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Aligning digital technology to support motivation, physical activity, and sports

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Aligning digital technology to support motivation, physical activity, and sports

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Aligning Digital Technology to Support Motivation, Physical Activity, and Sports

by
Emily Robinson Masters

Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education

May 2022

© Copyright by Emily Robinson Masters 2022
All Rights Reserved

The Committee for Emily Robinson Masters certifies the approval of this Dissertation

Bryant Adibe
Carrie LeCrom
Helena Seli
Patricia Tobey, Committee Chair

Rossier School of Education
University of Southern California
May 2022

iv

Abstract
One in four adults in the United States does not meet the recommended weekly physical activity
(PA) of 150 minutes of moderate-intensity aerobic activity. As a result, the lack of PA is one of
the more pressing public health problems of the 21st century. This study aimed to evaluate how
motivation plays a role in physical activity (PA) in adults, focusing on participation in organized
sports (OS) and digital health technology. Self-determination theory (SDT) is a significant
theoretical approach in physical activity motivation research, and this study uses a quantitative
approach to look at motivation and PA in adults. The study was based on a survey design,
including the Motives for Physical Activities Measure-Revised (MPAM-R) instrument,
measuring motivational regulation, and analyses were conducted with a sample of 765 adults.
ANOVA analyses, correlations, and post hoc Tukey-Kramer were used to present the differences
between all study variables. The results showed significant differences between the motivation
and groups regarding age, gender, ethnicity, daily PA, physical limitations, sitting habits,
participation in OS or PA in youth, current participation in OS or PA, digital health technology
use, and vitality. There were significant differences in motivation, primarily intrinsic and
integrated regulation, based on physical activity and digital technology. The findings reinforce
the basics of SDT and how the theory supports motivation for PA. Data analyses from the study
found that participants valued the motivators of fitness, appearance, and interest/enjoyment
relatively higher than those of competence and social. Findings from this study could aid health
practitioners, clinicians, researchers, or developers in understanding how the data can support
engagement in PA by focusing on motivators.

Keywords: self-determination theory, digital health, physical activity, motivation,
artificial intelligence, machine learning, AI/ML, digital health technology, organized sports,
vitality, quantitative survey.

vi

Dedication
To the BAMILY I chose and the FAMILY I was given. How do I even begin to express my
feelings, gratitude, and love? You came into my life, and I had no idea what it meant. Some
people wait a lifetime, and others have them for minutes. Health is wealth. Life is to love, live,
and give. So here's to what's ahead, the stories, the adventures, the lessons, and our family. And
B, you are the B in BAM. A family that plays together stays together!

vii

Acknowledgements
Each time I have had the opportunity to work with dynamic leaders, I take a part of them
with me, and this is a small list of some of those. This journey began in my home, where I
experienced firsthand the benefits of a healthy lifestyle, and the foundation started with the
leadership of my parents. They created an environment that fostered physical activity, lifelong
learning, creativity, kindness, healthy eating, and numerous habits that are ingrained in me today.
In addition, my brother and sister continue to inspire me, we are each other's most celebrated
advocates, and I wouldn't be where I am today if they weren't on my team. My dissertation
committee acted as coaches, spectators cheering on the side, pacers, support crews, and more
than I can even put into words. As a result, I have a new understanding of physical activity,
motivation, and research, and I look forward to what's ahead. The journey has felt comparable to
training for a marathon. Dr. Patricia Tobey, your encouragement to go beyond my comfort zone
will inspire me for years to come. Dr. Bryant Adibe, thank you for Dr. A's corner, the
soundtracks, and the leadership lessons. Dr. Helena Seli, thank you for your motivation, support,
and all you did to make my work stronger. Dr. Carrie LeCrom, I had no idea how influential it
would be to have you on this journey; thank you! As my unofficial fifth committee member, Dr.
Marc Pritchard, I would not be where I am today without your insight and leadership. Dr.
Michael Deer, I am grateful for your friendship, wisdom, support, and showing me what it is to
be part of the Fight On Family. Jennifer McElhinney, my first boss when I taught fitness classes
in the 1990s, continues to be a transformational leader in my life with her passion for health and
wellness, personal connection with others, and motivating the community. Kathy Hawkes-Smith
showed me how teamwork brings an event to life and how to personalize each experience. Dr.
George Davis reminded me to follow my path to serve. Nan Stockholm Walden and Bobbie

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Schorr showed me how to be a dynamic female leader in fields that men may dominate. As a
Peace Corps Volunteer, Patrick Henderson and my community partners taught me to have
confidence in my skills and learn what I do not know from others. Mike Ellis taught me to own
my weaknesses and be aware of how they can become my strengths. Greg Burton supported me,
and others, in possibilities that were just over the horizon. Stephen Phillips taught me that you
could catch the ferry if you miss the boat. Canyon Ranch Leadership and my fellow Ranchers
continue to reinforce my focus on health. Dr. Ken Blanchard, my mission is more defined
because of your time and attention. Dr. Cathy Greenberg and Dr. Dan Baker sparked my passion
for writing and how you can positively impact others. Patsy Arnett taught me that leaders would
see strengths in others, provide them with opportunities, and support them to grow past their
current roles. Joel Erb taught me that innovative thinking could change work cultures and
businesses. JBL taught me to feel comfortable with failing and how it steers you on your path,
and vision and passion are one thing; it's another when shared with the right people. SMGF, you
taught me that it takes teamwork to make the dream work and create time for the things you love
in your life. The Martin Agency leadership and my peers taught me that we are all in this
together, all work is significant, and taking risks pays off. Chelsfield taught me that leadership
lessons are universal and building a legacy is all about the people. Warren Gieck, taught me to
see data and explore the stories. Dr. Guadalupe Montano and Dr. Rodger Devine, having you on
my side helped strengthen my words. To My Sunday Scholarly Soul Sisters, your leadership
shaped me in ways that will be a part of me for years to come. I want to acknowledge the
mentors, Cohort 15 peers, professors, industry leaders, coaches, friends, and family that played a
pivotal role in my leadership journey. I do not know how to put the rest in words, so I will use
my future work to express my appreciation for those on this journey.

ix

Table of Contents
Abstract .......................................................................................................................................... iv
Dedication ...................................................................................................................................... vi
Acknowledgements ....................................................................................................................... vii
List of Tables ................................................................................................................................ xii
List of Figures .............................................................................................................................. xiv
List of Abbreviations .................................................................................................................... xv
Chapter One: Overview of the Study .............................................................................................. 1
Context and Background of the Problem ............................................................................ 2
Purpose of the Study ........................................................................................................... 3
Significance of the Study .................................................................................................... 4
Overview of Theoretical Framework and Methodology .................................................... 5
Definition of Terms ............................................................................................................. 7
Organization of the Study ................................................................................................. 11
Chapter Two: Review of the Literature ........................................................................................ 12
Past to Present: Medicine, Health, Wellness, and Sports ................................................. 13
Health Technology ............................................................................................................ 17
Self-Determination Theory and Motivation ...................................................................... 20
Motivation Continuum ...................................................................................................... 25
Motivation and Physical Activity ..................................................................................... 31
Conclusion ........................................................................................................................ 40
Chapter Three: Methodology ........................................................................................................ 41
Research Questions ........................................................................................................... 41
Overview of Design and Methodology ............................................................................. 42
Population and Survey Sample ......................................................................................... 43

x

Survey Link and Recruitment ........................................................................................... 43
Data Collection Procedures ............................................................................................... 44
Data Analysis .................................................................................................................... 49
Credibility and Trustworthiness ........................................................................................ 49
Ethics ................................................................................................................................. 50
Limitations and Delimitations ........................................................................................... 51
Summary ........................................................................................................................... 54
Chapter Four: Findings ................................................................................................................. 55
Survey Methodology ......................................................................................................... 56
Data Analysis .................................................................................................................... 57
Participant Stakeholders .................................................................................................... 57
Preliminary Analyses ........................................................................................................ 58
Results Pertaining to Sample ............................................................................................ 62
Interest/Enjoyment as a Motive for Physical Activity in Adults ...................................... 64
Competence as a Motive for Physical Activity in Adults ................................................. 68
Appearance as a Motive for Physical Activity in Adults .................................................. 72
Fitness as a Motive for Physical Activity in Adults ......................................................... 75
Social as a Motive for Physical Activity in Adults ........................................................... 78
Youth PA, Current PA, and Digital Health Technology Use ........................................... 82
Vitality, Youth PA, and Digital Health Technology Use ................................................. 82
Relationship Between Motives ......................................................................................... 83
Summary of Findings ........................................................................................................ 83
Chapter Five: Recommendations .................................................................................................. 88
Recommendations for Practice to Address Digital Health Tech and Motivation ............. 89
Implementation and Change Management ..................................................................... 110

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Strengths and Weaknesses of the Approach ................................................................... 117
Recommendations for Future Research .......................................................................... 117
Limitations and Delimitations ......................................................................................... 121
Conclusions ..................................................................................................................... 121
References ................................................................................................................................... 123
Appendix A: Demographic Questions ........................................................................................ 152
Appendix B: Motives for Physical Activities Measure-Revised (MPAM-R) and Scoring ........ 155
Figure C2 Examples of Targeted Post ........................................................................................ 158
Appendix D: Demographic Subcategories .................................................................................. 160
Appendix F: General Physical Activities Defined by Level of Intensity ................................... 171
Appendix H: Information Sheet for Exempt Research ............................................................... 181

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List of Tables
Table 1: MPAM-R Results for Question 13: “I Engage in Physical Activities, Sports, and
Exercise Because” 60
Table 2: MPAM-R Results for Motive for Physical Activities 62
Table 3: Summary of Inferential Findings Organized by Research Question 86
Table 4: Summary of Motivation Recommendation and Guiding Data 91
Table 5: Summary of Motivation Recommendation 96
Table 6: Summary of Sport Recommendation and Guiding Data 97
Table 7: Summary of Sport Recommendation 100
Table 8: Summary of Vitality Recommendation and Guiding Data 102
Table 9: Summary of Vitality Recommendation 105
Table 10: Summary of Multigenerational Family Recommendation 107
Table 11: Summary of Multigenerational Family Integration Recommendation 110
Table D1: Demographic Subcategories and Abbreviations for Analysis 160

Table D2: Descriptive Statistics and ANOVA - Combined Variables 161
Table D3: Descriptive Statistics and ANOVA - Interest Enjoument 162
Table D4: Descriptive Statistics and ANOVA - Competence 163
Table D5: Descriptive Statistics and ANOVA - Appearance 164
Table D6: Descriptive Statistics and ANOVA - Fitness 165
Table D7: Descriptive Statistics and ANOVA - Social 166
Table D8: ANOVA - Current Physical Activity 167
Table D9: ANOVA - Youth Physical Activity 167
Table D10: ANOVA - Alert 167
Table D11: ANOVA - Energized 168
Table D12: Pearson's Correlation Coefficients 168

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Appendix E: Theoretical Framework Alignment Matrix (Quantitative) 170

Table F1: General Physical Activities Defined by Level of Intensity 171

xiv

List of Figures
Figure 1: Types of Motivation 27
Figure 2: Basic Psychological Needs Self-Determination Theory: Human’s Three Basic
Needs 30
Figure 3: Summary of Survey Link and Recruitment 44
Figure 4: Summary of Survey Access and Engagement 45
Figure 5: Kotter’s Eight-Step Change Model 89
Figure 6: Self-Determination Theory Features 95
Appendix G: Informed Consent for Research 178

xv

List of Abbreviations
AI Artificial intelligence
ACSM American College of Sports Medicine
CDC Center for Disease Control and Prevention
HHS Department of Health and Human Services
IoT Internet of things
mHealth Mobile health
ML Machine learning
OSG Office of the Surgeon General
OS Organized sports
PA Physical activity
SDT Self-determination theory
WHO World Health Organization
WFT Wearable fitness tracker

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Chapter One: Overview of the Study
One in four adults in the United States does not meet the recommended weekly physical
activity (PA) of 150 minutes of moderate-intensity aerobic activity (CDC, 2020; Piercy et al.,
2018). As a result, lack of PA is one of the more pressing public health problems of the 21st
century, as insufficiently active people have a 20% to 30% increased risk of death than
sufficiently active people (WHO, 2020). In addition, low PA levels can contribute to heart
disease, type 2 diabetes, some kinds of cancer, and obesity (CDC, 2020; Nieman et al., 2019).
The COVID-19 pandemic has revealed significant health outcomes well past the virus itself,
including a decline in PA (Flanagan et al., 2021). The U.S. Department of Health and Human
Services (HHS) explains how adults should engage in at least 150 to 300 minutes a week of
moderate-intensity aerobic activity for increased health benefits, while exercising for more than
300 minutes a week can improve health even further (HHS, 2018). Moderate-to-vigorous PA has
an immediate positive impact on mental health, stress, and sleep. At the same time, regular PA
provides significant chronic disease prevention through brain health, heart health, cancer
prevention, healthy weight, bone strength, balance, and coordination (Jones et al., 2020; Nieman
et al., 2019; Schuch et al., 2018).
Recent research has revealed the health benefits of PA and how Americans can more
easily achieve them. The latest scientific evidence shows that PA has many health benefits
independent of other healthy behaviors, like good nutrition (HHS, 2021). For example, new
evidence notes that PA may help manage more preexisting health conditions, decrease pain for
those with osteoarthritis, reduce disease progression for hypertension and type 2 diabetes, reduce
symptoms of anxiety and depression, and increase cognition for those with dementia, multiple
sclerosis, ADHD, and Parkinson’s disease (ODPH, 2021). Thus, PA’s influential impact on
2

mental health and social well-being are immense, affecting every facet of life; innovative
approaches to foster motivation will continue to play a critical role in increasing PA and the
effects on individuals’ health (Quested et al ., 2021; Reed, 2021). This study addresses the
impact of previous participation in OS or PA, motivation on adults’ PA, and their use of digital
technology. This problem is critical because PA is a choice; researchers need to identify how to
support people in making healthy behavior choices and thriving when engaging in those
behaviors.
Context and Background of the Problem
Lack of PA comes with high health or financial costs, is associated with $117 billion in
health care costs every year, and contributes to about 10% of premature mortality (HHS, 2019).
In addition, regular PA favorably influences seven of the 10 most common chronic diseases,
though nearly 80% of adults do not meet the recommended guidelines for aerobic and muscle-
strengthening activity, and only about half meet the guidelines for aerobic PA (CDC, 2021).
Nevertheless, regular PA is one of the most effective actions that adults can take to improve their
health, and the physical activity guidelines for Americans are clear: PA promotes growth and
development, may allow people to feel better, function better, sleep better and reduce the risk of
a large number of chronic diseases (HHS, 2019). Following the COVID-19 pandemic, the
presidential executive order reestablished the President’s Council on Sports, Fitness, & Nutrition
to expand national awareness of mental health’s importance pertains to physical fitness and
nutrition (National Archives and Records Administration, 2021). Furthermore, comprehensive
and coordinated strategies will reverse the high rates of inactivity-related chronic diseases and
low rates of PA (HHS, 2019).
3

There are barriers to choosing even the simplest physical activity like walking (HHS,
2015), and developing health promotion programs that support PA requires comprehensive
planning and theories like self-determination theory (SDT; Deci et al., 2000; Deci et al., 2013;
Ryan et al., 2017a; Ryan et al., 2000a) may guide this process as they work with individuals,
groups, and communities (Simpson, 2015). Self-determination theory is a theory of human
motivation and personality that suggests that when fulfilling needs, people become self-
determined (Deci & Ryan, 1985a, 2000; Ryan & Deci, 2000a, 2017b). For example, motivation
can influence how individuals are physically active, stay involved in fitness programs, and
experience physical and mental health benefits (Quested et al., 2021). In addition, advances in
technology allow feedback and motivational support through text in apps or wearable technology
devices (Kang et al., 2021). Unfortunately, the development of behavioral interventions for PA
often does not offer systematic approaches, and, when reported, inadequate descriptions of the
components frequently become a barrier for replication and scaling up (Bengoechea et al., 2021).
Nevertheless, the data about the health benefits of regular PA are abundant, there are countless
resources, and research continues to seek insight into what motivates people to engage in PA.
Purpose of the Study
Lack of PA in adults outlines a complicated and significant problem in America.
Examined through SDT (Ryan & Deci, 1997, 2000a, 2017a), this study aims to evaluate the
degree to which motivation plays a role in PA in adults with a focus on organized sports (OS)
and digital health technology. While there are multiple stakeholders among physically active
adults, this study focuses on all adults over age 18, whether they self-identify as physically active
or intend to engage in PA. The quantitative analysis focuses on predictors and correlations
between adults and previous participation in OS or PA in their youth. It also examines why
4

people exercise regularly, their perceived competence, and their vitality. Furthermore, this study
seeks to discover the role of motivation and technology in PA among adults. The research
addresses the following questions:
1. Are there differences in why people are physically active based on prior participation
to OS/PA and current use of digital health technology?
2. Are there differences in current levels of PA based on prior participation to OS/PA
and current use of digital health technology?
3. Are there differences in participants’ perceived vitality based on prior participation to
OS/PA and current use of digital health technology?
4. What variables are the highest predictors of PA and digital health technology use in
adults?
Significance of the Study
Self-determined motivation is a good predictor of PA, providing an opportunity to focus
on how motivation may offer more central advantages and digital strategies to overcome barriers
to increase PA (Kang et al., 2021). Digital health technology can collect health and well-being
data outside of laboratory settings to promote PA and motivation strategies. Wearable and
mobile devices offer benefits like low-cost, objective PA measurements, relevant data for
assessment, and scalable behavior monitoring in vast populations (Perez-Pozuelo et al., 2021).
One of the main advantages of artificial intelligence (AI)/machine learning (ML) is its ability to
learn from real-world use and experience to improve its performance (U.S. Food & Drug
Administration, 2019).
Sports play a unique role in American culture, with more than 300 million youth and
adults participating in some form of sports, including traditional OS and free or casual and
5

unstructured play (Physical Activity Council, 2021). However, the Robert Wood Johnson
Foundation (RWJF, 2015) found that although almost three in four adults played sports when
they were younger (73%), only one in four (25%) continue to participate in sports as adults. In
2018, the National Health Interview Survey reported that 53.3% of adults aged 18 and over met
the physical activity guidelines for aerobic physical activity and only 23.2% for both aerobic and
muscle-strengthening activity (Centers for Disease Control and Prevention, n.d.). In addition,
sports are widely seen as a platform for development on many fronts, encouraging overall health
and contributing specific health benefits associated with increased PA.
Poor health and preventable deaths occur each year due to a lack of regular PA. Adults
should do at least 150 to 300 minutes (2 hours and 30 minutes to 5 hours) a week of moderate-
intensity aerobic activity, or 75 to 150 minutes (1 hour and 15 minutes to 2 hours and 30
minutes) of vigorous-intensity aerobic exercise throughout the week (HHS, 2018). The
equivalent combination of moderate- and vigorous-intensity contribute to the substantial health
benefits (HHS, 2018). Ideally, achieving or exceeding the upper limit of 300 minutes (5 hours) is
optimal (Rock et al., 2020). Unfortunately, inactivity tends to increase with age; women are more
likely to lead inactive lifestyles than men, and non-Hispanic White adults are more likely to
engage in PA than Hispanic and Black adults (Johns Hopkins, 2021). Therefore, despite the
many benefits of PA, levels of inactivity and their costs are high (American College of Sports
Medicine, 2015). Thus, harnessing AI/ML techniques in digital health technology development
may motivate adults across various populations to engage in PA.
Overview of Theoretical Framework and Methodology
The methodological approach used for this research study was quantitative through the
lens of SDT, which is one of the numerous models in contemporary behavioral science, offering
6

a framework for understanding the factors that support human motivation, well-being, and
healthy psychological and behavioral functioning (Ryan et al., 2017a; Ryan et al., 2000; Deci et
al., 2000; Deci et al., 2013). This study used SDT to examine conditions for intrinsic motivation
and subjective vitality, an indicator of mental and physical wellness (Ryan et al., 2017b; Ryan et
al., 2000; Deci et al., 2000; Deci et al., 2013). The theory assumes that all individuals have an
innate tendency for growth, self-integration, and psychological consistency (Markland et al.,
2005). First, the study reviewed the five distinct motives for physical activity: appearance, health
and fitness, social, competence, and enjoyment (Ryan et al., 1997). Next, the research examines
differences in PA levels based on prior exposure to OS and current use of digital health
technology to understand adults’ PA and motivation. Finally, in the perspective of SDT, the
study examined how PA can be innately rewarding, contributing to both happiness and
subjective vitality (Ryan & Frederick, 1997).
The methodological approach used for this research study is quantitative. A quantitative
approach was selected to endorse the view that psychological and social phenomena have an
objective reality independent of the study subjects, allowing the researcher to put distance
between themselves and subjects (Yilmaz, 2013). In addition, the quantitative methodology
assessed previous sports participation as a possible predictor for motivation to engage in PA and
improved well-being in adults. Finally, quantitative measuring and analysis determined the
relationships between variables within the SDT framework. Accordingly, quantitative analysis is
the preferred methodology as this research attempts to survey a substantial number of
participants to ascertain the relationship between adults’ PA, motivation, and sport. I chose not to
use qualitative methods because they would not allow for surveying large samples, thereby
limiting the ability to gain a broader perspective from participants. The quantitative design also
7

allowed for analysis of responses from multiple participants, hence determining connections and
statistical collection of data.
Definition of Terms
This section provides definitions of frequently used terms defined by the U.S.
Department of Health and Human Services (2018) used throughout the study and the dissertation
unless noted otherwise.
• Artificial Intelligence (AI): Algorithms that perform perceptual, cognitive, and
conversational functions typical of the human mind (Longoni et al., 2019).
• Big data: A collection of data elements whose size, speed, type, and/or complexity
require an attempt to use and discover new hardware and software tools to
successfully store, examine, and visualize the data (Amudhavel et al., 2015). Big Data
points to large, complex datasets exceed the capabilities of the traditional data
management system to store, manage, and process them (Yousef, 2021).
• Data mining: the ability to extract useful knowledge hidden in the large volume of
data by applying new techniques for discovering understandable patterns and
correlations from data and use it in making decisions and predicting likely outcomes,
such as association rule learning and regression analysis (Jothi et al., 2015).
• Digital health includes categories such as mobile health (mHealth), health
information technology (IT), wearable devices, telehealth and telemedicine, and
personalized medicine (U.S. Food & Drug Administration, 2020).
• Deep learning: Methods that allow a machine to be fed with large quantities of raw
data and to discover the representations necessary for detection or classification. Deep
learning methods rely on multiple layers of representation of the data with successive
8

transformations that amplify aspects of the input that are important for discrimination
and suppress irrelevant variations. Deep learning may be supervised or unsupervised.
Deep learning methods have been responsible for many of the recent foundational
advances in machine learning (LeCun et al., 2015).
• Exercise: A form of planned, structured, repetitive PA to improve health or fitness.
All exercise is PA, though not all PA is exercise (HHS, 2018).
• Health: A human condition with physical, social, and psychological dimensions;
positive health is a capacity to enjoy life and withstand challenges; it is not merely the
absence of disease. Conversely, negative health is associated with illness, and in the
extreme, with premature death (HHS, 2018).
• Intensity: The work performed or the magnitude of the effort required to perform an
activity or exercise. Light-intensity activity is non-sedentary waking behavior,
including walking at a slow or leisurely pace (2 mph or less), cooking activities, or
light household chores. Moderate-intensity activity includes walking briskly or with
purpose, mopping or vacuuming, or raking the yard. Vigorous-intensity activity
includes walking very fast (4.5 to 5 mph), running, carrying heavy groceries or other
loads upstairs, shoveling snow, or participating in a strenuous fitness class (HHS,
2018).
• Internet of Things (IoT): the point in time when more “things or objects” were
connected to the internet than people (Evans, 2011).
• Levels of physical activity: Describe how much regular aerobic PA a person gets.
Inactive is not getting any moderate- or vigorous-intensity PA beyond basic
movement from daily life activities. Insufficiently active is doing some moderate- or
9

vigorous-intensity PA but less than 150 minutes of moderate-intensity PA a week or
75 minutes of vigorous-intensity activity or the equivalent combination. This level is
less than the target range for meeting the key guidelines for adults. Active is doing the
equivalent of 150 minutes to 300 minutes of moderate-intensity PA a week. This level
meets the key guideline target range for adults. Highly Active is doing the equivalent
of more than 300 minutes of moderate-intensity PA a week. This level exceeds the
key guideline target range for adults. For the Qualtrics survey, minutes were
converted to hours (HHS, 2018).
• Machine learning (ML): A sub-discipline of AI, where computers programs
(algorithms) learn associations of predictive power from examples in data. Machine
learning is most simply the application of statistical models to data using computers.
Machine learning uses a broader set of statistical techniques than those typically used
in medicine. Newer techniques such as deep learning are based on models with less
assumptions about the underlying data and are therefore able to handle more complex
data (Panch et al., 2018).
• Mobile health (mHealth): software applications that run on mobile platforms, such as
smartphones and tablets, and are used to manage health and wellness (Food and Drug
Administration, 2019).
• Organized sports (OS): physical activities directed by adult or leaders and involve
rules, formal practice, and competition, including school and club sports (Logan et
al., 2019).
10

• Physical activity (PA): Any bodily movement produced by skeletal muscle
contraction that increases energy expenditure above a base or minimum level (HHS,
2018).
• Physical fitness: The ability to carry out daily tasks with vitality and alertness,
without undue fatigue, and ample energy to enjoy leisure-time pursuits and respond to
emergencies. Physical fitness includes several components: cardiorespiratory fitness
(endurance or aerobic power), musculoskeletal fitness, flexibility, balance, and
movement speed (HHS, 2018).
• Predictive analytics and predictive algorithms: Used to forecast what people are
likely to do and how to use those forecasts to decide how to treat people and use data
mining to develop tools to predict individual behaviors (Finlay, 2014).
• Sedentary behavior: Any waking behavior characterized by a low energy expenditure
while sitting, reclining, or lying (HHS, 2018).
• Strength: A health and performance component of physical fitness is the ability of a
muscle or muscle group to exert force (HHS, 2018).
• Wearables or wearable health devices: Technology that enables continuous
ambulatory monitoring of human vital signs during daily life (during work, at home,
during sport activities, etc.) or in a clinical environment, with the advantage of
minimizing discomfort and interference with normal human activities (Di Rienzo et
al., 2006).
• Wellness: Active pursuit of activities, choices, and lifestyles that lead to a state of
holistic health (Global Wellness Institute, 2020).
11

