su as the number of flights of stairs climbed and the distance one has traveled during the day. Discerning the preceding outcomes requires the processing of data and represents the next step in the data collection process. Movement information whi has been stored in raw form on the health wearable device can then be converted via mobile and Internet-based applications, whi display the previously raw movement information using easily interpretable metrics, su as steps per
day, calories burned, total active minutes, and sleep quality, among others. Notably, data analysis varies as different devices use different algorithms to calculate the gathered data in a precise and specialized way (Bogdanov, 2015). ese differences necessitate once again that consumers and researers/health professionals consider beforehand the type of PA information most important to them, so as to ensure that the health wearable provides this data. Finally, the third step in the data collection process concerns the interpretation by the user of PA data displayed on mobile/Internet-based apps. rough an examination of the data, individuals can read active versus sedentary time in order to facilitate modifications in their PA behavior, in addition to comparing current PA with previous statistics so as to derive motivation and examine progress toward their desired health goal (Barcena et al., 2014; Nield, 2016). Today, the vast majority of health wearable devices only perform a data collection function—requiring a separate computing device to allow the user to access the data analysis functions. In order to perform the previously reviewed data interpretation functions, most current health wearable devices synronize with mobile or Internet-based applications, giving users the ability to upload and interpret the data collected by the devices. Nevertheless, the ways the devices are synced with mobile and Internet-based apps are quite different. For example, Misfit Shine and Fitbit sync via Bluetooth, while health wearables like the Jawbone Up sync with a mobile device via an embedded mini ja to save the device’s baery life. Given the increasing use of health wearables in the collection, analysis, and interpretation of PA and related health metrics, developers of future health wearable devices need to focus on the design of devices whi deliver higher precision and more valid data. Increasing the accuracy and validity of these devices is paramount as health wearable users and researers have reaed a point where the collection of PA data is not sufficient. Indeed, an increasing number of individuals want devices that not only can accurately tra and interpret PA habits in real time, but whi can also provide moment-by-moment feedba that will help users modify their poor health
habits and improve their good habits—in order to optimize health and wellbeing.
Application of wearable devices in developing PA interventions Over the last five years, health wearable tenology has fast become a popular accessory. Rapid developments in tenology have encouraged the use of health wearable devices (i.e., fitness bands and sport wates) in PA resear and have offered new opportunities for promoting healthy lifestyles across diverse populations. Nowadays, commercially available health wearables have become popular devices for objectively assessing PA. ese devices typically provide PA feedba to users through the devices’ monitor displays or mobile and Internet-based applications to aid in motivating users to increase their PA. at is, these devices offer numerous features (e.g., goal seing, traing activity over time, reminders and prompts, and social connections) to promote greater PA participation and make being physically active more fun (Beebe & Harris, 2012). Health wearables su as Polar, Fitbit, and Jawbone, among others, are marketed to individuals trying to improve their health and physical fitness. ese health wearables promise greater convenience in connecting to social networks, oen via mobile device-based applications, and can be effective among various populations as an intervention tool. erefore, health wearables are of interest for use in scalable PA interventions, as these devices represent a novel and appealing approa to health promotion. Despite the novelty of health wearable tenology, these devices are beginning to be used frequently as a PA intervention tool among various populations. For example, orndike et al. (2014) designed a randomized controlled trial (RCT) using Fitbit to promote PA among 104 medical residents. Phase 1 was a 6-week RCT, with participants assigned to an intervention group receiving a traditional Fitbit with an external display or a control group given a Fitbit without an external display. Phase 2 was a 6-
week non-randomized trial immediately following Phase 1 during whi all participants wore traditional Fitbits. During both phases, median steps/day, blood pressure, high-density lipoprotein (HDL) olesterol, and proportion of days participants wore the health wearable were measured. Findings indicated that, although the health wearable intervention did not have a major impact on PA or health outcomes, the high implementation rates by physicians and the modest anges in steps, blood pressure, and HDL among patients suggested that more intensive wellness programs using health wearables might have potential in promoting healthier lifestyles among clinical populations. Similarly, a RCT by ompson et al. (2014) examined whether offering a health wearable capable of providing feedba regarding PA via an external display, while concurrently using Go4Life wellness education material for PA counseling, could promote health among 49 sedentary and overweight older adults. Researers observed no significant ange among intervention participants for PA, weight loss, glucose, lipids, blood pressure, or body fat, indicating alternative approaes to exercise counseling may be warranted for older adults to improve health. In addition, health wearable interventions have also taken place among women and overweight/obese adults. Cadmus-Bertram et al. (2015) evaluated the feasibility and preliminary efficacy of integrating the Fitbit and its associated Internet-based app into a PA intervention for 51 post-menopausal women. Findings indicated the intervention group (Fitbit and Internet-based-based self-monitoring intervention) to have significantly increased MVPA and steps minutes/week compared to non-significant increases in the control group (only received a standard pedometer). Additionally, the Fitbit was well accepted in this sample of women and associated with increased PA at post-intervention. In a 6-week RCT among African American college women, Melton and colleagues (2016) examined the effectiveness of the Jawbone UP platform to promote PA and sleep quality versus a comparison group using the MyFitnessPal smartphone application. Interestingly, at the 8-week follow-up, the intervention group experienced a significantly greater decrease in step counts versus the comparison group, with neither group demonstrating any significant anges
in sleep quality at the 6-week post-test or 8-week follow-up. Findings indicated that among African American female college students, there is no evidence to suggest health wearables were effective at improving PA participation or sleep quality—necessitating further study to discern whether gender or cultural differences act as mediating factors. Finally, Wang and colleagues (2015) assessed the utility of the Fitbit and short message service (SMS) text-messaging prompts to increase PA in overweight and obese adults. Sixty-seven participants were randomized to an intervention group receiving a Fitbit One in addition to three daily SMS-based PA prompts or to a comparison group receiving only the use of the Fitbit One. Findings showed the intervention group to have significantly higher steps per day (1266 steps), minutes/week of MVPA (17.8 minutes), and total PA per week (38.3 minutes), with the only significant increase among the comparison group being a 4.3 minutes/week increase in MVPA at the 6-week follow-up. Study findings suggested that Fitbit One was able to promote a small increase in MVPA. Overall, the market for health wearable devices continues to grow in popularity, and the potential for using these devices to promote health is great (Figure 8.4). Indeed, health wearable devices provide opportunities to overcome limitations of self-reported PA and may lead to more effective behavior ange (Hiey & Freedson, 2016). Although evidence regarding the effectiveness of health wearable devices in developing effective PA interventions is inconclusive, studies have demonstrated that these devices do hold great potential in improving PA and promoting healthier lifestyles in a diverse array of populations. While some preliminary work has been conducted using commercially available health wearable devices, more studies are warranted to beer understand the possible effects of these devices when used for developing efficacious PA interventions in the near future.
Figure 8.4
Validation of smart wates.
Source: Photo by Zan Gao.
Practical implications To date, health wearables play a significant role in health promotion. ese devices offer individuals the ability to tra and modify their health-related outcomes, su as PA and sleep paerns, as well as guide researers and health professionals in the design and implementation of health promotion interventions seeking to promote PA behavior ange. Nevertheless, using health wearables to effectively promote health behavior ange is currently facing allenges whi must be considered in future studies. To begin with, there is a paucity of evidence regarding the accuracy and precision of health wearables in free-living conditions. Although resear-grade accelerometers (e.g., ActiGraph and Actical) have been well investigated in traing PA and step counts during free-living conditions, commercially available health wearables (e.g., fitness wristbands and sport wates) capable of traing metrics su as step counts, energy expenditure, heart rate, or sleep quality, among others, have not been well validated (Powell, Landman, & Bates, 2014). It is important to note that recently ActiGraph has produced a wat called ActiGraph Link that has an app function on mobile devices, whi is convenient for resear and daily use. Second, convincing an individual who has never previously considered participating in health or fitness activities to use a health wearable to tra and assess their progress toward health goals is a allenge. Indeed, despite the initial novelty of health wearables to new users, the arition rate for use may be high. According to one survey (N = 6223), at least one-third of owners who purased a health wearable abandon their devices within 4 to 6 months (Ledger & McCaffrey, 2014). Notably, the individuals most likely to stop using these devices are those who are in poorer health but who need health behavior ange the most. Finally, a wearable device must be affordable, so that an individual is able to purase and use the device to implement a PA plan that is meant to aieve the desired health outcome. However, some devices, su as the Fitbit Surge and
the Apple Wat, can cost hundreds of dollars. is is a notable allenge as the individuals most likely to benefit from using these devices to self-regulate PA behavior for the purposes of improving health are most likely to be underserved, low-income populations (Patel, As, & Volpp, 2015). In the coming years, a concerted effort needs to be made to overcome these limitations if health wearables are truly able to aieve their full potential with regard to helping individuals live healthier and more active lives.
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9 Active video games and physical activity promotion Zan Gao, Nan Zeng, and Zachary Pope
Sedentary video games have become an increasingly integral part of individuals’ lifestyles since their advent in 1972. Presently, nearly three billion hours are spent playing video games by various populations worldwide every week. In the United States, the number of video game players who play at least one hour per day (via consoles and computers) reaed a stunning 183 million. Traditionally, video games have been blamed for individuals’ sedentary lifestyles, whi contribute to extremely high rates of overweight/obesity and associated ronic diseases including diabetes and cardiovascular diseases (Gao & Chen, 2014; Gao, Huang, Liu, & Xiong, 2012). Despite the negative impact that sedentary video games have on healthy behavior, active video games (AVGs) have great potential to promote physically active lifestyles (Barne, Cerin, & Baranowski, 2011; Gao, Chen, Pasco, & Pope, 2015). Briefly, AVGs (a.k.a., exergaming) refer to video games that are also a form of exercise. Indeed, the fast growth of AVGs (e.g., Dance Dance Revolution [DDR], Wii Sports/Fit, and Xbox Sports) has led to the development of new interactive exercise strategies, whi in turn have had a great impact on field-based physical activity (PA) interventions (Gao et al., 2012; Gao, 2012; Guy, Ratzki-Leewing, & Gwadry-Sridhar, 2011). ese games generally capitalize on individuals’ interest in computer and video interactions with gaming console-specific PA equipment to promote
increased PA and decreased sedentary time (Mears & Hansen, 2009; Prima et al., 2012). Recently, some AVGs su as DDR, Wii Just Dance, and Kinect Sports have been implemented outside of home and laboratory seings, and are beginning to find their way into sools and community centers as a potential solution to curbing physical inactivity and obesity (Gao et al., 2015). As AVGs have been and continue to be used by many individuals, particularly ildren and adolescents, it is of critical importance to synthesize resear findings derived from laboratory-based and field-based contexts, with the goal of providing meaningful practical implications and recommendations for health professionals and PA specialists. In response, this apter provides an overview of AVGs and health promotion in various populations (Figure 9.1), followed by a review of the effect AVGs have on PA and health-related outcomes. Additionally, the implementation and effectiveness of AVGs in different contexts, the application of AVGs in the healthcare field, and the emerging relationship of AVGs and augmented reality video games will be discussed. Finally, the apter will conclude with practical implications of applying AVGs in interventions promoting PA and health.
Figure 9.1
Elite athletes playing active video games.
Source: Photo by Zan Gao.
Active video games and health promotion in various populations Regular PA participation makes a significant contribution to healthy body weight among ildren and adolescents, a satisfactory quality of life in the general adult population, and the maintenance of physical functioning among older adults. Currently, AVGs have emerged as an innovative approa in promoting PA and health. Presently, numerous empirical studies and reviews have completed investigations regarding the effects of AVGs among ildren and adolescents, adults, and older adults.
Normal weight populations Children and adolescents
e increasing ildhood obesity rate has become a public health concern in the United States. Regular PA participation helps prevent and reduce ildhood obesity (McManus & Melleer, 2012). Hence, AVGs may be used to encourage ildren and adolescents to develop a physically active lifestyle (Baranowski, 2013; Gao & Podlog, 2012). As a result, AVGs have been implemented in laboratories, homes, sools, and community centers as an innovative solution to promote health and fight obesity among ildren and adolescents. Indeed, studying the efficacy and effectiveness of AVGs in this youth populations has become a popular resear topic over the past decade (Christison & Khan, 2012; Daley, 2009; Gao, 2012). Evidence from these studies proves that AVGs can generate some health benefits among ildren and adolescents. To date, resear studies conducted in laboratory-based
seings have documented the positive effects of AVGs on acute energy expenditure (Bailey & McInnis, 2011; Daley, 2009; Foley & Maddison, 2010). For example, it was found that energy expenditure values of DDR were equivalent to 7.0 METs—an intensity classified as a moderate-to-vigorousintensity level of PA. By contrast, other studies conducted in field-based seings appear to have diverse resear results, with some supporting the benefits of AVGs and others indicating no benefit (Baranowski et al., 2012; Gao, Podlog, & Huang, 2013; Graves, Ridgers, & Straon, 2008; Maddison, Mhuru, Jull, Prapavessis, & Rodgers, 2007).
Healthy adults and old adults
Although most AVG studies have focused on ildren and adolescents, a number of resear studies targeting adult populations have also been conducted to evaluate the physical, psyological, and cognitive effects of AVGs. Evidence from these studies has provend that AVGs can result in some health benefits (e.g., improving physical fitness, weight loss, and enjoyment) among healthy adults and older adults (Franco, Jacobs, Inzerillo, & Kluzik, 2012; Rendon et al., 2012, Touloe, Toursel, & Olivier, 2012). To date, the majority of AVG studies in adults were conducted in laboratory-based seings, whi observed positive effects of AVGs on outcomes su as energy expenditure, maximal oxygen consumption, heart rate and timed up and go compared to resting values. Additionally, it has been evident that the effects of AVGs on heart rate, maximal oxygen consumption, and energy expenditure were similar to those of traditional PA (Pope, Lee, Li, & Gao, 2016; Zeng, Lee, Pope, Li, & Gao, 2016). Details of su effects of the extant literature will be fully addressed in the following section.
Obese populations
e worldwide prevalence of obesity has increased dramatically over the past 30 years. Given the fact that AVGs have become an increasingly popular activity in whi both ildren and adults can participate, it is necessary to provide an overview of the current evidence regarding the effects of AVGs on health-related outcomes among overweight or obese individuals, with the ultimate goal of offering meaningful practical implications for future resear. Overall, evidence concerning the effects of AVGs on physiological and physical indicators is mixed, as some studies have indicated positive results while others have suggested otherwise (e.g., Goldfield et al., 2012; Staiano et al., 2016). Among the positive findings, evidence suggests that AVGs have the potential to aenuate weight gain for overweight or obese youth as AVGs can help ildren and adolescents be more physically active (Figure 9.2). Further, it has been observed that AVGs can improve ildren’s skill-related fitness and lead to increases in trunk and spine bone mass density among overweight and obese girls. Among the general obese populations, AVGs have demonstrated positive effects on psyological outcomes su as self-efficacy, body image, perceived solastic competence, and social competence. As was the case for the discussion of healthy populations, details regarding the study of the efficacy and effectiveness of AVGs among obese populations will be illustrated in a lat*er section of this apter.
Figure 9.2
Young adults playing active video games.
Source: Photo by Zan Gao.
Effects of active video games on PA and health-related outcomes Normal weight populations To determine the effectiveness of AVG on individuals’ health-related outcomes, randomized controlled trials and control trials were consulted first when examining the literature. Generally, the intervention dose varied considerably across studies. Explicitly, the intervention duration ranged from 6–52 weeks, with the majority of studies less than 30 weeks in duration. Moreover, the duration of ea AVG session ranged from 10–50 minutes, and the frequency varied from 1–5 sessions per week. Notably, given the nature of the AVGs, most empirical studies were conducted with ildren and adolescents. However, findings of AVG studies with adults were also reviewed.
Physiological and physical effects
Examining the physiological and metabolic effects of AVGs has been the focus of most laboratory-based resear (Bailey & McInnis, 2011; Daley, 2009; Foley & Maddison, 2010). Overall, the majority of AVGs have been reported to increase individuals’ exercise intensity to a point equivalent to light-tomoderate-intensity PA, as measured by heart rate, oxygen consumption, and rate of perceived exertion in ildren/adolescents and young adults (Graves et al., 2008; Maddison et al., 2007). Despite these findings, the effects of AVGs varied greatly, due to the nature of different AVGs (Biddiss & Irwin, 2010). Indeed, some AVGs are designed to be more physically demanding than
others. For instance, games that actively engage the lower body have higher energy costs due to the involvement of larger muscle mass when compared to games that only engage the upper body (Biddiss & Irwin, 2010). e preceding fact is none too surprising as resear shows that only one-third of the 68 Wii Sports and Wii Fit Plus games are capable of promoting moderateintensity PA. erefore, the AVGs requiring the use of more muscle mass (e.g., DDR, Wii boxing, Xbox Just Dance) should be encouraged for use in interventions with health promotion objectives. e effects of home-based AVG intervention on physiological/physical outcomes have yielded mixed findings. Some researers suggested that AVG interventions had significantly positive effects on individuals’ health-related outcomes, su as cardiovascular fitness, body composition, and PA participation. For example, Bethea and colleagues (2012) reported ildren’s cardiovascular fitness was enhanced and maintained aer a 30-week DDR intervention. Other researers asserted that the AVG treatment effects on PA, body mass index (BMI) and body fat favored the AVG (e.g., Eyetoy, Kinect Sport) intervention group over control ildren (Ni Mhuru et al., 2008). Working with healthy elderly, Rendon et al. (2012) found that participants in Wii Fit and exercise group had greater improvements in functional balance compared to the control group aer an 8-week program. e same was true for Touloe et al.’s (2012) study, when comparing Wii Fit or Wii Fit plus exercise to a control condition in a 20-week intervention among healthy elderly. However, several studies have reported that AVG interventions have no effect on ildren’s body composition (e.g., BMI, body fat percentage) and PA levels (Baranowski et al., 2012; Madsen, Yen, Wlasiuk, Newman, & Lustig, 2007). For instance, Baranowski et al. (2012) found no differences in accelerometer-determined PA levels between AVG intervention ildren and sedentary video game control ildren (Figure 9.3). Franco et al. (2012) also failed to find differences between Wii Fit or Wii Fit plus exercise and control conditions on balance in a short-duration intervention (i.e., 3 weeks). It is plausible that intervention fidelity may have caused the inconsistency of the findings across studies. Even with a randomized controlled trial design,
intervention fidelity may still be a concern. For example, Baranowski et al.’s (2012) study had two major limitations: (1) no instructions or prescriptions on how to be physically active were offered for ildren; and (2) no home visits and phone calls were performed to ensure the intervention fidelity. ese strategies are crucial and need to be used in home-based AVG studies as well as other population-based PA interventions. Hence, the implementation of standardized intervention fidelity procedures across studies presents an important allenge for AVG researers in the future.
Figure 9.3
Kids playing active video games.
Source: Photo by Zan Gao.
Additionally, the relatively low effectiveness of AVGs used in a nonstructured or self-directed manners (e.g., in home seings) for PA promotion among ildren may result from low self-regulation in ildren. Explicitly, ildren are not as adept as adults in the regulation of their thoughts and behaviors while playing AVGs. Indeed, ildren are not capable of contemplating the purpose of AVGs, how mu time to allocate to game play, and what benefits they could acquire from the AVG experiences. Instead, ildren may be aracted and engaged simply by the appealing features of
the games (Figure 9.4). us, devising strategies that could make self-directed use of AVGs more successful is a allenge.
Figure 9.4
Kids playing active video games.