Organization of the Study
The dissertation presents five chapters outlining the information and research conducted.
Chapter One introduces adults’ lack of PA and the impact on their health, the structure of the
study, defines the fundamental terminology, presents the theoretical framework and the
conceptual framework. Chapter Two reviews the peer-reviewed literature on the topic relevant to
this research, including the scope of the study, technology, AI/ML, PA, sport, motivation, and an
individual’s overall health based on the theoretical and conceptual framework. Chapter Three
focuses on the study’s methodology to determine the participants’ selection, data collection,
analysis, and ethical considerations. Next, Chapter Four discusses the results obtained from data
analysis. Finally, Chapter Five provides solutions based on data and literature, outlines
recommendations, and suggests areas for future research regarding PA and motivation.
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Chapter Two: Review of the Literature
The purpose of this literature review is to present the reader with a general overview of
the progression of medicine, health, wellness, PA, sport, and digital technology. The focus on
one’s health has become increasingly popular; wellness and health are now topics in everyday
conversations. However, there is an inherent necessity for non-medication intervention in health,
sedentary lifestyles are increasingly prevalent with the COVID-19 pandemic, and modern
lifestyles discourage PA (Flanagan et al., 2021). In addition, we are facing a national mental
health crisis that could yield severe health and social consequences for years to come (APA,
2020). Despite growing recognition of the social determinants of health, disparities exist, and
unconscious bias resulting from unrecognized social privilege is one contributor to these
disparities (Holm et al., 2017). Technologies such as smartphones, web apps, and streaming
online at-home-fitness classes promote and encourage regular PA through digital devices. When
the applications are correctly programmed to improve their physical condition, they empower
individuals to acquire beneficial and lasting results (Silva & Alturas, 2021). As technology
continues to change, there are opportunities to improve and positively influence their
development. Traditional and emerging health behavior-change theories, like SDT, may be
applied as frameworks to help developers, users, and researchers grow their understanding of
technology’s influence on exercise behaviors and PA (Herrmann et al., 2021).
Chapter Two also presents the application to the research centered on technology, AI/ML
PA, sport, motivation, and an individual’s overall health based on the theoretical and conceptual
framework of SDT. The first part of this chapter gives a brief overview of medicine, health,
wellness, PA, sport, and digital technology. Next, there is a review of current health and wellness
13

trends in digital health. Then, a presentation of the theoretical framework of SDT. Finally, this
chapter introduces the combination of Digital Health Technologies with a SDT framework.
Past to Present: Medicine, Health, Wellness, and Sports
Today’s health focus has grown from the cultures as far back as the dawn of human
civilization, unfolding over the years from moving for hunting and gathering, preparing for war,
and being reactive to treating illnesses. Treating illnesses was based on plants or the biology in
the geographic locations of the cultures, and traditionally spiritual terms explained medicine with
no disparity between the various medical systems regarding failure or success of treatments
(Grundmann, 2011). Often praised as Western medicine’s father, Hippocrates proposed a new
schema for natural explanations of illness, where before 400 B.C., there were supernatural or
religious explanations to one’s health (Mantri, 2008). In 50 B.C., Roman medicine emphasized
disease prevention, adopting the Greek belief that illness was a product of diet and lifestyle
(Mantri, 2008). Finally, a renaissance era began where doctors wrote their findings in journals.
As a result, the practice of medicine has become more indoctrinated in science, seeking unified
explanations for diseases, and PA’s benefits became apparent (Claridge & Fabian, 2005).
Wellness and Sports
Wellness was introduced as the opposite of illness or the state of being well or in good
health in the mid-17th century (Zimmer, 2010). Before the industrial revolution, fitness could
carry out the day’s activities without undue fatigue; now, it is considered one’s ability to function
efficiently and effectively in work and leisure activities, be healthy, resist hypokinetic diseases,
and meet an emergency (Chaudhary, 2017). In 1939, Abner Doubleday, the disputed father of
baseball, introduced the popular pastime when Civil War soldiers on both sides played it as a
diversion (Baseball Hall of Fame, 2021). In 1938, the first Cricket club was formed in New York
14

City, and by 1943 Cricket and Archery Grounds opened in East Boston that included wrestling
events (Bunk, 2019). Yale formed the first boat club in 1843, Princeton formed the first baseball
club in 1859, Rutgers and Princeton played the first intercollegiate football game in 1869, track
and field gained popularity in the 1870s, and by 1876 colleges had created associations for
conducting inter-collegiate competition (Lewis, 1970). Wellness and PA influenced leisure
lifestyles, team participation, and the development of sport.
Exercise dates back to Olympic training in Greece, tai chi in China, and yoga in India.
Some of the earliest examples of fitness are immigrant soccer games in New Orleans in the
1850s and an orthopedic machinist’s book about his portable home gym, made of mahogany
boards, cords, weights, and pulleys (Bunk, 2019; Ernst, 1861). In the 1880s, the YMCA
launched as one of the world’s first wellness organizations, with its principle of developing
mind, body, and spirit in London (YMCA, 2021). Play movement and PA in communities began
to grow, and the first public pool in the United States was built in Brookline, Massachusetts, in
1887 (Olsen, 2007). In the early 1900s, Dr. John Harvey Kellogg, Director of the Battle Creek
Michigan Sanitorium, advocates a healthy diet, exercise, fresh air, hydrotherapy, and learning to
stay well (Markel, 2017). In 1919, the Mayo brothers and their wives established Mayo Clinic as
a not-for-profit dedicated to patient care, research, and education (Mayo, 2021). Medicine,
health, and wellness began to integrate into private and public health settings; clinics began to
integrate into communities to promote health.
From Treatment to Preventive and Integrative
Health, medicine, and wellness moved from treatment to preventive and integrative. In
1920, CEA Winslow explained that public health is “the science and art of preventing disease,
prolonging life, and promoting health through the organized efforts and informed choices of
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society, organizations, public and private communities, and individuals” (CDC, 2021). The first
school of public health opened at Johns Hopkins in 1916; a new focus on promoting healthy
lifestyles, researching diseases and injury prevention, and detecting, preventing, and responding
to infectious diseases began to play a critical role in medicine, health, and wellness (Johns
Hopkins University, 2021). In 1929, the Mayo Clinic hired Dr. Halbert Dunn as the first
biometrician to initiate the IBM coding system in medical statistics (Mayo Clinic, 2021). Jack
LaLanne, the grandfather of modern fitness and the spiritual father of the health movement,
opened the nation’s first health spa club in 1936 (Luther, 2011). In 1948, the World Health
Organization (WHO) constitution explained that health is a state of complete physical, mental
and social well-being and not merely the absence of disease or infirmity (WHO, 2021). Then in
the 1950s, Jack LaLanne created the first nationally syndicated exercise show on television,
proposing the then-radical idea that women, the elderly, and even the disabled should work out
to retain strength (Luther, 2011). By the 1960s, Dr. Halbert Dunn from the Mayo Clinic was now
known as the father of the wellness movement, using the statistics to show how an individual
moves forward, climbing toward a higher potential of functioning and maximizing the potential
of which the individual is capable (Dunn, 1961). As treatment to preventive and integrative
began to develop, the statistics were on the forefront of the of the shift in multiple industries.
Gyms and the Fitness Revolution
Traditionally, gyms were designed to train boxers or were sport-specific until 1965, when
Joe Gold opened Gold’s Gym in a small concrete room in Venice Beach (Gold’s Gym, 2015).
This launched a fitness revolution that would bring exercise and healthy living to the forefront of
American culture. By the early 1970s, the phrase “quality of life” was introduced to describe
specific environmental and humanistic values neglected by industrialized societies in favor of
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technological advancement, industrial productivity, and economic growth (Walton, 1973).
However, pumping iron and bodybuilding in the 1970s grew with a competitive focus, based on
aesthetics and not on health concerns (Murtha et al., 2021). The 1970s also embraced
individualism or communitarianism; running had previously been for personal physical activity,
previously pursued alone for recreation and fitness, community races grew, and the sport grew in
recognition and popularity (Haberman, 2017).
By the early 1980s, health promotion programs concentrated on workplace wellness,
focusing on individual behavior modification through support, information, and skills
development (Motalebi et al., 2018). The interest in bodybuilding, workout techniques, aerobics,
and fitness, in general, exploded in the 1980s (Andreasson & Johansson, 2014). Multi‐purpose
gyms chains were standard, offering a wide range of services. Corporate gyms emergence came
as health promotion linked corporate attempts to reduce their share of medical expenditures and
solve problems of productivity and absenteeism that plague the American workforce (Alexander,
1988). The 1980s brought Chuck Norris endorsed Total Gym, Bowflex, Dennise Austin, Jane
Fonda, racquetball, 20-minute workout videos, Jazzercise, Buns of Steel, Richard Simmons, Step
Aerobics (Canning, 2018). Then the 1990s fitness focused on Thighmasters, Ellipticals, Curves
women’s only gyms, NordicTrack, Spinning, eight-minute abs with the Ab Roller, Tae Bo,
Dance Cardio, Barry’s Boot Camp, and Zumba. PA is not only something for our health; it also
generates significant economic activity (Yeung & Johnston, 2021).
Leisure-Time Physical Activity
Following the PA promotion of the 1990s, the early 2000s continued to encourage
incorporating PA into daily lifestyles and increased participation in various leisure-time activities
(Office of Disease Prevention and Health, 2000). Although in 2001, the President’s Council on
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Physical Fitness and Sport proposed a uniform definition of wellness, referring to wellness as a
multidimensional state of being, describing the existence of positive health in an individual as
exemplified by quality of life and a sense of well-being, there is still not a uniform definition
(Corbin & Pangrazi, 2001). Then outside fitness classes became popular, along with kickboxing,
yoga, pilates, strip aerobics, circuit training, SoulCycle, Jillian Michaels, fitness gaming with
WiiFit, kettlebells, P90X, FitBit, TRX, and the rise of the affordable gym (Canning, 2018;
Zoldan, 2016). The 2010s resulted in workplace wellness, boutique gyms, shaping shoes,
ClassPass, barre workouts, High-intensity interval training (HIIT), fitness apps, functional
fitness, CrossFit, Orangetheory, fit experiences, Tough Mudder, Spartan Races (Zoldan, 2016).
The Global Wellness Institute estimates that recreational physical activities generated $828.2
billion in private-sector economic activity in 2018 (GWI, 2019). Moving beyond the current state
of the science on wellness in medicine will require expanding methodologies to include those
utilizing direct interaction with participants and moving beyond solutions informed by a disease‐
model approach (Bynum et al., 2021).
Health Technology
Exercise and fitness evolved from the image-focused 1980s into a focus on physical
health while shifting to hold promise in treating and managing mental health conditions,
particularly depression and anxiety (Smith & Merwin, 2021). The COVID-19 pandemic changed
the course of fitness to a shift in online training, wearable technology, bodyweight training,
outdoor activities, HIIT, virtual training, exercise is medicine, strength training with free
weights, fitness programs for older adults, and personal training for the top trends (Thompson,
2021). As technologies change, a focus is moving from electronic health (eHealth)/mobile health
(mHealth) to Digital Health, emphasizing digital consumers. WHO’s vision embraces
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technologies that allow people to manage their health more effectively, improve communication
and monitor public health (2021). Amidst the constant increase in data available to individuals
through medical records, real-time streaming of information from monitoring devices, and
emerging technologies such as genomics, health practitioners, clinicians, researchers, and
developers need innovative solutions to help rapidly identify critical pieces of knowledge in this
“sea of information” (Mayo Clinic, 2021).
Wellness requires individuals to be intentional, proactive, responsible, and empowered to
engage in healthy behaviors; environments can limit and influence choices, and external
circumstances can make it challenging to practice healthy habits (GWI, 2020). Guidelines that
promote PA lead to public health campaigns encouraging people to move more; this messaging
may increase physical activity, though it may not lead to improved mental health or reduced
mental ill-health like depression (Teychenne et al., 2020). With a broader scope of smart devices,
the Internet of things (IoTs), comes more widespread use of technology. AI/ML, big data, deep
learning, data mining, predictive algorithms and analytics, and digital health are changing health
systems and health care delivery (WHO, 2021). Schools like the Mayo Clinic Alix School of
Medicine consider computer-augmented decision-making, AI, and ML technologies as tools that
support and amplify human knowledge and individual choices, allowing for rapid adoption and
integration of new data streams in individuals care (Mayo Clinic, 2021). Due to the complexity
of health and wellness, it is imperative to examine wellness from a subjective basis where
individuals report in conjunction with objective explorations (Stone et al., 2018). In order to
present healthy options for individuals, health practitioners, clinicians, researchers, and
developers have an array of tools to improve their decision-making and must be aware that
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evidence and data do not immediately translate into evidence-based practice (Claridge & Fabian,
2005).
Although fitness trackers are popular in the health and fitness industry, they may have
limited benefits to increase physical activity; if something is new and innovative, it does not
indicate that individuals will find it useful or usable (Brown et al., 2021). Nevertheless, wearable
fitness trackers (WFT), mHealth, and digital health technologies can contribute to PA
engagement with sustainable health outcomes; future scholars and practitioners have an
opportunity to focus on goal-based, social-based, and rewards-based gamification to increase
engagement (Cho et al., 2021). The growing use of fitness tracking apps, rising demand for
continuous health monitoring devices, and awareness about obesity drive the global market’s
growth; predicted WFT sales continue growing by 16.5 % annually until 2023 (Prescient &
Strategic Intelligence, 2018). However, as sales increase, the adaption rate of abandonment
illustrates how the device is functional though it fails to have a meaningful impact on users’
behaviors and habits; WFTs provide data and need more compelling value propositions to inspire
action (Windasari et al., 2021).
Rather than focusing on the adaption and engagement of health technology, researchers,
clinicians, and designers need to consider the value propositions that the device should provide
to encourage individuals to continue using it. Researchers strongly believe that the potential
benefit of new technology to support and create insight for individuals is significant (Keogh et
al., 2021). It is one thing to capture data from the digital technology, and it another it is to learn
from the data to improve the experience for the individuals. Supporting multi-disciplinary
research integrating academia, industry, and clinicians is a fundamental necessity to develop
other technology and protocols that match the varied needs of all stakeholders (Keogh et al.,
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2021). By understanding the barriers for usage, PA, and programming, the development of new
technology can implement the research. Research highlights how complicated it is to define
motivation within sport, fitness, exercise, and health. For example, motivation is one of the most
common barriers to participate, engage, or continue in PA (Woodruff et al., 2020; Stehr et al.,
2021; Brand et al., 2019). Nevertheless, motivation is critical for promoting health and
participation in physical activity (Quested et al., 2021). Furthermore, motivation to participate in
PA explains what energizes and directs human behavior (Ryan & Deci, 2017a) and considers
aspects related to activity adherence (Ryan & Frederick, 1997).
Self-Determination Theory and Motivation
The SDT came from Edward Deci and Richard Ryan, psychologists who first introduced
the idea of motivation in their book (1985a), Self-Determination and Intrinsic Motivation in
Human Behavior. Deci and Ryan developed a theoretical motivation framework, suggesting that
a need to grow and gain fulfillment drives individuals (2000). Ryan and Deci describe a broad
framework for studying human motivation through the SDT (Deci & Ryan, 1985b; Ryan & Deci,
1997; Deci & Ryan 2000; Ryan & Ryan 2000; Ryan & Deci, 2017b). SDT presumes that an
individual is motivated by three primary psychological needs: competence, autonomy, and
relatedness with others (Deci et al., 1991; Deci & Ryan, 1985a; Ryan & Deci, 2000b).
Furthermore, SDT declares that there are two main types of motivation—intrinsic and
extrinsic—both influential forces in developing who an individual is and how they act (Deci &
Ryan, 2008). SDT distinguishes between autonomous motivation and controlled motivation
(Ryan & Deci, 2008).
SDT contributes to the focus on goals, basic psychological needs, and qualities of
motivation as central organizing beliefs describing the implications of the individual- and
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system-level factors on well-being (Moller et al., 2021). The three basic psychological needs of
competence, autonomy, and relatedness form the foundation for self-motivation and self-
integration (Ryan & Deci., 2000a). SDT believes that all people have an innate tendency toward
growth, self-integration, and psychological consistency, and the researchers also believe that an
individual’s social environment can foster this innate drive or hamper it (Deci & Ryan, 2000).
SDT describes motivation as what drives people to act and proposes that it lies on a continuum
from controlled (external or coerced) to autonomous (internal or volitional) (Deci & Ryan,
1985a; Ryan & Deci, 1997; Deci & Ryan, 2000; Ryan & Deci, 2000a, 2017a).
Competence, autonomy, and relatedness explain the direction of an individual’s behavior
on a motivational continuum, fluctuating between autonomous motivation (intrinsic regulation,
integrated regulation, and identified regulation), then passing through more controlled forms of
motivation (introjected regulation and external regulation), and ending with the absence of
regulation or amotivation (Deci & Ryan, 2000; Ryan & Deci, 2000a, 2017a; Vansteenkiste et al.,
2010). Self-determination theory provides a framework for examining PA among adults to
engage in at least 150 minutes of moderate-to-vigorous activity a week. The concept of
motivation has increased popularity in PA research, where evidence shows that motivation
regulations are critical determinants of cognitive (e.g., attention, reasoning), emotional (e.g.,
enjoyment, anxiety), and behavioral (e.g., effort, persistence) outcomes (Quested et al., 2021).
SDT validates that the satisfaction of the basic psychological needs of competence,
autonomy, and relatedness links to positive behavioral outcomes and, therefore, may increase the
intention to be physically active. Competence, dependent on context, is a need to master a
behavior, be confident in one’s skills, and control their destiny (Ryan & Deci, 1997). Autonomy
is the need to self-regulate one’s behaviors and experiences to control their actions (Ryan &
22

Deci, 1997). Finally, individuals feel a sense of relatedness when socially connected, cared for,
supported through their relationship with others (Ryan & Deci, 1997). Autonomous is when all
three basic needs are satisfied and controlled, competence and relatedness are somewhat
satisfied, without autonomy (Ryan & Deci, 2000a). These needs can either be fulfilled or
neglected by an individual’s environment (Ryan & Deci, 2000b).
Studies have shown that although intrinsic motivation is a lifelong psychological growth
function, it is not automatic, and intrinsic motivation depends on support for basic psychological
needs competence, autonomy, and relatedness (Di Domenico et al., 2017). Autonomous
motivation includes motivation that comes from internal sources, from outside or extrinsic
sources for an activity’s value, and how it aligns with an individual’s sense of self (Stone et al.,
2009). Controlled motivation comprises external regulation, a type of motivation where an
individual acts out of the desire for external rewards or fear of punishment (Stone et al., 2009).
More than one type of motivation drives an individual; different goals, values, aspirations, and
beliefs determine what an individual wants or needs. Therefore, it is helpful to think of
motivation on a continuum ranging from non-self-determined to self-determined.
Intrinsic Motivation
The psychological needs of competence, autonomy, and relatedness collectively support
more autonomous motivation for a given behavior, like PA. Intrinsic motivation is when an
individual engages in a behavior or a PA because they find it rewarding, participating in it for its
own sake rather than the desire for some external reward, and the behavior or the PA itself is its
reward (Lee et al., 2012). Deci’s 1971 experiments on intrinsic motivation in humans and its
undermining by rewards stemmed from Harlow’s 1950’s research, where he coined the term
intrinsic motivation to describe his observation that monkeys would persist in playing with
23

mechanical puzzles even in the absence of external rewards. Intrinsic motivation is the key
outcome of self-determination; they are the internal drives that inspire individuals to behave in
specific ways, including our core values, interests, and a personal sense of morality (Deci &
Ryan, 1985b; Ryan & Deci, 2000a). The right end of the continuum presents an individual solely
motivated by intrinsic sources with intrinsic regulation (Ryan & Deci, 2000a). The individual is
self-motivated and self-determined, driven by enjoyment, interest, and the inherent satisfaction in
the behavior or activity they are engaging in (Ryan & Deci, 2000a).
SDT’s framework shows how intrinsic motivation influences individuals to develop,
continue, and engage in physical activities like exercise and sports (Frederick-Recascino et al.,
2003). SDT recognizes that individuals’ motivation may shift towards intrinsic motivation when
their psychological needs are satisfied. Ryan and Deci explain that intrinsic motivation is “to
seek out novelty and challenges, to extend and exercise one’s capacity, to explore, and to learn”
(2000, p.70). Therefore, the theory suggests that individuals develop their motivational approach
and gain confidence for a given PA based on how participation in the activity meets their basic
psychological needs for autonomy, competence, and relatedness (Mears & Kilpatrick, 2008). For
example, physical activities that are participated in for pleasure have the most autonomous form
of motivation, intrinsic (Ryan & Deci, 2000a).
Research shows that individuals experience less interest and exhibit less natural
engagement with physical activities for which they are initially intrinsically motivated after
receiving tangible rewards for performing the PA (Deci et al., 1999). Play, exploration, and
curiosity-spawned activities illustrate intrinsically motivated behaviors since they are not
dependent on external incentives or pressure; instead, they provide satisfaction and joy (Ryan &
24

Deci, 2020). Technically intrinsic motivation is when activities are done “for their own sake,” or
for an individual’s inherent interest and enjoyment (Deci & Ryan, 2000).
Extrinsic Motivation
Extrinsic motivation is when an individual is motivated to perform a behavior or engage
in a PA because they want to be rewarded or avoid punishment, not because it is satisfying or
enjoyable; instead, they expect to get something in return or avoid something unpleasant (Di
Domenico et al., 2017). Consequently, the most extreme form of extrinsic motivation is external
regulation, as behaviors or a PA stem from a desire to be obedient, compliant, and confirm or
receive external rewards or avoid external penalties (Ryan & Deci., 2000b). Therefore, extrinsic
motivation stems from outside of the individual, intrinsic motivation comes from within, and
each has a different effect on human behavior (Di Domenico et al., 2017). External motivation is
not a poor form of motivation, though the SDT framework explains how intrinsic motivation
influences behavior and PA.
According to Deci and Ryan, extrinsic motivation is a drive to act in a specific manner
based on external sources, resulting in external rewards (1985a). Extrinsic motivation behaviors
are for reasons other than inherent satisfaction, and the contrast is not straightforward because
instrumental motivations vary widely in content and character (Ryan & Deci, 2020). Externally
imposed rewards and punishments drive external regulation behaviors, a form of motivation
typically experienced as controlled and non-autonomous (Ryan & Deci, 2020). Although
introjected regulation concerns extrinsic motivation is partially internalized, the behavior is
regulated by the internal rewards of self-esteem for success by avoiding anxiety, shame, or guilt
for failure (Ryan & Deci, 2020).

25

Motivation Continuum
Between the opposite ends of intrinsic and amotivation lies four other forms of
motivation: external regulation, introjected regulation, identified regulation, and internalized
regulation (Ryan & Deci, 2000a). Intrinsic motivation is grounded in the individual’s interest,
satisfaction, and a sense of inherent comfort or enjoyment, and PA behaviors that are
extrinsically motivated differ considerably in the mechanisms that control them (Ryan & Deci,
2000b). On the other end of the continuum, when an individual is disinterested in the behavior, it
is amotivation (Ryan & Deci, 2000a). Amotivation can result from either lack of feeling
competent, lack of value, or lack of interest, and it has been a strong negative predictor of
engagement, learning, and wellness (Ryan & Deci, 2020). Thus, competence, autonomy, and
relatedness explain the direction of an individual’s behavior on a motivational continuum,
fluctuating between autonomous motivation (intrinsic regulation, integrated regulation, and
identified regulation), then passing through more controlled forms of motivation (introjected
regulation and external regulation), and ending with the absence of regulation or amotivation
(Deci & Ryan, 2000; Ryan & Deci, 2000a; Ryan et al., 2017b; Vansteenkiste et al., 2010).
Autonomous and Controlled Motivation
Autonomous motivation includes both intrinsic motivation and the types of extrinsic
motivation an individual has identified with an activity’s value, ideally integrated it into their
sense of self (Deci & Ryan, 2008). When people are autonomously motivated, they experience
volition, desire, or a self-endorsement of their actions, feeling free and ultimately endorsing
one’s actions (Deci & Ryan, 2008; Moller et al., 2021). Controlled motivation, in contrast,
consists of both external regulation and introjected regulation and feeling pressured by external
or internal forces (Deci & Ryan, 2008; Moller et al., 2021).
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Individuals experience pressure to think, feel, or behave in particular ways when they are
controlled (Deci & Ryan, 2008). Autonomous and controlled motivations energize and direct
behavior, unlike amotivation, which refers to a lack of intention and motivation (Deci & Ryan,
2000). Both forms can effectively motivate human behavior, and, in many contexts, each has fed
high-level accomplishments (Moller et al., 2021). Thus, SDT suggests that all behaviors range
from feeling completely controlled and non-self-determined to feeling fully autonomous and
self-determining (Deci & Ryan, 2000). Autonomous motivation and many positive psychological
and behavioral outcomes are associated with mindfulness (Deci & Ryan, 2008). Autonomous
extrinsic and intrinsic motivations are highly volitional or free and differ primarily because
intrinsic motivation is based on interest and enjoyment; individuals do behaviors because they
find them engaging and fun (Ryan & Deci, 2020).
External Regulation
External regulation is when an individual’s behavior is a function of external
contingencies of reward or punishment (Deci & Ryan, 2008). External regulation and
introjection are both controlled forms of motivation; extrinsic motivation may also be
autonomously enacted (Ryan & Deci, 2020). However, it is a reward or another external
constraint like money or praise and stands as the most blatant kind of control (Ryan & Deci,
2000a).
Introjected Regulation
Introjected regulation, in which the regulation of action has been partially internalized, is
energized by factors such as an approval motive, avoidance of shame, contingent self-esteem,
and ego-involvements (Deci & Ryan, 2008). In addition, this motivation is a drive for self-
control, a need to protect one’s ego, get internal rewards, or avoid internal punishments (Ryan &
27

Deci, 2000a). Finally, extrinsic motivation’s most autonomous form is integrated regulation,
when an individual recognizes and identifies with the value of the activity and finds it to be
congruent with other core interests and values (Ryan & Deci, 2020).
Identified Regulation
In identified regulation, the person consciously identifies with or endorses the value of an
activity and thus experiences a relatively high degree of volition or willingness to act (Ryan &
Deci, 2020). Identified and integrated motivations deal with a sense of value when people view
the activities as worthwhile, even when they are not enjoyable (Ryan & Deci, 2020). Identified
regulation occurs when the motivation is slightly internal and based on conscious values (Ryan
& Deci, 2000b). Extrinsic motivation is integrated regulation, the most internalized form of
motivation, driven by intrinsic sources such as the urge to act congruously with one’s values and
sense of self (Ryan & Deci, 2000a).