Source: Photo by Zan Gao.
Evidence of the effectiveness of sool-based AVG interventions are also inconclusive, with the majority of resear indicating significantly positive effects (Gao, Hannan, Xiang, Stodden, & Valdez, 2013; Gao et al., 2012; Gao & Xiang, 2014; Sheehan & Katz, 2013) while a few other studies reported the opposite (Gao, Hannon, Newton, & Huang, 2011; Gao, 2012). Specifically, two studies reported that ildren engaged in significantly more minutes of PA and had improved balance development as a result of AVG play than they did in the traditional physical education programs (Fogel, Miltenberger, Graves, & Koehler, 2010; Sheehan & Katz, 2013). ree other intervention studies also reported positive trends, indicating that AVG interventions significantly improved ildren’s cardiorespiratory fitness and PA over time (Gao, Hannan, Xiang, et al., 2013; Gao et al., 2012; Gao & Xiang, 2014). Conversely, however, one study found that an AVG intervention performed worse at
increasing PA participation and energy expenditure versus a comparison group (Duncan & Staples, 2010). Finally, evidence suggests playing AVGs does not result in as mu PA as traditional sports. Whiman (2010) reported AVGs resulted in greater increased PA levels in ildren compared to two traditional aer-sool activities. However, Gao et al. (2011) found adolescents had significantly higher percentages of time in moderate-to-vigorous PA in fitness and football when compared to a DDR unit during physical education classes. Two other studies carried out among elementary sool ildren demonstrated that ildren’s in-class PA levels during the AVG unit were significantly lower than during the fitness or aerobic dance units in physical education classes (Gao, Zhang, & Stodden, 2013; Sun, 2012). Lastly, Miller and colleagues (2013) suggested ildren’s energy expenditure was significantly greater during physical education compared to AVG classes. It is possible the contradictory findings may be aributable to the nature of AVGs (e.g., some games are more active than others), the resear design, the target population, instruments, and other confounding factors (e.g., the fidelity of the physical education or AVG programs, familiarity with AVGs). us, caution is advised when evaluating these mixed resear findings. While not all AVGs are physically demanding enough to result in health benefits, it is widely anowledged that AVGs can be a viable replacement for many mundane sedentary behaviors like sedentary video games. Pate (2008) argued that, unlike the 1950s when kids would go out to play if bored, today’s societies are enried by convenient tenologies su as video games, the Internet, cellphones, and television. ese tenologies have enabled people to live a sedentary lifestyle since a young age. erefore, AVGs, with their exercise utility, are believed to be a substitute for less active forms of entertainment (i.e., computer games), but not as a standalone replacement for traditional PA. Indeed, as AVGs are beginning to be seen with greater frequency in homes, studies have reported AVGs to be naturally replacing individuals’ sedentary screen time (Maloney et al., 2008). at said, care must be taken to ensure AVGs do not draw kids away from the traditional physical activities that they already participate in.
Psychosocial effects
e most appealing aspect of AVGs lie in their entertaining or motivating features. It is well documented that many AVGs, besides their requirement of physical exertion, are perceived as enjoyable by players (Bailey & McInnis, 2011). Su perceptions are critical to engage individuals’ participation in AVGs and, more importantly, sustain their continued participation in these games. us far, there have been only a handful of studies whi have examined individuals’ psyosocial outcomes. e majority of these studies seem to suggest that, generally, individuals enjoy playing AVGs (Gao, 2012; Sun, 2013). Gao (2012) reported that adolescents were intrinsically motivated to play DDR. Further, when provided with the opportunity to play DDR and other AVGs, ildren and adolescents were more interested and selfefficacious for these activities compared to conventional physical education classes learning fitness exercises or aerobic dance (Gao et al., 2013; Sun, 2012). Indeed, using a longitudinal study design, Gao and colleagues reported that an AVG intervention exerted a positive effect on ildren’s self-reported social support and self-efficacy over time (Gao et al., 2012). Furthermore, working with college students, researers (Pope et al., 2016; Zeng et al., 2016) suggested that exergaming may also increase perceived enjoyment and intrinsic motivation in this population compared to tread-mill exercise whi may improve exercise adherence. Undoubtedly, as an aractive form of modern tenology, AVGs allenge or complement conventional physical education classes in giving ildren enjoyable and psyosocially beneficial experiences (Figure 9.5). As su, AVGs have potential as a tenological tool in education as well as in public health promotion.
Figure 9.5
Kids playing active video games.
Source: Photo by Zan Gao.
Although AVGs are appealing to most game players, it remains questionable whether this effect is sustainable for the institution implementing the AVG program (e.g., sools) or whether the AVG program results in long-term PA adherence as only one study reported positive results over time (Gao et al., 2012). Unfortunately, two other studies reported that elementary sool ildren’s situational interest in AVGs declined significantly between the beginning and end of instruction (Sun, 2012, 2013). ese results suggest that AVGs may have strong motivational power at the beginning of their use, but it is premature to claim they will help ildren develop a physically active and healthy lifestyle in the long term. In fact, the sustainability of AVG use is a primary concern for the fieldbased PA interventions. Several studies illustrated that ildren’s enthusiasm toward AVGs waned over time (Duncan & Staples, 2010; Sun, 2012, 2013). As a result, some researers have suggested that AVG play exerts only acute effects and is not a sustainable activity to promote ildren’s long-term PA
participation (Duncan & Staples, 2010). Unfortunately, this perspective has not taken into account confounding factors su as study design problems, measurement issues, and other methodological concerns associated with previous studies. Indeed, most AVG studies were not randomized controlled trials and oen laed rigorous control of confounding factors. Specifically, previous studies have pinpointed a number of confounding factors (e.g., game types, game experience, age, and gender) associated with AVG studies that limit the strength of evidence. Engaging in AVGs could bring about other psyosocial benefits. Specifically, playing AVGs can significantly influence ildren’s PA aitudes, subjective norms (i.e., perceived social pressure from significant others), intention, and strenuous exercise behavior (Maddison et al., 2007). AVGs provide opportunities for social support among ildren and teaers who were involved in the gaming experience together (Fogel et al., 2010). For example, in one study, researers (Paw et al., 2008) implemented a dance AVG and compared the level of participation in the game between two different social groups. It was found that the multiplayer group (playing with peers) played approximately twice as many minutes as the home group (playing alone), with dropout significantly lower in the multiplayer group. is study points out that the self-directed use of AVGs over time might compromise the success of AVG intervention and subsequent PA behavior. Hence, it is recommended to promote structured play by seing up multiple AVG stations in the same seing. With a structured program, players rotate from one AVG station to another station with minimal waiting time. In this manner, all players would have the opportunity to play AVGs simultaneously, with the continuous ability to play different AVG activities during the program. Players can also configure the game in workout mode prior to the PA session to maximize exercise time while minimizing transition time. is can be accomplished through some game consoles su as DDR and Xbox One. To enhance the maintenance of long-term AVG participation it might also be suggested to involve family and friends. As mentioned earlier, AVGs can promote social support among ildren and their peers (Fogel et al., 2010; Gao et al., 2012; Paw et al., 2008). It is, therefore, desirable to get their family
members (parents and sibling) and friends involved in the AVG interventions. Additionally, once ildren are confident in their AVG skills, launing a tournament may be a fun way for them to demonstrate their psyomotor skills. For example, players usually compete for higher scores or number of perfect moves in DDR; and compete directly with other players in-person or online when playing Wii or Kinect. In fact, AVGs like iDance have networking capabilities, allowing the establishment of an online social group for gaming play. erefore, it is recommended that, in future resear, professionals help ildren build a global social network of AVG players, whi facilitate ildren’s shared experiences and socialization as they connect with new friends who share similar interests (Figure 9.6).
Figure 9.6
Kids playing active video games.
Source: Photo by Zan Gao.
Finally, successful AVG implementation is contingent on the appropriate instruction and training of players—an aspect of AVG implementation researers have neglected in field-based seings. at is, health
professionals should tea and guide individuals as they learn to play AVGs as opposed to just “rolling out the ball” and leing them play. Indeed, AVGs do not define the learning objectives and contents for players. Rather, health professionals must first decide what they want players to learn and accomplish through AVGs, with failure to do so resulting in marginal effects of AVGs. Decisions on the progression of skills, behavioral maintenance, and the learning environment should be carefully designed, arranged, and monitored. Health professionals should have the faith to manipulate AVG activities and the learning environment in a way to maximize individuals’ perceptions of enjoyment, self-efficacy, and motor skill development. Taken together, if professionals carefully plan and provide opportunities for all individuals’ engagement and quality learning, implementing motivating and sustainable AVGs is feasible in homes, sools, and community centers.
Effects on movement skill outcomes
Recently, AVGs have been considered an innovative approa to facilitate development of fundamental motor skills, whi include locomotor skills (e.g., running, hopping, jumping, and walking), object control skills (e.g., kiing, striking, throwing, and cating), and body management skills (e.g., static/dynamic balancing, landing, and twisting and turning) (Gallahue, Ozmun, & Goodway, 2012). us far, only one study has examined the effect of AVGs on locomotor skills. Interestingly, this study was part of a large study examining ildren’s PA during AVGs. Specifically, the researers (Barne, Hinkley, Okely, Hesketh, & Salmon, 2012) investigated whether presool ildren who reported playing the Nintendo Wii and PlayStation Eye Toy AVGs had higher movement skills than those who reported engaging in non-interactive electronic games (PlayStation and X-box) for 6 weeks. ey found no significant group differences on locomotor skills. In fact, it was not possible to ascertain whether playing AVGs could facilitate locomotor skill development since some participants might have had low levels of locomotor
skills prior to the study. Moreover, the small sample size of the study indicates the findings of the study should be interpreted with caution. Given the limitations of the preceding study, definitive conclusions cannot be ascertained regarding AVGs’ ability to promote improved locomotion skills. As su, future resear must concentrate on the ability of AVGs to promote locomotor skills. Resear evidence concerning the effectiveness of AVGs on object control skills is mixed, with half of the available literature indicating positive effects and the rest reporting otherwise. More specifically, two studies (Barne et al., 2012; Vernadakis, Papastergiou, Zetou, & Antoniou, 2015) suggested that ildren engaged in AVG interventions had significantly greater object control skills development than those in non-AVG or specific object control skill programs. Conversely, two studies (Barne, Ridgers, Reynolds, Hanna, & Salmon, 2015; Johnson, Ridgers, Hulteen, Melleer, & Barne, 2015) indicated that AVGs did not significantly improve intervention players’ perceived or actual object control skills over time as compared to the control group. Notably, however, these two investigations (Barne et al., 2015; Johnson et al., 2015) let ildren randomly play with designated AVGs with no gameplay instructions given, whereas the other study employed an experienced motor skill instructor at ea AVG intervention session, who taught ildren how to perform the required movements (Vernadakis et al., 2015). La of instruction in the former two studies may have led to the biased result and the appearance of AVGs as having no potential to develop an individual’s motor skills, while one may assume skill acquisition in the Vernadakis et al. (2015) study was aributable to the instructor as opposed to AVG itself. Further, we speculate that intervention dose may play a big role in the differences. Indeed, Vernadakis et al. (2015) conducted an 8-week intervention (twice a week, 30 minutes per session) with a total amount of 480 minutes of AVG-based program implementation. Yet, the intervention length in the other randomized controlled trials (Barne et al., 2015; Johnson et al., 2015) were 278 minutes and 300 minutes, respectively. e lower dose of the AVG interventions in the laer two studies may not have been
sufficient to trigger motor skill improvement. Finally, the nature of AVGs (i.e., Wii vs. Kinect) may result in different amounts of bodily engagement. e effectiveness of AVGs on body management of individuals with physical disabilities (e.g., Developmental Coordination Disorder, Autism Spectrum Disorder (ASD), and balance problems) is promising (Hilton, et al., 2014; Hammond, Jones, Hill, Green, & Male, 2014; Jelsma, Geuze, Mombarg, & Smits-Engelsman, 2014; Salem, Gropa, Coffin, & Godwin, 2012). However, studies investigating non-disabled ildren and adults are still warranted. Generally, studies among non-disabled populations reported that engaging in AVGs could improve balance (Giosidou et al., 2013; Vernadakis, Giosidou, Antoniou, Ioannidis, & Giannousi, 2012) and postural stability (Sheehan & Katz, 2012;, 2013) among ildren and college students. e preceding findings were congruent with findings that indicated AVGs were effective in improving overall balance capabilities in older adults (Bateni, 2012; Orsega-Smith, Davis, Slavish, & Gimbutas, 2012; Van Diest, Lamoth, Stegenga, Verkerke, & Postema, 2013). Additionally, the benefits of AVGs on flexibility (McCarthy, Brazil, Greene, Rendell, & Rohr, 2013), coordination, reaction time, speed and agility (Van Biljon & Longhurst, 2012) were also reported. In another qualitative study (Barne, Ridgers, Hanna, & Salmon, 2014), ildren indicated that they would consider AVGs a learning tool in developing movement skills because they thought skill acquisition through AVG to be highly transferable. In this way, AVGs might be used as a method by whi to promote motor control and overall physical functioning.
Obese populations Physiological and physical effects
Among the AVG studies in obese populations, seven examined whether AVGs affected individuals’ physiological outcomes. ree randomized controlled trials suggested AVGs had no effect on certain physiological outcomes.
Specifically, Staiano et al. (2016) found no significant anges in cardiovascular risk factors including blood pressure, olesterol, triglycerides, glucose and insulin, but did observe significant increases in trunk and spine bone mineral density post-intervention in overweight and obese girls. Adamo et al. (2010) also reported no significant differences in peak heart rate, peak workload, time to exhaustion, and blood assay aer an AVG intervention, suggesting AVGs may not help obese ildren improve cardiorespiratory fitness. Additionally, Maddison et al. (2011) indicated that obese individuals’ cardiovascular fitness improved aer AVG play but differences were not significant between the intervention and control groups. In contrast, Goldfield and associates (2012) indicated aerobic fitness su as peak heart rate and rate of perceived exertion significantly increased aer a 10-week AVG-based intervention. In addition, researers reported absolute energy expenditure was significantly higher, but maximal heart rate lower, in obese ildren compared to their lean counterparts during AVG play (Chaput et al., 2015). Lastly, one quasi-experiment study (Penko & Barkley, 2010) suggested obese individuals’ average heart rate and VO2 were significantly greater in Nintendo Wii intervention than during treadmill walking and traditional sedentary video game play, partially due to the greater mass of the ildren/individuals, while another study found no improvements in any physiological outcomes su as heart rate, rate of perceived exertion, VO2, and METs (Marti, Alvarez-Pii, Provinciale, Lison, & Rivera, 2015). Several studies examined the effects of AVG play on obese populations’ adiposity levels. Five randomized controlled trials supported that AVG interventions had a positive effect on BMI, body composition, or body fat percentage (Christison & Khan, 2012; Maddison et al., 2011; Staiano, Abraham, & Calvert, 2013; Staiano et al., 2016; Trost, Sundal, Foster, Lent, & Vojta, 2014). Conversely, three randomized controlled trials failed to report positive effects of AVGs on adiposity (Adamo et al., 2010; Goldfield et al., 2012; Wagener, Fedele, Mignogna, Hester, & Gillaspy, 2012). For example, Wagener et al. (2012) suggested no significant improvement in BMI aer a 10week (three times a week) DDR intervention.
As a final note, the effects of AVGs on overweight and/or obese individuals’ habitual PA and fitness are promising. One randomized controlled trial indicated AVG intervention ildren to have significantly increased moderate-to-vigorous PA post-intervention (Trost et al., 2014). Another randomized controlled trial found greater increased daily time spent playing AVGs, indicating AVG play may increase ildren’s PA (Maddison et al., 2011) (Figure 9.7). Lastly, one control trial revealed ildren in an AVG group showed significantly improved fitness including coordination, reaction time, and speed and agility aer a 6-week AVG-based intervention (Van Biljon & Longhurst, 2012).
Psychosocial effects
e psyological effects of AVG on overweight or obese individuals are very similar to those normal weight populations. Specifically, Staiano et al. (2013) reported that cooperative AVG players significantly increased in self-efficacy, and both cooperative and competitive AVG groups exhibited more peer support compared to a control group aer a 20-week AVG intervention. Wagener et al. (2012) stated participants in a dance-based AVG condition had significantly increased perceived competence to exercise regularly, with Goldfield et al. (2012) suggesting significant pre-post improvements for body image, perceived solastic competence, and social competence aer an AVGbased intervention. e preceding two studies are in contrast with the findings of one quasi-experimental study whi indicated improvements in global self-worth, but not in solastic competence, social acceptance, athletic competence, and physical appearance among obese population (Christison & Khan, 2012). Finally, a recent study suggested obese ildren scored significantly higher in expectations and satisfaction, but not in self-efficacy and perceived exertion, during an AVG intervention (Marti et al., 2015).
Figure 9.7
Kids playing an active video game.
Source: Photo by Zan Gao.
In summary, we conclude that some AVGs can be considered a strategic tool for promoting a physically active and healthy lifestyle, and may be useful in aenuating and reversing the obesity epidemic; as well as in improving psyological well-being among obese populations.
Application of active video games in different contexts AVGs are oen deemed as home entertainment, and are popular and available to younger generations. As su, one might expect, more AVG studies have been completed in home-based seings. However, based upon the extant literature, laboratories seem to be the most common context to test AVGs’ efficacy and validity, whereas sools appear to be a popular fieldbased venue to use AVG for PA and health promotion. Overall, more than two-thirds of the AVG intervention studies were conducted in laboratories and sools.
Laboratory-based contexts Based upon the large number of laboratory-based studies on AVGs, it is known that the physiological and psyological responses induced by AVGs are higher than those of sedentary behaviors like siing, resting, and sedentary video games. In detail, AVGs had positive effects on individuals’ physiological outcomes as compared to sedentary behaviors. ese findings suggested playing AVGs increased energy expenditure, heart rate, METs, VO2 max, and PA from rest, whi is in accordance with previous reviews (Barne et al., 2011; Biddiss & Irwin, 2010; Foley & Maddison, 2010; LeBlanc et al., 2013). Findings also favored AVGs over sedentary behaviors for rate of perceived exertion and enjoyment. Given that many people spend a large amount of time in sedentary behaviors on a daily basis, the findings represent an optimistic message for public health. Explicitly, the findings suggest that AVGs can be used to replace individuals’ sedentary behaviors, su as screen time, while maintaining game enjoyment and aractiveness over time. It is
important to note, however, this finding needs to be taken with caution in that the allenge facing health professionals is to encourage players to replace sedentary behaviors, but not other physical activities or sports, with AVGs. e physiological and psyological responses induced by AVGs were also similar to laboratory-based traditional exercises. According to Gao et al. (2015), in comparison to traditional exercise, heart rate considerably favored the AVGs, while all other variables were similar for both exercise modalities. Importantly, laboratory-based exercises can be further categorized into different types of exercise: walking (e.g.,1–2.6 km/hr), brisk walking (e.g., 4.2 km/hr), fast walking (e.g., 5.7 km/hr), running, and biking. Indeed, findings demonstrated that AVGs’ effect size margin over that of laboratory-based traditional exercises declined as the exercise intensity increased from slowpaced walking to biking and to running (Gao et al., 2015). at said, enjoyment favored AVGs versus laboratory-based exercises—an encouraging finding given that AVG gameplay produces not only the equivalent magnitude of effect as light-to-moderate-intensity PA, but is also fun and enjoyable for players (Figure 9.8). e increases the likelihood of long-term PA adherence.