Figure 1
Types of Motivation

Note. Adapted from The “What” and “Why” of Goal Pursuits: Human Needs and the Self-
Determination of Behavior by E. L. Deci & R. M. Ryan, 2000, Psychological Inquiry, 11(4),
227–268. (https://doi.org/10.1207/S15327965PLI1104_01_)
28

Competence
Per SDT, people strive towards growth, trying different challenges, and participating in
new experiences are essential for developing a sense of self. Thus, competence refers to the
experience of mastery, a challenge, curiosity, exploration, and stretching of one’s capacities (La
Guardia, 2009; White, 1959). Fulfillment of this need stands apart from the rewards and material
benefits competent behavior might result in, as fulfilling the need for competence does not rest
on expectations of success but instead relies on the act of simply doing or engaging in activity to
broaden one’s capacities (La Guardia, 2009; White, 1959). Accordingly, competence involves
the feeling of mastery, a sense that an individual can succeed and grow, and it is best satisfied
within well-structured environments that afford optimal challenges, positive feedback, and
growth opportunities (Ryan et al., 2019). Therefore, competence concerns an individual’s
accomplishments, experience, and abilities; people need to build their competence and develop
mastery over essential tasks (Ryan & Deci, 2000a).
Autonomy
Individuals are often extrinsically motivated by external rewards such as recognition,
money, awards, and prizes, while SDT concentrates fundamentally on internal sources of
intrinsic motivation such as a need to gain knowledge or independence (Ryan & Deci, 2000a).
Less autonomous behavior feels compelled or controlled as autonomy means “self-rule” and
refers to self-initiated and regulated acts; therefore, when autonomous, an individual is freely
endorsed and experienced as wholeheartedly engaged (deCharms, 1968; Deci, 1975; La Guardia,
2009). When autonomous motivation drives an individual, they may feel self-directed and
autonomous; when controlled motivation drives them, they may feel pressure to act in a certain
way and feel little to no autonomy (Ryan & Deci, 2008). Autonomy is a sense of initiative and
29

ownership in an individual’s actions, supported by one’s interest in experiences and value,
undermined by experiences of being externally controlled, whether by rewards or punishments
(Ryan et al., 2019). Individuals need to feel that they control their behavior (Ryan & Deci,
2000a).
Relatedness
People need to have a sense of belonging and connectedness with others in a network,
and an individual needs, other people to some extent (Deci & Ryan, 2008). Deci and Ryan
explain how individuals have differences in their personalities, resulting from the differing
degrees to which each need has been fulfilled—or thwarted (2008). Relatedness refers to the
feeling of connecting, belonging, and being notable or significant in the eyes of others (Ryan &
Deci, 2000a). Thus, relatedness is a sense of belonging, a connection, aided by the transfer of
respect and caring (Ryan et al., 2019). Relatedness refers to uniting with an individual for who
they genuinely are, not based on an individual’s behavior, appearance, status, or possession
(Ryan & Deci, 2000a).
Basic Psychological Needs
Individuals need autonomy, competence, and relatedness, motivating them to engage in
activities naturally that they value, with their capacities and skills to seek connectedness with
others. Therefore, SDT suggests that individuals naturally explore and apply their energies
toward activities, roles, and relationships that promote the three basic psychological needs
autonomy, competence, and relatedness (Ryan & Deci, 2000a). In turn, individuals will avoid
situations that have significant costs to their well-being or threaten their basic needs (Deci &
Ryan, 2000; Ryan & Deci, 2000a). Individuals are more fulfilled and successful when they
pursue goals in their way rather than according to strict external systems. Even when pursuing
30

extrinsic rewards like wealth or fame, individuals are more satisfied and self-actualized when
they pursue them autonomously, for their reasons and methods (Deci & Ryan, 2000b).

Figure 2
Basic Psychological Needs Self-Determination Theory: Human’s Three Basic Needs

Note. Adapted from The “What” and “Why” of Goal Pursuits: Human Needs and the Self-
Determination of Behavior by E. L. Deci & R. M. Ryan, 2000, Psychological Inquiry, 11(4),
227–268. (https://doi.org/10.1207/S15327965PLI1104_01_)

31

SDT assumes individuals are inherently inclined to psychological growth and integration
toward learning, mastery, and connection with others; nevertheless, these proactive tendencies
require supportive conditions to be robust and not automatic (Ryan & Deci, 2020). Thus, SDT
expressly explains that individuals need support for basic psychological needs, autonomy,
competence, and relatedness for healthy development to unfold (Ryan et al., 2019). Preventing
autonomy, competence, or relatedness is seen as damaging to motivation and wellness; therefore,
SDT’s is primarily focused on the degree to which they meet or frustrate these basic needs (Ryan
et al., 2019). Motivation is consistently shown to be related to PA (Choi et al., 2017; Haible et
al., 2019; Fernández-Espínola et al., 2020).
Motivation and Physical Activity
Motivation theories such as SDT may further explain the mechanisms behind the barriers
due to lack of motivation and time when engaging in PA (Thomas et al., 2021). Self-
determination theory shows how not all motivations are created equal, and individuals develop
their motivational approach for a PA based on how participation in that exercise meets their
basic psychological needs for autonomy, competence, and relatedness (Mears et al., 2008; Deci
et al., 2002). Meeting the three basic psychological needs increases confidence, encouraging a
healthy motivational approach to increase enjoyment, effort, and continuity to the activity. In
connection to PA, more autonomous forms of motivation support persistent engagement in
moderate-to-vigorous PA (Ryan & Deci, 2017a; Teixeria et al., 2021). In addition, understanding
an individual’s level of motivation enables health practitioners, clinicians, researchers, or
developers to provide guidance that promotes improved health behaviors or increased PA
(Ntoumanis et al., 2021). While sports fulfill competence, autonomy, and relatedness needs to a
differing degree, especially in individuals who participated in youth or collegiate athletics, new
32

approaches will provide other strategies for engaging in PA through adulthood (Smith et al.,
2018).
Lack of Motivation
In addition, SDT suggests that individuals are less likely to adhere to an exercise program
or PA if they perceive others are controlling their actions, feel unskilled, or have minimal or
negative social connections to the behavior (Mears & Kilpatrick, 2008). Individuals can be
motivated to engage in PA for various reasons, including enjoyment of exercise, health benefits,
avoiding letting oneself or others pressuring them, or not want to let them down (Ntoumanis et
al., 2021). Specifically, former athletes have unique challenges transitioning to lifetime PA
(Smith et al., 2018). Multiple research groups have assessed behavior-change techniques that can
impact psychological needs found in different WFT models, mHealth, and similar digital health
technologies (Lyons et al., 2014; Mercer et al., 2016; Chia et al., 2019; Lewis et al., 2020). There
are ways to utilize apps, WFT, and other digital health technologies as partners in staying
motivated, and the big data/AI/ML will help health practitioners, clinicians, researchers, or
developers understand how the data can support engagement in PA (Segar, 2019). Using the
established SDT to create more strategic design increases the value and integration of PA into
daily living (Segar et al., 2019).
There is increasing research on the frustration that autonomy, competence, and
relatedness may result in controlled motivation, amotivation, and ill-being (Ng et al., 2013).
Amotivation is a state where individuals lack any intention or motivation to engage in PA (Ryan
& Deci, 2017b). Regarding motivation, a health practitioner, clinician, researcher, or developer
may support PA or attempts to improve health by offering meaningful opportunities, positive and
informative feedback, and empathizing with or recognizing the individual’s perspective
33

(Ntoumanis et al., 2021). According to Deci and Ryan, healthy growth and PA are likely when
the basic psychological needs for autonomy, competence, and relatedness are met (Ryan & Deci,
2000a, 2017b). Furthermore, when individuals can choose in their PA, their environment
supports their basic need of competence, and the people around them are responsive, rather than
aloof and inattentive, then their basic need of relatedness is met. Thus, the theory postulates that
the three basic needs mediate the relationship between need supportive environments for PA
(Thomas et al., 2021). In addition, the self-monitoring of the PA may have contributed to
autonomous motivation (Nuss & Li, 2021).
Measuring Motivation
In 1993, an essential aspect in studying these theoretical relationships, Fredrick and Ryan
developed an instrument to measure motivation, the Motivation for Physical Activity Measure
(MPAM). Later, adding the social and health fitness subscales to the competence, enjoyment,
and body relations representative of intrinsic and extrinsic motives, relevant to potential reasons
for exercise participation (MPAM-R; Ryan et al., 1997). The MPAM-R evaluates five motives
for engaging in PA with differing degrees of psychological need satisfaction from low
(appearance) to high (social, competence-challenge, fitness health, and interest enjoyment)
(Ryan et al., 1997). The self-reporting instrument uses 30-items containing five subscales
measuring competence-challenge, appearance, social, health fitness, and enjoyment-interest
motives to determine motivation for involvement.
The MPAM-R assesses the strength of five motives for participating in PA, like OS,
walking, yoga, housework, and cycling, to name a few. Within the MPAM-R, there are
measurements for intrinsic and extrinsic motivation (Ryan et al., 1997). For example, the
competence scale and interest/enjoyment scale measure intrinsic while fitness, appearance-based
34

motives, and social scales measure extrinsic motivation (Ryan et al., 1997). Fitness and health
motives refer to engaging in PA with a desire to be physically healthy, strong, and energetic
(Frederick et al., 1993; Ryan et al., 1997). Appearance motives refer to PA engagement
becoming more physically attractive, making muscle definition, looking better, and reaching or
holding the desired weight (Ryan et al., 1997). Additionally, engaging in PA because of the
desire to improve at an activity, meet a challenge, or acquire new skills is a
competence/challenge motive (Frederick et al., 1993). Further, the social motive is to be
physically active to meet friends or meet new people, connecting with others (Frederick et al.,
1993; Ryan et al., 1997). Finally, the enjoyment motive is engaging in PA because it is fun,
makes you happy, is interesting, stimulating, or enjoyable (Frederick et al., 1993). The MPAM-R
scales have been used in various research to predict diverse behavioral outcomes associated with
PA.
Integrating SDT
Improving the health and fitness of individuals is a challenge that health practitioners,
clinicians, researchers, or developers face daily. Large amounts of data and research confirm the
countless benefits of PA; however, approximately 80% of adults and adolescents in the United
States do not meet the physical activity guidelines for Americans (Thompson & Eijsvogels,
2018). A healthy approach to motivation is essential for exercise adoption and adherence; a
slight integration of SDT could have vast implications for disease prevention and management
(Ntoumanis et al., 2021). Although there is no immediate impact of the SDT-based interventions
on physical health and PA outcomes, there is a small benefit to follow-up (Ntoumanis et al.,
2021). Individuals need autonomy that exercise or PA is a choice, competence, or a desire to
improve skills and master a practice, and relatedness to feel socially connected to others and
35

sport has the potential to fulfill all three needs for an individual (Ryan & Deci, 2017a). Health
practitioners, clinicians, researchers, and developers have a significant position to incorporate an
assessment of PA to promote positive health outcomes (Thompson & Eijsvogels, 2018).
Promoting PA in later life might be best achieved by promoting sport participation earlier in the
life course (Aggio et al., 2017). In addition, various types of PA can meet basic psychological
needs for autonomy, competence, and relatedness; the practitioners, clinicians, researchers, or
developers have an opportunity to facilitate the use of a PA to achieve improved health or draw
on previous sports experience with SDT. Finally, SDT-based interventions can integrate health
promotion, health care systems, fitness programs, and health technology to facilitate motivation
(Ntoumanis et al., 2021).
Market Growth With WFT, mHealth, and Digital Health Technologies
The growth in WFT, mHealth, and digital health technology markets has attracted and
motivated health-focused individuals to monitor, store and transmit their health-related
information and data to keep the obtain goals, engage in PA, and increase health outcomes
(Barua & Barua, 2021; Villalobos-Zúñiga & Cherubini, 2020). The expansion of WFT, mHealth,
and digital health technology has gained attention in both theory and practice. For example,
health and fitness organizations’ integration of this technology provides opportunities to study its
influence on sports, PA, and fitness experiences (Pizzo et al., 2021). In addition, WFT’s small
and reasonably low-cost design and mHealth accessibility encourage inactive individuals to
improve their PA engagement and overall health (Degroote et al., 2018). mHealth is a vital tool
for health-oriented behavioral change interventions, increasing positive health outcomes have
increased use of exposure as wristwear (watches and wristbands) are projected to reach almost
230 million units in 2024 (Vailshery, 2021).
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In a study of 288 behavior-change apps, only one-fourth of the apps (i.e., 25.5%) include
features that trigger all three basic psychological needs of SDT in some form, showing room for
growth in the field (Villalobos-Zúñiga & Cherubini, 2020). By linking health practitioners,
clinicians, researchers, or developers with motivation theories like SDT is essential for
cultivating a desire to engage in PA with the growth in big data (Segar, 2019). SDT organizes the
different motivation among the control–autonomy continuum: amotivation (or absence of
intention to act), external regulation (to obtain a reward), introjected regulation (to avoid guilt),
identification (accepted external regulation), integration (self-determined action), and provide
insight for integration into innovative technology (Villalobos-Zúñiga & Cherubini, 2020).
Popularity and Decline
WFTs and wearables have been extremely popular among businesses, military forces,
and medical professionals who have been using wearable technology for decades (Vailshery,
2021). As a result, the most thriving wearable devices on the market are smartwatches and health
and fitness trackers, estimated to be 10 billion units by 2025 (Sujay, 2021). In addition, there
were 68.7 million smartphone users in the United States in 2019 who used at least one health or
fitness app monthly, and forecasts predict that in 2022, there will be 86.3 million health or fitness
apps users (Statista, 2021). However, although millions of individuals purchase or engage in
digital health technology annually, research has shown that at least one-third of WFT users
abandon the devices within 6 to 12 months of purchase and slow adoption rate of WFT
developers need to understand the determinants behind the adoption and use of WFT, and these
numbers may be similar to other technology (Barua & Barua, 2021; Ledger, 2014).
Notwithstanding the mix of positive and negative research and concern that WFT, mHealth, and
digital health technologies do not adequately motivate; however, they promise to increase PA
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when scientifically supported principles in motivation like SDT and consumer behavior integrate
into the design (Segar, 2017). Few design features engage in pre-commitment and intergroup
competition, suggesting that PA motivation may underpin the connection; therefore, targeting
motivation as part of PA behavior-change interventions is more effective than not addressing
motivation (Schoeppe et al., 2016; Schoeppe et al., 2021). The 2020 global guidelines on
physical activity from WHO provide an opportunity to communicate in new ways that align with
behavioral science to better support individuals’ three basic psychological needs for motivation
to incorporate PA into their lives (Bull et al., 2020; Segar et al., 2020).
Health practitioners, clinicians, researchers, or developers must also avoid being a barrier
to PA, and an assessment or plan for PA should be part of each individual’s health strategy
(Thompson & Eijsvogels, 2018). In addition, encouraging individuals to start small and use
tracking to understand goals allows individuals to access data to be flexible with PA and goals as
the need arises (Segar, 2019). However, some former users have negative impacts on their
mental health due to their inability to meet their WFT goals or their doubt in the technology to
assess their PA accurately, and this may be due to poor development of the technology that may
benefit from integrating SDT elements (Nuss & Li, 2021). WFTs, mHealth, and digital
technologies continue to grow in popularity, offering various forms of data-tracking, including
physical activities like golf, swimming, cycling, dance, yoga, and more, therefore providing the
opportunity for choice can enhance autonomy for PA and support more autonomous forms of
motivation for PA in WFT users (Moustaka et al., 2012; Wulf et al., 2014; Nuss & Li, 2021). In
addition, technology-enhanced options are an approach to motivate people and promote PA
(Eysenbach et al., 2021).
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Sustained Engagement and Motivation
The novelty of a new tool may quickly wear off for those not inherently enchanted by
numbers or graphs; an individual’s relationship with PA determines whether or not they will stay
motivated is a scientific reason why the technology integration may decline (Segar, 2019).
Furthermore, given the increased popularity of WFT, mHealth, and digital health technologies,
individuals may bring their data to future consultations, generating additional confusion and
anxiety for health practitioners, clinicians, and individuals. On the other hand, if individuals
believe the data obtained from WFT and mHealth devices are accurate, their perceived reliability
will be strengthened and encourage technology adoption (Barua & Barua, 2021). Finally, the
current data suggest the significance of considering various behavior regulation outlines when
examining the effects of behavioral regulations on PA-related outcomes, and SDT may be
applied to improve the understanding of the psychological tools that develop exercise motivation
(Emm-Collison et al., 2020; Matsumoto, 2021; Villalobos-Zúñiga & Cherubini, 2020).
In addition, health practitioners, clinicians, researchers, or developers can support former
athletes transition to PA beyond OS by emphasizing SDT in transitional programs and exposure
to lifetime physical activities (Smith et al., 2018). Participation in PA for former and current user
participation is similar, and autonomy may increase motivation due to WFT feedback; however,
it is unclear how the WFT and digital health technology impacts PA levels (Nuss & Li, 2021).
Data may determine who is most suited for a PA intervention using WFTs or mHealth to screen
individuals to determine if technology might be an impactful intervention to increase PA in
underactive people (Nuss & Li, 2021). Devices have embedded features that align with providing
feedback and self-monitoring of behavior and can support more autonomous forms of
39

motivation for PA (Lyons et al., 2014; Mercer et al., 2016; Chia et al., 2019; Lewis et al., 2020;
Friel & Garber, 2021; Villalobos-Zúñiga & Cherubini, 2020). Individuals with self-determined
motivations who use WFTs, mHealth, and digital health technologies may benefit in terms of
higher levels of PA (O’Loughlin et al., 2021; Friel & Garber, 2021). Incorporating guidelines or
industry standards for different technologies based on behavior-change theories, with AI/ML,
data mining, deep learning, predictive algorithms, and big data may allow for a more
personalized exchange, increasing tailored interactions for individuals to engage in PA to
promote positive health changes (Schoeppe et al., 2021; Villalobos-Zúñiga & Cherubini, 2020).
SDT proposes that individuals have different amounts of motivation towards a particular
PA; more importantly, different motivations or orientations with underlying views and goals give
rise to action (Deci & Ryan, 1985a). Both current and former WFT users met recommended PA
levels and had high levels of autonomous motivation for PA (Nuss & Li, 2021). More
autonomous forms of motivation are connected with steadfast engagement in moderate-to-
vigorous PA (Ryan & Deci, 2017a). Current users do not necessarily consider themselves
motivated by their WFT, mHealth app, or digital health technology; however, they appreciate the
visibility of the PA data (Nuss & Li, 2021). These technologies have the opportunity to support
SDT-based motivation for PA by fulfilling psychological needs in the design and implementation
phases (Schaben & Furness, 2018; Friel & Garber, 2021; Villalobos-Zúñiga & Cherubini, 2020).
There has been a growing trend with continued use exploring different theories, using persuasive
design, motivational affordance, and framework (Chuah, 2019; Hassan et al., 2019; Suh, 2018;
Windasari et al., 2021; Villalobos-Zúñiga & Cherubini, 2020). WFT, mHealth, and digital health
technology interventions may effectively improve specific health outcomes for individuals.
However, more SDT research is needed in the design elements that incorporate proven behavior-
40

change theories, including using social media to stimulate usage through social interactions and
past behavior as a predictor of future PA engagement (Schoeppe et al., 2021; Emberson et al.,
2021; O’Loughlin et al., 2021; Friel & Garber, 2021; Rodrigues et al., 2021; Villalobos-Zúñiga
& Cherubini, 2020).
Conclusion
Research suggests that WFT, mHealth apps, and digital health technologies alone do not
help individuals achieve long-term motivation; they provide a visible incentive for PA and need
the self-generating fuel of internalized motivation to sustain use (Segar, 2017; Friel & Garber,
2021). Combining these strategies with AI/ML algorithms seems promising to improve the state
of inactivity linked to health risks, supporting the goals of comprehensive health programs,
increase PA in adults, sustained sports engagement, and Exercise is Medicine programs (Nuss &
Li, 2021; Nuss et al., 2021). Determining integrative methods for engagement in WFT, mHealth
apps, and digital health technologies may motivate individuals to continue using the technology
to generate sustained value, encourage PA, and positive health outcomes. SDT provides an
opportunity to understand continued use intention instead of focusing on adoption may help
health practitioners, clinicians, researchers, or developers identify strategies to create features
prompting the sustained use of technology and engagement in PA, exercise, fitness, and sports
(Windasari et al., 2021; Villalobos-Zúñiga & Cherubini, 2020).
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Chapter Three: Methodology
The purpose of this chapter is to describe the research methodology used to assess the
reasons why adults participate in PA, to illuminate the significance, or lack thereof, of PA and
motivation. The research is rooted in SDT as a framework for highlighting potential reasons and
motivation for exercise participation and influences that contribute to PA in adults. Adult subjects
participated in an anonymous survey of standard Likert-type items to share their experience.
Ethical implications associated with the research are taken into consideration as well. The study
aimed to understand how adults are motivated to engage in PA to provide insight toward
understanding intentional actions. Through the survey, the research provided a quantitative or
numeric description of physically active adults by studying a sample of that population (Fowler,
2013). It focused on sport, technology, motivation, and adults’ PA.
Research Questions
The study evaluated adults over age 18 who are physically active or intend to engage in
PA. The only exclusions were individuals was under age 18. The research used a quantitative
approach with surveys to systematically report findings from a sample population of adults over
18 years old to understand who participates in or intends to engage in at least 150 minutes of
moderate-intensity activity a week. The purpose of the study and the research questions was to
review adults’ PA and motivation. This section explains the background and demographics of the
target population, adults who participate insufficiently active physical activity (>150 minutes),
active physical activity (150–300 minutes), and highly active (300 + minutes) each week. This
study utilized a cross tab quantitative design with descriptive analysis. Chapter Three outlines the
research design and methodology for data collection and instrumentation. The data was analyzed
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and the results presented in Chapter Four. Finally, all applications addressed the conceptual
framework presented in Chapter Two, directed by the following research questions:
1. Are there differences in why people are physically active based on prior participation
in OS/PA and current use of digital health technology?
2. Are there differences in current levels of PA based on prior participation in OS/PA
and current use of digital health technology?
3. Are there differences in participants’ perceived vitality based on prior participation in
OS/PA and current use of digital health technology?
4. What variables are the highest predictors of PA and digital health technology use in
adults?
Overview of Design and Methodology
Quantitative research methods presented standardized procedures to increase the study’s
objectivity, based on questions and answers, to apply the results to a broader setting and
population (Creswell & Creswell, 2018). In addition, descriptive statistics served to organize and
describe the data (Salkind & Frey, 2019). Data were collected via a survey available to all active
adults to describe, compare, and correlate conditions of their level of PA. The study represented
physically active adults and those who intend to engage in PA to illustrate how SDT applies in
real-life settings and how technology may communicate or support adaptive motivation among
adults. The study explored physically active adults, their relationship to OS/PA in their past, and
their current use of digital health technology. The examination of background information was
essential to this study to identify characteristics and factors contributing to differences in PA.
Therefore, the survey asked respondents to identify their motivation, why they engage in
physical activities, sports, and exercise, and how they feel.
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Population and Survey Sample
The online survey was open to U.S.-based English-speaking adults who have access to
take the survey. The survey began by asking participants to verify key demographic details about
themselves for eligibility in the study (Appendix A). Once they state that they are over 18 years
of age, the survey invited them to self-report on the following areas: why they engage in physical
activities, sports, and exercise with the Motives for Physical Activities Measure-Revised
(MPAM-R) with a Cronbach Alpha of 0.78 to 0.92 (Ryan et al., 1997) (Appendix B). Thus, the
study sought the participation of all adults who were active or intend to be active in PA.
Survey Link and Recruitment
The survey was hosted on Qualtrics.com, and a Bit.ly link was created to shorten the link.
The Bit.ly link provided information about geographic locations and where clicks were coming
from. Figure 3 provides an overview of the survey access and engagement. Of the 863 clicks
from the United States, 765 completed the survey with an 88.64% click-through rate. Of the 863
unique clicks that met the research requirements, less than 50 participants (5.79%) did not
complete the survey questions. In addition, there were 23 clicks (2.67%) on the link that did not
meet the research requirements to be living within the United States (e.g., Japan, Singapore). Of
the 886 total who clicked the link, Facebook groups and targeted communications referred to 806
visits, the most significant number of clicks at 90.97%. The success of recruiting female
participants may be due to American adults using Facebook; around three-quarters of U.S.
women (77%) use the platform, compared with 61% of men (Gramlich, 2021). LinkedIn referred
to 41 clicks at 5%, Facebook Messenger referred 36 clicks at 4%, WeChat referred to one click,
Google referred to one click, and another source referred one click. After the research was
closed, those who clicked on the link informed them that the survey was not active.
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Figure 3
Summary of Survey Link and Recruitment

Note. N = 886 link clicks

Data Collection Procedures
Participant recruitment took place through social media posts, individual messages, and a
snowball effect. Generic messages and individualized messaging allowed for more targeted
approaches (Appendix C). A request to participate in a soft launch of the survey was sent to a
small group from Sunday, January 23, 2022, through Tuesday, January 25, 2022. Then, from
Wednesday, January 26, 2022, through Thursday, January 27, 2022, I targeted specific groups on
Facebook with social media posts. In addition, a LinkedIn post included a link to the survey and
messages informing a few individuals in the health equity space; the social media posts and
45

personal communications also encouraged individuals to share the link with others. A final push
for participants took place from Sunday, February 6, 2022, through Tuesday, February 8, 2022.

Figure 4
Summary of Survey Access and Engagement

Note. n = 863 original clicks

46

Instrumentation
To maintain survey validity, the survey questions were based on the revised Motivation
for Physical Activity Measure (MPAM-R; Ryan et al., 1997). The MPAM-R is a 30-item survey
assessing participation motivation for sport and exercise activities, determining motivation from
interest/enjoyment, competence, appearance, fitness, and social categories. Interest/enjoyment
and competence motives reflect intrinsic motivational orientation, while appearance, fitness, and
social motives reflect extrinsic motivation (Deci & Ryan 1985b; Frederick-Recascino et al.,
2003; Fredrick & Ryan, 1993; Ryan et al., 1997). A Likert scale 7-point measurement was
developed in MPAM-R to understand the motives for physical activity using the theoretical
background of the SDT, ranging from not at all true for me to very true for me. The original
scale used “Because” at the beginning of each question. The modified parent question in the
survey included “because” in the question, I engage in physical activities, sports, and exercise
(Appendix B). Modifications in the wording to the original instrument asked participants to
answer based on physical activities. The original scale showed acceptable psychometric
properties by exploratory factor analysis and Cronbach’s alpha ranging between 0.78 to 0.92
(Ryan et al., 1997). The following sections highlight the results and summarize the findings in
detail.
An online survey questionnaire was selected as the data collection instrument based on
the target population size and presence of COVID-19. The survey sample was drawn from social
and professional networks. A broad spectrum of demographics, engagement in physical
activities, and digital health technology was expected to be reflected in the survey participant
responses. Therefore, the survey included some baseline demographic questions (gender, age,
ethnicity) to contextualize the research and discoveries. Also included in the survey were
47

questions about daily physical activities, limitations to PA, daily time spent sitting, and weekly
physical activities. In addition, two questions inquired about PA and sports participation, past
and present. An additional question was included to understand digital health technology usage.
Finally, two questions were also included in the survey questionnaire on subjective vitality.
Initially, a prototype survey was tested, reviewed, and revised to improve the question clarity,
respondent interpretation, and response time.
The survey included sections that address motivation for PA, why one would engage in
physical activities, sports, and exercise, and a subjective vitality scale. The Motives for Physical
Activities Measure-Revised (MPAM-R) explored why they would or currently engage in
exercise, sports, or PA (Ryan & Deci, 1997). The survey identified motives for participating in
physical activities, and the five motives assess fitness, appearance, competence, enjoyment, and
social (Appendix B). In addition, a portion of the questionnaire addressed if the participant
intends to begin a permanent regimen of exercising or maintain their regular exercise regimen
permanently along with their perceived vitality (Ryan & Deci, 2017b, 2000a; Deci & Ryan,
2000, 2013). The study examined the participant’s previous experience with OS, PA, and their
current PA. Lastly, the data reviewed motivation, PA, and use of digital health technology. The
survey included a Likert-type questionnaire to review data findings and increase the credibility
of the research: the data sources and identification of questions explored in the study.
Survey Completion and Gift Card Drawing
All participants had an option to complete their survey and submit their email addresses
for a drawing for one of the two $50 Amazon gift cards. To enter their email address in the raffle
drawing, they needed to click yes or no at the end of the anonymous survey. Forty-seven
participants did not respond. These 47 completed 94% of the official study, through the final
48

question, including them in the data analysis. If they answered yes, they were directed to a new
link. There were 517 participants who entered their email addresses for one of the two $50 gift
cards, a 67.58% participation rate in the drawing. Upon closing the Qualtrics survey,
participants’ data were exported to an Excel document. The random drawing was formulated in
the spreadsheet to ensure privacy and fairness. The two participants selected were informed via
email and asked to confirm the correct address before providing the gift card. The drawing took
place after the closing of the survey, and the two participants were notified via email.
The Researcher
I closely identify with the study participants and constantly evaluate PA in my daily life. I
am passionate about health and wellness, with a personal mission to help others put their healthy
intentions into action. I have a career path with experience across various industries and
understand how different factors negatively impact PA, like fatigue, lack of resources (time,
money, equipment), and stress. On the other hand, I also understand how time management,
access to facilities, a supportive community, and work culture are also common factors that
positively impact PA. My positionality and identity support a logical approach to identifying
quantitative analysis on the topic of PA. I have contacts in numerous industries, and the survey
took place across these industries. The study was available to a large pool and was not limited to
one group of physically active adults. Therefore, my positionality did not affect the participants
or their overall environment (Merriam & Tisdell, 2016). The survey did not take place with
individuals within my organization, providing an opportunity for individuals to be open and
honest. Participants were not associated with a specific organization, and responses were
anonymous and not identifiable.
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Data Analysis
The survey respondents shared data about their demographics, their motivation, why they
engaged in physical activities, and their vitality. In addition, the Qualtrics XM® online survey
provided legitimacy to the data collection and storage processes through proprietary and secure
software that allows researchers to design and distribute surveys, and generate analytic reports
based on the data received (Qualtrics, 2021). Each section separately examined the data received
and sorted using Qualtrics XM®, Microsoft Excel, IBM ® SPPS ®, and JMP® software.
The MPAM-R scoring uses a scale with the 30 items in the survey, with seven that assess
interest/enjoyment, seven that assess controlled competence, six that assess appearance, five that
assess fitness, and five that assess social (Appendix B). The analysis of variance (ANOVA) and
post-hoc Tukey-Kramer were preformed to interpret the statistical significance of the data.
Credibility and Trustworthiness
It is essential to ensure credibility and trustworthiness in this study to limit researcher
bias. The quantitative research design is one strategy to increase credibility and trustworthiness
while limiting researcher bias. In addition, the use of data from multiple sources increases the
credibility and trustworthiness of the study while limiting researcher bias (Creswell & Creswell,
2018; Merriam & Tisdell, 2016). Further measures were implemented to improve the credibility
and trustworthiness of the study, with multiple data analysis points. The survey was distributed
through the University of Southern California Qualtrics XM® Online Survey. Data were not
labeled with any personal identifying information, nor with a code that the research team could
link to personal identifying information. The data were electronically stored on local
computers/laptops, removeable drives, University of Southern California Qualtrics, and
University of Southern California Google Drive.
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Electronic data were stored with appropriate electronic safeguards, such as unique
usernames/passwords, and limited to authorized study personnel. Dual factor authentication was
used when feasible. Security software (firewall, antivirus, anti-intrusion) was installed and
regularly updated in all servers, workstations, laptops, and other devices used in the study. All
computers with access to study data were scanned regularly (for viruses and spyware, etc.) and
problems were resolved if needed. Data stored on a removable drive was encrypted and have
proper access controls and data transfer was encrypted.
The study utilized unbiased quantitative resources to solicit feedback on the findings
through a network of data scientists and Quants in the Big Data field (Merriam & Tisdell, 2015).
The research incorporated inquiry-seeking data to challenge expectations or emergent findings
(Merriam & Tisdell, 2015). Self-determination theory research occurs in laboratory experiments
and field studies across various settings. The Center for Self-Determination Theory (CSDT) is a
non-profit organization created to advance SDT by disseminating the philosophy, research, and
practices has worked with researchers to develop many questionnaires to assess different
constructs within the theory. The MPAM-R questionnaire in this study is an industry-standard,
scientifically valid, and generally used in the motivation study. All developed questionnaires
provided by CSDT are copyrighted, and they welcome the use of the instruments for academic
(non-commercial) research projects. All Likert-type questionnaires in this study use the
suggested metrics and methods as a basis for the research.
Ethics
Research participants may be apprehensive to provide honest and forthcoming
information about themselves with concern that it may be traceable, and emphasizing voluntary
participation was essential. Informed consent was obtained at the beginning of the survey to
51

guarantee the research participants’ confidentiality, during and after the study, to protect
participants. Furthermore, ensuring confidentiality hopefully relieved participants’ fear of
presenting accurate and straightforward responses and improve the credibility and
trustworthiness of the study (Creswell & Creswell, 2019). All participants had the option to leave
the online study at any time. Quantitative research was selected to minimize influence on the
participants and be mindful of how the research impacts the researcher and participants (Merriam
& Tisdell, 2016). The snowball sampling method allowed participants to feel greater autonomy
in a larger pool of participants (Goodman, 1961). When there is a large number of participants,
they are less identifiable (Nowell et al., 2017).
Electronic records were stored on a password-protected computer or on third-party
servers utilizing two-factor authentication and encryption to protect the data. Furthermore,
sharable information required obtaining permission by presenting a valid need to review the data.
The Qualtrics XM® servers are protected by high-end firewall systems, offering password
protection, and regular scans ensure additional precautions took place to reduce the likelihood of
a breach of data may occur, making the research artifacts accessible to unauthorized parties;
notifying participants promptly (Qualtrics, 2021). Approval from the institutional review board at
the University of Southern California allowed the study to proceed.
Limitations and Delimitations
This section will review the limitations and delimitations of the study. Limitations for this
study are the aspects associated with the survey that were beyond the researcher’s control. In
contrast, the researcher made delimitations to limit the scope of the study, addressed in this
section. First, this section addresses the potential impact of the COVID-19 pandemic. The second
52

discusses participant recruitment. The third presents the timing of when participants self-reported
on the survey.
It is noteworthy to address that the impact of COVID-19 may have been a challenge as
participants may be overwhelmed with daily tasks and may not have the time available to answer
the survey. In addition, the pandemic also impacted face-to-face social interactions and
potentially limited participant recruitment. Finally, participants might have various factors that
affect their regular PA due to the pandemic. For example, the survey may contain responses at
different points in their year, month, or day that may have been standard in the past.
Outreach for participation was through a snowball sampling method using personalized
emails, social media posts, and professional networks. The strategically designed emails, social
media posts, and individual correspondences prevented overlap or overcommunication. It also
allowed participants to promote the survey in their networks to encourage survey engagement.
As the surveys were anonymous, there is no way to know where participation was recruited and
most significant. However, monitoring allowed identifying participants with different
backgrounds. I worked with various contacts to promote the survey in her networks, reaching
more vast and diverse audiences (Appendix C). However, there were limitations to target groups
to get an extensive sampling, restricting the generalizability of the results providing an
incomplete view of the data (Creswell & Creswell, 2019).
The hurdles lie in the information provided and determining how true or reliable it is
when self-reporting may be subject to recall bias (Tarrant et al., 1993). It was also possible that
the participants may not have included information on leisure-time versus nonleisure-time
physical activities, and total activity may be underestimated. In addition, participants completed
an online Qualtrics survey where there was no way for the researcher to control the truthfulness
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of answers. Beyond the participant sending an email, there was no option to help if they did not
understand the question. Finally, these data were limited by coverage- and nonresponse-related
errors (Lee, 2006). Limitations, issues, and concerns that the researcher could no control were
addressed in the analysis phase (Simon & Goes, 2013). Self-selection due to voluntary
participation in the study may have resulted in participation bias. Study participants may also
have only included those interested in filling out the online survey and not include individuals
who would have felt more comfortable sitting for an interview.
The researcher limited the survey’s availability to around two weeks, and a more
extended period may have resulted in more participants. In addition, I chose not to send out
group emails or reminders to potential participants, limiting the number of participants who may
need more time or want multiple reminders. Additionally, I limited the survey questions based on
the initial design of 54-questions. Finally, the researcher restricted the survey to 10 demographic
questions, two on vitality and the 30 from the MPAM-R (Fredrick & Ryan, 1993; Ryan et al.,
1997). The 16 Exercise Self-Regulation Questionnaire (SRQ-E; Ryan & Connell, 1989; Ryan &
Deci, 2000a) was removed from the survey after running it through the Qualtrics expert review
to evaluate and recommend improvements. The scale would assess external regulation,
introjected regulation, identified regulation, and intrinsic motivation to determine the motivation
for exercise. Testing a prototype survey resulted in removing the SRQ-E scale based on the
Qualtrics evaluation and the length of time it took to complete the survey. Feedback from the
prototype survey also indicated that the complete seven questions from the State Level Version
Vitality Scale (Bostic et al., 2000; Ryan & Frederick, 1997) were excessive. Thus, the validity of
the vitality scale was eliminated and treated as a demographic question.
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Results from the prototype survey also showed confusion with the wording of the
questions. Participants were confused by the word “because” in the prototype survey. The survey
design included “because” at the beginning of question 13, removing it from the beginning of
each sub-question and allowing the questions to flow easier. The prototype survey participants
were also confused by the wording about their primary physical activity/sport; editing it to
physical activities was updated in the Qualtrics survey. Ultimately, the prototype survey allowed
for feedback from individuals and the Qualtrics expert review where they evaluate the survey,
making recommendations for improvements before beginning the research.
This study included more female participants at 556 (72.68%) than males at 205
(26.80%). Of the 765 participants, 761 noted their age, with a mean of 42.39 years. In addition, it
should be noted that 638 (83.40%) participants identified as Caucasian/White. These numbers
may be seen as limitations, though the study did not strive to differentiate based on age, gender,
or ethnicity/race.
Summary
The methodology allowed for the collection of data to understand motivation and PA
among adults, past participation in OS/PA, current PA, participation in OS, and use of digital
health technology. The surveys tell a story of the bigger picture. Thus, the data presents valuable
insight into respondents’ PA and the role of motivation. As mentioned, framing the problem with
the lack of PA in adults generates clarity and understanding to develop resources to motivate
adults to engage in at least 150 minutes of moderate PA a week.

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Chapter Four: Findings
This quantitative study aimed to understand how the lack of PA in adults outlines a
complicated and significant problem in America. Examined through SDT (Deci & Ryan, 2000),
this study aimed to evaluate how motivation plays a role in PA in adults, with a focus on sports
and digital health technology. In addition, this quantitative study aimed to understand how
interest/enjoyment, competence, appearance, fitness, and social motivations illuminate PA’s
significance or lack thereof. The quantitative analysis focused on differences and correlations
between adults and previous participation in sports or PA in their youth and current digital
technology use. It also examined why people exercise regularly and their perceived vitality.
Furthermore, this study sought to discover the role of motivation and technology in PA among
adults. The following questions guided the research:
1. Are there differences in why people are physically active based on prior participation
in OS/PA and current use of digital health technology?
2. Are there differences in current levels of PA based on prior participation in OS/PA
and current use of digital health technology?
3. Are there differences in participants’ perceived vitality based on prior participation in
OS/PA and current use of digital health technology?
4. What variables are the highest predictors of PA and digital health technology use in
adults?
While limited in terms of participants’ demographics, the data analysis demonstrated
trends and relationships between digital health technology, prior participation in sports, and the
current level of PA. This chapter outlines the following elements of the study: participating
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stakeholders; demographics; physical activities, sports, and exercise; digital health technology;
vitality, survey results, and findings of the quantitative study.
Survey Methodology
The 765 research participants were all surveyed remotely using Qualtrics capabilities for
data analysis and coding. The researcher had to specifically enable the anonymous functionality
for the surveys and note that no IP addresses were collected. Once the participants clicked on the
survey link, the researcher thanked them for their interest in the health, physical activity, and
motivation survey. To get started, the participants had to follow prompts and instructions. All
visuals in Qualtrics were also noted with alternative text to ensure accessibility. Finally, a
description was provided for the participants to decide whether they should participate. The
description included requirements, payments, and compensation; voluntary participation; length
of study; privacy and confidentiality; withdrawal from study instructions; contact information;
and a consent statement. Following the consent, participants were provided with a question to
confirm they agreed to take the survey.
After the survey, participants were thanked and provided an option to enter a drawing for
an Amazon gift card. Participants who chose to enter their email addresses into an anonymous
drawing link submit their email addresses in a new link that does not collect IP addresses. The
researcher then collects the de-identified raw data and exports it from the surveys. The researcher
asked 13 questions; 10 were demographic questions, two were on vitality, and one included 30
Likert scale sub-questions (Appendix A) based on a pre-validated survey. A total of 43 questions
were presented, excluding the question of whether the participant was interested in the drawing
for the Amazon gift card.

57

Data Analysis
This research aimed to reveal the participants’ motivation with experience with sports,
physical activity, and digital health technology. The survey design helped flow and facilitated the
data analysis process; the question groups were in distinct categories: participant demographics,
current physical activities, previous physical activity in participants’ youth, current digital health
technology use, vitality, and motives for PA. The results revealed in this section came from the
data analysis of 765 different quantitative surveys that focused on past participation in PA/OS,
current participation in PA/OS, motivation, and digital health technology. In addition, the
questions from the survey protocol sought to understand the current PA through the lens of Deci
and Ryan’s (2000, 2017a) SDT, offering a perspective on different types of motivation.
Participant Stakeholders
Quantitative data collection came from 765 survey responses from adults over age 18
who are physically active or intend to engage in PA. The only exclusion was if they were under
the age of 18, they did not have the option to proceed with the survey. In addition, the online
survey was open to U.S.-based English-speaking adults who have access to take the survey.
Once they stated that they were over 18 years of age, the survey invited them to self-report.
Quantitative data were collected from survey responses from January 23, 2022, through February
8, 2022. The participants could use the back button to change their responses, had an option to
finish later, and the survey response time was calculated at the last time they edited a response.
The average time to complete the survey was 14 minutes and 52 seconds. The survey access was
open to anyone; it was not password-protected, utilizing Qualtrics security to prevent multiple
submissions and index and anonymize responses that did not record IP addresses, location data,
or contact information.
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Although 765 participants are a relatively small sample considering the significant
number of adults in the United States, this study covered various participant demographics
through snowball sampling. Following the descriptive demographics overview, an overview of
the data categories and abbreviations analyzed in Excel, SSPS®, and JMP® software.
Additionally, a descriptive overview of the survey participants shows what was examined in the
inferential analysis (Appendix D). The participant population was 72.68% female and 26.80%
male. In addition, 638 participants identified as Caucasian/White, making up 83.40% of the
responses. It is worth noting that there was no target ratio for gender identity or ethnicity in this
study.
Preliminary Analyses
This survey aimed to gauge how participants engage in physical activities. Using
quantitative methods, data about daily physical activities gave walking, running, yoga,
swimming, housework, playing with children, and home repair as examples. To understand PA
in adults, understanding their limitations was necessary to gauge if any imitations may have
impacted their PA engagement. Therefore, the survey addressed injury, health conditions,
disabilities, or limitations that would restrict their physical activity. In contrast to understanding
PA, the survey sought to understand how much time participants were sitting each day.
The recommended weekly PA of at least 150 minutes of moderate-intensity aerobic
activity (CDC, 2020) was the foundation to measure participants’ PA. In addition, adults should
engage in at least 150 to 300 minutes a week of moderate-intensity aerobic activity for increased
health benefits, while exercising for more than 300 minutes a week may improve health (HHS,
2018). The survey exhibited time in hours based on these CDC and HHS recommendations. The
survey asked participants to recall times that they had increased breathing, heart rate, leg fatigue,
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or perspiration for at least 10 minutes over the last week. In addition, the study examined
engaging in OS/PA in the participants’ youth and currently. The research explored the regular
use of fitness and health tracking technology with smart devices (e.g., Apple Watch, Garmin,
Oura Ring, Joule), fitness trackers (e.g., Fitbit, Smart Clothing, GPS Tracker, Heart Rate
Monitor, Cadence Sensor) and other forms of digital health technology. In addition, the
participants' energy and vitality was assessed.
There is no calculated total motivation score, and the study used mean item scale scores
in the analysis. The five scales of the MPAM-R determined the items associated with each scale.
First, the totals from each section were added up and then divided by the number of questions in
the subsection, calculating the means for five scales of the MPAM-R. Finally, the five
subcategories were averaged and compared to determine the stronger motive. For example, five
questions measure fitness as a motive. The scores of questions 1, 13, 16, 19, and 23 were added
up and then divided by five to determine the average for the fitness scale. All five scales utilized
the same scoring manner. For example, interest/enjoyment totaled questions 2, 7, 11, 18, 22, 26,
29, and divided the total by seven; the total for the competence questions (3, 4, 8, 9, 12, 14, and
25) was divided by seven; questions 5, 10, 17, 20, 24, 27 were summed and divided by six to
determine the score for the appearance motive; and the social motive score added questions 6,
15, 21, 28, 30 and split the total by five. Table 1 provides an overview of the results for question
13 to understand why participants engage in physical activities, sports, and exercise.

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Table 1
MPAM-R Results for Question 13: “I Engage in Physical Activities, Sports, and Exercise
Because”
Variable Enjoyment/interest Mean SD Variance
2 Physical activities are fun. 5.39 1.41 1.99
7 I like to do physical activities. 5.39 1.44 2.07
11 Physical activities make me happy. 5.62 1.39 1.93
18 I think physical activities are interesting. 5.18 1.51 2.28
22 I enjoy physical activities. 5.32 1.48 2.2
26 I find physical activities stimulating. 5.35 1.50 2.26
29 I like the excitement of participation. 4.58 1.73 3.01
Competence
3
I like engaging in activities that physically
challenge me.
5.08 1.56 2.45
4 I want to obtain new skills. 4.99 1.61 2.59
8 I want to improve my existing skills. 5.27 1.49 2.23
9 I like the challenge. 5.17 1.57 2.47
12 I want to keep up my current skill level. 5.16 1.62 2.63
14 I like activities that are physically challenging. 5.02 1.61 2.58
25 I want to get better at physical activities. 5.20 1.47 2.16
Appearance
5
I want to look better or maintain weight, so I look
better.
5.88 1.37 1.88
10 I want to define my muscles so I look better. 5.29 1.72 2.95
17 I want to improve my appearance. 5.56 1.53 2.33
20 I want to be attractive to others. 5.12 1.64 2.70
24 I want to improve my body shape. 5.57 1.48 2.18
27 I will feel physically unattractive if I don't. 4.33 1.83 3.36
Fitness
1 I want to be physically fit. 6.05 1.32 1.75
13 I want to have more energy. 5.92 1.33 1.77
16 I want to improve my cardiovascular fitness. 5.72 1.32 1.74
19
I want to maintain my physical strength to live a
healthy life.
6.07 1.23 1.52
23
I want to maintain my physical health and well-
being.
6.05 1.24 1.53
Social
6 I want to be with my friends. 4.84 1.83 3.34
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15
I like to be with others who are interested in
physical activities.
4.78 1.65 2.71
21 I want to meet new people. 4.18 1.79 3.21
28
My friends want me to engage in physical
activities.
3.67 1.87 3.49
30
I enjoy spending time with others doing physical
activities.
4.58 1.78 3.17

Note. N = 765 completed survey responses

The survey found that adults have various reasons people engage in physical activities,
sports, and exercise. The responses were to the 30 questions from the MPAM-R instrument. The
results aligned with participants’ engagement in physical activities, sports, and exercise because
of interest/enjoyment, competence, appearance, fitness, or social motives. The scoring indicated
that fitness was the highest motivator, with an average score of 5.96. Fitness was followed by
appearance, averaging 5.29. Then came interest/enjoyment with an average score of 5.26.
Competence followed with an average score of 5.13. Finally, the least motivating factor was
social, with an average of 4.41. The standard deviations for the measurements ranged from 1.29–
1.77. The validity and reliability of the MPAM-R and the internal consistency measures for each
subscale achieved Cronbach’s alpha scores above .80 (Frederick and Ryan, 1993; Ryan et al.,
1997). Table 2 provides an overview of the survey participant’s MPAM-R scores in relation the
Cronbach alphas based on the question in the group.