Field-based context In home-based seings, two studies indicated that AVG interventions were more effective than control/comparison groups in increasing energy expenditure, PA, and METs (Murphy et al., 2009; Ni Mhuru et al., 2008). However, three home-based randomized controlled trials found no significant differences for PA, BMI and weight loss, between the intervention and control/comparison groups (Baranowski et al., 2012; Maloney et al., 2008; Ni Mhuru et al., 2008). Finally, one randomized controlled trial indicated mixed results in that AVG intervention ildren had greater increased selfreported PA, but not objectively measured PA, compared to control ildren (Maloney, Stempel, Wood, Patraitis, & Beaudoin, 2012).
Figure 9.8
Elite athletes playing active video games.
Source: Photo by Zan Gao.
In sool seings, two randomized controlled trials and two control trials found AVG interventions were more effective than control/comparison groups in promoting a variety of health-related outcomes, including PA, cardiorespiratory fitness, and weight loss (Gao & Xiang, 2014; Sheehan & Katz, 2013; Staiano, Abraham, & Calvert, 2013). Furthermore, three control trials suggested that AVG interventions had positive effects on some outcomes (e.g., PA, self-efficacy, enjoy/liking, intrinsic motivation) but no significant effects on other outcomes (e.g., outcome expectancy) as compared to control/comparison groups (Gao, Hannan, Xiang, et al., 2013; Gao et al., 2012; Lwin & Malik, 2014). Conversely, one control trial revealed that an AVG intervention was worse than a comparison group at influencing PA participation and energy expenditure (Duncan & Staples, 2010). When comparing the effectiveness of AVGs with traditional sports or PA, one study (Whiman, 2010) indicated AVGs were beer while four studies (Gao et al., 2011; Gao, Zhang, & Stodden, 2013; Miller et al., 2013; Sun, 2012) suggested otherwise. Finally, within other field-based seings, one randomized controlled trial (Wagener, Fedele, Mignogna, Hester, & Gillaspy, 2012)
indicated positive effects of an AVG intervention compared to control while two other studies found no differences between the groups (Bethea, Berry, Maloney, & Siki, 2012; Paw et al., 2008). e aforementioned findings appear inconclusive with regard to physiological and physical outcomes suggesting more resear studies utilizing AVGs in field-based contexts are warranted and encouraged. Psyologically, however, AVGs produced improved self-efficacy, enjoyment/liking, and aitudes as compared to tradition field-based PA programs—a result in line with many empirical studies (Gao et al., 2012; Gao, Podlog, & Huang, 2013; Gao, Zhang, & Podlog, 2013; Gao, Zhang, & Stodden, 2013; Sun, 2012). Taken together, it is recommended that health professionals employ AVG interventions as a viable option to promote individuals’ PA and health, as AVGs are more aractive and enjoyable for players in comparison with traditional PA (Figure 9.9). at is, AVGs can be used as an excellent addition to, but not a replacement for, traditional intensive PA or sports in individuals’ daily lives (Gao, Zhang, & Stodden, 2013).
Figure 9.9
Kids playing active video games.
Source: Photo by Zan Gao.
Regarding the implementation of AVG interventions, the findings suggest that AVG interventions in field-based seings should be developed based upon study aracteristics whi appear to have the greatest effect (i.e., intervention length). A meta-analysis demonstrated that ildren/adolescents benefit more from AVG interventions when the interventions are longer (Gao et al., 2015). is finding is consistent with the previous PA intervention studies (Gao, Hannan, Xiang, et al., 2013; Maloney et al., 2008; Sun, 2012). e involvement of greater muscle mass during AVG play should also be considered. Currently, only AVGs that mainly involved whole body movements and lower body movements (primarily DDR) have been implemented in field-based studies. Gao et al. (2015) suggested that these two types of AVGs generated similar health benefits, partially supporting findings by Peng et al. (2011). e finding further confirms that AVGs requiring large muscle group movement (lower body or whole body) are most effective in promoting PA (Biddiss & Irwin, 2010). Lastly, PA assessment methods did not moderate the relationship between PA interventions and health outcomes. Given the limited sample for assessing PA, future studies examining this moderator with larger samples are warranted.
Clinic-based context Given the fact that increased PA has been shown to be a viable approa to prevent or lessen the risk of ronic diseases among a variety of populations, AVGs may represent an alternative means in prompting PA participation and improving the quality of life and life satisfaction (Baranowski, Maddison, Maloney, Medina, & Simons, 2014; Gao & Chen, 2014; Liang & Lau, 2014; Lu, Kharrazi, Gharghabi, & ompson, 2013). As mentioned in the previous section of this apter, the positive effects of AVGs on health-related outcomes have been reported among healthy populations (Gao et al., 2015; Peng et al., 2011). More recently, however, AVGs have received considerable
aention from healthcare professionals and solars alike as a rehabilitative tool in clinical seings to promote physical, psyological, and cognitive functioning among various populations (Giosidou et al., 2013; Hung et al., 2014). e reader is referred to the next section of this apter to gain a beer understanding of the application of AVG in the healthcare field.
Application of active video games in the healthcare field As a result of the unique and enjoyable experience of game-based rehabilitation, AVGs may have the potential to increase motivation and adherence to physical and cognitive therapy at a low cost compared to traditional forms of treatment (Lange, Flynn, & Rizzo, 2009). Below we present the effects of AVG-based rehabilitation on various outcomes in different populations, the implications of AVG-based rehabilitation in healthcare field, as well as the limitations and recommendations for future resear.
Effects of AVG-based rehabilitation on different populations is section of the apter will address the current literature with regard to the effectiveness of AVG-based rehabilitation on health and cognitive outcomes among various populations, namely, youth/young adults (5–25 years old) (Figures 9.10 and 9.11), middle-aged adults (40–65 years old), and older adults (≥65 years old). AVG-based balance rehabilitation, specifically Wii Fit programs, have demonstrated a positive effect among youth and young adults with Down Syndrome (Rahman, 2010) or with a history of lower limb injury (Sims, Cosby,
Figure 9.10
Young adults playing active video games.
Source: Photo by Zan Gao.
Figure 9.11
Young adults playing active video games.
Source: Photo by Zan Gao.
Saliba, Hertel, & Saliba, 2013) compared to control conditions whi included traditional therapy-based strength exercises to improve balance using standard care procedures. In middle-aged adults, the vast majority of AVGbased rehabilitation used the Wii (Gil-Gómez, Lloréns, Alcañiz, Colomer, 2011; Kim, Kang, Park, & Jung, 2012; Kramer, Demers, & Gruber, 2014; Nilsagard, Forsberg, & von Ko, 2013; Robinson, Dixon, Macsween, van
Saik, & Martin, 2015) with one rehabilitation program among stroke patients using the Sony PlayStation Eye Toy (Yavuzer, Senel, Atay, & Stam, 2008). Specifically, AVGs were used for physical functioning and/or balance rehabilitation among patients with multiple sclerosis (Kramer et al., 2014; Nilsagard et al., 2013; Robinson et al., 2015), acquired brain injuries (GilGomez et al., 2011) and stroke (Kim et al., 2012; Yavuzer et al., 2008). Generally, AVG-based rehabilitation had lile effect on patients’ physical functioning versus control conditions in middle-aged adults. However, AVGbased rehabilitation demonstrated positive effects on balance over control and comparison conditions. e gaming systems used for rehabilitation in older adults included the Nintendo Wii (Daniel, 2012; Franco et al., 2012; Pluino, Lee, Asfour, Roos, & Signorile, 2012), researer-developed gaming platforms (Chen et al., 2012; Szturm, Betker, Moussavi, Desai, & Goodman, 2011), or the Microso Xbox Kinect (Sato, Kuroki, Saiki, & Nagatomi, 2015). Among older adults, AVGbased rehabilitation was used to improve physical functioning, balance, and falls efficacy for lower limb rehabilitation in frail elderly at risk of falls (Chen et al., 2012), pre-frail elderly at risk of falls (Daniel, 2012; Franco et al., 2012; Pluino et al., 2012; Sato et al., 2015), or balance/mobility in impaired elderly (Szturm et al., 2011). Compared with control or comparison conditions, AVGbased physical functioning rehabilitation yielded similar effects in older adults. However, AVG-based balance rehabilitation had a small, marginally positive effect as compared to control conditions. Also, AVGs had a positive effect on falls efficacy versus control among older adults.
Effect of AVG-based rehabilitation on health and cognitive outcomes Physiological and physical effects of AVGs
A number of AVG studies investigated a wide range of physiological and physical outcomes in patients, with the majority of the studies focusing on whether AVG-based rehabilitation has a positive effect on balance. Indeed, a number of studies examined the use of AVGs on static and dynamic balance ability, with most indicating AVG-based rehabilitation could improve balance performance. Among them, three experimental studies found positive results on balance (Esculier, Vaudrin, Bériault, Gagnon, & Tremblay, 2012; Pompeu et al., 2012; van den Berg et al., 2016) as compared to control/comparison groups aer AVG-based rehabilitation. Yet, Bainbridge et al. (2011) failed to find balance ange aer an AVG-based rehabilitation program. Many studies examined other components of physical functioning besides balance. Among them, seven studies comparing AVG-based rehabilitation with other treatment protocols observed comparable or equal improvements for several outcomes. In detail, one control trial found significant improvements for the Sit to Stand Test, the ten-meter Walk Test and Tinei’s Performance Oriented Mobility Assessment for the AVG group (Esculier et al., 2012). Further, four of six randomized controlled trials observed significant anges in the 2-minute Walk Test (Imam, Miller, Finlayson, Eng, & Jarus, 2015); the Unified Parkinson’s disease rating scale (Pompeu et al., 2012); the Functional Independence Measure (Yavuzer et al., 2008); and the Senior Fitness Test, Late Life Function and Disability Index (Daniel, 2012) as compared to the control/comparison groups. Notably, two other randomized controlled trials indicated no significant differences in the Active Range of Knee Motion, the 2-minute Walk Test, the Lower Extremity Functional Scale (Fung, Ho, Shaffer, Chung, & Gomez, 2012) and the Nursing Home Physical Performance Test (Hsu, ibodeau, Wong, Zukiwsky, Cecile, & Walton, 2011) between AVG-based rehabilitation and conventional therapy—providing evidence that the use of the AVGs might best be used as an adjunctive therapy to standard treatment.
Psychological effects of AVGs
Studies investigating psyological rehabilitative outcomes included enjoyment, quality of life, and depression. Specifically, three studies investigated enjoyment in relation to AVG-based rehabilitation with two randomized controlled trials indicating a positive effect on enjoyment (Hsu et al., 2011; van den Berg et al., 2016) for the AVG groups, compared to the comparison groups. Interestingly, two non-experimental studies seem to suggest AVGs could be an effective rehabilitative tool in improving quality of life but not to a degree sufficient to trigger anges in mood (Herz, Mehta, Sethi, Jason, Hall, & Morgan, 2013; Mhatre et al., 2013). Conversely, one randomized controlled trial stated no significant ange was found for the Wii group in quality of life despite a slight upward trend indicating improvement aer AVG-based treatment (van den Berg et al., 2016).
Cognitive effects of AVGs
A number of studies examined patients’ cognitive functions, with most concerned with balance confidence. Generally, the findings are equivocal for balance confidence, with one randomized controlled trial (Daniel, 2012) indicating improvements while one control trial (Esculier et al., 2012) and two other randomized controlled trials (Fung et al., 2012; van den Berg et al., 2016) observing no anges following AVG-based rehabilitation as compared to control/comparison groups. Other cognitive rehabilitative outcomes included falls efficacy, pain intensity, and cognitive performance. Specifically, falls efficacy was examined exclusively in older adults (Chen et al., 2012; Pluino et al., 2012) with AVGs demonstrating a positive effect over control/comparison groups. With regard to pain intensity, one randomized controlled trial demonstrated that AVGs were not effective for upper extremity pain management aer an AVG intervention (Hsu et al., 2011) while another randomized controlled trial revealed no significant difference in lower extremity pain between an AVG group and physiotherapy group (Fung et al., 2012). Finally, one control trial demonstrated significantly improved cognitive performance for Wii groups for aention and decision-
making (dos Santos Mendes et al., 2012) while one randomized controlled trial indicated significant improvements in executive function versus a comparison group (Pompeu et al., 2012).
Implications of AVG-based rehabilitation in the healthcare field e findings indicate that AVG-based balance rehabilitation was most common across all age groups. e vast majority of findings favored AVGbased balance rehabilitation over control conditions among youth and young adults. While findings regarding AVG-based balance rehabilitation among middle-aged adults still favored AVGs in comparison with control conditions, the magnitude of effect decreased. e same was true when using AVG-based balance rehabilitation versus control conditions among older adults. Notably, previous experience with AVG may be a factor limiting the effectiveness of AVG-based balance rehabilitation among older populations in comparison to youth and young adults. Given this situation, AVGs may demonstrate greater effectiveness for balance rehabilitation among youth/young adults, as less time is required to learn how to play the game(s) and more time can be spent engaging in rehabilitation-oriented gameplay. AVG-based physical functioning rehabilitation was observed only among middle-aged and older adult populations but not youth/young adults. is is logical given the fact that functional limitations among middle-aged and older adults are more common compared to youth and young adults (Holmes, Powell-Griner, Lethbridge-Cejku, & Heyman, 2009). Notably, although the majority of randomized controlled trials reported favorable results for AVGs with regard to physical functioning, a limited number of publications and the use of different instruments measuring dissimilar physical outcomes made it allenging to compare ea study and draw a definite conclusion on the effectiveness of AVG-based rehabilitation. Additionally, as some randomized controlled trials (Daniel, 2012; Imam, Miller, Finlayson, Eng, & Jarus, 2015) were designed as feasibility trials, with the primary goal of assessing the
feasibility of AVG-based rehabilitation as opposed to drawing conclusions regarding the efficacy of this treatment modality, the results cannot readily be interpreted to suggest AVG-based interventions are more effective than usual care. Moreover, the discrepancies between study findings might be aributable to the fact that some studies did not isolate the AVG intervention from traditional rehabilitation exercises. Not isolating the AVG-based physical functioning rehabilitation may have reduced the effects of AVGs. Finally, a generalized conclusion must be regarded with caution as the aforementioned studies had a wide range of sample sizes and relatively short intervention durations, whi may limit the generalizability and practical implications of the findings. Consequently, the decision on the effectiveness of AVGs on non-balance physical functioning among patients is still inconclusive. Given the current aging population, continued investigation regarding how to promote balance and physical functioning among patients via AVGs is paramount in improving the ability to engage in activities of daily living and reducing later life disability. One advantage of AVG-based rehabilitation over traditional physical therapy is that AVGs not only provide physical benefits, but also act as a form of entertainment. e most appealing aspect of AVGs lies in its motivating feature––meaning AVGs offer players a mu higher level of engagement, whi can significantly reduce players’ level of perceived exertion (Gao, Gerling, Mandryk, & Stanley, 2014). As su, motivation to sti with AVGs is higher than with traditional rehabilitation. In this way, players may experience increased enjoyment during AVG gameplay, leading to improved quality of life over time and, more importantly, sustained participation in AVG-based rehabilitation activities. at said, resear evidence regarding the effectiveness of AVGs on psyological rehabilitative outcomes is mixed, with some studies indicating positive effects while a small portion of studies reporting no effect. Taken together, the findings were not in line with previous studies indicating positive psyological effects of AVGs on enjoyment among healthy youth and adults (Li, eng, & Foo, 2016; Verhoeven, Abeele, Gers, & Seghers, 2015). Notably, extrinsic factors in the included studies may have affected the results due to the high proportion of
patients with preexisting depression (Mhatre et al., 2013). Additionally, it is plausible that the intensity and length of AVG-based rehabilitation were not high enough to elicit anges in mood. It is also possible that health professionals did not provide a variety of AVGs to the patients during the intervention periods—potentially increasing the monotony of the program and reducing enjoyment. Meanwhile, as modern tenology is not part of daily life for many seniors, with many unfamiliar with tenology, the motivation to learn how to use AVGs is rather weak, as the learning process may be frustrating for many older adults—particularly among older patients already burdened enough by treatments related to their diseases or impairments. erefore, findings concerning the psyological rehabilitative effects of AVGs on older patients are inconclusive and, at times, contradictory. At this point, it is unclear whether AVGs are a viable rehabilitative tool to improve cognitive outcomes in older patients. First, as previously stated, despite findings demonstrating improved balance confidence postintervention, studies did not indicate whether these improvements were statistically significant (Broadbent, Crowley-McHaan, & Zhou, 2014; Clark & Kraemer, 2009; Daniel, 2012). Without providing inferential statistics, we cannot conclude that AVG-based rehabilitation is effective for balance confidence. Second, most current studies have been examining the acute effects of AVG-based rehabilitation without follow-up to assess the sustainability of intervention adaptations, whi might cause researers to under- or over-rate the potential of AVGs and result in inaccurate conclusions regarding the effectiveness of AVGs on patients’ cognitive outcomes. Additionally, as individuals with higher education levels or socioeconomic status may possess greater cognitive ability than individuals of lower education levels or socioeconomic status, some factors su as education, occupation, and even personality could have affected individuals’ cognitive performance within those studies. Finally, the nature of AVGs could also be a confounding factor influencing intervention results as some types of AVGs (e.g., games that require more executive functions) may have stronger cognitive demands than others. In this regard, patients may receive varying
stimulation intensities from different AVG-based treatments leading to different results in cognitive rehabilitative outcomes. Falls efficacy was another common cognitive outcomes between studies within any age group and was seen exclusively in older adults (Chen et al., 2012; Pluino et al., 2012). Findings that revealed AVG-based rehabilitation to improve falls efficacy favored AVGs compared to control conditions. is finding is promising due to the following reasons. First, falls are prevalent among older adults. Second, older adults may not be able to commute to a clinic for rehabilitation to reduce fall risk and increase falls efficacy due to functional disabilities (Levasseur et al., 2015). AVG-based rehabilitative programs are likely to be beer adhered to and can be implemented at home without the need of constant supervision by a physical therapist/nurse or patient travel, AVGs might represent an aractive and effective alternative in promoting greater falls efficacy and possible reduction in fall prevalence. Further, healthcare professionals may monitor the progress of patients engaging in AVG-based rehabilitation by having patients upload AVG data to an online portal. Healthcare professionals could then monitor the data and provide relevant feedba to increase the effectiveness of the rehabilitation program.
Limitations and recommendations is section has synthesized the available evidence regarding the effectiveness of AVG-based rehabilitation in the therapeutic treatment of physiological/physical, psyological and cognitive ailments. e strength of this section lies in the provision of a comprehensive synthesis of the effects of AVG-based rehabilitation in the healthcare field in a systematic manner, yet some limitations should be noted. First, the heterogeneity of samples, outcomes, interventions, effects, measurement instruments, and resear designs lessened the ability to detect significant differences and summarize overall significant findings. Second, the variety of AVGs limited the ability to
investigate whi specific games, and whi aspects of those games, are most useful for the rehabilitation of specific diseases and/or impairments. ese findings have implications for healthcare professionals as AVG-based rehabilitation may be more easily adhered to, given the enjoyable nature of AVG play and the ability of this rehabilitation to be played within the comfort of the patients’ home. Further, once trained in how to use AVGs as part of a rehabilitation program, patients can implement the program without assistance from health practitioners while health practitioners may be able to remotely monitor, via an online portal, the progress of the patient— potentially decreasing healthcare costs for both parties. To beer understand and evaluate the effects of AVG-based rehabilitation on patients, future studies should continue to determine guidelines regarding the ideal dose of AVG gameplay among patients with specific diseases and/or impairments. is could be aieved through the use of more high-quality study designs and more follow-up testing with patients to discern the sustainability of the adaptations stimulated by certain durations and frequencies of AVG-based rehabilitation. Second, researers may want to isolate AVG-based rehabilitation and standard therapy procedures during rehabilitation with patients. Isolating these treatments will permit beer examination of the standalone benefits that AVG-based rehabilitation may have in comparison to standard therapy. Additionally, researers may integrate newer tenology and employ multiple types of AVGs to distinguish and surmise the effectiveness of specific AVGs on specific diseases and/or impairments. Lastly, larger samples and greater concentration on the cognitive rehabilitative outcomes resulting from AVG gameplay are warranted with a larger and more diverse number of variables assessed. Methodological improvements su as those outlined previously will ensure the development of AVG-based rehabilitation programs that are most effective and capable of treating a wider array of health-related outcomes.