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Table 2
MPAM-R Results for Motive for Physical Activities
Motive for physical activities measure M SD
Survey
Cronbach’s
alpha
Original
Cronbach’s
alpha
Interest/enjoyment 5.26 1.49 .91 .92
Competence 5.13 1.56 .88 .88
Appearance 5.29 1.60 .87 .91
Fitness 5.96 1.29 .82 .78
Social 4.41 1.77 .84 .83

Note. N = 765 completed survey responses

Each variable was aggregated and compared with levels of PA weekly/daily, motives for
PA, perceived vitality, prior and current participation in OS/PA, and digital health technology.
This analysis reviewed multiple categorical independent variables and multiple dependent
variables. An ANOVA was carried out to identify differences in motives among participant
variables of gender, ethnicity, age, OS/PA, sitting, vitality, and digital health technology. Finally,
post hoc tests with Tukey-Kramer were performed to reveal significant differences.
Results Pertaining to Sample
Analyzing motivators through the entire sample of variables presented the data
differently than breaking the sample into subcategories. In order to gain a stronger understanding
of the data, the combined scores were initially reviewed, and subcategories were designed
(Appendix D, Table 1). The comparison was made by age, gender, daily, physical limitations,
weekly PA, hours sitting, participation in OS/PA in youth, current participation in OS/PA, digital
health technology, and vitality. Age (< = 41, 42+) based on the mean age, hours sitting a day
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(<6S, >6S), those who do not feel or maybe alert and awake (NMAA), and those who feel yes to
being alert and awake (YAA) do not significantly differ in what motivates people to engage in
PA.
Other variables show significant differences worth looking into to understand motivations
further (Appendix D, Table 2). For example, the data shows a significant difference between the
male (M; Mean (M) = 5.33, Standard Deviation (SD) = 0.96) and female (F; M = 5.17, SD =
0.95) participants. The data also suggests that there are differences in motives for ethnicity,
t(765) = 2.44, p = .02, White (W; M = 5.23, SD = 0.96) and non-Whites (NW; M = 4.98, SD =
0.94). Motivation to engage in PA also varies based on if participating in PA, t(765) = 6.37, p =
.00, in less than one hour (<1hrD; M = 4.88, SD = 0.97) or more than one hour (>1hrD; M =
5.35, SD = 0.91) of PA a day. The data also suggests that those with that replied about physical
limitations, t(765) = 3.06, p = .00, those who replied with yes to physical limitations (PLY; M =
5.04, SD = 0.99) and those who replied with no limitations due to injury, health conditions,
disabilities, or other factors (PLN; M = 5.27, SD = 0.93) have significant differences in what
motivates them to engage in PA. In line with past research, there is a significant difference in
motivation based on if there was youth participation in OS/PA, t(765) = 3.24, p = .00, those who
participated in either OS or PA or both (YOS/PA; M = 5.24, SD = 0.96) attaining higher scores
than those who did not participate (YN; M = 4.82, SD = 0.91). These are a few variables that
impact motivation, contributing to how people engage in PA.
In addition, there was also a significant difference in motivation based on current
participation in PA between those who participate in either OS/PA or both t(765) = 8.73, p = .00,
(CPAY; M = 5.32, SD = 0.90) and those who do not engage in OS or PA (CPAN; M = 4.47, SD
= 0.90). The use of digital health technology also showed significant differences between those
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who do not use any digital health technology, t(765) 3.85, p = .00, (NDHT; M = 4.99, SD = 1.02)
and those who currently use digital health technology (YDHT; M = 5.29, SD 0.92). There are no
significant differences in how people feel energized when engaging in PA between those who
maybe or do not feel energized t(765) 7.90, p = .42, (NME; M = 4.48, SD = 1.02). There are
significant differences between those who scored higher noting they do feel energized, t(765) =
7.90 , p = .00, (YME; M = 5.30, SD = 0.90). An overview of demographic subcategories,
abbreviations, and ANOVA combined variables for analysis show details of the sample
(Appendix D).
Interest/Enjoyment as a Motive for Physical Activity in Adults
Interest and enjoyment are low motivators for people engaging in PA, sports, or exercise.
The data shows that interest and enjoyment were low motivators for why adults currently engage
in physical activities, sports, and exercise (Appendix D, Table 3). However, adults who
participated in PA in their youth and currently participate in PA were more likely to note an
interest/enjoyment. This measure indicates that they were moderately motivated to engage PA,
OS, and exercise due to being intrinsically motivated by interests and enjoyment.
Interest/enjoyment refers to being physically active simply because it is fun, makes one happy,
and is interesting, stimulating, and enjoyable (Ryan et al., 1997). The MPAM-R scale can be
used to predict various behavioral outcomes, such as attendance, persistence, or maintained
participation in some sport or exercise activity, or to predict mental health and well-being (Ryan
et al., 1997). Interest/enjoyment did not rank high on the scale for reasons adults want to
participate in PA, OS, or exercise. However, these numbers show that current participation is
prominent in all reported interest/enjoyment motives. Interest/enjoyment is not a high motivator
compared to the other motives presented. Showing that being physically active simply because it
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is fun, makes one happy, or is interesting is not a high motivator to engage in PA, OS, or
exercise as an adult. Once again, adults motivated by interests and enjoyment do not currently
engage in OS at the same rate at PA, even though they participated in their youth.
Age
There is no significant difference for age, t(761) = 2.09, p = .81 between those over the
age of 42 (M = 5.25, SD = 1.28) and those under 41 years (M = 5.27, SD = 1.15) motivated by
interest/enjoyment.
Gender
There was significant effect for sex, t(761) = 2.09, p = .04, males (M = 5.41, SD = 1.15)
attaining higher scores than females (M = 5.21, SD = 1.24) when physically active simply
because it is interesting, fun, enjoyable, and makes one happy. The significant differences could
be based on the activities that participants engage in or even the types of facilities or
organizations where they engage in exercise. The data suggest differences in if males or females
are motivated by interest/enjoyment because of the type of exercise or location where they are
participating in PA. For overweight women, there is a stigma that prevails in their everyday lives
and is often associated with physical activity (Myre et al., 2021), contributing to if they enjoy or
are interested in PA. The significant differences in the data point to differences in males and
females being motivated by interest or enjoyment when it comes to PA.
Ethnicity
When reviewing the initial data, there was no significance when looking at ethnicities'
motivation by interest/enjoyment. However, a significant difference appeared once the data was
separated between Whites and non-Whites. There was significant effect for ethnicity, t(765) =
2.26, p = .02, non-Whites (M = 4.99, SD = 1.22) scored lower than Whites (M = 5.29, SD =
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1.21). Being motivated by interest/enjoyment to engage in physical activity is associated with
cultural differences, individual, interpersonal, or community-level barriers, and social influence
or social support-related themes (Choi et al., 2021). Cultural influences, including leisure-time
PA, are associated with the differences in Whites and non-Whites being motivated by
interest/enjoyment.
Daily PA
The data indicates that motivation to engage in PA daily shows a difference in those who
get up and engage in even one hour of PA a day. For example, there was a significant effect for
daily participation in PA, t(765) = 8.34, p = .00, as participants who engaged in less than one
hour a day (M = 4.72, SD = 1.23) attained lower scores than those who participated in more than
one hour of PA a day (M = 5.49, SD 1.13). Lifestyle choices are vital to physical health, mental
health, and well-being. Some research indicates that the benefits of activities like nature walks
contribute to overall health (Anderson, 2022), providing examples of how daily walks that
engage in at least one hour of PA a day are motivated by interest/engagement.
Physical Limitations
There was significant effect for physical limitations, t(765) = -2.40, p = .02, as those with
physical limitations (M = 5.09, SD = 1.17) was lower than those who have no limitations (M =
5.32, SD = 1.23). The physical limitations in this study were associated with injury, disabilities,
health, or other limitations. People living with disabilities are 16–62% less likely to meet PA
guidelines, placing them at a higher risk of serious health problems (Ginis et al., 2021). Interest
and enjoyment for adults living with limitations is not a strong motivator to engage in PA.

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Sitting
Those who sat for less than 6 hours a day had a significant effect for sitting habits, t(765)
= 3.22 = , p = .00, (M = 5.40, SD = 1.16) and lower scores than those who sat for more than 6
hours a day (M = 5.12, SD = 1.25). New evidence shows there is potential for health benefits by
reducing sitting time and moving more (Dunstan et al., 2021). Interest or enjoyment contribute to
those motivated to move based on their sitting habits.
Youth PA
PA is significant for the health and wellbeing of youth, though participation declines
across the teenage years (Rullestad et al., 2021). The data shows a significant effect for
participation in OS/PA in their youth, t(765) = 3.71, p = .00, those who engaged in OS/PA in
their youth (M = 5.30, SD = 1.19) attaining higher scores than those who did not participate in
OS/PA (M = 4.70, SD = 1.12). In addition, it is noteworthy to point out that other research data
suggests that youth sports participation could be more critical among females than males for
predicting physical activity in adulthood (Kwon et al., 2021).
Current PA
There was a significant difference between those who currently engage in OS/PA and
those who do not, t(765) = 11.58, p = .00, those currently engaging in OS/PA (M = 5.44, SD =
1.10) attaining higher scores than those who do not participate in OS/PA (M = 4.04, SD 1.25).
Being motivated by interest and enjoyment was impacted by the COVID-19 pandemic;
decreased gym access, effects on outdoor PA, and increased dependence on at-home PA (Folk et
al., 2021) contributed to what motivates interest/enjoyment in PA.

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Digital Health Technology
There was significant effect for motivation from interest/enjoyment for use of digital
health technology, t(765) = 3.72, p = .00, those who use digital health technology (M = 5.36, SD
= 1.13) attaining lower scores than those who do not (M = 4.99, SD = 1.37). Research shows
digital health is disadvantaging women, contributing to lower access and exclusion from app
design, gender imbalance in digital health leadership, and harmful stereotypes —especially for
women with racial or ethnic minority backgrounds (Figueroa et al., 2021). Tackling digital
health's gender inequities contribute to women's motivation based on interest/enjoyment.
Additionally, digital health technology can help adults interact with similar-mind people to
involve themselves in a fun activity that can also be effective for improving their health (Ahmad
et al., 2022).
Vitality
Feeling alert and energized contributes to self-determination, and there are significant
differences in motivation based on interest/enjoyment. There was significant effect for vitality,
t(765) = 2.58, p = .01, those who do not feel alert or may feel alert (M = 5.02, SD = 1.23)
attaining lower scores than those who do feel alert (M = 5.31, SD = 1.20). In addition, there is
significant effect for energy, t(765) = 10.65, p = .00, those who feel energized when engaging in
PA (M = 5.42, SD = 1.12) scoring higher than those who do not (M = 4.05, SD = 1.25). Physical
inactivity is a public health issue, and understanding vitality is a factor in motivation based on
interest/enjoyment.
Competence as a Motive for Physical Activity in Adults
Competence did not rank highest on the scale for reasons adults want to participate in PA,
OS, or exercise (Appendix D, Table 4). Nevertheless, numbers show that PA’s current
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participation is prominent in all reported competence motives. Among all participants,
Competence is one of the lower motivators compared to the other motives presented. The data
show that the participants are not as motivated to be physically active because of the desire to
improve at an activity, meet a challenge, and acquire new skills. Adults continue to report that
they engage in PA and are not as active in OS. Competence guides an experience of mastery,
challenge, curiosity, exploration, and stretching capacities (La Guardia, 2009; White, 1959).
Age
There is no significant effect for age, t(761) = -2.62, p = .11 between those over the age
of 42 (M = 5.05, SD = 1.25) with lower scores those under 41 years (M = 5.20, SD = 1.26)
motivated by competence.
Gender
There was significant effect for sex, t(761) = 4.21, p = .00, males (M = 5.43, SD = 1.10)
attaining higher scores than females (M = 5.02, SD = 1.21) when physically active and motivated
by accomplishments, experience, and abilities. The data suggest differences in if males or
females are motivated by competence. For females, research shows that experiences appear to be
limited by a fear of judgment and an overarching sense of gender inequality (Cowley et al.,
2021), contributing to if they are motivated by competence to engage in PA. The significant
differences in the data point to differences in males and females being motivated by competence
when it comes to PA.
Ethnicity
There was no significance when looking at ethnicities' motivation by competence when
reviewing the initial data. However, a significant difference appeared once the data was
separated between Whites and non-Whites. There was significant effect for ethnicity, t(765) =
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2.10, p = .04, non-Whites (M = 4.88, SD = 1.25) attaining lower scores than Whites (M = 5.15,
SD = 1.19, p = .04). Being motivated by competence to engage in physical activity is a key topic
when focusing on public health. With the benefits of PA and the availability of evidence-based
interventions, measures must now concentrate on implementing the knowledge to improve public
health and reduce inequities through PA (Keadle et al., 2021). Cultural influences are associated
with the differences between Whites and non-Whites being motivated by competence.
Daily PA
Like being motivated by interest/enjoyment, the data indicates that motivation to engage
in daily PA reveals a difference in those who get up and engage in even one hour of PA a day.
For example, there was a significant effect for daily PA, t(765) = 7.61, p = .00, participants who
engaged in less than one hour a day (M = 4.64, SD = 1.22) attained lower scores than those who
participated in more than one hour of PA a day (M = 5.33, SD 1.12). Examined through SDT,
competence is one of the basic psychological needs (Deci et al., 2002). However, motivations are
not identical, and these data point to differences in why people are motivated to engage in PA for
at least an hour a day.
Physical Limitations
There was no significant effect for those with physical limitations, t(765) = -0.62, p = .54
(M = 5.08, SD = 1.14) and those with a higher score who have no limitations (M = 5.14, SD =
1.22) who are motived by competence to engage in PA.
Sitting
There was a significant effect for sitting, t(765) = 4.08, p = .00, those who sat for less
than 6 hours a day (M = 5.30, SD = 1.12) and those who sat for more than 6 hours a day (M =
4.95, SD = 1.24). In addition, a recent study reveals that perceived competence and task values
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toward PA were predictors of depressive symptoms, and physically active college students
consistently demonstrated higher PA motivation, displaying fewer depressive symptoms (Zhang
et al., 2021). Competence contributes to those motivated to move based on their sitting habits.
Youth PA
The data shows a significant effect for those who engaged in OS/PA in their youth, t(765)
= 3.33, p = .00, those who engaged in OS/PA in their youth (M = 5.17, SD = 1.18) and those who
did not participate in OS/PA scored lower (M = 4.63, SD = 1.26). Expanding on sport
participation tracking and how past movement experiences influence adult PA, movement
outside of regular physical education classes, and supplemental movement activities aid in
developing competencies that might influence future participation (Martin et al., 2021). As youth
sports become increasingly expensive, there is an opportunity to invest in programs and
organizations that increase physical activity and access to sports for all children (Watson et al.,
2021), encouraging motivation to engage in PA as adults.
Current PA
There was a significant effect for OS/PA, t(765) = 10.56, p = .00, those who engage in
OS/PA (M = 5.29, SD = 1.09) scored higher than those who do not participate in OS/PA (M =
4.02, SD 1.26). Being motivated by competence is influenced by overall health, opportunities for
skill improvement, or general knowledge. With increased fitness influencers online, watching
videos and exercising are related to those who already exercise and are not associated with those
who intend to engage in PA (Sokolova et al., 2021). The data in this study show that improving
skills and competence has significant differences in what motivates participants to engage based
on current participation in PA.

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Digital Health Technology
Once again, similar to interest/enjoyment, motivation from competence had a significant
effect for digital health technology use, t(765) = 3.26, p = .00, those who use digital health
technology (M = 5.21, SD = 1.12) scored higher than those who do not (M = 4.90, SD = 1.35).
Research shows that there is still considerable skepticism about the prospect of digital sports
solutions to improve the motivation to engage in OS/PA, increase performance, or raise a sense
of team spirit when done in groups (Ruth et al., 2022). This study points to differences in digital
health technologies used to be motivated by competence to engage in PA.
Vitality
Feeling alert and energized contributes to self-determination, and there are significant
differences in motivation based on competence. There was a significant effect for vitality, t(765)
= 2.97, p = .00, those who do not or may feel alert (M = 4.86, SD = 1.17) attaining lower scores
than those who do feel alert (M = 5.19, SD = 1.19). In addition, there was a significant effect for
energy, t(765) = 9.84, p = .00, those who feel energized when engaging in PA (M = 5.27, SD =
1.11) and attaining higher scores than those who do not (M = 4.02, SD = 1.24). Initially, the
COVID-19 pandemic prompted adults to engage in health-promotion activities (Cummings et al.,
2022), and the differences in the data show that interpretation vitality is an aspect of
understanding competence motivation.
Appearance as a Motive for Physical Activity in Adults
Appearance was not a strong motivator for why adults engage in PA, referring to
engagement in becoming more physically attractive, making muscle definition, looking better,
and reaching or holding the desired weight (Ryan et al., 1997). Instead, the data show that the
participants are not as motivated to be physically active because of the desire to become more
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physically attractive, have defined muscles, look better, and gain or maintain the desired weight
(Ryan et al.,1997). Adults continue to report that they engage in PA and are not as active in OS.
The data for appearance and being extrinsically motivated show fewer significant differences
(Appendix D, Table 5).
Age, Gender, Ethnicity, Sitting, and Youth PA
There is no significant difference between age, gender, ethnicity, daily PA, sitting, and
youth PA. There was no significant effect for age t(761) = -0.78, p = .44, despite those over the
age of 42 (M = 5.25, SD = 1.28) attaining lower scores than those under 41 years (M = 5.32, SD
= 1.22) motivated by appearance. There was no significant effect for gender, t(761) = -1.46, p =
.14, despite males (M = 5.19, SD = 1.25) attaining lower scores than females (M = 5.33, SD =
1.25) when physically active and motivated by how they looked. There was no significant effect
for ethnicity, t(765) = 1.25, p = .21, despite non-Whites (M = 5.13, SD = 1.34) attaining lower
scores than Whites (M = 5.30, SD = 1.24) motivated by appearance. There was no significant
effect for sitting, t(765) = -1.62, p = .11, despite those who sat for less than 6 hours a day (M =
5.22, SD = 1.25) attaining lower scores than those who sat for more than 6 hours a day (M =
5.36, SD = 1.24). In addition, there was no significant effect for OS/PA youth engagement and
being motivated by appearance, t(765) = 0.44, p = .66, despite those who engaged in OS/PA in
their youth (M = 5.30, SD = 1.24) attaining higher scores than those who did not participate in
OS/PA (M = 5.22, SD = 1.38).
Daily PA
Once again, similar to interest/enjoyment and competence, appearance data indicates that
motivation to engage in daily PA reveals a significant difference in those who get up and engage
in even one hour of PA a day. For example, there was a significant effect for daily PA, t(765) =
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2.47, p = .01, participants who engaged in less than one hour a day (M = 5.12, SD = 1.36) was
lower than those who participated in more than one hour of PA a day (M = 5.36, SD 1.19, p =
.01). Studies reveal the importance of appearance and performance in shaping women’s
participation in physical activity (Su et al., 2022)—understanding the significance of the
differences in how appearance motivates daily PA.
Physical Limitations
Unlike interest/enjoyment and competence, there were significant differences in the data
for appearance as a motivator based on physical limitations. There was a significant effect for
physical limitations, t(765) = -3.30, p = .00, those with physical limitations (M = 5.05, SD =
1.23) attaining lower scores than those with no limitations (M = 5.38, SD = 1.25) when
motivated by appearance to engage in PA. Research shows implications for PA as a tool for
maintaining or enhancing mental health during a time of trauma and uncertainty (Maher et al.,
2021), and the differences in the data explain how appearance motivates those with physical
limitations differently.
Current PA
In line with interest/enjoyment and competence, there was a significant effect for current
PA, t(765) = 4.13, p = .00, those who currently engage in OS/PA (M = 5.36, SD = 1.20) attaining
higher scores than those who do not participate in OS/PA (M = 4.81, SD 1.48). Being motivated
by appearance is influenced by the desire to look good and have defined muscles, and body-
positive advertising raises acceptance of diverse body types. Understanding how media shapes
attitudes or beliefs that contribute to weight stigma and body ideals (Selensky et al., 2021)
explains the differences in the data on how appearance motivates current PA.

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Digital Health Technology
Appearance, interest/enjoyment, and competence show significant effects in the data for
use of digital health technology, t(765) = 3.81, p = .00, those who currently use digital health
technology (M = 5.40, SD = 1.18, p = .00) attaining higher scores than those who do not (M =
5.01, SD = 1.38, p = .00). Evidence shows the potential benefits of engaging with body-positive
content on social media and advertising (Cohen et al., 2021). This also illustrates differences in
data for those motivated by appearance who use digital health technology and those who do not.
Vitality
There was significant effect for vitality, t(765) = -2.37, p = .02, those who do not or may
feel alert (M = 5.51, SD = 1.16) attaining higher scores than those who do feel alert (M = 5.24,
SD = 1.26). In addition, there was a significant effect for feeling energized, t(765) = 3.74, p =
.00, those who feel energized when engaging in PA (M = 5.35, SD = 1.21) attaining higher
scores than those who do not (M = 4.83, SD = 1.45). Moreover, the differences in the data show
that vitality is an aspect of understanding appearance motivation.
Fitness as a Motive for Physical Activity in Adults
Fitness ranked the highest on the scale for reasons adults want to participate in PA, OS,
or exercise. The numbers show that nine variables had no significant differences and show how
fitness was the highest motivator (Appendix D). Social and fitness subscales were added to the
SDT instrument with competence, enjoyment, and body relations representative of intrinsic and
extrinsic motives relevant to potential reasons for exercise participation in the revised instrument
(Ryan et al., 1997). In addition to the original SDT instrument, fitness and health motives are
shown to guide PA engagement with a desire to be physically healthy, strong, and energetic
(Frederick et al., 1993; Ryan et al., 1997). The data show that the participants are motivated to be
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physically active because of the desire to be physically active. Cardiorespiratory fitness,
musculoskeletal fitness, flexibility, balance, and movement are indicators of fitness motivation.
Adults continue to report that they engage in PA and are not as active in OS. Fitness,
extrinsically motivated, guides incentive to be physically healthy, strong, and energetic.
Gender, Ethnicity, Sitting Habits, Weekly PA, and Digital Health Technology
There is no significant difference between gender, ethnicity, sitting habits, and digital
technology use for those motivated by fitness. There were no significant effect for gender, t(761)
= -0.22, p = .83, despite males (M = 5.95, SD = 1.03) attaining lower scores than females (M =
5.97, SD = 0.98) when physically active and motivated by fitness. There were no significant
effect for ethnicity, t(765) = 1.23, p = .22, non-Whites (M = 5.29, SD = 1.15) attaining lower
scores than Whites (M = 5.96, SD = 0.98) in being motivated by fitness. There were no
significant effect for sitting habits it fitness motivation, t(765) = -1.92, p = .06, those who sat for
less than 6 hours a day (M = 5.89, SD = 1.04) attaining lower scores than those who sat for more
than 6 hours a day (M = 6.03, SD = 0.94). In addition, there were no significant effect in being
motivated by fitness between those who engaged in OS/PA, t(765) = -1.03, p = .30, those who
participated in OS/PA in their youth (M = 5.95, SD = 1.01) attaining lower scores than those who
did not participate in OS/PA (M = 6.09, SD = 0.76). Unlike appearance, interest/enjoyment, and
competence, fitness motivation shows no significant effect for digital health technology use,
t(765) = -0.78, p = .44, those who use digitahl heath technology (M = 5.94, SD = 1.02) attaining
lower scores than those who do not (M = 6.01, SD = 0.93).
Age
In contrast to interest/enjoyment, competence, and appearance, there is a significant
difference between age and fitness motivation. There was a significant effect for age, t(761) =
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6.94, p = .00, those over the age of 42 (M = 6.20, SD = 0.81) attaining higher scores than those
under 41 years (M = 5.72, SD = 1.10) who are motivated by fitness. Research shows that efforts
are needed to ensure evidence-based public health messages are available for older adults
(Harden et al., 2022), and motivation by fitness aligns with the shift in focus in the analysis of
active aging (Fanning et al., 2022).
Daily PA
In line with interest/enjoyment, competence, and appearance, fitness also shows
significance for those engaging in PA for more than an hour or less than an hour each day. There
was a significant effect for daily PA in fitness motivation, t(765) = 3.27, p = .00, despite
participants who engaged in less than one hour a day (M = 5.78, SD = 0.90) attaining lower
scores than those who participated in more than one hour of PA a day (M = 6.04, SD = 0.99).
Studies about youth's relationship between morning cortisol and perceived stress with physical
activity (Naya et al., 2021) show the significant differences in daily PA for those motivated by
fitness. Decreased PA has powerful and potentially recoverable impacts (Christensen et al.,
2022), and the significance of the data show how fitness plays a role in motivation.
Physical Limitations
Comparable to interest/enjoyment and appearance, there were significant differences in
the data for fitness as a motivator based on physical limitations. Significant differences were
between those with physical limitations (M = 5.50, SD = 0.08, p = .00) and those with no
limitations (M = 6.13, SD = 0.82, p = .00) motivated by fitness to engage in PA. Research
identifies no consensus for the optimal physical activity level for mitigating negative mental
symptoms, neither for the frequency nor the type of physical activity (Marconcin et al., 2022).
The data support differences in PA based on physical limitations and fitness motivation.
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Current PA
In addition to interest/enjoyment, competence, and appearance, there was a significant
effect for fitness motivation in those who currently engage in PA, t(765) = 3.08, p = .00, despite
those who currently engage in OS/PA (M = 6.00, SD = 0.99) attaining higher scores than those
who do not participate in OS/PA (M = 5.67, SD = 0.96). Differences in being motivated by
fitness coincide with studies that exercise frequency varied significantly during the pandemic
(Wijngaards et al., 2022).
Vitality
There was no significant effect for motivation by fitness for alterness, t(765) = -0.54, p =
.59, despite those who do not or may feel alert (M = 6.00, SD = 0.84) attaining higher scores than
those who feel alert (M = 5.95, SD = 1.03). However, there was significant effect for feeling
energized when engaging in PA, t(765) = 4.38, p = .00, those who feel energized when engaging
in PA (M = 6.01, SD = 0.97) attaining higher scores than those who do not (M = 5.52, SD =
1.06). The differences in the data show that vitality is an aspect of fitness motivation in adults
based on feeling alert and their energy when engaging in PA.
Social as a Motive for Physical Activity in Adults
Social ranked the lowest on the scale for reasons adults want to participate in PA, OS, or
exercise (Appendix D, Table 7). Similar to the addition of the fitness subscale, social motivation
was added to the SDT instrument with competence, enjoyment, and body relations representative
of intrinsic and extrinsic motives (Ryan et al., 1997). SDT acknowledges that people have an
innate preference for growth, self-integration, and psychological consistency; researchers also
believe that a social environment can foster this innate drive or hamper it (Deci & Ryan, 2000).
The data shows that the participants are motivated to be physically active because of social
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variables. Individuals feel a sense of relatedness when socially bonded, cared for, and supported
via their connections with others (Ryan & Deci, 1997). Unfortunately, adults continue to report
engaging in PA and are not as active in OS. Social, extrinsically motivated directs people to
connect with others.
Age
There was significant effect in social motivation for age, t(761) = -4.28, p = .00, those
who are over the age of 42 (M = 4.17, SD = 1.41) attaining higher scores than those who are
under 41 years (M = 4.65, SD = 1.34). SDT suggests that people are less likely to continue to
engage in PA if they perceive others control their actions, feel unskilled, or have minimal or
negative social connections to the behavior (Mears & Kilpatrick, 2008). Data demonstrates how
social motivation changes with age.
Gender
There was significant effect for gender, t(761) = 2.90, p = .00, males (M = 4.66, SD =
1.29) attaining higher scores than females (M = 4.33, SD = 1.43) when physically active and
motivated by social. Research shows that the size of one's network is important in motivating PA
participation, and encouraging an increase in the size of one's network may increase PA
participation (Huang et al., 2022). These data show gender differences in social motivation.
Ethnicity
There was significant effect for ethnicity in social motivation, t(765) = 2.60, p = .01, non-
Whites (M = 4.07, SD = 1.36) attaining lower scores than Whites (M = 4.46, SD = 1.39). Social
isolation and social distancing practices have impacted depression, anxiety, and even though it
may not decrease, it is occasionally associated with poor mental health (Peterson et al., 2021).
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Significant differences in Whites and non-Whites show that social motivation impacts PA and
can be associated with social isolation and social distancing during the research.
Daily PA
Comparable to interest/enjoyment, competence, appearance, and fitness, social also
shows significance for those engaging in PA for more than an hour or less than an hour each day.
There was a significant effect in social motivation for daily PA, t(765) = 3.48, p = .00,
participants who engaged in less than one hour a day (M = 4.14, SD = 1.35) attained lower scores
than those who participated in more than one hour of PA a day (M = 4.53, SD = 1.41). Research
shows that daily PA patterns increased and decreased over the pandemic (Wunsch et al., 2022),
signifying that social motivation had significant differences in those who engage in PA.
Physical Limitations
There was no significant effect in social motivation for physical limitations, t(765) =
0.82, p = .42, despite those with physical limitations (M = 4.48, SD = 1.32) attaining higher
scores than those with no physical limitations (M = 4.38, SD = 1.44).
Sitting
There was a significant effect in social motivation for sitting habits, t(765) = 2,77, p =
.01; those who sat for less than 6 hours a day (M = 4.55, SD = 1.38) attained higher scores than
those who sat for more than 6 hours a day (M = 4.27, SD = 1.41). Research demonstrates that
sitting habits change with PA (Zhang et al., 2021), and competence contributes to those
motivated to move based on their sitting habits. In addition, digital health technology should
enable significant interactions between users and reduce the time people spend sitting down
(Marchant et al., 2021; Nuss & Li, 2021).
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Youth PA
There was a significant effect in social motivation for youth PA, t(765) = 5.37, p = .00;
those who engaged in OS/PA in their youth (M = 4.5, SD = 1.18) attained higher scores than
those who did not participate in OS/PA in their youth (M = 3.48, SD = 1.26). These data
reinforce sport-based social capital programs to add significance to adults’ general happiness for
successful aging (Kim et al., 2021).
Current PA
There was a significant effect in social motivation for current PA, t(765) = 4.93, p = .00,
those who currently engage in OS/PA (M = 4.51, SD = 1.40) attaining higher scores than those
who do not currently participate in OS/PA (M = 3.77, SD 1.26). Being motivated by social is to
be physically active to meet friends or meet new people, connecting with others (Frederick et al.,
1993; Ryan et al., 1997). Pointing to the data for the lowest motivation for PA, golfers with
stronger social motives were 60% more likely to consume alcohol (Courtney et al., 2021). The
data in this study show that social motives have significant differences in what motivates
participants to engage based on current participation in PA.
Digital Health Technology
Social, interest/enjoyment, competence, appearance, and fitness all show significant
differences. There was a significant effect for digital health technology use, t(765) = 4.25, p =
.00; those who currently use digital health technology (M = 4.54, SD = 1.36) attained higher
scores than those who do not (M = 4.07, SD = 1.46). Data points to significance in current and
former WFT users differing in exercise motivation and their use of digital health technology to
track data instead of for motivation (Nuss & Li, 2021).