Augmented reality games and active video games Augmented reality games, su as Zombies, Run! and Pokémon Go, have successfully aracted the aention of youth and adults alike in recent years, with particular excitement noted aer the release of Pokémon Go in July 7, 2016. Indeed, Pokémon Go generated lots of public interest, with an estimated 65 million users downloading the game within one week of its release, including thousands of young ildren (Baranowski, 2016) (Figure 9.12). While a full review of augmented reality games is beyond the scope of this book, we would like to briefly introduce these augmented reality games and their relationships with AVGs. Augmented reality games are unique because they integrate the physical and virtual worlds into one interface using mobile devices applications (a.k.a., apps). Unlike the traditional sedentary video games that encourage solitary and sedentary activity, augmented reality games require players to walk around and explore their local surroundings su as parks, sools, gyms, and home neighborhoods, and hence offer potential physical, social, and emotional benefits to players (Serino, Cordrey, McLaughlin, & Milanaik, 2016). In this way, augmented reality games serve very similar functions to those of AVGs, and consequently can be considered a type of AVG that elicits PA. In fact, augmented reality games have been around since the early 1990s but lile aention had been given to this tenology in promoting PA and health. For instance, an early augmented reality game, Finding Yosh, aempted to elicit PA through a geo-caing approa but failed to expand its use to a larger population. In 2002, a then-pioneering game named Majestic made significant contributions to the field, followed by several aempts to advance this tenology. As a result of the contributions made by Majestic, there are a number of available augmented reality games on the
market presently, including but not limited to: Ingress, Zombies, Run!, Zombies Everywhere, e Walk, Superhero Workout, SpecTrek, Clandestine Anomaly, Temple Treasure Hunt Game, Geocaing, Real Strike, Night Terrors, and Pokémon Go, among others.
Figure 9.12
A kid playing Pokémon Go.
Source: Photo by Zan Gao.
Clearly, this tenology has been gaining momentum in the past decade, particularly with the advances in virtual reality and augmented-reality headsets. Prior to Pokémon Go’s arrival, another augmented reality game entitled “Zombies, Run!” aieved some popularity. is immersive game motivates players up off their cou, and gets them fit with the help of
zombies. Specifically, the game partially immerses players into a world full of zombies, requiring players to run or jog to survive zombie aas while listening to the undead scream. Players also have to collect supplies and unlo daily missions too. Overall, this game provides an experience in whi the players’ real world blends into a virtual one on the screen of their mobile device while simultaneously encouraging individuals to be physically active. In 2012, Niantic Labs, the developers behind Pokémon Go, released a new augmented reality game: Ingress. Ingress encourages players to leave their houses and embrace the environment to gain game experience. Niantic later developed Pokémon Go, to an unprecedented level of fanfare given Pokémon’s consistent and commied fan base—making it the most popular augmented reality game of all time. Pokémon Go is a free app available on iPhone and Android devices that is also a GPS-based augmented reality game. Users are encouraged to tra and cat Pokémon that are virtually superimposed onto physical and real-world surroundings, su as local parks, sools, historical sites, and gyms. Contrary to the original Pokémon sedentary video game, Pokémon Go encourages its users be physically active through exploration of the physical environment while simultaneously engaging in the game’s fantasy world. Similar to Zombies, Run!, Pokémon Go is also deemed an innovative AVG (Baranowski, 2016). e newly available augmented reality games are promising in bringing potential health benefits to the users, particularly among ildren and adolescents. For instance, Pokémon Go encourages a physically active lifestyle and promotes active learning as well through the interaction with the local neighborhoods and historical sites. e game also enhances social interaction by playing against nearby players. Additionally, the game may exert a positive effect on mood and emotion (Serino et al., 2016). Despite the aractiveness and benefits of aforementioned augmented reality games, readers should be aware of the adverse consequences of these games. Based upon the media reports, it has been observed that physical harm may occur as a result of playing Pokémon Go while walking or driving. Playing the game also increases economic burden as a result of in-app purasing and heavy data usage, and potentially leads the users to inappropriate or
dangerous areas. e most concerning downside of playing Pokémon Go may be due to its geo-locating feature (i.e., the “lure” function) an interactive aspect of the game whi can lead to crime. For example, criminals have taken advantage of the game and conducted a number of recent first-degree robberies and felonies. It is, therefore, imperative to be cautious and alert all the time while playing su augmented reality games (Serino et al., 2016). Overall, augmented reality games can improve positive health behavior (i.e., PA behavior), socialization, and emotion. ese games have the potential to promote PA and other aspects of health in the naturalist contexts. As the tenology advances, the efficacy and effectiveness of emerging augmented reality games su as Pokémon Go deserve further investigation. Baranowski (2016) has proposed a series of future resear questions and directions in the new field. It is expected that greater number of empirical studies will emerge in the next five years.
Practical implications Although the increased frequency of publications in this field indicate AVGs’ increased popularity, AVG resear is still lagging behind, given the rapid development of the gaming industry and the volume of AVGs on the market. According to the preceding, extensive review, resear design and other methodological issues of AVG studies need improvement—particularly as AVGs have more frequently been taken from laboratories and placed in fieldbased contexts, su as sools, community centers, homes, and health clinics. Resear studies reviewed in this apter have suggested that, in laboratory-based contexts, AVGs had a positive effect on healthy individuals’ physiological and psyological outcomes compared to rest, with these effects similar to those of light-to-moderate-intensity PA. Studies have also supported some types of AVGs in field-based contexts whi, when implemented with careful planning and guidance, are efficacious in providing physical, psyosocial, motor, and cognitive benefits (Figure 9.13). In this regard, AVGs offer more benefits than downsides with regard to the promotion of individuals’ PA and health outcomes.
Figure 9.13
Elite athletes playing active video games.
Source: Photo by Zan Gao.
Notably, while PA as a result of AVG use can contribute toward daily recommendations of PA duration, solely depending on AVG as a PA promotion strategy among individuals is not realistic, as the light-tomoderate intensity of PA generated from AVG play is, at times, insufficient to help individuals meet the recommended PA intensity needed for the greatest health benefit. ese findings have public health implications that can help inform healthcare stakeholders regarding AVG interventions. For example, health professionals may integrate AVGs in sools, community centers, and homes to help individuals develop a healthy lifestyle habits—striving only to replace sedentary behavior, but not the traditional PA and sports, with AVGs. Meanwhile, the fun and/or boredom factors of AVGs should be taken into consideration during program implementation as well. In particular, AVGs need to be constantly upgraded or updated to hold ildren’s interest and promote sustainability with regard to anges in health behaviors. However, given the fun component programmed into the games, AVGs are desirable as
a promising addition to promote PA and health by replacing these sedentary behaviors. Additionally, when implementing AVGs, health professionals should provide systematic instructions on AVG use and provide PA opportunities for players. e ultimate goal of this instruction is to take advantage of the enthusiastic nature of AVGs, and aieve long-term success in making AVG play part of individuals’ daily workout routine. Finally, it also needs to be recognized that the potential of AVGs in field-based contexts might have been underestimated due to a variety of limitations inherent in many published studies. Future resear and practice should take into account these limitations to unravel and exploit the maximal effectiveness of AVGs. Simply stated, high quality and well-designed resear is warranted to investigate how various AVGs may affect individuals’ health-related outcomes from a longitudinal perspective (Figures 9.14, Figure 9.15).
Figure 9.14
Kids playing active video games.
Source: Photo by Zan Gao.
Figure 9.15
Kids playing active video games.
Source: Photo by Zan Gao.
is apter provides an overview and synthesis of the health benefits of AVGs in healthy and clinical populations. Given the limitations inherent in the empirical studies reviewed, researers and practitioners have a number of resear questions to answer prior to being able to conclude if AVGs can effectively promote healthy individuals’ moderate-to-vigorous PA in fun and innovative ways. Gao (2017) has offered some recommendations for future resear in different contexts among various populations, including but not limited to: (1) examine the long-term efficacy of AVG use in non-structured home seings for PA promotion using high-quality randomized controlled trials, and the potential benefits of family/group play and potential barriers in su seings; (2) determine whether ildren/adolescents with access to AVGs actually replace their screen time with AVGs as opposed to traditional sports or PA; (3) conduct process evaluation for AVGs to ensure the intervention fidelity; (4) examine the efficacy and effectiveness of augmented reality games su as Pokémon Go on individuals’ PA behavior and health outcomes, particularly youth and young adults; (5) investigate other gaming consoles su as the Xbox and Play Station on rehabilitation; and (6) examine the effectiveness of home-based, patient-implemented AVG-based
rehabilitation as compared with clinic-based rehabilitation resear. More details can be found in Gao (2017).
Anowledgments Please note that a small portion of statements and sentences are abstracted from Gao and Chen (2014) and Gao et al. (2015). Copyright permission has been secured from Obesity Reviews.
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10 Virtual reality in physical activity promotion Nan Zeng and Zan Gao
Among emerging tenologies poised to aid in the assessment and promotion of physical activity (PA) and health, virtual reality and augmented reality (a.k.a., simulation tenology) are arguably the most exciting and tenologically advanced. Virtual reality is a digital tenology that replicates a real or imagined environment, and simulates a user’s physical presence in this environment allowing for user interaction (Isaac, 2016). Two types of virtual reality exist. e first is immersive virtual reality whi frequently uses head-mounted displays, body-motion sensors, real-time graphics, and advanced interface devices (e.g., specialized helmets) in the simulation of an environment for the user. Conversely, non-immersive virtual reality uses interfaces su as flat-screen televisions/computer screens with associated keyboards, game pads, and joystis—not simulating an environment to as deep a degree as immersive virtual reality. Simply stated, virtual reality creates an environment in whi visual, auditory, and other perceptual stimuli are incorporated in a sequence of manipulated events to whi a person is expected to react (Pasco, 2013). at is, virtual reality replaces the real world with a simulated world. e latest commercially available virtual reality headsets, su as the Oculus Ri, PlayStation VR, Samsung Gear VR, or HTC Vive artificially generate sensory experiences, whi may include visual, auditory, tou and
scent stimuli, while allowing a user to manipulate objects within the virtual environment (Isaac, 2016). Notably, virtual reality environments are split into two categories. Simple virtual reality environments consist of a twodimensional (2-D) viewing environment, whereas a complex virtual reality environment can include three-dimensional (3-D) digital objects and usercontrolled avatars displayed in real-time (i.e., without any time delay in movement between the users’ and avatars’ actions). Since a 3-D environment provides a condition where individuals are immersed in close-to-reality situations while interacting with digital objects and other avatars, virtual reality interaction concepts within these environments have been widely used to help understand virtual reality applications and their features— immersion, interaction, and presence—in PA and health promotion (Pasco, 2013). By contrast, augmented reality is a direct or indirect live view of a physical, real-world environment whose elements are supplemented by computer-generated sensory input su as sound, video, graphics or GPS data (Graham, Zook, & Boulton, 2013). Augmented virtual reality games, like Pokémon Go and Zombies, Run!, are unique in that they overlay aspects from the physical and virtual worlds into one cohesive experience. In fact, virtual reality and augmented reality are the inverse of one another as virtual reality offers a digital recreation of a real-life seing, while augmented reality delivers virtual elements as an overlay onto the real world (Augment, 2015) (Figure 10.1). Investigation and understanding of virtual reality and augmented reality interaction concepts are of paramount importance when applied to the PA and health promotion fields as it increases the likelihood of effective interventions.
Figure 10.1
An adult playing a virtual reality active game.
Source: Photo by Nan Zeng.
To date, virtual reality is extensively used in many health domains, with most virtual reality systems designed to facilitate cognitive learning, motor function, and psyological well-being. Additionally, virtual reality is also considered a cuing-edge tenology possessing great potential in PA and health promotion. Specifically, virtual reality systems present opportunities for the implementation of interventions aimed at increasing PA and promoting health in environments including, but not limited to, sool-based physical education classes, community-based fitness programs, or homebased rehabilitation, among others. To this end, virtual reality has received considerable aention in the promotion of PA participation in healthy
individuals in addition to being used for rehabilitation purposes in patients (Miller et al., 2014). Indeed, in an aempt to provide diverse workout experiences, some researers and health professionals have even integrated traditional PA equipment with virtual reality tenology. For example, combining virtual reality with standard gym-based exercise equipment, su as a stationary exercise bike, may serve to enhance the psyological benefits of exercise (Mestre, Dagonneau, & Mercier, 2011). ese psyological benefits may increase the ances of long-term adherence to an exercise program through the provision of a sense of allenge and regulated competition, and result in a more enjoyable exercise experiences (Plante, Aldridge, Bogden, & Haneli, 2003). erefore, virtual reality as an emerging tenology is rapidly becoming a popular intervention tenique for PA and health promotion among various populations. is apter will mainly review the literature regarding the application of virtual reality in PA and health promotion in various seings (i.e., physical education, sports, and rehabilitation) and populations (i.e., young, middle-aged, and older adults with/without disabilities).
Application of virtual reality in different PA settings Physical education Due to support from organizations su as SHAPE America, in addition to the increasing presence of modern tenology in healthcare, virtual reality is becoming widely accepted by physical educators for use in promoting ildren’s PA (Hansen & Sanders, 2012). Physical educators nationwide have been integrating immersive and non-immersive forms of virtual reality systems into their curricula through provision of virtual reality exercise bikes, rhythmic dance maines, sporting games, martial arts simulators, balance boards, and other similar products—thereby increasing PA appeal and motivation in youth engaged in physical education/fitness programs (Hansen & Sanders, 2012). As su, a new area of resear regarding virtual reality’s ability to be integrated within the physical education seing has arisen over the last several years. While the traditional physical education teaing methodology still predominates within most current physical education curricula, virtual reality in sool-based physical education has the potential to improve students’ self-training awareness, prevent injuries, and overcome gym space limitations, among other benefits (Liao, 2015) (Figure 10.2). In fact, some K-12 sools have begun using su devices in their physical education classes. For example, students in the San Francisco Unified Sool District and Florida’s Polk County Public Sools are among the first to use Nearpod VR virtual reality lesson plans. Using branded Google Cardboards, teaers can send classes on over 25 virtual field trip lessons across many curricular areas from math to science and from foreign language to health and physical education.
Unfortunately, there is a paucity of experimental resear on using virtual reality as an intervention tool in the K-12 physical education seing. As su, the effects of virtual reality in physical education classes are unclear. Despite initially positive results, the development and utilization of virtual reality within sool-based physical education continue to be limited by many sools’ reluctance to allocate gym/classroom space and funds to physical education (Liao, 2015)—explaining the relatively few virtual reality studies seen in physical education seings. erefore, researers and practitioners should address the following questions: (1) given the high cost of implementing sool-based virtual reality programs, how do physical educators make sool administration understand the potential effectiveness of virtual reality to improve PA and health outcomes among ildren in physical education?; (2) once implemented in physical education, how can virtual reality be used as a teaing medium capable of strengthening and improving teaing methods and optimizing teaing content?; and (3) using virtual reality-based teaing methodology and content provision, how effectively does virtual reality aid physical educators in helping youth meet national physical education standards (e.g., developing physical literacy and providing the knowledge and skills necessary to be “lifelong movers”) (SHAPE America, 2013).
Figure 10.2
From the lens of a virtual reality game.
Source: pixabay.com.
Sports Biofeedba (e.g., heart rate, breathing meanics, etc.) was previously the main tenology used to provide information regarding athlete performance (Lieberman & Breazeal, 2007). Other previous tenologies focused on transferring sports-related skills from a video game training context to performance of the sports-related skill on the actual field of play (Rosser et al., 2007). However, advances in virtual reality tenology have offered new possibilities for sports performance training (Bailenson et al., 2008). Specifically, improvements in visualization, motion capture, and computing power have paved the way for the development of virtual reality simulations capable of training sensorimotor components of sports (Bideau et al., 2010). Indeed, virtual reality has been demonstrated to be useful for enhancing athletic performance when aiming to improve an existing skill (Bergamasco, Bardy, & Gopher, 2012; Bideau et al., 2010). Coaes and athletes find virtual reality training useful as this tenology allows for a systematically controlled environment—likely reducing injury risk—and can be used to manipulate factors (e.g., creating simulated adverse weather conditions) that affect athletes’ performance on the actual field of play. For example, a feasibility study by Stinson and Bowman (2014) used virtual reality to aid in improving soccer athletes’ performance during high-pressure situations. To conduct this study, researers developed an immersive virtual reality soccer goalkeeping environment in whi users defended against simulated penalty kis using their bodies. Findings indicated that a virtual reality sportoriented system can induce increased anxiety (as assessed by physiological and subjective measures) compared to a baseline condition. ese findings suggest that inducing anxiety (i.e., adverse conditions) using virtual reality systems is possible and may be used to train athletes in psyologically taxing situations that they might face on the actual field of play. Further resear, however, is needed to determine if simulated virtual reality sporting environments can lead to improvement in sports psyology variables aside from anxiety. Nonetheless, through systematically manipulating these factors and analyzing the instant feedba generated during virtual reality training
sessions whi simulated these adverse conditions, coaes and athletes can then develop beer training programs on the actual field of play that allow for improved performance despite the adverse conditions the athletes might face (Hoffmann, Filippesi, Ruffaldi, & Bardy, 2014). Aside from simulating potentially adverse conditions on the field of play, virtual reality has been used as a training aid in many sports (e.g., golf, rowing, skiing, cycling, etc.) to help evaluate athletic performance and analyze tenique. For example, 3-D virtual reality systems can pinpoint certain aspects of an athlete’s unique biomeanics and tenique whi need to be modified. In recent years, studies using virtual reality as a training tool have become more common. For instance, Hoffmann and colleagues (2014) investigated the use of virtual reality with novice rowers as a means to learn about energy management. Fieen participants were assigned to an avatar group (n = 7) asked to tra a boat on the screen, whose speed was set individually to follow the appropriate to-be-learned speed profile. e control group (n = 8) followed a traditional indoor training program. Findings demonstrated decreased racing times among rowing participants within the avatar group, whereas the control group did not obtain the same improvements—suggesting virtual reality may be an effective means of learning energy management and enhancing performance. In fact, virtual reality training has been widely recognized by some professional sports teams and e National Collegiate Athletic Association (NCAA) Division I institutions. Recently, the Minnesota Vikings announced that they had incorporated virtual reality into their quarterba training, joining the New York Jets, Dallas Cowboys, San Francisco 49ers, and Arizona Cardinals in the virtual realm. At the NCAA level, the Stanford, Auburn, Clemson, Vanderbilt, Dartmouth, Rice, and the University of Arkansas football programs are also using the system provided by STRIVR Labs (focusing on using virtual reality to improve the performance of athletes). Although there is a paucity of resear on this topic, with the tenology still in its infancy, virtual reality shows promise for a wide range of athlete training. What is clear from the preceding literature review regarding the use of virtual reality in various PA seings is the fact that virtual reality
tenologies show initial potential in physical education and sports seings to promote PA and athletic performance. Nevertheless, few large-scale, methodologically rigorous studies have been conducted in the literature, with no study completed in physical education classes. While improvements to virtual reality tenology have been significant in recent years, the tenology is still young. erefore, more resear is warranted to prove the effectiveness of virtual reality tenologies prior to adoption by physical educators and coaes for promoting PA and athletic performance, respectively.