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Vitality
There was no significant effect in social motivation for feeling alert, t(765) = 0.47, p =
.64, despite those who do not or may feel alert (M = 4.36, SD = 1.36) attaining lower scores than
those who do feel alert (M = 4.42, SD = 1.41). In addition, there was a significant effect in social
motivation on those who feel energized when engaging in PA, t(765) = 2.98, p = .00; those who
feel energized (M = 4.47, SD = 1.40) attained higher scores than those who do not (M = 4.00, SD
= 1.34). Adults are interested in developing the ability to use digital health technology to stay
healthy, independent, and socially connected (Suh & Li, 2022).
Youth PA, Current PA, and Digital Health Technology Use
There was significant effect in current PA, t(765) = 6.31, p = .00, those who do not use
digital health technology (M = 0.78, SD = 0.03) attaining lower scores than those who do use
digital health technology (M = 1.00, SD = 0.02) (Appendix D, Table 8). In addition, there was
significant effect in youth PA, t(765) = 2.36, p = .02, those who participated in either OS/PA (M
= 0.81, SD = 0.44) attaining lower scores than those who did not participate in OS/PA (M = 0.95,
SD = 0.44) (Appendix D, Table 9). As technology use is rapidly growing, there is a need for it to
be a core component of the education curriculum for sports and physical activity (van Tuyckom
& Vos, 2022).
Vitality, Youth PA, and Digital Health Technology Use
There was no significant effect in vitality for currently feeling alert and awake for those
who participated in OS/PA in their youth, t(765) = 1.38, p = .16; those who did not participate
(M = 1.64, SD = 0.08) attained lower scores than those who participated (M = 1.75, SD = 0.02)
(Appendix D, Table 8). There was no significant effect in vitality for currently feeling alert and
awake with digital health technology use, t(765) = 0.96, p = .33, those who did not use digital
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health technology (M = 1.71, SD = 0.04) attaining lower scores than those who did use digital
health technology (M = 1.75, SD = 0.02) (Appendix D, Table 8). In addition, there was a
significant effect in vitality for feeling energized when engaging in PA for those who
participated in OS/PA in their youth, t(765) = 2.05, p = .04; those who did not participate (M =
1.76, SD = 0.54) attained lower scores than those who participated (M = 1.87, SD = 0.39)
(Appendix D, Table 9). There was a significant effect in vitality for feeling energized when
engaging in PA with digital health technology use, t(765) = 4.68, p = .00, those who did not use
digital health technology (M = 1.90, SD = 0.02) attaining higher scores than those who did use
digital health technology (M = 1.75, SD = 0.03). Engaging in more PA results in higher chances
of reporting high levels of well-being and vitality (Ginoux et al., 2021), and positive interactions
with digital health technology increase positive expectations while decreasing fatigue
(Skvortsova et al., 2022).
Relationship Between Motives
As illustrated in Appendix D, there was a strong positive correlation between motives,
running a Pearson's correlation coefficient to assess the linear relationship between motives. The
most significant correlation was competence and interest/enjoyment (.84). All statistics were
significant, except for social, showing that those motivated by social are less likely to be
motivated to engage in PA. On the other hand, interest/enjoyment, competence, appearance, and
fitness show that the higher the motivation, the more likely they are to engage in PA.
Summary of Findings
This study breaks data into subgroups to identify significant differences in the data and
gain a better picture of the story. Table 3 presents an overview of the inferential findings from
the data.
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Research Question 1
The first research question asked, “Are there differences in why people are physically
active based on prior participation in OS/PA and current use of digital health technology?” There
was no statistical difference in the means for fitness and appearance connected to youth PA. In
contrast, there were significant differences in the means for interest/enjoyment, competence,
social, and combined motives in those who participated in OS or PA in their youth. There was no
significant effect on the means of digital health technology used for the fitness motive. Data
shows fitness, appearance, and interest enjoyment are the highest motivators with significant
differences in the means. Adults showed statistical differences in interest/enjoyment,
competence, appearance, social, and the combined motives of digital health technology use.
Adults were not as motivated by social and competence motivates.
Research Question 2
The second research question asked, “Are there differences in current levels of PA based
on prior participation in OS/PA and current use of digital health technology?: Again, data note
significant differences in the means of all five combined motives in current participation in
OS/PA and daily PA. In addition, those who currently engage in PA were more likely to be
motivated by significant differences in all five and combined motives except appearance,
indicating that PA engagement is not motivated by appearance. Regarding those who
participated in OS/PA in their youth, there were no significant differences in the means
motivated by appearance and fitness. However, there are significant differences in means for
motivation from interest/enjoyment, competence, social, and combined motives for those who
participated in OS/PA in their youth. Adults who currently use digital health technology show no
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significant difference in the mean for being motivated by fitness, even though fitness was the
highest motivator.
Research Question 3
The third research question asked, “Are there differences in participants' perceived
vitality based on prior participation in OS/PA and current use of digital health technology?”
There was no significant effect on means of feeling alert and awake for those who participated in
OS/PA in their youth and those who did not participate in OS/PA. There was a significant effect
on means for feeling energized and engaging in PA; those who participated in OS/PA in their
youth and those who did not show significant differences. There was no significant effect on the
means of feeling alert and awake when using digital health technology. However, there was a
significant effect on the means shown with digital health technology use for feeling energized
when engaging in PA. Means are significant in feeling alert and awake when motivated by
interest/enjoyment, competence, and appearance. In contrast, there is no significant difference in
the means for fitness, social, or combined motives when feeling alert or awake. Adults feel
energized when engaging in PA, showing significant differences in the means for all combined
motives.
Research Question 4
The fourth research question asked, “What variables are the highest predictors of PA and
digital health technology use in adults?” This study evaluated the motives of interest/enjoyment,
competence, appearance, fitness, and social to engage in PA. The data suggested statistically
significant relationships with daily PA (<1hrD, >1hrD), current PA (CPAY, CPAN), and vitality
regarding energy (YME, NME) across all five motives and the combined. Fitness ranked as the
highest motivator, and there were no significant differences in gender (M/F), ethnicity (W/NW),
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physical limitations, sitting habits, youth OS/PA, current PA, or digital use. There was statistical
significance when correlating motives, except for social, indicating that social was not a
motivator for PA engagement.

Table 3
Summary of Inferential Findings Organized by Research Question
Are there differences in why people are physically active based on prior participation in
OS/PA and current use of digital health technology?
Finding 1: Motives: Fitness, appearance, and interest/enjoyment are the highest motivators
Finding 2: Motives: Adults were not as motivated by social and competence motives
Finding 3: Youth OS/PA: No significant difference in fitness and appearance motives
Finding 4: Youth OS/PA: Significant differences in interest/enjoyment, competence, social,
and combined motives
Finding 5: Digital Health Technology Use: No significant difference in the fitness motive.
Finding 6: Digital Health Technology Use: Significant differences in interest/enjoyment,
competence, appearance, social, and combined motives
Are the differences in current levels of PA based on prior participation in OS/PA and current
use of digital health technology?
Finding 1: Current PA: Data note significant differences in all five and combined motives in
current participation in OS/PA
Finding 2: Daily PA: There are significant differences in all five and combined motives with
the exception of appearance.
Finding 3: Youth OS/PA: There were no significant differences in appearance and fitness
Finding 4: Youth OS/PA: There were significant differences in interest/enjoyment,
competence, social, and combined motives
Finding 5: Digital Health Technology Use: No significant difference in the fitness motive.
Are the differences in participants’ perceived vitality based on prior participation in OS/PA
and current use of digital health technology?
Finding 1: Youth OS/PA: There was no significant effect for currently feeling alert and awake
for those who participated in OS/PA in their youth and those who did not
Finding 2: Youth OS/PA: there was a significant effect for feeling energized when engaging in
PA for those who participated in OS/PA in their youth and those who did not
Finding 3: Digital Health Technology Use: There was no significant effect for currently feeling
alert and awake with digital health technology use
Finding 4: Digital Health Technology Use: There was a significant effect for feeling energized
when engaging in PA with digital health technology use
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Finding 5: Motives: Data is significant in feeling alert and awake in interest/enjoyment,
competence, and appearance motives, not in fitness, social, or combined motives
Finding 6: Motives: Feeling energized when engaging in physical activities was significant for
all combined motives
What variables are the highest predictors of PA and digital health technology use in adults?
Finding 1: Motives: Statistically significant relationships with daily, current PA, and vitality
regarding energy across all five motives and the combined.
Finding 2: Motives: Fitness ranked as the highest motive, and there were no significant
differences in gender, ethnicity (W/NW), physical limitations, sitting, youth, PA, or digital
use.
Finding 3: Motives: There was statistical significance when correlating motives, except for
social.

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Chapter Five: Recommendations
This study describes physical activities, sports participation, sitting habits, motivation,
and digital technology use in adults at all fitness levels. To better understand motivation, PA, and
digital health technology, Chapter Two explored topics relevant to this research, including the
scope of the study, technology, AI/ML, PA, sport, motivation, and an individual’s overall health
based on Ryan and Deci’s (1997) SDT. Next, chapter three explained this study’s research
methodologies and principles. Chapter Four included data from the 30-question survey with 765
adults data organized by age, daily and weekly PA, youth PA, demographics, motives to engage
in PA, and perceived vitality. Finally, this chapter provides solutions based on data and literature,
outlining recommendations and suggestions for future research regarding PA and motivation.
This chapter details recommendations for PA and motivation organized by the SDT. In
addition, this chapter includes an evaluation plan constructed from Kotter’s (1996) eight-step
model of change. Kotter’s model provides a theoretical evaluation plan with eight
implementation levels. The levels, presented in detail, provide direction for implementing this
study’s recommendations. The eight steps in the change process include urgency, coalitions,
vision, communication, actions, short-term wins, momentum, and culture (Kotter, 1996). In
conclusion, this chapter discusses the learning and evaluation plan covering the specific
implementation recommendations, expected outcomes, and metrics to measure progress.

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Figure 5
Kotter’s Eight-Step Change Model

Recommendations for Practice to Address Digital Health Tech and Motivation
The data also shows that adults’ motivation to engage in PA was reported in the order of
the following motives: fitness, appearance, competence, interest/enjoyment, and social. The use
of digital health technology was self-reported, though no features or applications were assessed.
It is important to consider whether digital health technology was influential in motivating PA.
The findings indicate respondents engaged in PA and OS in their youth, though there was no
direct method to assess if this contributed to current PA. Overall, the data provided a real-time
representation of motivation, current PA, and use of digital health technology; all data provide
insight into physical activities among respondents.
Drawing on SDT (Deci & Ryan, 1985b; Deci et al., 2000; Deci et al., 2013; Ryan et al.,
2017a; Ryan et al., 2000), emphasizing that motivation to participate in PA explains what
energizes or directs human behavior (Ryan & Deci, 2017b) and considers aspects related to
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activity adherence (Ryan & Frederick, 1997). Motivation is one of the most common barriers to
participating, engaging, or continuing in PA (Brand et al., 2019; Stehr et al., 2021; Woodruff et
al., 2020) and potentially a key to successfully shaping the positive impact of digital health
technology and AI/ML in the future. The concept of motivation has grown in PA research, where
evidence shows that motivation regulations are essential determinants of cognitive (e.g.,
attention, reasoning), emotional (e.g., enjoyment, anxiety), and behavioral (e.g., effort,
persistence) results (Quested et al., 2021). This study reviewed the MPAM-R five motives scale
for engaging in PA with differing degrees of psychological need satisfaction from low
(appearance) to high (social, competence-challenge, fitness health, and interest enjoyment)
(Ryan et al., 1997), and the motives varied. This section presents recommendations to increase
PA in adults with digital health technology. The data from the study found significant effects on
self-reported variables, and more research would guide the following recommendations even
further to identify specifics in the differences. An overview of the motivation recommendation
with guiding data is provided (Table 4) and discussed in the following suggestions.

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Table 4
Summary of Motivation Recommendation and Guiding Data
Recommendation Guiding data Development
Integrate
motivation in the
development of
digital health
technology
Motives: Fitness, appearance, and
interest/enjoyment are the highest
motivators
Motives: Adults were not as motivated
by social and competence motives
Digital health technology use:
Significant differences in
interest/enjoyment, competence,
appearance, social, and combined
motives
Features based on SDT,
motives, and basic
psychological needs.

Recommendation 1: Integrate Motivation in the Development of Digital Health Technology
This study revealed that fitness, appearance, competence, interest/enjoyment, and social
motives span the 765 participants; moreover, as the three basic psychological needs of
autonomy, competence, and relatedness are fulfilled, PA motivation shifts toward internal
behavior regulation (Deci & Ryan, 2017a). Digital health technologies have the opportunity to
support SDT-based motivation for increased PA by fulfilling psychological needs in the design
and implementation phases (Friel & Garber, 2021; Schaben & Furness, 2018; Villalobos-Zúñiga
& Cherubini, 2020).
Motivation Features in Digital Health Technology
While the competence and interest/enjoyment scales measure intrinsic motivators, the
fitness, appearance, and social scales measure extrinsic motivation (Ryan et al., 1997). SDT has
proposed that people have diverse motivations towards PA; more notably, different motivations,
views, and goals move towards action (Deci & Ryan, 1985a). Therefore, developing digital
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health technology with motivation features supports programming to improve their physical
condition, empowering individuals to acquire beneficial and lasting results (Silva & Alturas,
2021). Furthermore, applying change theory frameworks allows help developers, users, and
researchers to grow their understanding of technology’s influence on exercise behaviors and PA
(Herrmann et al., 2021). Existing users do not consider themselves motivated by their digital
health technology; however, they appreciate the visibility of the PA data (Nuss & Li, 2021),
providing room for improvement. There are opportunities to improve or positively influence
technology development to harness theoretical change models incorporating motivation features,
messaging, or interventions that may effectively improve specific health outcomes for
individuals (Villalobos-Zúñiga & Cherubini, 2020).
The literature shows that more SDT research to design elements that incorporate proven
behavior-change theories, including using social media to stimulate usage through social
interactions and past behavior as a predictor of future PA engagement, is needed (Emberson et
al., 2021; Friel & Garber, 2021; O’Loughlin et al., 2021; Rodrigues et al., 2021; Schoeppe et al.,
2021; Villalobos-Zúñiga & Cherubini, 2020). Consequently, most commercially available apps
are not evidence-based; therefore, using scientific approaches is not part of the evaluation
(Cowan et al., 2013), though interventions may effectively improve specific health outcomes for
individuals. The Food and Drug Administration (FDA) does not regulate apps and has a hands-
off approach not to stifle innovation; a regulatory framework is needed for digital health
technology, clinical promise, unique user interface, and new product introductions (Gottlieb,
2017). Unfortunately, this lack of direction points out that not all apps are safe and effective, and
some may pose significant risks to users. In addition, due to the heterogeneity of the apps, it is
hard to define a core set of features to assess app quality (Wisniewski et al., 2019). As a result,
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app ranking models have emerged to fill this gap and supply a source of transparency and
objectivity in evaluating digital health technology applications; a framework tailored to
clinicians' and individuals' demands is needed (Lagan et al., 2020).
The apps and digital health technology that adults are using may not be helpful, practical,
or effective (Torous et al., 2018). From an SDT perspective, messages can help the user reflect
on or be motivated to engage in a specific activity, supporting the basic need for autonomy
(Villalobos-Zúñiga, 2020). As digital health technology continues to develop, there is a greater
need to research the efficacy of technology-based interventions and define a framework for
quality assessment and labeling of health apps (Neal et al., 2022). Digital health technology can
potentially offer incorrect or misleading information, and a clinical foundation should indicate if
research supports the apps' claims (Torous et al., 2019). Furthermore, as continued WFT and
digital health technology usage decline, there is an emphasis on the value of sustaining usage
(Torous et al., 2019; Windasari et al., 2020). As a result, technology that applies different
theories incorporates motivational affordance and framework (Chuah, 2019; Hassan et al., 2019;
Suh, 2018; Villalobos-Zúñiga & Cherubini, 2020; Windasari et al., 2021).
The Basic Needs of Autonomy, Competence, and Relatedness
Designing technology around autonomy, competence, and relatedness allows users to
move towards self-determined actions and PA. Incorporating these needs contributes to the
motivation of the user. Logging activities allow them to see they have maintained an activity,
feeling competent. Supporting autonomy occurs in the data input stage, indicating an intention
and ability. Developing pre-set motivational messages can explain why completing the detailed
activity benefits their health or well-being. The basic psychological need for relatedness in
shared challenges, social media posts, or team engagement would also contribute to the user
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features. A 2020 study on digital health technology found that only one-fourth of the sample
supported the three basic needs (Villalobos-Zúñiga & Cherubini). Design that supports the
individual in reaching a higher level of intrinsic motivation and tailoring the interventions that
resonate with the constructs would support SDT (Villalobos-Zúñiga & Cherubini, 2020).
Target group-specific preferences, motivation-related app features, and demographic
differences should be considered, focusing on goal achievement–related features like goal setting
and monitoring (Yang & Koenigstorfer, 2021). Studies have not examined the connection
between motivational aspects of users and user susceptibility/responsiveness to different features
(Alqahtani et al., 2022). More in-depth attention to the theoretical support of SDT-based features
and measures would encourage knowledge gains, and SDT has intuitive concepts that are easy
for researchers to understand without any motivational psychology background (Tyack &
Mekler, 2020). Figure 6 provides an overview of features based on SDT’s basic psychological
needs.

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Figure 6
Self-Determination Theory Features

Note. Adapted from Apps that motivate: A taxonomy of app features based on self-determination
theory by G. Villalobos-Zúñiga & M. Cherubini, 2020. International Journal of Human-
Computer Studies, 140, Article 102449.

A recent study revealed that adults with various types of motivation prefer different
features to aid them in improving their health with digital health technology, and tailoring the
features to individuals' motivation types is more likely to improve the effectiveness of the apps
(Alqahtani et al., 2022). As features become available in digital health technology, it should be
noted that smartphone ownership is not a barrier to reaching lower socio-economic populations
(Pontin et al., 2021), and more research on these populations should be considered when
developing features based on SDT. The field of digital health technology research and the
research available on fitness apps is still scarce (Liu & Avello, 2021), and Table 5 presents
recommendations to develop motivation features to increase PA in adults.

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Table 5
Summary of Motivation Recommendation
Outcome Metrics Methods Timing
Increased PA
in adults
with digital
health
technology.

Data on sustained usage,
activities, and other
features associated
with motivation.
Fitness
Social
Interest/enjoyment
Competence
Appearance
Subject matter experts work
with digital health
technology developers to
create features based on
SDT.
Autonomy
Competence
Relatedness
Controlled/autonomous
motivation
Amotivation
Extrinsic motivation
External regulation
Introjection
Identification
Integration
Intrinsic motivation
Ongoing

Recommendation 2: Integrate More Sport Fundamentals Iinto Digital Technology
There is growing evidence that sports participation can have health benefits beyond the
physical and significantly improve mental and social health (Westerbeek & Eime, 2021).
Consistent with this study, a 2015 study found that although almost 3 in 4 adults played sports
when they were younger (73%), only 1 in 4 (25%) continue to participate in sports as adults
(RWJF, 2015). Playing a sport in mid-life was more strongly associated with PA than other PA
domains in old age. Increasing opportunities for PA, such as walking, may also be crucial as
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people transition into old age (Aggio et al., 2017). PA/OS in participants’ youth was high
throughout this study, and OS participation declined. Play activities illustrate intrinsically
motivated behaviors providing satisfaction and joy (Ryan & Deci, 2020), and play is commonly
associated with youth. The data from the study found significant effects on participation in
organized sports, and more research would guide the following recommendations even further to
identify specifics in the differences. An overview of the sport recommendation with guiding data
is provided (Table 6) and discussed in the following suggestions.

Table 6
Summary of Sport Recommendation and Guiding Data
Recommendation Guiding data Development
Integrate more
sport
fundamentals
into digital
technology.
Youth OS/PA: No significant difference
in fitness and appearance motives
Youth OS/PA: Significant differences in
interest/enjoyment, competence,
social, and combined motives
Features based on sport
and motivation.

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Play and Sport Features in Digital Health Technology
Youth sports are often associated with a foundation for healthy lifestyles, and some youth
populations are unable to reap the benefits of sports because of existing inequities (Kuhn et al.,
2021). For over 15 years, the American Heart Association and the National Football League have
supported mental and physical wellness through their PLAY 60 initiative, encouraging kids to
get physically active for at least 60 minutes a day. Personal enjoyment, improving health, getting
in shape, or losing weight are the top reasons adults play sports (RWJF, 2015). In contrast,
reasons for not playing sports are health-related, lack of time, and lack of interest (RWJF, 2015).
Learning about individuals’ perspectives on sports participation may support digital health
technology in the future. In addition, sports participation is a strategy for maintaining/boosting
older adults' social and psychological health (Kim et al., 2021).
Self-Determination Theory and Sport
A healthy path to motivation is critical for PA adoption, and commitment and a small
integration of SDT and sport into digital health technology may have extensive implications for
disease prevention and management (Ntoumanis et al., 2021). Supporting multi-disciplinary
research integrating academia, industry, and clinicians are required to develop other technology
and protocols that match the adaption of sport for adults (Keogh et al., 2021). By understanding
the barriers to usage, PA, and programming, research can examine the development of new
technology. Although digital health technology is popular in the health and fitness industry, it
may have limited usefulness to increase physical activity. If something is new and innovative, it
does not indicate that individuals will find it useful or usable (Brown et al., 2021), and adapting
sport into the technology would allow for play and skill development. Still, digital health
technology can contribute to PA engagement with sustainable health outcomes. Future scholars,
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practitioners, and developers might concentrate on goal-based, social-based, and rewards-based
gamification to increase engagement (Cho et al., 2021) while incorporating sports fundamentals.
Digital health technology and smartphone app design features should increase the
probability of usage, increasing PA by supporting users in reaching their goals and facilitating
habit formation (Yang & Koenigstorfer, 2021). In addition, features like blood oxygen detection
are becoming common in digital health technology; different ways of using this information
might further enhance the motivational drive to establish fitness habits through sport (Oc &
Plangger, 2022). Gamification interventions like Peloton's Lanebreak (2021) are growing, and
little is known about the interventions' application status and efficacy for designing elements to
increase motivation and engagement potential to increase motivation for PA (Xu et al., 2022).
Research shows that continuous lifelong participation in organized sport is unlikely, and
encouraging participation in sport is one way to increase physical activity levels (Woods et al.,
2022). Furthermore, as adults are a growing population group currently underrepresented in
sports, digital health technology strategies and features designs assist with re-engaging adults in
sports (Jenkin et al., 2021).
Given the percentage of adults who do not engage in sports, there is an option to
encourage greater participation and increase PA. Sport provides an outlet for all of the basic
psychological needs to be met, encouraging motivation. Sport has the potential to fulfill the need
for autonomy, competence, and relatedness (Ryan & Deci, 2017a). Incorporating sports
techniques with AI/ML algorithms appears promising to enhance the state of inactivity linked to
health risks, keeping the goals of comprehensive health programs, increasing PA in adults,
sustaining sports engagement, and encouraging Exercise is Medicine programs (Nuss & Li,
2021; Nuss et al., 2021). Distinguishing integrative sport approaches for engagement with digital
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health technologies motivate and encourage individuals to continue using the technology to
develop maintained value, promote PA, and improve health outcomes. There are numerous
similarities between smart fitness and sports; monitoring and maintenance are needed when
dealing with ordinary and non-professional people who do not want to have coaches (Farrokhi et
al., 2021). Table 7 presents the recommendation regarding sport.

Table 7
Summary of Sport Recommendation
Outcome Metrics Methods Timing
Increased PA in
adults with
digital health
technology.

Data on sustained
usage, activities,
and other features
associated with
sport.
Balancing
Running
Jumping
Catching
Hopping
Throwing
Galloping
Skipping
Leaping
Kicking
Teamwork
Self-discipline
Endurance
Leadership
Subject matter experts work
with digital health technology
developers to create features
based on sport.
Sport-specific
Body management
Locomotor skills
Object control skills
Fundamental movement skills
Goal-based skills
Social-based skills
Rewards-based Skills
Ongoing

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Recommendation 3: Integrate Vitality in the Development of Digital Health Technology
The HHS (2018) defined physical fitness as the ability to carry out day-to-day tasks with
vitality and alertness, without excessive exhaustion, and abundant energy to enjoy leisure-time
pursuits or respond to emergencies. By understanding vitality, developers, users, and researchers
can expand their knowledge of technology’s impact on exercise behaviors and PA (Herrmann et
al., 2021). This study reviewed SDT to examine conditions for motivation and measure vitality,
an indicator of mental and physical wellness (Deci et al., 2000; Deci et al., 2013; Ryan et al.,
2017a; Ryan et al., 2000). Why one would engage in physical activities, sports, and exercise on a
subjective vitality scale gave insight that participants felt alert and energized.
The data from the study found significant differences in feeling energized while
participating in OS/PA, and more research would guide the following recommendations even
further to identify specifics in the differences. Therefore, an overview of the vitality
recommendation with guiding data is provided (Table 8) and discussed in the following
suggestions.
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Table 8
Summary of Vitality Recommendation and Guiding Data
Recommendation Guiding data Development
Integrate vitality in
the development
of digital health
technology
Youth OS/PA: There was no significant
effect for currently feeling alert and
awake for those who participated in
OS/PA in their youth and those who
did not
Youth OS/PA: there was significant
effect for feeling energized when
engaging in PA for those who
participated in OS/PA in their youth
and those who did not
Digital health technology use: There was
no significant effect for currently
feeling alert and awake with digital
health technology use
Digital health technology use: There was
significant effect for feeling energized
when engaging in PA with digital
health technology use
Motives: Data is significant in feeling
alert and awake in interest/enjoyment,
competence, and appearance motives,
not in fitness, social, or combined
motives
Feeling energized when engaging in
physical activities was significant for
all combined motives
Features based on
vitality,
controlled/autonomous
motivation,
amotivation, extrinsic
motivation: external
regulation,
introjection,
identification,
integration, and
intrinsic motivation.

Vitality Features in Digital Health Technology
Studies have shown that although intrinsic motivation is a lifelong psychological
development process, it is not intuitive, and intrinsic motivation relies on support for basic
psychological needs of competence, autonomy, and relatedness (Di Domenico et al., 2017).
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Autonomous motivation includes motivation that comes from internal sources, from outside or
extrinsic sources for an activity’s value, and how it aligns with an individual’s sense of self
(Stone et al., 2009). Controlled motivation comprises external regulation, a type of motivation
where an individual acts out of the desire for external rewards or fear of punishment (Stone et al.,
2009). More than one type of motivation drives an individual; different goals, values, aspirations,
and beliefs determine what an individual wants or needs. Therefore, it is helpful to think of
motivation on a continuum ranging from non-self-determined to self-determined.
Vitality and Health Promotion
Subjective vitality refers to individuals’ felt energy, factoring into physical, mental, or
social contextual worlds that influence the potential to organize and initiate action (Ryan &
Frederick, 1997). By incorporating ratings or a scale into digital health technology, individuals
could rate if they feel alive or invigorated in certain circumstances or following certain events.
The subjective vitality could also be examined in longitudinal research in real-time to contribute
to resilience, understanding stress, chronic disease management, immunological status, overall
energy, sleep and serve as indicators of physical or mental status. As the mental health crisis
continues to grow, this could allow for examining how individuals feel in the moment. The
research could examine low health literacy and low commitment to gaining data for AI/ML
development and vitality features for wellbeing, comparable to the Happify Health platform
(Parks et al., 2020). Wellbeing research shows AI plays a positive and impactful role in real-time
and accurate stress recognition and interventions through chatbots, virtual therapists, biofeedback
systems, wearable devices with sensor technology, and smartphone apps effectively reduce stress
at the workplace (Solanky & Gupta, 2022).
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The data captured directly from individuals could encourage engagement with other
specific digital health technology features. Past research showed a limited ability to gauge in-
depth engagement with the behavior-change process or how participants have acquired new
behavior-change skills (Nicholas et al., 2021; Short et al., 2018), and this may be based on how
features were initially developed. Working together with health practitioners, clinicians,
researchers, or developers to design and develop “precision mobile health” products may offer
higher personalized and participatory levels, therefore improving the population’s health (Ye &
Ma, 2021). Additional autonomous forms of motivation are associated with dedicated
engagement in moderate-to-vigorous PA (Ryan & Deci, 2017b), and understanding an
individual’s vitality provides an opportunity to influence health promotion interventions. Health
Literacy can help users to make conscious choices about their PA and influence health promotion
interventions (Buja et al., 2020), while vitality or energy scales could be an easy entry into
increased health literacy with an adaption of digital health technology. Table 9 presents the
recommendation regarding vitality.

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Table 9
Summary of Vitality Recommendation
Outcome Metrics Methods Timing
Increased PA in
adults with
digital health
technology.

Data on sustained
usage, activities,
and other features
associated with
vitality.
Vitality rating/scale
Subject matter experts
work with digital
health technology
developers to create
features based on
vitality through the
motivation continuum.
Controlled/autonomous
motivation
Amotivation
Extrinsic motivation:
External regulation,
introjection,
identification,
integration
Intrinsic motivation

Ongoing

Recommendation 4: Integrate Smart Devices for Multigenerational “Families”
Inactivity tends to progress in a lifetime; women are more likely to lead inactive lifestyles
than men, and non-Hispanic White adults are more likely to engage in PA than Hispanic and
Black adults (Johns Hopkins, 2021). Digital health technologies, SDs, and applications are
becoming more accessible and affordable. The expanded market of digital health technologies
has extended with options to collect health data continuously outside of clinics. When digital
health technologies are accurately programmed to encourage PA, they empower individuals to
gain practical and lasting outcomes on their health (Silva & Alturas, 2021).
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To reduce the gaps in health disparities and inequities, there are opportunities to improve
and positively impact the development of digital health technologies. Traditional and emerging
health behavior-change theories, like SDT, may be applied as frameworks to help developers,
users, and researchers expand their comprehension of technology’s influence on PA (Herrmann
et al., 2021), family health, and costs associated with poor health. Despite the numerous benefits
of PA, levels of inactivity and their costs are increasing (American College of Sports Medicine,
2015). Through AI/ML, big data, deep learning, data mining, predictive algorithms, and
analytics, digital health technologies are changing health systems, health care delivery, and
communities (WHO, 2021).
Multigenerational “Family” Features in Digital Health Technology
Instead of concentrating on the adaptation and engagement of digital health technology,
researchers, clinicians, and designers should consider the value offers that the device should
deliver to encourage individuals and families to continue using them. By understanding barriers
for usage like time, money, and other resources, features can be integrated to motivate families to
engage in PA. Motivation is one of the most common barriers to participating, engaging, or
continuing in PA (Brand et al., 2019; Stehr et al., 2021; Woodruff et al., 2020), and it is critical
for promoting health and participation in PA (Quested et al., 2021). Furthermore, because the
motivation to participate in PA explains what energizes and directs human behavior (Ryan &
Deci, 2017b), aspects related to activity adherence (Ryan & Frederick, 1997) and SDT features
for a family may encourage increases in PA. Education and income are social determinants of
health associated with digital health technology use, and PA and integration or interventions may
be more beneficial, as health goals, data visualization, real-time support, and feedback results in
interpretation, to increase health literacy (Ye & Ma, 2021).
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Results indicate differences in motivation, digital health technology use, and youth
engagement in OS/PA across all demographics, indicating significance in PA engagement; more
research on multi-generations would guide the following recommendations further to identify
specifics in the differences. Therefore, an overview of the multigenerational family
recommendation with guiding data is provided (Table 10) and discussed in the following
suggestions.

Table 10
Summary of Multigenerational Family Recommendation
Recommendation Guiding data Development
Integrate smart
devices for
multigenerational
“families.”
Digital health technology use: No
significant difference in the fitness
motive.
Current/Daily PA: Data note significant
differences in all five and combined
motives in current participation in
OS/PA
Current/Daily PA: There are significant
differences in all five and combined
motives with the exception of
appearance.
Digital health technology use: No
significant difference in the fitness
motive.
Features based on group
PA for various ages
and extrinsic
motivation

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Multigenerational “Family” Engagement
Through multidisciplinary and cross-sectorial collaboration, a comprehensive approach
with revolutionary change at social, environmental, and systems levels are needed to attack low
PA (van Sluijs et al., 2021). In addition, a recent study suggests that parental involvement in PA,
particularly among mothers, is essential for children’s PA and healthy outcomes (Zovko et al.,
2022). Evidence-based solutions focus on three critical components of the adolescent PA:
supportive schools, the social and digital environment, and multipurpose urban environments
(van Sluijs et al., 2021).
Health information in a social and digital environment is extensive and challenging to
navigate information; as a result, young people often look to trusted adults to assist them in
making sense of the information they find online and providing alternative sources of
information and support (Lupton, 2021). In addition, adolescence is a period of growing
independence when social support becomes influential, providing an opportunity for
multigenerational digital engagement (van Sluijs et al., 2021). To comprehend the
multigenerational transmission of socioeconomic achievement and health in America, we must
consider how behaviors and group-based norms, identities, and experiences of health impact
health lifestyles (Mollborn & Modile, 2022). Finally, innovations and strategies should
concentrate on developing the reach of digital health technology across all demographics to
assure that its growth does not worsen existing health inequalities (Mahajan et al., 2021).
Harnessing AI/ML techniques within a family environment may motivate adults across
various populations to engage in PA. Tools that sustain and amplify knowledge and choices
allow for rapid adoption and integration of new data streams in individuals’ care (Mayo Clinic,
2021). Due to the complexity of health and wellness, wellness must be explored from a
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subjective basis where individuals report data simultaneousness with objective explorations
(Stone et al., 2018). Family engagement provides an opportunity to present healthy options for
individuals. Health practitioners, clinicians, researchers, and developers have an array of tools to
enhance decision-making and be aware that evidence and data do not directly translate into
evidence-based practice (Claridge & Fabian, 2005). Family-based PA interventions may also
benefit from helping parents identify value in exercise while sidestepping the use of external
command or force to motivate behavior (Solomon-Moore, 2017).
This study showed that external motivation was higher than intrinsic, with fitness as the
highest motivator. Accordingly, the most extreme form of extrinsic motivation is external
regulation, as behaviors or a PA stem from a desire to be obedient, compliant, and confirm or
receive external rewards or avoid external penalties (Ryan & Deci., 2000b). Factors that predict
PA are a positive attitude, perceived support from parents, and how the youth rate their health
(Rullestad et al., 2021). Being encouraged by the family could encourage extrinsic motivation.
Extrinsic motivation stems from outside the individual and is not a poor form of motivation, and
the SDT framework explains how intrinsic motivation influences behavior and PA. Extensive
amounts of data and analysis demonstrate the numerous benefits of PA, yet approximately 80%
of adults and adolescents in the United States do not meet the physical activity guidelines for
Americans (Thompson & Eijsvogels, 2018). A healthy practice of motivation continues to be
necessary for PA adoption and commitment; therefore, the integration of SDT may encourage
health promotion, disease prevention, and chronic health management (Ntoumanis et al., 2021).
Table 11 presents the recommendation regarding family integration.

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Table 11
Summary of Multigenerational Family Integration Recommendation
Outcome Metrics Methods Timing
Increased PA
in adults
with digital
health
technology

Data on sustained usage,
activities, and other features
associated with multi-
generational families.
Group PA for various ages
Extrinsic motivation
Subject matter experts
work with digital
health technology
developers to create
features based on
health promotion.
Health equity
Health literacy
Health inequality
Ongoing

Implementation and Change Management
Changing an industry is no small task, though it may be necessary to increase PA in
adults. Kotter’s (1996 ) eight-step change model provides a framework to evaluate and apply
gradual shifts. Kotter’s model outlines how to systematically and effectively execute change by
simplifying the process into eight steps. Change can come as the latest technologies, mergers,
acquisitions, new systems, additional techniques, or cultural shifts. When implementing change,
barriers arrive in different stages, and this model addresses that aspect as well. Kotter provides a
framework to overcome challenges and set large-scale transformation into effect. The steps
create a process for change, especially knowing that it is often not fast or easy.
Creating the Environment for Change
Physical inactivity has been identified as the fourth leading risk factor for global
mortality, and it has been shown to be related to individual environments; thus, it is important for
governments to provide more sports facilities and parks to promote active PA and to reduce
sitting time in adults by creating an active built environment (Zhang et al., 2022). A recent study
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showed how creating the right environment and developing a plan can demonstrate the potential
for increasing PA in adults (Ezenwankwo et al., 2022). Creating an environment for change
starts with creating a sense of urgency, building coalitions, and developing vision or strategies.
Step 1: Creating a Sense of Urgency
The first step in applying change is creating a sense of urgency, as this helps others see
the necessity for change through statements that communicate the importance of acting
immediately. As this study presents, adults should do at least 150 to 300 minutes a week of
moderate-intensity aerobic activity (HHS, 2018), achieving or exceeding the upper limit of 300
minutes (5 hours) is optimal (Rock et al., 2020). SDT research is needed in the configuration of
proven behavior-change theories, and recent research also highlighted the importance of non-
conscious mechanisms to form habits and modify behaviors (Pinder et al., 2017). There is a need
for an industry shift towards motivation and PA, and research suggests that digital health
technologies alone do not help individuals achieve long-term motivation; instead, they provide a
visual stimulus for PA and require the self-generating fuel of internalized motivation to maintain
use (Friel & Garber, 2021; Segar, 2017).
Now more than ever, machine learning to predict future adherence to PA programs is
needed to set users up for success. Creating an open dialogue with health practitioners,
clinicians, researchers, and developers about the urgency to encourage more diversity of the
individuals engaged and building the technology. Often features like reminders are overused to
encourage the use of the application, and a recent study found that one-fourth of the sample
provided users support for the three basic needs of SDT (Villalobos-Zúñiga & Cherubini, 2020).
The urgency of PA is well informed, and understanding motivation levels will enable health
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practitioners, clinicians, researchers, and developers to provide guidance that promotes improved
health behaviors for increased physical activity.
Step 2: Building Coalitions
Step 2 is about bringing together a qualified team with the proper skillsets, qualifications,
expertise, relationships, and leadership to supervise the transformation measures and influence
stakeholders. Health and fitness organizations’ integration of digital health technologies provides
opportunities to study its influence on sports, PA, and fitness experiences (Pizzo et al., 2021).
This is also a time to focus on the reasonably low-cost design and accessibility of digital health
technologies for individuals to improve their PA engagement and overall health (Degroote et al.,
2018). This is the time to build the team to focus on technology, health promotion, and other
matter experts. Essentially, PA interventions take place at the individual level and have been
among the earliest types of interventions that have been tested systematically in the physical
activity promotion field (HHS, 2018), and building the right team can encourage leadership,
team-building, and commitment. Linking health practitioners, clinicians, researchers, or
developers with motivation theories like SDT is essential for cultivating a desire to engage in PA
with the growth in big data (Segar, 2019).
Based on the high usage of smart devices and digital health technology, awareness,
diversity, equity, and inclusion (DEI, communication, and long-term goals increase physical
activity in adults. Bringing awareness to the topic will bring attention to health practitioners,
clinicians, researchers, developers, and the general population. In order to ensure representation
matters, a continued focus on communication will allow for diverse stakeholders to align for
setting up development and implementation for success. These goals all lead to the long-term
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goals to develop metrics and strategic plans for increasing physical activity in adults across
various populations and industries.
Step 3: Developing Vision and Strategies
The purpose of this step is to assemble a realistic vision to lead the initiative to develop
effective strategies or methods to help achieve it. The right vision can help achieve change by
inspiring and guiding team actions and decisions. It should also define clear and realistic targets
to help measure success and appeal to the interest of the company stakeholders. Kotter (1997)
encourages this step to describe how the future will be different from the past, and the initiative
and vision will make the future a reality. This is the time to ensure that the vision can be
communicated efficiently and is easy to understand. Self-determination theory organizes the
different motivations among the control–autonomy continuum, providing a strategy and vision
for change. Combining these strategies with AI/ML algorithms appears promising to enhance the
state of inactivity linked to health risks, maintaining goals, developing health programs,
increasing PA in adults, and sustaining sports engagement (Nuss & Li, 2021; Nuss et al., 2021).
In addition, recent work on fairness can help reveal problems with prediction-based decision-
making and provide useful tools for addressing them (Mitchell et al., 2018), and the opportunity
exists to employ quantitative methods to make meaningful progress on policy goals (Fussell
2018; Mitchell et al. 2018).
Implementation of Change
Devices have embedded features that align with providing feedback and self-monitoring
of behavior to support more autonomous forms of motivation for PA (Chia et al., 2019; Friel &
Garber, 2021; Lewis et al., 2020; Lyons et al., 2014; Mercer et al., 2016; Villalobos-Zúñiga &
Cherubini, 2020) and now is the time to implement change. This phase allows for
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communicating how individuals with self-determined motivations who use digital health
technologies may benefit from increased PA (Friel & Garber, 2021; O’Loughlin et al., 2021).
This phase will also incorporate guidelines or industry standards for different technologies based
on behavior-change theories, with AI/ML, data mining, deep learning, predictive algorithms, and
big data to allow for a more personalized exchange, increasing tailored interactions for
individuals to engage in PA to promote positive health changes (Schoeppe et al., 2021;
Villalobos-Zúñiga & Cherubini, 2020). Communication buy-in, enabling action, creating short-
term wins are key at this phase in change.
Step 4: Communicating the Vision
The fourth step is about communicating the change in the vision while connecting the
vision with all the essential aspects like AI/ML development, generating data, and
confidentiality. Advanced digital health technologies can be utilized to generate algorithms
supporting precision health, though the algorithms need to be based on users’ representative data
(Kristoffersson et al., 2022; Rajagopalan et al., 2017). Individuals are generating a huge amount
of data far exceeding the market’s present-day potential to produce value from it, and the real
challenge is to produce value and benefit from data collected to analyze to make people’s lives
healthier and easier (Asri, 2022). There is increasing interest in AI/ML, particularly predictive
analytics based on machine learning, and ensuring realistic expectations is also essential when
communicating the vision. This step is about projection, addressing concerns, applying the
change, and leading by example (Kotter, 1997), and it is crucial to involve members of
communities in the development process (Mitchel et al., 2020). Finally, the communication goes
beyond DEI and extends to panels, outreach groups, researchers, and clearinghouses to ensure
the message meets those building and receiving the motivation features
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Step 5: Enable Action, Removing Obstacles, and Barriers
This step encourages action; the growth of digital health technology has attracted and
motivated health-focused individuals to monitor, store and transmit their health-related
information and data to obtain goals, engage in PA, and increase health outcomes (Barua &
Barua, 2021; Villalobos-Zúñiga & Cherubini, 2020). The era of big data is upon us, and the
volume of data available to clinicians and researchers for predictive analytics will continue to
expand as we increasingly incorporate information from electronic health records and digital
health technology (Weaver et al., 2021). Privacy of data, vulnerability for attacks, and costs for
storing data are all valid concerns for individuals. Starting with clearly articulated goals can
improve fairness and accountability (Mitchel et al., 2020).
Step 6: Short-Term Wins
Step 6 in the change model focuses on short-term wins, and achieving encourages the
transformation over time. To keep the momentum moving and to motivate others to support the
initiative, it is important to have short-term goals to accomplish and celebrate early in the change
process (Kotter, 1997). If individuals believe the data obtained from digital health technologies
are accurate, their perceived trustworthiness will be strengthened and encourage technology
adoption (Barua & Barua, 2021). Digital health technologies are indispensable tools for health-
oriented behavioral change interventions, and increasing favorable health outcomes has
increased the use of exposure, as SDs are projected to reach almost 230 million units in 2024
(Vailshery, 2021). With SDT, self-reporting of tasks is positively associated with an increased
intrinsic motivation towards the target activity (Ryan & Deci, 2000a).
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Incorporating and Sustaining Change
Aligning healthcare and community partners to implement change may be beneficial for
implementing PA as a core prevention strategy to slow, stop and potentially reverse their risk of
future disease (Porter et al., 2022). The WHO, UN SDG, healthcare organizations, and
community non-profits are a few stakeholders in incorporating and sustaining change in PA.
Building the change and making it stick is often as complex as the problem and Kotter breaks
this phase down into two steps that can guide change (1997).
Step 7: Sustain Acceleration
The seventh step focuses on measuring and evaluating the change and sustaining the
implementation of change (Kotter, 1997). Health data and digital health technologies can support
challenges, health promotion, and drive insights into health conditions. To sustain momentum,
data needs to be input from users, access needs to be available, guidelines need to be established
to protect data, and development should encourage intrinsically motivated users to engage in
increased PA. Peloton is a current example of how millions purchase or engage in digital health
technology annually, yet research shows at least one-third abandon the devices within 6 to 12
months of purchase. Thus, developers need to understand the determinants behind the adoption
and use of digital health technology (Barua & Barua, 2021; Ledger, 2014).
Step 8: Incorporating Change
The final step focuses on incorporating, making change a part of the culture, and
strengthening habits (Kotter, 1997). The current data indicates the importance of evaluating
various behavior regulation strategies when analyzing the effects of behavioral regulations on
PA-related results, and SDT may be applied to improve the interpretation of the psychological
tools that develop exercise motivation (Emm-Collison et al., 2020; Matsumoto, 2021; Villalobos-
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Zúñiga & Cherubini, 2020). To sustain and strengthen change, it is useful to look at AI/ML to
automate processes, gain insight through data analysis, and engage with users (Davenport et al.,
2018). Integrating the change with the existing culture, ensuring it is discussed, and
communicating with change agents are integral to maintaining the change.
Increasing Motivation and PA in Adults
Kotter (1997) helped mobilize change through an eight-step model. When collecting big
data and implementing it into a PA culture, it is incredibly advantageous to implement long-
lasting change focusing heavily on the individuals going through the change rather than solely
focusing on the project itself. Kotter emphasized preparing for change, how to succeed across
various stakeholders, and implementing change successfully. Beyond the health benefits of
implementing SDT into digital health technology, and despite the many benefits of PA, levels of
inactivity and their costs are high (American College of Sports Medicine, 2015), and this change
has a large impact on individuals and industries.
Strengths and Weaknesses of the Approach
The SDT (1997), along with Kotter’s (1996) eight-step model of change, provided a
comprehensive method for identifying, organizing, and validating motivation as well as
implementing and evaluating recommendations to increase PA. However, the design of the SDT
model proved difficult to organize results when the motivation related to a combination of digital
health technology and participation in PA/OS. Furthermore, the SDT model did not readily
permit for influences external to understanding the influence of motivation on PA.
Recommendations for Future Research
Through SDT, this study examined motivation, past participation in PA/OS, vitality, and
the use of digital health technology’s influence on current engagement in PA. This study
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revealed multiple areas that could contribute to future research involving motivation, PA, and
digital health technology, specifically for targeted populations. Another area of focus could be
designing research on a specific type of digital health technology usage. In addition, future
research on AI/ML programming would be beneficial for predictive trends and positive impacts
on health promotion. Eventually, a longitudinal study would also provide more insight into PA in
adults throughout their lifetime. Finally, future research would also be beneficial to understand
the benefits of incorporating sport into an adult PA practice or lack thereof. These are just a few
of the areas of focus to understand how PA plays a role in the declining health of adults.
When developing research, representation and health equity matters, and future research
must ensure these parameters target specific populations. This type of research may be necessary
for increasing PA in all adults, presenting methods to integrate AI/ML and motivation into
digital health technology available for all demographics. Research designed within communities
would promote input into the research and create an opportunity to develop a more sustainable
implementation plan. In addition, PA measurement has relied too heavily on self-report
instrumentation, and future research may benefit from objective measures and data collection.
Ultimately, the demographics and socioeconomic status may be vital in understanding the role of
physical activities in health. A greater sense of the context in which PA emerges would benefit
and inform forthcoming health promotion campaigns that target PA as a preventive health
behavior within this population.
As digital health technology evolves, it is essential to understand how it may contribute
to adults engaging in PA. For example, the more physically active participants used SDs over
fitness trackers. However, the data also showed that digital health technology was not essential to
those engaged in PA. With the fourth industrial revolution, technology is changing, health and
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physical activity is changing, how we relate to one another, work, our economies and
governments function, and even what it means to be human are changing (Schwab & Davis,
2018). Therefore, future research could focus on the specific tools, applications, or resources
digital health technology provides or if they are needed. In addition, as the market changes and
the IoT increases, future technology development must be based on theoretical models and
research that contribute to health promotion.
As researchers work with AI/ML programming, specific development and features in
digital health technology should consider PA. Specifically, further examining how and why
adults engage in PA would help shape future development. Finally, designing the research based
on specific datasets would also allow for predictive algorithms established on specific data
gathered. Real-time data are integral in AI/ML algorithms as multiple changing factors come into
play for analyzing data, automating processes, and contributing to engagement. Future research
should look at electronic medical records (EMR), body mass index (BMI), weight, height, and
biometric identifiers to understand PA. Artificial intelligence/machine learning designed to tailor
interventions, health promotion programs, and digital health technology should target the
differences rooted in demographic factors, curbing increases in the prevalence of poor health to
reduce disparities among sub-demographic populations.
This study assessed adults’ participation in PA/OS and their youth, giving insight into an
opportunity for future longitudinal research to understand PA/OS participation at various stages
in a person’s life. This research is essential because it would identify how PA participation
evolves over an individual’s life alongside significant predictors at various stages. In addition,
the various stages would give an understanding of an individual’s overall health and preventive
measures they may take with their health. The longitudinal study would also provide insight by
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bringing in various groups from diverse backgrounds, locations, ages, socioeconomic statuses,
fitness levels, health literacy, and overall health. Additionally, comparing it globally to
understand how to create societal structures that support lifelong sport better. Finally, given the
change in PA manners that occur from childhood to young adult, to parenthood and beyond,
accurate measurement is prudent to understand how to adapt to different periods in one’s life.
Over time, a greater understanding of PA engagement would contribute to health promotion.
Additionally, this study sought to understand how adults self-assessed sport with their
current PA. Unfortunately, the participants in this study did not have an opportunity to review
this in-depth, and measurements were lacking. Therefore, recommending future research
supports understanding the role of sport and the impact on PA. Understanding the lack of OS
participation in adults may provide a clearer understanding of sports, what factors contribute to
participation, and adult PA. Research would contribute to how sports is a foundation for adults to
incorporate into their daily habits and understand how sports motivation could benefit their
mental and physical health.
In conclusion, future research on specific populations, types of digital health technology,
AI/ML development, a longitudinal approach, sport, motivation, vitality, and PA as a leisure-
time activity is suggested. Vitality and energy may play a more significant role, though future
studies might not be beneficial. In addition, this study reviewed PA in adults and did not
differentiate leisure-time PA; this might also provide insight for future research. Finally, this
study concluded that future research would benefit from a greater focus on PA in adults to
understand why PA has increased over the years, and health has declined.
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Limitations and Delimitations
Potential limitations were apparent at the study’s outset and as it progressed. The scope
of the study was broad, and self-selection due to voluntary participation in the study may have
resulted in participation bias. The truthfulness of participants when providing answers may have
been aspirational and was subjective to their answers. The study included participants within
various networks and may lack generalizability beyond those surveyed. Finally, with limited
outreach and resources, the study was conducted in a short time, preventing a larger sample size.
Many, if not all, participants may have high health literacy and not represent the general
population.
It is important to note that using an instrument developed 25 years ago may include
common challenges associated with online surveys. Other important delimitations that may have
affected data collection included survey question clarity. In addition, the MPAM-R was limited
in measuring intrinsic motivation with the competence and interest/enjoyment motive scales and
extrinsic through the fitness, appearance, and social motives scales (Ryan et al., 1997).
Additional instruments with a non-Likert scale would be advised for AI/ML integration. The
Physical Activity and Leisure Motivation Scale (PALMS) may be beneficial as it includes those
in the MPAM-R (Ryan et al., 1997) and competition/ego, psychological condition, and others’
expectations as additional factors (Zach et al., 2012). Research on different types of motivation
would also be beneficial to understand why people engage in PA, OS, or exercise.
Conclusions
Physical activities in adults and motivation are increasingly essential due to their effects
on an individual’s health. Although active adults are healthier and less likely to develop many
chronic diseases, there is a need to understand that increasing PA may contribute to better
122

aerobic fitness than inactive adults. Furthermore, although there is an increase in adults meeting
aerobic activity guidelines, it has a significant negative impact on their overall health when they
do not go beyond the minimum guidelines. This study examined Ryan and Deci’s (1997) SDT
motives to engage in PA, use digital health technology, past participation in PA/OS, and their
influence on adults PA. Also, this study provided specific recommendations and an evaluation
plan for implementing changes in the health culture. These recommendations were grounded in
the literature and offered paths to motivate adults to engage in PA. By expanding the body of
knowledge on PA, OS, digital health technology, and motivation, this research expects to form a
foundation for future studies on increasing PA in adults and developing future AI/ML
technology.
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152