Application of virtual reality tenology in rehabilitation Physical rehabilitation Physical rehabilitation strives to aid patients with disabilities in the acquisition of lost motor skills that may have come about due to injury or illness—with the main goal being to ensure these individuals can perform activities of daily living (Weiss, Keshner, & Levin, 2014). Virtual reality-based physical rehabilitation tenology shares the preceding objectives, with the improvement of functional capacity paramount, while also offering an easierto-implement physical rehabilitation option to a diverse array of populations, su as adults suffering from stroke and Parkinson’s disease and ildren/adolescents with developmental disabilities (Weiss, Keshner, & Levin, 2014). To date, approximately 15 million people annually have a new or recurrent stroke worldwide, and nearly two-thirds of stroke survivors have motor deficits associated with diminished quality of life (Feigin et al., 2016; Krishnamurthi et al., 2015). As a result of motor deficits, most patients are unable to perform independent actions, tasks, and activities needed for selfcare, home management, employment, and social activities. Conventional stroke rehabilitation teniques, su as motor relearning, neurodevelopmental therapy, or proprioceptive neuromuscular facilitation, are similarly effective in improving motor function (Saposnik, 2016). However, traditional physical rehabilitation can be resource-intensive and costly—oen requiring specialized facilities not always widely available (Jutai & Teasell, 2003). Resulting from the limitations of traditional physical rehabilitation, virtual reality is now seen as a novel rehabilitation strategy
and is regarded as an enjoyable alternative to promote motor recovery. Indeed, promising evidence from previous resear has shown the effectiveness of virtual reality for improving motor function aer stroke. In a recent randomized controlled trial, Saposnik and colleagues (2016) found a non-immersive virtual reality group using the Nintendo Wii gaming system (VRWii) improved upper extremity motor performance time from baseline and posest. Yet, there was no significant difference between the virtual reality and control group playing recreational activities (e.g., cards, ball games, Jenga). e study suggests that innovative non-immersive virtual reality tenologies might be as effective as recreational activities in enhancing upper extremity motor function among stroke patients. Similarly, Subramanian et al. (2013) revealed that endpoint speed, overall performance on a rea-to-grasp task, and activity levels increased in both virtual reality environment and physical environment groups. However, only participants in the virtual reality environment group improved shoulder horizontal adduction and flexion at post-test. As su, virtual reality training might be a suitable alternative for stroke patients to promote upper-limb recovery. Additionally, the effects of virtual reality-based physical rehabilitation on lower extremity function (e.g., to improve balance and reduce risk of falls) among stroke populations has also been proved. For example, McEwen and colleagues (2014) investigated whether a 3-week adjunct virtual reality therapy improves balance, mobility, and gait in stroke rehabilitation inpatients. e treatment group (n = 30) received a standard stroke rehabilitation therapy plus virtual reality exercises that allenged balance (e.g., soccer goaltending and snow-boarding) performed while standing. e control group (n = 29) received identical treatment but did not allenge balance and, instead, performed treatments while siing. Findings indicated the treatment group had reduced impairment in the lower extremity compared to the control group. Further, Cho, Lee, and Song (2012) conducted a 6-week randomized controlled trial to examine the effects of virtual reality training on the balance of ronic stroke patients. Although there was no increase in static balance for either group, greater improvements for dynamic balance in the virtual reality training group were observed, indicating virtual
reality balance training is feasible and suitable for ronic stroke patients with balance deficit in clinical seings. Virtual reality-based physical rehabilitation has also been used among patients with Parkinson’s disease. For example, Yang et al. (2016) examined whether a 6-week home-based virtual reality balance training program was more effective than conventional home balance training for improving balance and walking in patients with Parkinson’s disease. Although no significant differences were seen between these two groups, researers found both groups to demonstrate improved balance aer training. e study suggests the two training approaes were equally effective in improving balance and walking among community-dwelling Parkinson’s disease patients. In a randomized controlled study by Robles-García and colleagues (2016), the impact of 4 weeks of imitation-therapy on movement hypometria in Parkinson’s disease was assessed. While the experimental group imitated full-amplitude repetitive finger-tapping movements presented by the virtual reality avatar at three different tapping rates, the control group performed a protocol mating the same features presented to the experimental group, but without imitation. Findings indicated significantly increased movement amplitude aer the therapy in the experimental group for the trained and untrained hands, suggesting that virtual reality-based therapy has the potential to enhance the effect of motor functioning in patients with Parkinson’s disease. With regard to ildren and adolescents with developmental disabilities, the positive effects of virtual reality-based physical rehabilitation in these populations have been presented. In a recent study, increased upper-limb motor skills on the affected side and bilateral coordination ability were observed by Do and associates (2016), following virtual reality-based bilateral arm training. is study concluded that bilateral arm training based using virtual reality can be an effective intervention method for enhancing motor function in ildren with hemiplegic cerebral palsy. Ashkenazi et al. (2013) explored the feasibility of using a low-cost, off-the-shelf virtual reality game to treat young ildren with developmental coordination disorder. Following the intervention, significant anges were observed in fine and gross motor
skills, including hand writing, manual dexterity, aiming, cating, balance, and walking. Furthermore, the ildren seemed to be motivated and to enjoy the interaction with the virtual reality environment. Similarly, Gonsalves and colleagues (2015) examined the effect of virtual reality on movement quality in ildren with developmental coordination disorder. Researers found ildren with developmental coordination disorder display a slower hand path speed, greater wrist extension, and greater elbow flexion compared with ildren with typical development when engaging in virtual reality. e study suggests virtual reality is favorable for facilitating desired motor learning outcomes. As seen, of the reviewed high-quality literature regarding the use of virtual reality in physical rehabilitation among a diverse array of populations, results are promising in relation to the effectiveness of virtual reality-based rehabilitation. It appears that virtual reality rehabilitation is favorable or of equal quality as compared to conventional rehabilitation in part because of the ability to provide the intensive repetition of meaningful task-related activities. at is, virtual reality is beneficial in improving individual motor functioning. Nevertheless, the virtual reality interventions are different due to divergent virtual reality instruments. Future resear should compare the effects between dissimilar virtual reality treatments.
Psyological rehabilitation Over the past two decades, virtual reality-based psyological rehabilitation has experienced increasing application in treating individuals with anxiety disorders, with particular emphasis on specific phobias (Morina, Ijntema, Meyerbröker, & Emmelkamp, 2015). In detail, this type of virtual realitybased psyological rehabilitation incorporates computer graphics, visual displays, and sensory inputs to create an immersive virtual environment capable of approximating the feeling of being in the real world (McCann et al., 2014). Aer immersing a patient in this virtual world, therapists can then introduce the anxiety-eliciting stimuli into the environment. e advantage
of using virtual reality-based psyological rehabilitation is the therapists’ ability to control the quality, intensity, duration, and frequency of exposure to the anxiety-eliciting stimuli (Emmelkamp, 2005). For instance, in a 12month randomized controlled trial by Anderson and colleagues (2013), the effects of virtual reality exposure therapy on 97 participants’ social anxiety disorder was evaluated. ese researers found that patients who were exposed in the virtual world reported less peak anxiety and spoke longer at the post-treatment spee, indicating virtual reality exposure therapy might be effective for treating social fears. Mialiszyn et al. (2010) assessed the efficacy of virtual reality in virtuo exposure and in vivo exposure on spider phobia. ese two treatment conditions were compared to a wait-list condition. Researers found participants in both the in virtuo and in vivo exposure therapies demonstrated significant improvements on objective and subjective measures of fear aer eight 90-minute treatment sessions. at is, both in vivo and in virtuo exposure are efficient methods of treating spider phobia. Notably, a slight advantage of in vivo exposure over in virtuo exposure was found, as revealed by significant and continued improvement regarding catastrophic beliefs about spiders aer posest in the in vivo group. Further, Gaggioli and colleagues (2014) conducted a 5-week randomized controlled trial involving 121 teaers and nurses (i.e., all highly exposed to stress) to evaluate the effects of virtual reality for the management and prevention of psyological stress. ese researers found the virtual reality group to report significant reductions in ronic “trait” anxiety and a significantly increased coping and emotional support skills when compared to the wait-list group. Findings lead the authors to conclude that virtual reality protocol yields beer effects than the traditionally accepted gold standard for psyological stress treatment (i.e., cognitive behavioral therapy). In fact, previous meta-analyses have shown the effectiveness of virtual reality-based psyological rehabilitation on various mental disorders. For example, Powers and Emmelkamp (2008) conducted a meta-analytic review investigating the impact of virtual reality-based psyological rehabilitation on mental illnesses, with nine studies on specific phobias, two studies on
social phobia, one study on panic disorder, and one study on post-traumatic stress disorder. Researers found that virtual reality-based psyological rehabilitation is highly effective in treating phobias in comparison to traditional psyological rehabilitation (e.g., relaxation and bibliotherapy) or wait-list control conditions. Additionally, Opris and colleagues (2012) included 23 studies in a meta-analysis comparing virtual reality-based psyological rehabilitation for anxiety disorders as compared to standard rehabilitation procedures. e majority of the included studies were on specific phobias (n = 12), with the remaining studies investigating the use of virtual reality-based psyological rehabilitation on social phobia (n = 5), panic disorder with or without agoraphobia (n = 5), and post-traumatic stress disorder (n = 1). Similar to the aforementioned meta-analytic review, high effect sizes in favor of virtual reality-based psyological rehabilitation were observed when this novel rehabilitation methodology was compared to waitlist control conditions. As a result, the researers concluded that virtual reality-based psyological rehabilitation is efficacious in treating anxiety disorders, in addition to demonstrating a powerful real-life impact and good stability of results over time. In the most recent meta-analysis conducted to date, Morina et al. (2015) included 14 studies regarding the effectiveness of virtual reality-based psyological rehabilitation in the treatment of aranophobia (i.e., fear of spiders) and acrophobia (i.e., fear of heights). Findings revealed that patients undergoing virtual reality-based psyological rehabilitation for specific phobias (e.g., fear of flying, fear of spiders, etc.) resulted in significantly beer behavioral assessments than patients undergoing standard rehabilitation or serving as wait-list controls. As reviewed, present evidence indicates that virtual reality-based psyological rehabilitation has the potential to promote psyological wellbeing and can be a valuable tool in treating various mental disorders. It is also clear that virtual reality-based psyological rehabilitation shows considerable utility in allowing patients to engage in novel, highly interactive, and effective simulations of active face-to-face conditions (Figure 10.3). Nevertheless, most resear regarding virtual reality and psyological rehabilitation has been conducted among adults or older adults. More studies,
therefore, are warranted in determining the effects of virtual reality on mental health among ildren and adolescents.
Cognitive rehabilitation Cognitive impairment is a considerable health concern worldwide, with a number of people unable to live independently due to these impairments (Seelye, Smier-Edgecombe, Das, & Cook, 2012). Virtual reality-based cognitive rehabilitation has experienced increasingly widespread use for the treatment of cognitive impairments. is emerging tenology offers new rehabilitation options whi can be tailored to suit specific clinical or resear purposes (Rizzo et al., 2001; Tarnanas et al., 2013). Specifically, the engaging nature of virtual reality tasks makes this tenology ideal for a wide range of applications including cognitive screening and evaluation (Zygouris et al., 2015). In fact, the potential of virtual reality-based cognitive rehabilitation is currently being investigated, with a focus on the treatment of cognitive processes, su as aention, working memory, verbal and visual memory, and general cognitive functioning (Gamito et al., 2011; Kim,
Figure 10.3
Experiencing virtual reality in CAVE.
Source: Photo by Denis Pasco.
Chun, Kim, & Park, 2011; Man, Chung, & Lee, 2012; Manzoni et al., 2016; Optale et al., 2010, Sorita et al., 2013; Yip & Man, 2013; Zygouris et al., 2015). For example, Optale and colleagues (2010) compared a 6-month virtual reality memory training to music therapy in the treatment of cognitive decline for 36 older adults, with the objective being to improve memory. ese researers found participants who underwent virtual reality memory training showed significant improvements in memory tests—particularly with regard to long-term recall, while the control group demonstrated continued cognitive decline. e beneficial effects observed in cognitive functions support the efficacy of using virtual reality in elderly adults and suggest that the virtual reality memory training protocol can be a valid and integral part of a rehabilitative strategy aimed at encouraging memory recovery. Similarly,
in a 5-week randomized controlled study by Man et al. (2012), 44 older adults in the early stages of dementia were recruited to investigate the effectiveness of virtual reality-based memory training. At study’s end, both the virtual reality-based cognitive rehabilitation group and traditional rehabilitation group demonstrated positive training effects, with the virtual reality group showing greater improvement in objective memory performance and the non-virtual reality group displaying beer subjective memory tests, indicating the use of virtual reality is acceptable for treating older adults with dementia. Finally, Kim and colleagues (2011) evaluated the effectiveness of additional fully immersive virtual reality in the promotion of greater cognitive functioning. An intervention group comprised of 15 cognitive impairment patients underwent virtual reality-based cognitive rehabilitation and computer-based cognitive rehabilitation while 13 patients in a control group received only computer-based cognitive rehabilitation over four weeks. Researers observed both groups to have improved neuropsyological outcomes following treatment. However, compared to the controls, the intervention group demonstrated improvements on a visual continuous performance test and on a baward visual span test—both positive findings in relation to visual aention and memory, leading the authors to conclude that virtual reality training combined with computer-based cognitive rehabilitation may be of additional benefit for treating patients with cognitive impairment. Given the positive results reviewed above, the use of virtual reality-based cognitive rehabilitation promises to continue to be a resear area of interest. Virtual reality-based cognitive rehabilitation offers great benefit as the immersive nature of the simulated environment capable of being created allows clinicians to approximate real-life situations in a well-controlled manner whi reduces the risk of adverse patient outcomes (Rizzo et al., 2001). Further, as a result of being able to highly control the environment, clinicians can enhance the transfer of skills learned in the simulated environment of virtual reality to real-world conditions (Rose, Aree, Brooks, & Andrews, 2001). As su, future resear using virtual reality-based
cognitive rehabilitation should continue to increase the number of different applications of this tenology in the field of cognitive rehabilitation.
Practical implications Traditional informational approaes for promoting PA and health are to disseminate information through printed materials and mass media. However, tenology sprawl has allenged this tradition, and advanced health promotion professionals are integrating email, text messaging, web sites, and online support groups into their repository (Tomnay, 2005). A less familiar, yet aractive, platform for PA and health promotion specialists is virtual reality. Nowadays, virtual reality is widely used in PA and health promotion seings among various populations (Figure 10.4). An appealing feature of virtual reality is to allow participants to see, hear, use, and modify simulated objects in a computer-created world (Tomnay, 2005). Specifically, the potential of virtual reality in PA and health promotion has been explored, with improved motor function and patients’ rehabilitative outcomes have been observed among various seings. Nevertheless, the use virtual reality tenology to promote PA and health is still in its infancy (Liu, 2011; Mohnsen, 2003; Pasco, 2013). erefore, researers and practitioners should continue to explore the benefits of virtual reality and harness its potential in the promotion of PA and health among a variety of populations in a trustworthy manner.
Figure 10.4
Playing an augmented reality game—Pokémon Go.
Source: pixabay.com.
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St-Jacques, J., Bouard, S., & Bélanger, C. (2010). Is virtual reality effective to motivate and raise interest in phobic ildren toward therapy? A clinical trial study of in vivo with in virtuo versus in vivo only treatment exposure. The Journal of Clinical Psychiatry, 71(7), 924–931. Subramanian, S. K., Lourenço, C. B., Chilingaryan, G., Sveistrup, H., & Levin, M. F. (2013). Arm motor recovery using a virtual reality intervention in ronic stroke randomized control trial. Neurorehabilitation and Neural Repair, 27(1), 13–23. Tarnanas, I., Slee, W., Tsolaki, M., Müri, R., Mosimann, U., & Nef, T. (2013). Ecological validity of virtual reality daily living activities screening for early dementia: Longitudinal study. Journal of Medical Internet Research Serious Games, 1(1), e1. Tomnay, J. E. (2005). New tenology and partner notification – why aren’t we using them? American Journal of Health Promotion, 16(1), 19–22. Weiss, P. L., Keshner, E. A., & Levin, M. F. (2014). Virtual reality for physical and motor rehabilitation. New York, NY: Springer. Yang, W. C., Wang, H. K., Wu, R. M., Lo, C. S., & Lin, K. H. (2016). Homebased virtual reality balance training and conventional balance training in Parkinson’s disease: A randomized controlled trial. Journal of the Formosan Medical Association, 115(9), 734–743. Yip, B. C., & Man, D. W. (2013). Virtual reality-based prospective memory training program for people with acquired brain injury. Neurorehabilitation, 32(1), 103–115. Zygouris, S., Giakoumis, D., Votis, K., Doumpoulakis, S., Ntovas, K., Segkouli, S., … & Tsolaki, M. (2015). Can a virtual reality cognitive training application fulfill a dual role? Using the virtual supermarket cognitive training application as a screening tool for mild cognitive impairment. Journal of Alzheimer’s Disease, 44(4), 1333–1347.
Part III Applications for physical activity and health promotion
11 Negative aspects of emerging tenologies in physical activity promotion Zachary Pope and Zan Gao
roughout this book, the reader has been presented with a number of emerging tenologies used in promoting physical activity (PA) and health. From active video games to wearable tenology and mobile device apps to social media and state-of-the-art GPS/GIS systems as well as virtual reality, the current generation of tenology is redefining how individuals improve their health and well-being and prevent or aenuate diseases and conditions precipitated by poor health behaviors (e.g., physical inactivity, smoking, poor diet). Despite the promise many of these tenologies hold for PA promotion, negative aspects, limitations, and allenges for researers and clinicians are still present and need to be considered before further effective integration of emerging tenology into the field of PA and health can take place. Notably, the negative aspects, limitations, and allenges reviewed below do not constitute an exhaustive list. e content is, however, representative of the major obstacles currently facing emerging tenology in the promotion of PA for improved health and well-being.
Negative aspects and limitations of emerging tenologies Privacy Perhaps the greatest negative aspect of emerging tenologies for use in PA promotion is the privacy of health information when using these novel tenologies (Lobelo et al., 2016; Riardson & Aner, 2015). For example, if a clinician is having a patient use a free, commercially available smartphone app to tra their steps per day, the app is likely not subject to the confidentiality and security regulations stipulated in the Health Insurance Portability and Accountability Act (HIPAA), to whi apps specifically developed by physicians for healthcare purposes are required to adhere (Lobelo et al., 2016). is la of regulation exposes the patient to unlawful use and sharing of private health information. As a result, questions regarding the security and privacy of personal health information assessed and provided to health professionals via different forms of emerging tenology are a limiting factor with regard to client/patient use of emerging tenology for health promotion—as is the fact that many applications require log-in information whi further exposes the user to identity the. erefore, it is up to researers to find ways in whi to develop tenology whi protects a user’s health information while still being able to acquire enough information from users to inform personal health practices (Figure 11.1).
Validity and reliability of PA and physiological data
Figure 11.1
Designing websites for Internet-based intervention.
Source: pixabay.com.
e validity and reliability of PA and physiological data (e.g., METs, calories, steps) provided by emerging tenologies su as mobile device health apps (i.e., mHealth apps) are, at times, questionable (Lobelo et al., 2016)—a term that might also be used to describe the PA and physiological data provided during active video game play or during health wearable use. For instance, in a recent study by Bai et al. (2016), consumer health wearables used to tra PA (e.g., Fitbit Flex, JU24, etc.) had error values of 15%–20% for energy expenditure when compared to energy expenditure values provided by a portable metabolic analyzer—errors whi have major implications for health professionals using these devices to develop PA and dietary plans to improve client/patient health. Further, with numerous tenologies currently being released with the intention to promote health and well-being in ways that are more fashionable (e.g., nelaces, belts, and upscale smartwates capable of capturing physiological data; see Charara, 2016), more resear will be needed to discern whether or not these newer tenologies are addressing some of the measurement errors present in past tenologies of
the same type. Boom line, any health professional who wants to use emerging tenology for health promotion purposes needs to keep in mind the error rates for heart rate, calories, and other physiological variables related to the type of tenology being implemented. Indeed, not taking into account physiological data error rates for specific tenologies might produce unwanted outcomes among patients—particularly patient populations where outcomes su as weight loss is of utmost importance or in populations where baseline fitness is poor.
Stigma Stigma is also a barrier. For example, reviews on active video games have pointed out that while active video games are anging the perception that all video games contribute to poor health—as opposed to just sedentary video games contributing to poor health—continued work needs to be done to prove the effectiveness of this PA modality in the promotion of improved health (Guy, Ratzki-Leewing, & Gwadry-Sridhar, 2011). Fortunately, more recent meta-analytic reviews have statistically proven the effectiveness of active video games in PA and health promotion across numerous studies among ildren and adolescents (Gao, Chen, Pasco, & Pope, 2015)—results of whi can be used to further decrease the stigma some individuals hold regarding the use of active video games to improve health. Within the context of active video games, King, Glanz, and Patri (2015) have posited that health professionals need to “frame” this PA modality in terms of health, rather than just defining the games as a way to have fun. Extrapolating this “health framing” idea to other emerging tenologies currently being used for PA promotion may improve the perception of these tenologies’ ability to improve health, while also decreasing the stigma or resistance of key stakeholders (e.g., sool and hospital administrators) to adopting these tenologies.
Integration framework and compensation for personnel Lobelo et al. (2016) makes the point that if we are to more successfully integrate tenology into healthcare treatments—of whi PA is oen a component—a framework through whi to integrate this tenology is needed. A proper integration framework is important for at least three reasons. First, this framework may allow us to discern the best ways by whi to protect patient/client health information when placed within electronic medical records. Second, we will be able to increase the effectiveness of interventions using certain types of novel tenology (e.g., health wearables, mHealth apps, etc.) for PA promotion. Finally, a proper integration framework will pemit new policies regarding the compensation of healthcare providers for time spent providing care via tenological mediums su as mHealth apps. Indeed, as West (2012) indicated, most established mHealth programs do not compensate physicians and other healthcare professionals (e.g., physicians assistants, physical/occupational therapists) for the time they spend providing care to patients via a mHealth app—increasing resistance among this cohort to integrate this tenology into practice. While more reasons could be listed regarding the importance of an established integration framework, one thing is clear: if the adoption of emerging tenology into healthcare is to become more mainstream and greatly impact and revolutionize the manner by whi health professionals provide care, an integration framework will be vital.
La of studies using an established theoretical framework or intervention fidelity While some emerging tenology fields, su as active video gaming, have numerous intervention literature whi has properly integrated established theory and ensured proper intervention fidelity measures (e.g., puing
methodology in place to assess, monitor, and correct any threats to the reliability and internal validity of an intervention; see Borrelli, 2011), other newer fields of emerging tenology resear have not been as successful in this task. Establishing theory allows researers to develop and implement interventions with greater effectiveness due to the indirect effects theory has on the intervention’s main objective (Brug, Oenema, & Ferreira, 2005). Further, theory allows for a more accurate interpretation of the phenomena seen as a result of the intervention’s implementation (Paen, 2014). Finally, more studies using emerging tenology to promote PA and improve health need to implement intervention fidelity measures to ensure the reliability and internal validity of the study. Intervention fidelity measures include the proper selection of intervention design and the implementation of provider/resear team training, in addition to the ongoing assessment of intervention delivery (i.e., how will providers deliver the intervention systematically and consistently among all participants?), intervention receipt (i.e., do the participants understand the different components of the intervention?), and intervention enactment (i.e., does the participant enact the behavior anges taught during the intervention in a “real-world” seing?). Importantly, interventions with sound theoretical baing may still not be effective if the proper intervention fidelity measures were not set in place. e reader is directed to Chapter 6 and the authors’ detailed discussion of intervention theory and fidelity within the context of mHealth app-based PA interventions.
Internet gaming disorder In the 5th edition of their Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the American Psyiatric Association included, for the first time, the diagnosis of Internet Gaming Disorder (IGD; American Psyiatric Association, 2013). According to the DSM-5, IGD has nine distinct criteria and, of these nine criteria, five must be present for greater
than 12 months for a diagnosis with IGD. ese criteria include: (1) gaming preoccupation; (2) foregoing other hobbies or activities to play games; (3) continued gameplay despite other life problems; (4) using gaming to cope with poor moods; (5) lying about the amount of time spent gaming; (6) reluctance to curtail or discontinue gaming habits; (7) experiencing withdrawal when unable to game; (8) becoming tolerant to an unusually high amount of gaming play; and (9) placing oneself in jeopardy of losing relationships, jobs, or education as a result of gaming (Figure 11.2). Depending upon the population studied, the prevalence of IGD can be as low as 0.2% (Festl, Sarkow, & andt, 2013) to nearly 9% (Choo et al., 2010). Recently, work has been completed to validate the Internet Gaming Disorder estionnaire in multiple populations so health professionals can beer assess and treat this disorder (Jeromin, Rief, & Barke, 2016). Nonetheless, health professionals using gaming tenology to promote PA need to be aware of the signs of IGD in order to properly intervene if and when these signs start to appear in participants. at said, health professionals implementing game-based interventions (e.g., active video games and augmented/virtual reality games) need to make clear to participants that the games are only to be used to supplement a wellrounded PA plan.
Tenology-related accidents, unlawful behavior, and safety
Figure 11.2
Playing video games.
Source: pixabay.com.
Emerging tenology in the promotion of PA and health has many benefits whi do, in general, outweigh the potential negative aspects of using these tenologies. at said, use of these tenologies does have downsides. For example, Pokémon Go, a hugely popular augmented reality game played using a smartphone, has facilitated increased PA among players. Unfortunately, as Serino and colleagues (2016) state, Pokémon Go has resulted in accidents due to players not paying aention while walking, riding a bike, or even driving a car to cat Pokémon, as well as increasing trespassing activity on private properties by players with an extreme desire to cat their next Pokémon. Moreover, as a result of using this game’s “lure” function, criminals have used this game to aract other players to their location aer whi these criminals rob players who were simply looking for the nearest Pokémon aracter (Serino, Cordrey, McLaughlin, & Milanaik, 2016). While other emerging tenologies su as GPS-based geocaing might also result in unlawful acts (e.g., trespassing on private property), Pokémon Go is a superb example of how the misuse of potentially healthbenefiting tenology can result in negative outcomes. Given these
potentially negative outcomes, health professionals need to clearly outline to participants the need to remain aentive to their environment and engage in law-abiding actions. Finally, more resear will also be needed regarding the safety of emerging tenology when used to promote PA and health. Indeed, innovative new te su as skateboard helmets whi light up during activity have been developed (see Exertion Games Lab, 2016a), yet no empirical resear has been conducted to ensure that tenologies akin to this form of wearable tenology are as safe as their traditional counterparts.
Challenges for researers and clinicians Now that several negative aspects/limitations associated with the implementation of emerging tenology in PA promotion and, more broadly, health promotion, have been reviewed, it is worthwhile briefly discussing what allenges will face researers and clinicians within this field over the next decade. While one could think of an endless list of allenges, many can fit into five broad categories: (1) stakeholder buy-in; (2) reduction of health disparities; (3) proving the validity and reliability of emerging tenology in promotion of health behaviors su as PA; (4) dissemination of tenology observed to be efficacious in promoting health; and (5) incorporation of Big Data analysis to improve health at the population level. Stakeholder buy-in is likely the most pressing allenge for health professionals seeking to promote health via emerging tenology. Indeed, until researers and/or clinicians can develop proper security measures to protect the electronic health information (e.g., the amount of PA an individual engages in) provided by some emerging tenologies for analysis by wellness teams/physicians, institutions su as hospitals or primary care clinics will be reluctant to integrate tenology into their healthcare practices. As we have seen, integration of emerging tenology has the potential to provide great benefits to the patient/client (see West, 2012), meaning ensuring individuals’ electronic health information security is vital before administrators would consider fully adopting emerging tenology as a means of treatment delivery (Figure 11.3). Sools, institutions whi have great ability to use emerging tenology to promote health among the large percentage of youth who aend, also need stakeholder buy-in. Particularly, sool administrations and teaers need to see data on the effectiveness of emerging tenology, su as mHealth apps and active video games, in the promotion of health within physical education and the classroom. is will
go a long way in allenging the stigma associated with tenology and tenology’s perceived negative relationship with youth health—providing opportunities for PA and health interventions to be implemented in novel and impactful ways within the sool seing.
Figure 11.3
Application of a smart wat in health promotion.
Source: pixabay.com.
Second, a allenge for health professionals will be to decrease the health disparities present in society due, in part, to la of access to healthcare. As Lobelo et al. (2016) noted, due to the greater access that individuals with higher socioeconomic status have compared to individuals of lower socioeconomic status, health professionals need to be careful that the use of emerging tenology to improve health does not widen the health disparities between the two aforementioned populations. Frameworks for integrating emerging tenology into healthcare need to be aware of this potential to increase the health disparity between populations of high and low socioeconomic status and concentrate efforts on underserved populations by creating avenues by whi emerging tenology is available to them.
Fortunately, in recent years, researers have been working on emerging tenology whi can be used by individuals in high- and low-income populations. For example, Gao and associates (2013) used active video games to promote health and improved academic performance in low-income Latino elementary sool students finding that a Dance Dance Revolutionbased PA intervention improved math scores and cardiorespiratory endurance versus control. Further, the tablet-based “Discovery Tool” has been used among low-income populations to document barriers in these individuals’ PA environment (Buman et al., 2013). e users simply take a picture of a location where their PA is impeded (e.g., a sidewalk that is broken or ends abruptly) at whi point the Discovery Tool geocaes the picture—providing direct coordinates to the object in the environment whi is limiting PA. is information can then be used to influence modifications in the built environment to beer facilitate PA. Nonetheless, until more resear teams focus their aention on not widening the health disparities gap with the implementation of emerging tenology, this gap will remain a allenge to overcome. Greater study of the validity and reliability of emerging tenology when assessing variables su as PA intensity/type, heart rates and energy expenditure needs to be conducted to further improve upon the accuracy of devices used for these assessment purposes. While some resear has been completed to validate emerging tenology, su as health wearables (Bai et al., 2016), only a paucity of literature is available on testing these devices in the provision of physiology-related information. Further, health wearables (e.g., Fitbits, and Jawbones) are not the only devices being used to provide information on PA intensity/type and energy expenditure. As other devices, su as smartphones, are more frequently being used to provide the aforementioned type of data to health professionals by way of mHealth apps, more resear also needs to be devoted to the validation of these devices in the traing of physiological outcomes (i.e., heart rate, calories, etc.) as well as the ability of patients/clients to accurately report PA and related health information via mHealth apps (Figure 11.4). While examples within the context of other emerging tenologies could be provided, the
need for greater study of the validity and reliability of emerging tenology in the provision of health information is vital. Indeed, if health professionals do not prove the data provided by emerging tenology for healthcare purposes to be valid and reliable, stakeholder buy-in will always remain elusive—particularly within primary care seings where provision of accurate information via these tenologies is of paramount importance. Finally, dissemination of tenology that has been proved efficacious in the promotion of PA and health, in addition to the integration of Big Data analysis in tenology-based interventions, represent two grand allenges to researers in the future. Dissemination of efficacious tenology is oen troublesome in health tenology resear. While some laboratories investigating the use of tenology to improve health and well-being are working hard with industrial collaborators to put these tenologies onto the market (see Exertion Games Lab, 2016b), most laboratories la the resources, reputation, or desire to commercialize their health tenology. In order to reverse this trend, more emphasis needs to be placed on rewarding researers for placing their health tenology onto the public market, in the form of resear grant monies whi stipulate researers shuld have a detailed plan for tenology dissemination at the conclusion of the study.
Figure 11.4
Combination of a mobile device and a computer.
Source: pixabay.com.
As a final and potentially related allenge, integration of emerging tenology needs to place emphasis on Big Data analysis. In the simplest terms, Big Data analysis refers to our ability to used advanced teniques to capture, store, and analyze the rapidly accumulating information present in numerous formats (e.g., text, audio, and video) in our tenologically advanced society (Te-America Foundation’s Federal Big Data Commission, 2012). In brief, the ability to analyze large volumes of health data produced by emerging tenologies like the Fitbit might have major implications for healthcare. Specifically, examination of the individualenvironment relationship using these large volumes of data could be used in the healthcare system to inform health behavior decisions while also facilitating the development of novel innovations whi improve our ability to prevent, treat, and manage diseases. One thing is clear, however. Specifically, the general population needs to decide upon the trade-off between the privacy they wish when using emerging tenology (e.g., health wearables) to tra health indices as opposed to the amount of personal health information that could potentially be shared by these devices— information whi could ultimately be evaluated using Big Data analysis teniques and used by health professionals to improve population-level health and well-being (Kvedar, 2015). Despite the allenges whi face numerous areas of emerging tenology in the promotion of health and well-being, one thing remains certain: tenology will continue to transform how health professionals treat/train patients/clients. Emerging tenology has the power to improve intervention outcomes and facilitate quier communication and feedba between interventionists and patients/clients. However, just as with any worthwhile endeavor, barriers and allenges are still present. As su, future health professionals need to keep in mind the barriers and allenges outlined in this apter—striving to overcome the barriers whi will subsequently
make meeting the allenges of using emerging tenology in PA and health promotion more manageable.
References American Psyiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psyiatric Association. Bai, Y., Welk, G., Nam, Y., Lee, J., Lee, J.-M., Kim, Y., … Dixon, P. (2016). Comparison of consumer and resear monitors under semistructured seings. Medicine and Science in Sport and Exercise, 48(1), 151–158. Borrelli, B. (2011). e assessment, monitoring, and enhancement of treatment fidelity in public health clinical trials. Journal of Public Health Dentistry, 71, S52–S63. Brug, J., Oenema, A., & Ferreira, I. (2005). eory, evidence, and intervention mapping to improve behavior, nutrition, and physical activity interventions International Journal of Behavioral Nutrition and Physical Activity, 22(2), 1–7. Buman, M., Winter, S., Sheats, J., Hekler, E., Oen, J., Grieco, L., & King, A. (2013). e Standford healthy neighborhood discovery tool: A computerized tool to assess active living environments. American Journal of Preventive Medicine, 44(4), e41–e47. Charara, S. (2016). Fashion tech: 20 wearables that are more chic than geek. Retrieved from www.wareable.com/fashion/wearable-te-fashion-style. Choo, H., Gentile, D., Sim, T., Li, D., Khoo, A., & Liau, A. (2010). Pathological video-gaming among Singaporean youth. Annals Academy of Medicine, 39(11), 822–829. Exertion Games Lab. (2016a). LumaHelm-interactive helmet. Retrieved from hp://exertiongameslab.org/projects/lumahelm. Exertion Games Lab. (2016b). Projects. Retrieved from hp://exertiongameslab.org/projects.
Festl, R., Sarkow, M., & andt, T. (2013). Problematic computer game use among adolescents, younger and older adults. Addiction, 108, 592–599. Gao, Z., Chen, S., Pasco, D., & Pope, Z. (2015). A meta-analysis of active video games on health outcomes among ildren and adolescents. Obesity Reviews, 16, 783–794. Gao, Z., Hannan, P., Xiang, P., Stodden, D., & Valdez, V. (2013). Video gamebased exercise, Latino ildren’s physical health, and academic aievement. American Journal of Preventive Medicine, 44(3S3), S240– S246. Guy, S., Ratzki-Leewing, A., & Gwadry-Sridhar, F. (2011). Moving beyond the stigma: systematic review of video games and their potential to combat obesity. International Journal of Hypertension, 2011, 1–13. Jeromin, F., Rief, W., & Barke, A. (2016). Validation of the Internet Gaming Disorder estionnaire in a sample of adult German-speaking Internet gamers. Cyberpsychology, Behavior, and Social Networking, 19(7), 453– 459. King, A., Glanz, K., & Patri, K. (2015). Tenologies to measure and modify physical activity and eating environments. American Journal of Preventive Medicine, 48(5), 630–638. Kvedar, J. (2015). e privacy trade-offs. In The internet of healthy things. Boston, MA: Partners HealthCare Connected Health. Lobelo, F., Kelli, H., Tejedor, S., Pra, M., McConnell, M., Martin, S., & Welk, G. (2016). e wild wild west: A framework to integrate mHealth soware applications and wearables to support physical activity assessment, counseling, and interventions for cardiovascular disease risk reduction. Progress in Cardiovascular Diseases, 58(6), 584–594. Paen, M. (2014). e role of theory in resear. In M. Paen (Ed.), Understanding research methods: An overview of the essentials (9th ed., pp. 27–29). Glendale, CA: Pyrczak Publishing. Riardson, J., & Aner, J. (2015). Public health perspectives of mobile phones’ effects on healthcare quality and medical data security and privacy: A 2-year nationwide survey. Paper presented at the e
American Medical Informatics Association Annual Conference Proceedings, 2015. Serino, M., Cordrey, K., McLaughlin, L., & Milanaik, R. (2016). Pokémon Go and augmented virtual reality games: A cautionary commentary for parents and pediatricians. Current Opinions in Pediatrics, 28(5), 673–677. TeAmerica Foundation’s Federal Big Data Commission. (2012). Demystifying big data: a practical guide to transforming the business of government.
Retrieved from www.304.ibm.com/industries/publicsector/fileserve?contentid=239170. West, D. (2012). How mobile devices are transforming healthcare. Issues Technology Innovation, 18(1), 1–11.
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12 Emerging tenologies in promoting physical activity and health Zan Gao
roughout this book, an illustration of how emerging tenologies have anged and continue to ange our lives has been provided. Indeed, emerging tenologies have le no scientific field untoued, including the field of physical activity (PA) and health. As stated a number of times within this book, however, tenology is like a double-edged sword when viewed through the lens of PA and health promotion. On one hand, tenology contributes significantly to physical inactivity and sedentary behavior. On the other hand, researers are striving to “fight fire with fire”—aempting to use emerging tenology to promote PA participation. Notably, as a result of the work of intrepid researers, emerging tenologies have contributed tremendously to the understanding and promotion of PA behaviors— particularly over the past decade. is apter will conclude the book with an overview of the most pressing issues and relevant considerations related to the implementation of emerging tenologies in the promotion of PA and health.
Adoption of social and behavioral theories in promoting PA To beer understand and predict individuals’ PA behaviors using emerging tenologies, it is imperative to adopt appropriate social and behavioral theories for different target populations. In Chapter 3, we thoroughly elaborated three classifications for these theories: personal-level theories, behavior micro-environmental theories, and behavior macro-environmental theories (King, Stokols, Talen, Brassington, & Killingsworth, 2002). Although only seven out of the numerous social and behavioral theories available were discussed in this book, it is apparent that one personal-level theory (Transtheoretical Model) and one behavior micro-environmental theory (Social Cognitive eory) have received the most aention in the literature. Undoubtedly, these two theories have greatly enhanced our understanding concerning key correlates and/or determinants of PA behavior. at said, researers have reaed a consensus that human behavior is not exclusively influenced by factors present only at the intrapersonal level or only at the interpersonal level. Rather, PA behavior ange is a complex and multifaceted phenomenon influenced by a number of factors at multiple levels. Consequently, relying exclusively on certain intrapersonal or interpersonal level theories does not necessarily lead to successful behavior anges among target populations included in interventions. With the preceding fact in mind, researers should consider embracing the Social Ecological Model of behavior ange as this theory includes intrapersonal (i.e., self-efficacy) and interpersonal level variables (i.e., social support) as well as environmental and policy variables (Buan, Ollis, omas, & Baker, 2012). Notably, however, recent emerging tenologies, including online social media, active video games, and health wearable devices and apps, have
brought new allenges to the implementation of theory-based PA intervention strategies—providing a new area of potential exploration for future resear. Nonetheless, the oice of assessment and intervention strategy will always be paramount, no maer what emerging tenology is employed by researers implementing PA interventions. In the next section, the reader will be treated to an overview of PA assessment and intervention using emerging tenologies to promote PA at different levels (Figure 12.1). Specifically, tenology-based PA assessment and intervention will be illustrated at the intrapersonal level (individual) first, followed by an overview of assessment and intervention at the interpersonal and ecological levels (groups and larger population segments and locales).
Figure 12.1
Robot, the future direction in tenology in promoting health.
Source: Photo by Zan Gao.
Emerging tenologies for PA assessment and intervention Emerging tenologies at the intrapersonal level Use of emerging tenologies at the intrapersonal level oen include tenology whi captures an individual’s personal PA data, beliefs, health status, and environments—typically in conjunction with proximal contextual information and real-time feedba (King, Glanz, & Patri, 2015). Given the fact mobile devices are becoming smaller, faster, and less expensive as tenology advances, the ubiquity of these devices is increasing daily. In fact, the prevalence of smartphones and other health wearable devices provides tremendous opportunities for assessing a variety of PA data, health outcomes, social interactions, and built environments at the intrapersonal level. For example, MapMyFitness, a popular smartphone exercise app, can tra PA variables spatially through use of smartphones’ embedded GPS in addition to other aractive functions. In addition to assessing PA, some emerging tenologies can provide platforms for using dynamic personal PA data in combination with local contextual information to provide real-time feedba and guidance for behavior ange, thus making intervention programs possible. For instance, in a mobile device intervention study, subjects traed their walking data, as well as personal and perceived environments (Hekler et al., 2012). Findings suggested that subjects walked, on average, approximately 20 minutes more during instances when they had direct access to a walking path. Further, when employing active video games, empirical studies have indicated that intervention ildren reported higher levels of self-efficacy and enjoyment and had similar PA levels as compared to control ildren—perhaps due to
the built-in real-time feedba meanisms of the games (Gao, Huang, Liu, & Xiong, 2012; Gao, Zhang, & Podlog, 2013) (Figure 12.2).
Emerging tenologies at the interpersonal and ecological level Emerging tenologies at interpersonal and ecological levels can assess relevant data in the following ways: (1) use of web-based tools su as Google Earth, Google Streetview, and Walk Score to gain a more comprehensive view of individuals’ built environments; (2) use of portable mobile or health wearable devices to collect PA data and travel information about larger groups and the environmental factors that might collectively influence individuals’ health; (3) designing mobile device apps to facilitate capture of observational PA data (e.g., System for Observing Play and Recreation in Communities); (4) use of self-reported information paired with tenological data and aggregate the data among large numbers of people with the goal of traing PA behavior paerns and determinants for a large population; and (5) traing a variety of PA data (e.g., duration, distance, frequency, location information, and daily paerns) through Facebook or Twier or other similar programs (King et al., 2015).
Figure 12.2
Young ildren playing active video games.
Source: Photo by Zan Gao.
PA interventions at interpersonal and ecological level usually employ online social networking, active video games (Figure 12.3), GPS/GIS, and web-based virtual worlds and community-oriented social networking games to gather data in the abovementioned manners. For example, iDance, an active video game, implemented within sool physical education classes, is an example of a PA intervention at the interpersonal level. In detail, iDance allows up to 32 ildren to dance interactively with the game console, while providing instant scores and voice feedba to all players and stimulating competition among ildren. Regarding an example of an ecological level PA intervention, one might consider the newly released Pokémon Go app/game. Specifically, Pokémon Go allows users to explore community environments while also building social networks when outside competing with different people for the newest and best Pokémon aracters. e preceding examples represent two of many different manners by whi researers and health professionals can intervene to promote PA at the interpersonal and ecological levels. at said, the target level of the PA intervention must always be kept in mind as some emerging tenologies lend themselves
beer to intrapersonal level interventions (e.g., health wearables) while others are more effective at interpersonal or ecological level interventions (e.g., social media, augmented reality mobile device apps.su as Pokémon Go) (Figure 12.4).
Figure 12.3
Young ildren playing active video games.
Source: Photo by Zan Gao.
Figure 12.4
A kid playing Pokémon Go.
Source: Photo by Zan Gao.
e Internet of health things A handful of emerging tenologies, as well as the allenges and opportunities associated with these tenologies’ integration into PA and health promotion, have been elaborated throughout this book. During this review, the following questions might have been posed: What emerging tenologies will we use 5 to 10 years from now to promote more healthful behaviors? Relatedly, what should we be doing currently to prepare for that future? Fortunately, new smart mobile devices and other Internet-based emerging tenologies make it possible for all parties (e.g., researers, healthcare providers, tenology companies) to work together in promoting PA and health in powerful and innovative ways that were previously unimaginable. Indeed, many of the cuing-edge studies reviewed in this book have demonstrated the power of smart mobile devices and Internet-based tenologies in transforming PA assessment and intervention. Akin to the anging landscape of healthcare over the past decade from reactive to preventive forms of treatment, we can build our own Internet of Health ings with tenology and PA to aenuate and prevent ronic diseases. For example, we can connect PA specialists with clients via smart devices su as health wearable devices, smartphones, and wireless weight scales, among other tenologies, whi these clients possess. Specifically, we would have the potential to provide clients with access to their own realtime health data (e.g., weight taken via a wireless scale, blood pressure taken with a wireless blood pressure cuff) via mobile device apps while using a health wearable device to monitor PA. e preceding data could then be uploaded daily to the Internet via a secure server to healthcare providers. On the basis of the PA specialists’ review of a client’s real-time health data, health professionals could then proactively develop well-designed,
personalized exercise prescriptions to improve clients’ health and well-being —offering external incentives to clients for staying physically active and adhering to exercise protocols. It should be noted, however, that tenological advances and the prevalence of ronic diseases are currently driving the development of thousands of apps, devices, sensors, wearables, and Internet-based tools. Despite the well-meaning reasons behind organizations and entities development of these products, resear into the effectiveness of many of these emerging tenologies is still in its infancy. For instance, lile longitudinal data is available concerning how successful these emerging tenologies are at enabling users to lose weight, get fit, or sleep beer. Further, how emerging tenologies empower clients to ange health behaviors (e.g., participate in greater PA) and become healthier remains largely unexplored. erefore, while pursuing this area of inquiry, it is important for researers and health professionals to keep in mind that improved tenology, data, and connectivity will not promote PA and health per se. Rather, emerging tenologies and their applications have the potential to enable users to ange behavior and/or work more effectively when health professionals take care to implement a PA and health intervention using sound theoretical baing and proper intervention fidelity measures.
Cross-tenology issues Advances in emerging tenology also raise cross-tenology issues. In some cases, health wearable or mobile devices incorporate several tenologies, su as accelerometers, GPS, gyroscopes, cameras, light and sound sensors, and even physiological sensors for assessing electrocardiography and heart rate, to beer understand correlates and determinants of PA behavior. Recently, platforms integrating multiple sensors have been developed in order to explain complex phenomena that fuse data simultaneously from one or more sensors and one or more behaviors (e.g., traing both PA and diet data like MapMyFitness). For example, MapMyFitness, a GPS-enabled smartphone app, has been used to assess PA, dietary intake, and other health-related outcomes in a growing number of users. In fact, this type of app may provide detailed contextual information on PA environments on a sizable geographic scale (Hirs et al., 2014). Indeed, the Personal Activity Location Measurement System, another fully integrated instrument, can provide estimated PA energy expenditure based upon data collected with accelerometers and heart rate monitors along with location data from GPS data loggers (Ellis et al., 2014). erefore, it seems the next step in this area of inquiry is to more fully integrate data from motion sensors (e.g., accelerometer), contextual sensors (e.g., GPS), and physiological sensors while also including self-reported indicators of health as well (e.g., perceived PA environment, self-efficacy to adhere to an exercise program). As a result of this integration, researers will be capable of providing a deeper understanding of the interactive effects of contextual and individual-level factors on PA behavior. at said, the following two issues need to be kept in mind when considering cross tenology integration. First, smartphones typically run out baery quily when GPS is utilized. e alternative options would be to use power banks, if possible, or use smartwates or
sports wates with associated app functionality. Second, few studies have employed crowdsourcing to evaluate and manage large datasets in an effort to promote PA and health. Yet, crowdsourcing data in PA and health is the newest and among the most powerful tools presently at researers’ disposal. It is warranted to take advantage of crowdsourcing data in promoting PA in the future. A brief review of crowdsourcing is provided in Chapter 7.
Promote and model digital citizenship As alluded to above and mentioned throughout the book, it is allenging to assure present-day privacy and anonymity in an era when geo-coded data can be easily associated with behavioral and social data (Gutman & Stern, 2007). Further, although self-traing and mobile device–based data have been increasingly used for PA promotion, few studies reported how subjects’ privacy and confidentiality were handled. In fact, there are substantial concerns regarding how the new digital era is compromising anonymity. Although most people take actions to reduce identifying information associated with their online activities, re-identification of anonymous individuals in separate databases can be accomplished with advanced mathematical strategies (Pew Resear Center, 2013). As a result, the expectations of anonymity in traditional PA and health resear may be mu more difficult to maintain when using present-day emerging tenology. To resolve these issues, we must increase the use of tenological safeguards su as robust passwords, encryption, and use of secure servers while improving industry vigilance in the prevention of misuse of information. Further, more rigorous consumer education regarding threats to privacy should be offered to facilitate users’ expectations concerning data security and protection. According to King et al. (2015), many of the data the and user identification problems might be alleviated if data security companies offer, at lile to no cost, information tenology services su as the installation of basic data protection programs to decrease data access vulnerabilities. at said, we advocate that legislative and policy action take place to speed this process. Additionally, PA resear using self-traing mobile devices and other emerging tenologies raises new issues regarding the need to get informed consent from participants. More specifically, if researers collect PA data
over a long period of time or re-use the data in successive experiments, questions arise regarding whether they still need to obtain consent from subjects. Traditionally, informed consent has generally been used for timelimited studies where assessments occurred infrequently. As su, protocols may need to be anged if researers collect daily PA data from participants using emerging tenology. erefore, it is recommended to fully inform participants of the multiple uses of their data and ensure the participants that the data will be destroyed upon completion of a study. Notably, ethical issues pertinent to new forms of digital data have been investigated since the inception of Internet. Currently, however, Institutional Review Boards hold different perspectives concerning whether issues related to the collection of data via emerging tenologies are unique, and thus there is a need to develop formal universal guidelines to address these issues (Buanan, 2010). As complex allenges and opportunities regarding the use of emerging tenologies in PA and health interventions have been thoroughly discussed in Chapter 2, we wll not reiterate this information here; nor will we restate the directions for future resear discussed in ea emerging tenology’s individual apter. Yet, it is important to note that careful consideration of these allenges, opportunities, and directions for future resear set the stage for transformative approaes to scientific discovery and effective intervention implementation in the field of PA and health. Indeed, tenology is continuously anging our lives, with the use of these tenologies in the promotion of PA taking place most frequently over the last decade. As tenology has become more advanced, however, these emerging tenologies offer numerous exciting opportunities to assess and promote PA from an innovative paradigm when properly implemented to ange individuals’ PA behaviors. at said, to effectively assess and promote PA using emerging tenology, we need to take into consideration the design, cost, and behavioral theories, as well as build partnerships with the tenology industry, communities, interdisciplinary teams, and other relevant public sectors. In this manner, the promise of emerging tenology
in the facilitation of a more physically active and healthy global population can be more fully realized.
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physical activity into neighborhood context. Frontiers in Public Health, 2, 1–9. hp://doi.org/10.3389/fpubh.2014.00019. King, A. C., Glanz, K., & Patri, K. (2015). Tenologies to measure and modify physical activity and eating environments. American Journal of Preventive Medicine, 48(5), 630–638. hp://doi.org/10.1016/j.amepre.2014.10.005. King, A. C., Stokols, D., Talen, E., Brassington, G. S., & Killingsworth, R. (2002). eoretical approaes to the promotion of physical activity. American Journal of Preventive Medicine, 23(2), 15–25. hp://doi.org/10.1016/S0749-3797(02)00470-1. Pew Resear Center (2013). Anonymity, privacy, and security online. Retrieved from www.pewinternet.org/Reports/2013/Anonymityonline.aspx.
Index
2-minute Walk Test 186 Abioye, A. 42 accelerometers 7, 9, 19, 26, 29–30, 34, 73, 80, 131, 136–7, 146–8, 156, 161, 240 ActiGraph 10, 19, 153, 155–7, 161 Active Range of Knee Motion 186 Active Teen Leaders Avoiding Screen-time 109 activity traers 9, 33, 148, 162–3 active video games (AVGs) 3–4, 10–12, 14, 18, 20, 22, 26, 36, 42, 45, 50, 58–65, 106, 165–75, 177, 179–85, 187, 189–203, 205, 223, 225–7, 229–30, 232, 235–8 Adamo, K.B. 164, 196, 198 Adams, J. 62 Addy, C. 62, 145 adolescents 14, 22, 38, 44, 56, 66, 76–7, 81–2, 85, 87, 92–3, 100, 102–3, 109–10, 114, 127, 144–5, 149, 166–7, 169, 172–3, 182, 195, 197–8, 200, 203, 209, 211, 213, 225, 232 adults 6, 13–14, 22–3, 30, 32, 34–6, 38, 43–6, 65–6, 69, 75, 78–9, 81, 84–6, 88, 92–6, 103–4, 107, 110–13, 117, 125–6, 131–3, 135, 141, 143–4, 147, 149, 152, 156, 160, 162–4, 166–9, 171, 177, 183–90, 195–7, 199–2, 206, 209, 213–14, 217–18, 232 agility 177–8 Ajzen, I. 62, 80 Alharbi, M. 162 Allen, J. 125 Aller, E.E. 22 Almalki, M. 43, 162 American Psyiatric Association 226, 232
amotivation 50–1, 59 An, R. 125, 144 anaerobic enzymes 15–16 Anderson, P.L. 216 Apple 5, 7, 10, 20, 22, 34, 107, 119, 149–50, 152, 162 Arsand, E. 125 Asbrenner, K.A. 102 Ashkenazi, T. 216 aitudes 20, 52, 57, 59–60, 62, 65, 88, 104, 128, 174, 182 augmented reality 12, 22, 38, 41, 43, 106, 115, 133, 166, 190–2, 195, 202, 204–5, 216–17, 227, 237 Autism Spectrum Disorder 177 autonomy 42, 50 Awi, E.A. 22 Baele, T.R. 22 Bai, Y. 232 Bailey, B.W. 43, 196 Bainbridge, E. 196 balance capabilities 177 Bandura, A. 62, 125 Baranowski, T. 12, 22, 83, 115, 125, 144, 196 Barcena, M.B. 157, 158, 162 Barnes, S.B. 99, 102 Barne, A. 196 Barne, L.M. 165, 196, 199 Baroni, A. 162 Bateni, H. 196 Beebe, L.H. 162 Beets, M. 63, 83 Benne, J.B. 81 Bensley, R.J. 81 Bethea, T.C. 196, 200
Biddiss, E. 196 Big Data 40, 107–8, 117–18, 120, 126, 128, 162, 228, 230–1 Blair, S. 45, 63, 104, 203 Blasco, A. 81 blood pressure 29, 74–5, 79, 112, 159–60, 177, 209 Bluetooth 10, 158 body fat percentage 15, 170, 178 Bogdanis, G. 22 Bogdanov, V. 162 Bond, G.E. 81 bone mineral density 177 Booth, A.O. 81 Borrelli, B. 125, 144, 232 Borth, D. 22 Bosak, K.A. 81 Bouc, A. 102 Boyce, B.A. 63 Brassington, G.S. 65, 242 Bravata, D.M. 43 Bridle, C. 63 Brindal, E. 102 Broadbent, S. 196 Broekhuizen, K. 81 Bronfenbrenner, U. 63, 81 Brouwer, W. 81 Brown, H.E. 22 Brown, J. 43, 102, 162 Brownstein, J. 144 Brug, J. 95, 103, 125, 232 Buan, D.S. 242 Buanan, E.A. 242 Buanan, R. 22 Buholz, S.W. 43
Buman, M. 44, 163–4, 232, 242 Cadmus-Bertram, L.A. 43, 45, 162, 164 calories burned 30, 33, 148, 150–4, 153–1, 158 cancer survivors 75, 85, 91–2, 100, 103–4 cardiorespiratory endurance 15, 230 cardiorespiratory health 14 cardiovascular disease 13, 26, 72–4, 165, 233 Carlson, J. 81, 144, 163 Carr, L.J. 81 Carter, M. 125 Case, M.A. 163 Cavallo, D.N. 43, 102 Centers for Disease Control and Prevention 14, 46 Centola, D. 45, 102, 105 Chang, M. 22 Chaput, J.P. 23, 197, 199 Charara, S. 232 Chatzisarantis, N.L. 63–4 Chen, P.Y. 107 ildren 14, 30, 38, 42, 58–9, 76, 80, 109, 114–15, 131–2, 143, 149, 152, 165–80, 182, 190, 192, 194–5, 206–7, 209, 211, 213, 225, 236–8 Children’s Use of the Built Environment (CUBE) study 132 olesterol 15, 159, 177 Choo, H. 232 Christison, A. 197 ronic disease 13, 16, 18, 26, 107, 111–12, 115–16, 129, 165, 183, 239 Chunara, R. 144 Cialdini, R.B. 102 Ciccolo, J.T. 81 Clark, R. 197 Claude, B. 23 clinical populations 7, 34, 73, 75, 108, 111, 140, 143, 159, 195
cognition 17, 40, 55, 60 cognitive performance 187 Colberg, S.R. 23 competence 51, 121, 168, 178–9 Cook, R.F. 81 Cook, T. 82 coordination 177–8, 211 Cornbla, J. 102 Cous, A. 144 Craig, C.L. 82, 104 Crutzen R. 102 Daley, A.J. 197 Dance Dance Revolution 11, 38, 42, 49, 58, 165, 230 Daniel, K. 197 Danova, T. 163 Davies, C.A. 43, 82 Davison, K.K. 63 Defore, B.I. 102 deJong, A. 43 dementia 17, 44, 214–15 depression 13, 17–18, 75, 78, 186, 188 Desai, M. 102 Developmental Coordination Disorder 177, 211 Devi, R. 82 Diagnostic and Statistical Manual of Mental Disorders 226 Direito, A. 125 Dishman, R.K. 63 Do, J.H. 217 Dominic, D. 23 Dowda, M. 63 Downs, D.S. 63 Dummer, G. 23
Duncan, G.E. 23 Duncan, M. 43, 197 Durant, N.H. 83 Edberg, M. 63 electronic devices 19, 26, 29, 42 Ellis, K. 242 Ellison, N.B. 102 emerging tenologies 3, 9, 18–22, 26, 38–42, 58, 62, 67, 72, 129, 142, 204, 223–5, 227–37, 239– 42 Emmelkamp, P.M. 217 endothelial function 15 energy expenditure 13, 15, 19, 29–30, 37, 48, 78, 96, 106, 111, 143, 152, 156–7, 161, 167, 172, 178, 180–1, 224, 230, 240 enjoyment 5, 36, 38, 167, 173, 175, 180, 182, 186, 188, 236 Errison, S.P. 197 Esculier, J.F. 197 Evenson, K. 144, 163 executive function 187, 189 exergaming 165, 173 Exertion Games Lab. 148, 228 extrinsic motivation 19, 50 EyeToy 11, 169 Facebook 5, 20–1, 30–2, 88–95, 98–9, 101, 118, 154, 236 Fallows D. 102 Farr, C. 102 Feigin, V.L. 217 Ferguson, T. 163 Festl, R. 232 Field, A. 126 Fitbit 3, 7, 9–10, 34, 131, 149–50, 152–60, 162, 224, 230–1 Fle, M. 144
Flores, M. 126 Fogel, V. 63, 197 Foley, L. 197 Foster, D. 102 Fox, J. 102 Fox, S. 163 Franco, D.L. 82 Franco, J.R. 197 Freedson, P.S. 43 Fukuoka, Y. 126 fundamental motor skills 176 Fung, V. 197 Gaggioli, A. 43, 217 Gallahue, D.L. 197 Gamito, P. 217 Gandomi, A. 126 Gant, N. 127 Gao, Y. 197 Gao, Z. 43, 63–4, 198, 232, 242 Garmin 151 Gasser, R. 126 Gear VR 11, 38, 204 Geocaing 38, 135–6, 191, 228 George, E.S. 43 Geman, L.R. 23 Ghosh, A.K. 23 Gil-Gómez, J.A. 198 Giosidou, A. 198 GIS 7–9, 20, 24, 26, 34–6, 40, 58, 60, 101, 108, 129–44, 223 Glanz, K. 82 Glasgow, R.E. 82 global positioning systems see GPS glucose 15, 74, 112, 160, 177
Glynn, L. 126 goal seing 32, 46–9, 58–60, 71, 76, 91, 149, 159 Godin, G. 82 Goessens, B.M. 82 Gonsalves, L. 217 Goode, A. 43 GPS 7–9, 19–20, 24, 26, 29, 34–6, 38, 40, 58, 60, 108, 113, 129–44, 150–1, 192, 205, 223, 228, 236–7, 240 Graham, D.J. 23 Graham, M. 43, 217 graphic information systems 7, 129 Greene, J. 102 Grim, M. 82 GT9X 10, 157 Gutman, M. 242 Guy, S. 199, 232 Haerens, L. 82 Hales, S.B. 43 Hallal, P. 126 Hamel, L. 43, 83 Hammond, J. 199 Hands, B.P. 44 Handy, S. 23 Hanjagi, A. 144 Hansen, L. 217 Hargreaves, E.A. 83 Harries, T. 126 Hartman, S.J. 83 Harvey-Berino, J. 83 Hasson, R. 44 Hausenblas, H.A., 64 Havey, M.L. 163
health behaviors 7, 9–10, 30, 33, 50, 53–4, 78, 80, 96, 100–1, 110, 112, 118, 120, 125 Health Insurance Portability and Accountability Act 223 health promotion 3–4, 9, 11, 34–6, 38, 46, 53, 55, 69, 95, 107, 110, 114–15, 129–31, 133, 136, 138–40, 142, 144, 152, 159, 161, 166, 169, 173, 179, 205–6, 221, 223, 225, 232, 234, 239 health wearable devices 9–10, 18–19, 26, 33–4, 58, 62, 125, 148–9, 152–64, 224, 235–6, 239 Healthy People 2020 77 heart rate monitors 19, 29–30, 240 Hebden, L. 126 Herz, N.B. 199 Heyward, V.H. 23, 44 Hiey, A.M. 163 Hills, A.P. 163 Hillsdon, M. 144 Hilton, C.L. 199 Hirs, J. 144, 242 Hirvensalo, M. 145 Hoffmann, C.P. 217 Hohepa, M. 64 Holmes, J. 199 homeostasis 15 Hong, A.R. 23 Hood, L. 126 Hsu, J.K. 199 HTC 11, 38, 204 Huberty, J. 83 human body movement 14, 29 Humpel, N. 63 Hung, J.W. 199 Hurkmans, E.J. 83 Imam, B. 199 Instagram 88 Institute of Health Informatics 7, 107
Intercontinental Marketing Services 7, 107 Internet Gaming Disorder 227 intrinsic motivation 50–1, 59, 173, 181 iPad 7, 106 iPhone 106, 152, 192 Isaac, J. 44, 217 iStepLog 33 Jago, R. 83 Jakicic, J.M. 44 Jawbone 10, 34, 131, 150, 152, 155–60, 230 Jennings, D. 145 Jepson, R.G. 103 Jeromin, F. 232 Jobe, J.B. 66 Johnston, R. 145 Jones, L.W. 103 Joseph, R.P. 83 Just Dance 20–1, 27, 42, 165, 169 Kantomaa, M.T. 103 Karvinen, K.H. 103 Kaushal, N. 103 Kempton, T. 44, 145 Kervin, L. 126 Kessler, R.C. 103 Khaylis, A. 103 Kim, B.R. 217 Kim, E.K. 199 Kinect 11, 20, 22, 27, 42, 165, 169, 175, 177, 185 King, A. 44, 65, 233 Kirtland, K. 145 Kirwan, M. 44, 126
Kohl III, H. 23 Kokkinos, P.F. 23 Kramer, A. 199 Krenn, P. 24, 145 Krishnamurthi, R.V. 127 Kumanyika, S.K. 103 Kvedar, J. 126, 233 Laausse, R.G. 83 Lamkin, P. 24 Lange, B. 199 Late Life Function and Disability Index 186 Leahey, T.M. 103 LeBlanc, A.G. 24, 199 Ledger, D. 163 Lee, J.M. 163 Lefebvre, R.C. 103 Levasseur, M. 200 level of perceived exertion 178, 188 Lewis, B.A. 65, 84 Liang, Y. 200 Liao, T. 217 Lieberman, J. 217 Ligge, L. 163 light PA 17, 19, 76, 91, 110, 157 lipoprotein 15, 159 Liu, J.K. 217 Loe, E.A. 65 locomotor skills 176 Lower Extremity Functional Scale 186 Lowood, H. 24 Lowry, B. 103 Lu, A.S. 200
Lubans, D. 126–7 Lwin, M.O. 200 Lyons, E.J. 163 MacLeod, H. 24, 145 Macutkiewicz, D. 44, 145 Maddison, R. 200 Madsen, K.A. 200 Magoc, D. 84 Maher, C.A. 103 Mailey, E.L. 84 Malone, J. 24, 145 Maloney, A.E. 200 Mammen, G. 24 Man, D. 217 Manios, Y. 82 Manzi, V. 145 Manzoni, G.M. 217 MapMyFitness 19–20, 22, 59, 133, 236, 240 Marcus, B. 84 Marks, J.T. 84 Marti, A.C. 200 Martin, C. 127 Martin, J.J. 65 Martin, N. 127 mass media 27–8, 215 Mahews, C. 145 Maila, E. 127 Matzka, M. 24 McCann, R.A. 218 McCarthy, H. 200 McEwen, D. 44, 218 McLaren, L. 65
Mears, D. 200 Meetup 97 Mehta, P. 84 Melton, B. 44, 163 mental disorders 16–17, 91, 213, 226 mental health 16–17, 78, 91, 213 Merant, G. 103 Mestre, D.R. 218 metabolic adaptations 16 METs 157, 167, 178, 180, 224 Mhatre, P.V. 200 mHealth 7, 10, 91, 106–25, 224–6, 229–30 Mialiszyn, D. 218 Miie, S. 163 Microso Bands 149 Middelweerd, A. 103 Miller, H. 145 Miller, K.J. 218 Mio 151 Mohnsen, B. 218 Montfort, N. 24 mood 16–18, 91, 99, 186, 188, 192, 226 Mooney, R.P. 65 Moore, L.V. 163 Morgan, J.P. 84 Morina, N. 218 Mossberg, W.S. 24 motivation 19, 29, 37, 49–53, 59–60, 79–80, 91, 100, 120–1, 137, 142, 153–4, 158, 173, 181, 183, 188, 206 Motl, R.W. 104 musculoskeletal health 16 Myers, J. 24
MySpace 5, 88, 98 Nakhasi, A. 104 Napolitano, M.A. 84, 104 National Council on Aging 112 National Geographic Society 8, 129 Nelson, T. 104 Nguyen, D. 146 Ni Mhuru, C. 201 Nield, D. 163 Nielsen, R. 146 Nigg, C. 24 Nilanont, Y. 44 Nilsagard, Y.E. 201 Nintendo 11, 176, 178, 185, 210 normal weight populations 167, 169, 178 Norman, G.J. 84 Nursing Home Physical Performance Test 186 obese populations 100, 168, 177–9 obesity 13, 18, 26, 29, 88, 93, 107, 110–12, 114–15, 165, 167, 179, 196 object control skills 176 Ocular Ri 11 Ogden, C. 85, 127 Okazaki, K. 85 older adults 79, 112–13, 117, 131, 143 Oliver, M. 146 online social media 26, 30–2, 58–60, 62, 88–9, 91–3, 95–01, 235 Opris, D. 218 Optale, G. 218 Oreskovic, N. 146 Orsega-Smith, E. 201
osteoporosis 16, 29 PA behaviors 18, 20, 33, 37, 46, 53, 55, 56–8, 60–1, 94, 152, 234 PA contexts 19, 47, 49, 51, 53, 56 PA correlates 55, 58 PA determinants 47, 57–8, 62, 77, 240 PA interventions 6, 16, 19, 21, 28–33, 35, 46, 49, 51, 69–80, 89–93, 95, 97–101, 114, 116, 120, 130, 133, 135, 142–3, 159–60, 165, 170, 174, 183, 226, 235, 237 PA levels 18–19, 29, 49, 51, 58–9, 61, 72, 75–6, 78–80, 93, 112, 115, 132, 142–3, 153, 170, 172, 236 PA measures 73, 101, 137, 153 PA recommendations 46, 69, 80, 153 Papandonatos, G. 65 Pasco, D. 24, 44, 218 Pate, R.R. 201 Patel, M.S. 164 patients 73–5, 90–1, 111–12, 116–17, 119–20, 123, 125, 142, 149, 153, 159, 185–6, 188–90, 206, 209, 210–13, 215, 225, 230, 232 Paen, M. 127, 146, 223 Paw, M.C.A 65, 201 pedometers 9, 19, 26, 29–30, 80 Pekmezi, D.W. 85 Pellegrini, C.A. 85 Peng, W. 201 Penko, A.L. 201 perceived behavioral control 52–3 Perrin, A. 44, 85, 104 Personal Activity Location Measurement System 240 Petrella, R. 127 Pew Resear Center 5, 32, 106, 111–12, 133, 142, 149, 241 Physical Activities Guidelines Advisory Commiee 15, 17 physical activity and health 3, 18, 26–41, 67, 221 physical education 30, 39, 114–15, 171–3, 206–7, 209, 229, 237
physical environmental factors 57 physical functioning 166, 177, 185, 187–8 physical rehabilitation 209–11 Pietrzak, E. 85 Pipe, A.L. 85 PlayStation 11, 38, 176, 185, 204 Plsek, P.E. 65 Pluino, A. 201 Poirier, J. 85 Pokémon Go 11, 13, 35, 38, 41, 106, 115, 133–4, 190–2, 195, 205, 216, 227–8, 237–8 Polar 19, 151, 159 Pompeu, J.E. 201 Pope, Z. 65, 201 Portnoy, D.B. 85 Powell, A.C. 164 Powers, M.B. 218 practical implications 80, 89, 99, 107, 124, 130, 143, 161, 165–6, 168, 193, 215 presool ildren 14, 176 Proaska, J.O. 65 igg, R. 146 Rabin, C. 85 Rahman, S.A. 201 Ranard, B. 146 randomized controlled trials 28, 73, 76, 80, 109, 114, 169, 174, 177–8, 180–1, 186–7, 195 Rawassizadeh, R. 24 Real Strike 191 Reid, R.D. 85 Reilly, J. 127 relatedness 51 reliability 27, 35, 121, 125, 131, 137, 142, 153, 224, 226, 228, 230 Rendon, A.A. 201
Resnicow, K. 65 Riardson, J. 127, 233 Rideout, V. 85 Rizzo, A.A. 218 Robinson, J. 202 Robles-García, V. 218 Rodgers, W.M. 66 Rodriguez, D. 44, 146 Rose, F.D. 218 Rosenberger, M.E. 164 Ross, R. 24 Rosser, J.C. 219 Rovniak, L.S. 66 Ruotsalainen, H. 104 Ryan, R.M. 66 Salem, Y. 202 Sallis, J.F. 66, 85, 144, 164 Samsung 11, 38, 148, 152, 204 Saposnik, G. 44, 219 Sarwar, M. 127 Sasaki, J.E. 164 Sato, K. 202 Slaer, B. 146 Sneider, K.L. 104 Seelye, A.M. 219 Seidl, D. 146 self-determination 50–1, 58–9, 109 self-efficacy 235–6, 240 self-esteem 17–18, 40 self-monitoring 28, 32, 58, 71, 91, 93, 95–6, 149, 152, 160 Senior Fitness Test 186 SenseCam 19
Serino, M. 202, 233 SHAPE America., 206–7 Sheehan, D.P. 202 Sims, J. 202 Sirard, J.R. 45, 164 situational motivation 50 sleep 10, 17, 33, 34, 119–20, 148, 150–3, 155–8, 160–1, 239 smartphone 3, 5–7, 11, 18–20, 30, 32–3, 41, 59, 106–7, 110–13, 117, 124, 133, 137, 139, 142, 152–3, 155, 160, 223, 227, 230, 236, 239–40 Smith, J. 146 Snapat 88 Social Cognitive eory 47–8, 55–6, 58–60, 62, 78, 100, 109, 121, 234 Social Ecologic Model 42, 46, 56–60, 62, 235 social environmental factors 56, 60 social media 5–6, 19–20, 26, 30–2, 40, 58–60, 62, 88–101, 154–5, 223, 235, 237 social networking 98, 101, 237 social support 28, 55–7, 59–60, 70, 77, 89, 91, 94–7, 100, 109, 111, 148, 173–4, 235 Sorita, E. 219 SpecTrek 191 speed 40, 135–6, 150–1, 177–8, 208–11, 141 sports 5, 11, 19, 33–5, 39, 48–9, 134–5, 140, 143, 150–1, 157, 165, 169, 172, 180–2, 194–5, 206, 208–9, 240 Staiano, A.E. 202 stationary cycling 30 step counts 71, 89, 94, 109, 111–12, 148, 152–4, 156, 160–1 Stigma 225, 229 Stokols, D. 66 Stonero, G.L. 25 Strekalova, Y. 45 Stuey, M. 127 Subramanian, S.K. 219 Sun, F. 127 Sun, H. 45, 202
Superhero Workout 191 Suon, S. 66 systematic review 27, 54, 73, 79 Szturm, T. 202 Tarnanas, I. 219 Teen Environment and Neighborhood (TEAN) study 132 Temple Treasure Hunt Game 191 Terrel, K. 25 eory of Planned Behavior 48, 51–3, 58–60 omas, J. 104, 128 ompson, W.G. 164 orndike, A. 45, 164 Tomnay, J.E. 219 TomTom 34, 150, 154–5 Toscos, T. 128 Touger-Deer, R. 86 Touloe, C. 202 Transtheoretical Model 48, 53–4, 58, 60, 62, 100, 234 Troiano, R. 128 Troped, P. 45 Trost, S.G. 202 Tsai, C. 128 Tuer, P. 25 Tudor-Loe, C. 45, 104 Turing, A. 25 Turkle, S. 104 Twier 5, 20, 30, 88–9, 96, 101, 154, 236 type 2 diabetes 18, 26, 73, 111–12, 119, 141 U.S. Department of Health and Human Services 13, 57, 109 U.S. Government Information 8, 25
validity 10, 20, 29, 35, 101, 121, 125, 131, 134, 137, 142, 153, 156, 158, 179, 224, 226, 228, 230 Vallerand, R.J. 66 Van Biljon, A. 202 van den Berg, M. 86, 203 Van der Weegen, S. 86 Van Diest, M. 203 Van Kessel, G. 104 Van Loon, L.J. 25 Vandelanoe, C. 86 Vella, C.A. 25 Verheijden, M.W. 66, 104 Verhoeven, K. 203 Vernadakis, N. 203 Vernooij, J.W. 86 Verwey, R. 128 vigorous PA 56 virtual reality 10–12, 21, 38–9, 41, 58, 106, 191, 204–15, 223, 227 VO2 max 15–16, 180 Voorhees, C. 66 Wadsworth, D.D. 86 Wagener, T.L. 203 Wang, J.B. 45, 164 Wanner, M. 86 Watson, A. 86 Waerson, T. 128 Webber, S. 147 Weat 88 weight loss 31, 34, 50, 72, 74, 77, 80, 88–9, 91, 94, 96, 100, 110–13, 116, 119, 155–6, 160, 167, 180–1, 225 weightliing 30 Weinberg, R.S. 66 Weinstein, P.K. 86
Weiss, P.L. 219 Welk, G. 203 West, D. 128 West, R. 66 Wheeler, B. 45, 147 Whiemore, R. 87 Whiman, G. 203 Wii 11, 42, 165, 169–70, 175–8, 183, 185–7, 210 Wikis 5 Williamson, D.A. 87 World Health Organization 14 World Wide Web 5, 28, 70 Yang, W.C. 219 Yavuzer, G. 203 Yip, B.C. 219 Yoo, H.J. 87 young adult 30, 36, 39, 75, 78–9, 91, 93, 110–11, 149, 168–9, 183–4, 187, 195 youth 29, 35, 76–7, 79, 109–10, 114–15, 131–3, 141, 167–8, 183, 187–8, 190, 195, 206–7, 229 YouTube 30, 32, 118 Zabinski, M.F. 105 Zaaria, S. 87 Zeng, N. 203 Zhang, J. 45, 105 Zhang, T. 66 Zhu, W. 25 Zombies Everywhere 191 Zombies, Run! 11, 38, 109, 115, 190–2, 205 Zutz, A. 87 Zygouris, S. 219