Appendix A: Demographic Questions
1. What is your age? (in whole numbers)__________________

2. What is your gender?
Female
Male
Transgender Female
Transgender Male
Non-binary
Other
Prefer Not to Say/ Decline Responding

3. What is your ethnicity/race?
Caucasian/White
Asian
Black/African American
Chicano/a • Latino/a/x • Hispanic
Native American/Alaskan Native
Pacific Islander
Two or More Races
Some Other Race
Prefer Not to Say/ Decline Responding

4. How many hours per day do you spend doing physical activities? (e.g., walking,
running, yoga, swimming, housework, playing with children, home repair)
Not at all
Less than 1 hour per day
1–3 hours per day
3–5 hours per day
5–7 hours per day
7+ hours per day

5. Due to an injury, health condition, or disability, do you have any limitations where
medical providers have restricted your physical activity?
No limitations
Limitations due to injury
Limitations due to health condition
Limitations due disability
Other

6. About how many hours per day do you spend sitting?
Not at all
Less than 3 hour per day
3–6 hours per day
153

6–8 hours per day
8–10 hours per day
10+ hours per day

7. Think back to the last week when you engaged in physical activities, sports, or
exercise for at least 10 minutes. Now think of the total time you had increased
breathing, heart rate, leg fatigue, or perspiration. What was your estimated total time?
Less than 1-hour last week
1 hour – 2.5 hours last week
2.5 hours – 5 hours last week
5+ hours last week
Don’t know

8. Were you physically active in your youth? Please click all that apply:
I participated in organized sports.
I participated in physical activities.
Not at all

9. Are you currently physically active? Please click all that apply:
I participate in organized sports.
I participate in physical activities
Not at all

10. Do you regularly use fitness and health tracking technology? Please click all that
apply:
Smart Devices (e.g., Apple Watch, Garmin, Oura Ring, Joule)
Fitness Tracker (e.g., Fitbit, Smart Clothing, GPS Tracker, Heart Rate Monitor,
Cadence Sensor)
Other (e.g., blood oxygen saturation, posture wearable)
I do not currently use any fitness tracking technology

11. At this moment, I feel alert and awake.
Yes
Maybe
No

12. I feel energized when I engage in physical activities.
Yes
Maybe
No

13. I engage in physical activities, sports, and exercise because:
a. I want to be physically fit.
b. Physical activities are fun
c. I like engaging in activities that physically challenge me.
154

d. I want to obtain new skills.
e. I want to look better or maintain weight so I look better.
f. I want to be with my friends.
g. I like to do physical activities.
h. I want to improve my existing skills.
i. I like the challenge.
j. I want to define my muscles so I look better.
k. Physical activities make me happy.
l. I want to keep up my current skill level.
m. I want to have more energy.
n. I like activities that are physically challenging.
o. I like to be with others who are interested in physical activities.
p. I want to improve my cardiovascular fitness.
q. I want to improve my appearance.
r. I think physical activities are interesting.
s. I want to maintain my physical strength to live a healthy life.
t. I want to be more attractive to others.
u. I want to meet new people.
v. I enjoy physical activities.
w. I want to maintain my physical health and well-being.
x. I want to improve my body shape.
y. I want to get better at physical activities.
z. I find physical activities stimulating.
aa. I will feel physically unattractive if I don’t.
bb. My friends want me to engage in physical activities.
cc. I like the excitement of participation.
dd. I enjoy spending time with others doing physical activities.

155

Appendix B: Motives for Physical Activities Measure-Revised (MPAM-R) and Scoring

Section 2:

The following is a list of reasons why people engage in physical activities, sports and exercise
(Fredrick et al., 1993; Ryan et al., 1997). Keeping in mind your primary physical activity/sport,
respond to each question (using the scale given), on the basis of how true that response is for
you.

1 Not at all true for me
2
3
4
5
6
7 Very true for me

___ 1. Because I want to be physically fit.
___ 2. Because it’s fun.
___ 3. Because I like engaging in activities which physically challenge me.
___ 4. Because I want to obtain new skills.
___ 5. Because I want to look or maintain weight so I look better.
___ 6. Because I want to be with my friends.
___ 7. Because I like to do this activity.
___ 8. Because I want to improve existing skills.
___ 9. Because I like the challenge.
___ 10. Because I want to define my muscles so I look better.
___ 11. Because it makes me happy.
___ 12. Because I want to keep up my current skill level.
___ 13. Because I want to have more energy
___ 14. Because I like activities which are physically challenging.
___ 15. Because I like to be with others who are interested in this activity.
___ 16. Because I want to improve my cardiovascular fitness.
___ 17. Because I want to improve my appearance.
___ 18. Because I think it’s interesting.
___ 19. Because I want to maintain my physical strength to live a healthy life.
___ 20. Because I want to be attractive to others.
___ 21. Because I want to meet new people.
___ 22. Because I enjoy this activity.
___ 23. Because I want to maintain my physical health and well-being.
___ 24. Because I want to improve my body shape.
___ 25. Because I want to get better at my activity.
___ 26. Because I find this activity stimulating.
___ 27. Because I will feel physically unattractive if I don’t.
___ 28. Because my friends want me to.
156

___ 29. Because I like the excitement of participation.
___ 30. Because I enjoy spending time with others doing this activity.

Scoring Information
Interest/Enjoyment: 2, 7, 11, 18, 22, 26, 29
Competence: 3, 4, 8, 9, 12, 14, 25
Appearance: 5, 10, 17, 20, 24, 27
Fitness: 1, 13, 16, 19, 23
Social: 6, 15, 21, 28, 30

157

Appendix C: Social Media Recruiting
Figure C1
Example of Generic Post

158

Figure C2
Examples of Targeted Post

159

Example of LinkedIn Message and Reply

Inspired by your profile!

[Name Removed for Privacy] - your profile popped up on my LinkedIn feed, and your
focus on #dataleadership caught my attention. The more I look at your profile, the more
I am impressed and inspired by all you do. And as a [Location Removed for Privacy]
native, your location made my heart happy! I’m doing my research for my doctorate and
would love your insight into having more representation in my study. I value your
support and wisdom on a 10-minute anonymous survey for my doctorate focusing on
health, physical activity, and motivation. Do you have any connections you could direct
me to, or would you be interested in posting a link to my research? I’m happy to provide
more details if needed. #representationmatters I’m looking for a range of ages and
physical activity levels. US-based adults over the age of 18 are the only requirement.
https://bit.ly/PhysicalActivityResearch

Hi Emily, Thanks for reaching out. I just commented on your post to help boost
the reach! I might reach out to local organizations like [Name Removed for
Privacy] for suggestions on how to continue amplifying the reach. Another idea is
adding a visual to the poster when sharing on social medial. By the way, I really
like how you used images to share the informed consent details of the study. I am
going to leverage that approach in the future!

160

Appendix D: Demographic Subcategories
Table D1
Demographic Subcategories and Abbreviations for Analysis
Category Subcategory Abbreviation n
Age
41 and younger
42 and older
<=41
42+
380
381
Gender
Female
Male
F
M
556
205
Ethnicity
Non-White
White
NW
W
127
638
Daily physical
activity
<1 hour
>3 hours
<1hrD
>1hrD
229
536
Physical
limitations
No limitations
Limitations due to injury, health
condition, disability or other
PLN
PLY
549
216
Hours sitting
Less than 6 hours
More than 6 hours
<6S
>6S
86
679
Youth activity
Not at all
Organized sports/Physical activity
YN
YOS/PA
58
707
Current activity
Not at all
Organized sports/Physical activity
CPAN
CPAY
99
666
Digital health
technology
No digital health technology use
Smart devices/Fitness tracker/Other
NDHT
YDHT
211
554
Alert
No/Maybe
Feel alert and awake
NMAA
YAA
147
618
Energized
No/Maybe
Energized with PA
NME
YE
88
677

Note. N = 765 completed survey responses. Four participants opted-out for including their age.

161

Table D2
Descriptive Statistics and ANOVA: Combined Variables
Comparison n M SD df t F p*
Sample 765 5.26 1.21
<=41
42+
380
381
5.23
5.19
0.96
0.95
1 -0.66 0.43 .51
F
M
556
205
5.17
5.33
0.95
0.96
1 1.99 3.99 .05*
NW
W
127
638
4.98
5.23
0.94
0.96
1 2.44 5.94 .02*
<1hrD
>1hrD
229
536
4.88
5.35
0.97
0.91
1 6.37 40.58 .00*
PLN
PLY
549
212
5.27
5.04
0.92
0.99
1 3.06 9.37 .00*
<6S
>6S
383
382
5.15
5.27
0.95
0.95
1 1.82 3.31 .07
YN
YOS/PA
58
707
4.82
5.24
0.91
0.95
1 3.24 10.47 .00*
CPAN
CPAY
99
666
4.47
5.32
0.95
0.90
1 8.73 76.20 .00*
NDHT
YDHT
211
554
4.99
5.29
1.02
0.92
1 3.85 14.80 .00*
NMAA
YAA
147
618
5.15
5.22
0.91
0.96
1 0.80 0.64 .42
NME
YE
88
677
4.48
5.30
1.03
0.90
1 7.90 62.37 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

162

Table D3
Descriptive Statistics and ANOVA: Interest/Enjoyment
Comparison n M SD df t F p*
Sample 765 5.26 1.21
<=41
42+
380
381
5.27
5.24
1.15
1.28
1 2.09 4.35 .81
F
M
556
205
5.21
5.41
1.24
1.15
1 2.09 4.35 .04*
NW
W
127
638
4.99
5.28
1.22
1.21
1 2.26 5.09 .02*
<1hrD
>1hrD
229
536
4.72
5.49
1.23
1.13
1 8.34 69.58 .00*
PLN
PLY
549
212
5.33
5.09
1.23
1.17
1 -2.40 5.78 .02*
<6S
>6S
383
382
5.21
5.40
1.25
1.16
1 3.22 10.38 .00*
YN
YOS/PA
58
707
4.70
5.31
1.32
1.20
1 3.71 13.79 .00*
CPAN
CPAY
99
666
4.04
5.44
1.25
1.10
1 11.58 134.08 .00*
NDHT
YDHT
211
554
5.00
5.36
1.37
1.33
1 3.72 13.82 .00*
NMAA
YAA
147
618
5.03
5.31
1.23
1.20
1 2.58 6.66 .01*
NME
YE
88
677
4.05
5.42
1.25
1.12
1 10.65 113.50 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

163

Table D4
Descriptive Statistics and ANOVA: Competence
Comparison n M SD df t F p*
Sample 765 5.26 1.21
<=41
42+
380
381
5.20
5.06
1.13
1.25
1 -2.62 2.61 .11
F
M
556
205
5.02
5.43
1.21
1.10
1 4.21 17.71 .00*
NW
W
127
638
4.88
5.15
1.25
1.29
1 2.10 4.40 .04*
<1hrD
>1hrD
229
536
4.64
5.33
1.22
1.12
1 7.61 57.93 .00*
PLN
PLY
549
212
5.14
5.08
1.22
1.14
1 -0.62 0.38 .54
<6S
>6S
383
382
4.92
5.30
1.24
1.12
1 4.08 16.66 .00*
YN
YOS/PA
58
707
4.63
5.12
1.25
1.18
1 3.33 11.10 .00*
CPAN
CPAY
99
666
4.02
5.29
1.26
1.09
1 10.56 111.54 .00*
NDHT
YDHT
211
554
4.90
5.21
1.35
1.11
1 3.26 10.60 .00*
NMAA
YAA
147
618
4.86
5.19
1.17
1.19
1 2.97 8.85 .00*
NME
YE
88
677
4.02
5.27
1.24
1.11
1 9.84 96.84 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

164

Table D5
Descriptive Statistics and ANOVA: Appearance
Comparison n M SD df t F p*
Sample 765 5.26 1.21
<=41
42+
380
381
5.32
5.25
1.22
1.28
1 -0.78 0.60 .44
F
M
556
205
5.34
5.19
1.25
1.25
1 -1.46 2.14 .14
NW
W
127
638
5.13
5.30
1.34
1.24
1 1.25 1.56 .21
<1hrD
>1hrD
229
536
5.12
5.36
1.36
1.91
1 2.47 6.11 .01*
PLN
PLY
549
212
5.38
5.05
1.25
1.23
1 -3.30 10.89 .00*
<6S
>6S
383
382
5.36
5.21
1.24
1.25
1 -1.62 2.61 .11
YN
YOS/PA
58
707
5.22
5.30
1.38
1.24
1 0.44 0.19 .66
CPAN
CPAY
99
666
4.81
5.36
1.48
1.20
1 4.13 17.06 .00*
NDHT
YDHT
211
554
5.01
5.40
1.38
1.18
1 3.82 14.57 .00*
NMAA
YAA
147
618
5.51
5.24
1.16
1.26
1 -2.37 5.60 .02*
NME
YE
88
677
4.83
5.35
1.44
1.21
1 3.74 13.98 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance
165

Table D6
Descriptive Statistics and ANOVA: Fitness
Comparison n M SD df t F p*
Sample 765 5.26 1.21
<=41
42+
380
381
5.72
6.20
1.10
0.81
1 6.94 48.18 .00*
F
M
556
205
5.97
5.95
0.98
1.03
1 -0.22 0.05 .83
NW
W
127
638
5.82
5.96
1.15
0.98
1 1.23 1.51 .22
<1hrD
>1hrD
229
536
5.78
6.04
0.98
0.99
1 3.27 10.72 .00*
PLN
PLY
549
212
6.14
5.50
0.82
1.23
1 -8.29 68.79 .00*
<6S
>6S
383
382
6.03
5.89
0.94
1.04
1 -1.92 3.68 .06
YN
YOS/PA
58
707
6.09
5.95
0.77
1.01
1 -1.03 1.06 .30
CPAN
CPAY
99
666
5.67
6.00
0.96
0.99
1 3.08 9.51 .00*
NDHT
YDHT
211
554
6.01
5.94
0.92
1.02
1 -0.78 0.60 .44
NMAA
YAA
147
618
6.00
5.95
0.84
1.03
1 -0.54 0.29 .59
NME
YE
88
677
5.53
6.02
1.06
0.97
1 4.38 19.14 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance
166

Table D7
Descriptive Statistics and ANOVA: Social
Comparison n M SD df t F p*
Sample 765 5.26 1.21
<=41
42+
380
381
4.65
4.17
1.34
1.42
1 -4.82 23.22 .00*
F
M
556
205
4.33
4.66
1.43
1.29
1 2.90 8.41 .00*
NW
W
127
638
4.07
4.46
1.36
1.39
1 2.60 6.77 .01*
<1hrD
>1hrD
229
536
4.14
4.53
1.35
1.41
1 3.48 12.11 .00*
PLN
PLY
549
212
4.39
4.48
1.43
1.39
1 0.82 0.67 .42
<6S
>6S
383
382
4.27
4.55
1.41
1.38
1 2.77 7.70 .01*
YN
YOS/PA
58
707
3.48
4.89
1.35
1.38
1 5.37 28.83 .00*
CPAN
CPAY
99
666
3.77
4.51
1.26
1.40
1 4.93 24.32 .00*
NDHT
YDHT
211
554
4.07
5.54
1.46
1.36
1 4.25 18.02 .00*
NMAA
YAA
147
618
4.36
4.32
1.36
1.41
1 0.47 0.22 .64
NME
YE
88
677
3.99
4.47
1.34
1.40
1 2.98 8.88 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

167

Table D8
ANOVA: Current Physical Activity
Comparison n M SD df t F p*
Sample 765 5.26 1.21
NDHT
YDHT
211
554
0.78
1.00
0.03
0.02
1 6.31 39.78 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

Table D9
ANOVA: Youth Physical Activity
Comparison n M SD df t F p*
Sample 765 5.26 1.21
NDHT
YDHT
58
707
0.95
0.81
0.44
0.44
1 2.36 5.56 .02*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

168

Table D10
ANOVA: Alert
Comparison n M SD df t F p*
Sample 765 5.26 1.21
YN
YOS/PA
58
707
1.64
1.75
0.08
0.02
1 1.38 1.92 .16
NDHT
YDHT
211
554
1.71
1.75
0.04
0.02
1 0.96 0.92 .33

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

Table D11
ANOVA: Energized
Comparison n M SD df t F p*
Sample 765 5.26 1.21
YN
YOS/PA
58
707
1.76
1.87
0.54
0.39
1 2.05 4.19 .04*
NDHT
YDHT
211
554
1.75
1.90
0.03
0.02
1 4.68 21.88 .00*

Note. 95% CI utilized for calculating p values.
*p < 0.05 significance

169

Table D12
Pearson’ s Correlation Coefficients
Measure 1 2 3 4 5
Interest/enjoyment –
Competence 0.84 –
Appearance 0.45 0.46 –
Fitness 0.59 0.57 0.57 –
Social 0.54 0.56 0.40 0.31 –

Note. All values significant at the 99% confidence level.

170

Appendix E: Theoretical Framework Alignment Matrix (Quantitative)
Research questions Type of
analysis
Theoretical
framework +
data instrument
question
Scoring/Reliability
Are there differences in
why people are physically
active based on prior
participation in OS/PA
and current use of digital
health technology?

ANOVA,
Tukey-
Kramer

Self-determination
theory

MPAM-R

(Ryan et al., 1997)

Interest/Enjoyment:
2, 7, 11, 18, 22, 26, 29
Competence:
3, 4, 8, 9, 12, 14, 25
Appearance:
5, 10, 17, 20, 24, 27
Fitness:
1, 13, 16, 19, 23
Social:
6, 15, 21, 28, 30

Cronbach alpha Of
0.78 To 0.92
Are there differences in
current levels of PA
based on prior
participation in OS/PA
and current use of digital
health technology?
ANOVA,
Tukey-
Kramer

Demographics

Questions 1–10

Are there differences in
participants’ perceived
vitality based on prior
participation in OS/PA
and current use of digital
health technology?
ANOVA,
Tukey-
Kramer

Self-determination
theory

Vitality scale - State
level version

Modified questions
11 and 12

What most describes PA
and digital health
technology use in adults?
ANOVA,
Tukey-
Kramer,
Correlations

Self-determination
theory

MPAM-R

(Ryan et al., 1997)

171

Appendix F: General Physical Activities Defined by Level of Intensity
The following is in accordance with CDC and ACSM guidelines (Ainsworth et al., 1993;
CDC, 1999).

Table F1
General Physical Activities Defined by Level of Intensity
Moderate activity+
3.0 to 6.0 METs*
(3.5 to 7 kcal/min)
Vigorous activity+
Greater than 6.0 METs*
(more than 7 kcal/min)
Walking at a moderate or brisk pace of 3 to
4.5 mph on a level surface inside or outside,
such as
Walking to class, work, or the store;
Walking for pleasure;
Walking the dog; or
Walking as a break from work.
Walking downstairs or down a hill
Racewalking—less than 5 mph using
crutches
Hiking
Roller skating or in-line skating at a leisurely
pace
Racewalking and aerobic walking, 5 mph or
faster
Jogging or running
Wheeling your wheelchair
Walking and climbing briskly up a hill
Backpacking
Mountain climbing, rock climbing,
rappelling Roller skating or in-line skating
at a brisk pace
Bicycling 5 to 9 mph, level terrain, or with
few hills
Stationary bicycling, using moderate effort
Bicycling more than 10 mph or bicycling on
steep uphill terrain
Stationary bicycling, using vigorous effort
Aerobic dancing, high impact
Water aerobics
Aerobic dancing, high impact
Step aerobics
Water jogging
Teaching an aerobic dance class
Calisthenics, light
Yoga
Gymnastics
General home exercises, light or moderate
effort, getting up and down from the floor
Jumping on a trampoline
Using a stair climber machine at a light-to-
moderate pace
Using a rowing machine, with moderate
effort
Calisthenics, push-ups, pull-ups, vigorous
effort
Karate, judo, tae kwon do, jujitsu
Jumping rope
Performing jumping jacks
Using a stair climber machine at a fast pace
Using a rowing machine—with vigorous
effort
Using an arm cycling machine, with
vigorous effort
172

Weight training and bodybuilding using free
weights, Nautilus- or universal-type
weights
Circuit weight training
Boxing, punching bag
Boxing, in the ring, sparring
Wrestling, competitive
Ballroom dancing
Line dancing
Square dancing
Folk dancing
Modern dancing, disco ballet
Professional ballroom dancing—energetically
Square dancing—energetically
Folk dancing—energetically
Clogging
Table tennis, competitive
Tennis, doubles
Tennis, singles
Wheelchair tennis
Golf, wheeling or carrying clubs ––––
Softball, fast pitch or slow pitch, basketball,
shooting baskets
Coaching children’s or adults’ sports
Most competitive sports, football game
Basketball game, wheelchair basketball
Soccer
Rugby
Kickball
Field or rollerblade hockey
Lacrosse
Volleyball—competitive Beach volleyball—on sand court
Playing frisbee
Juggling
Curling
Cricket, batting and bowling
Badminton
Archery (nonhunting)
Fencing
Handball, general or team
Racquetball
Squash
Downhill skiing, with light effort
Ice skating at a leisurely pace (9 mph or
less) Snowmobiling
Ice sailing
Downhill skiing, racing or with vigorous
effort ice skating, fast pace or speedskating
Cross-country skiing
Sledding
Tobogganing
Playing ice hockey
Swimming, recreational
Treading water, slowly, moderate effort
Diving, springboard or platform
Aquatic aerobics
Waterskiing
Snorkeling
Surfing, board or body
Swimming, steady paced laps, synchronized
swimming
Treading water, fast, vigorous effort
Water jogging
Water polo
Water basketball
Scuba diving
Canoeing or rowing a boat at less than 4 mph
Rafting, whitewater
Sailing, recreational or competition
Canoeing or rowing, 4 or more mph kayaking
in whitewater rapids
173

Paddle boating
Kayaking, on a lake, calm water
Washing or waxing a powerboat or the hull
of a sailboat
Fishing while walking along a riverbank or
while wading in a stream, wearing waders
––––
Hunting deer, large or small game
Pheasant and grouse hunting
Hunting with a bow and arrow or
crossbow— walking
––––
Horseback riding, general
Saddling or grooming a horse
Horseback riding, trotting, galloping,
jumping, or in competition
Playing polo
Playing on school playground equipment,
moving about, swinging, or climbing
Playing hopscotch, 4-square, dodgeball, T-
ball, or tetherball
Skateboarding
Roller skating or in-line skating, leisurely
pace
Running
Skipping
Jumping rope
Performing jumping jacks
Roller skating or in-line skating, fast pace
Playing instruments while actively moving;
playing in a marching band; playing guitar
or drums in a rock band
Twirling a baton in a marching band
Singing while actively moving about, as on
stage or in church
Playing a heavy musical instrument while
actively running in a marching band
Gardening and yard work: raking the lawn,
bagging grass or leaves, digging, hoeing,
light shoveling (less than 10 lbs per
minute), or weeding while standing or
bending
Planting trees, trimming shrubs and trees,
hauling branches, stacking wood
Pushing a power lawn mower or tiller
Gardening and yard work: heavy or rapid
shoveling (more than 10 lbs per minute),
digging ditches, or carrying heavy loads
Felling trees, carrying large logs, swinging
an ax, hand-splitting logs, or climbing and
trimming trees
Pushing a nonmotorized lawn mower
Shoveling light snow Shoveling heavy snow
Moderate housework: scrubbing the floor or
bathtub while on hands and knees, hanging
laundry on a clothesline, sweeping an
outdoor area, cleaning out the garage,
washing windows, moving light furniture,
packing or unpacking boxes, walking and
putting household items away, carrying out
heavy bags of trash or recyclables (e.g.,
glass, newspapers, and plastics), or carrying
water or firewood
Heavy housework: moving or pushing heavy
furniture (75 lbs or more), carrying
household items weighing 25 lbs or more
up a flight of stairs, or shoveling coal into a
stove
Standing, walking, or walking down a flight
of stairs while carrying objects weighing 50
lbs or more

174

General household tasks requiring
considerable effort
Putting groceries away—walking and
carrying especially large or heavy items
less than 50 lbs.
Carrying several heavy bags (25 lbs or more)
of groceries at one time up a flight of stairs
Grocery shopping while carrying young
children and pushing a full grocery cart, or
pushing two full grocery carts at once
Actively playing with children—walking,
running, or climbing while playing with
children Walking while carrying a child
weighing less than 50 lbs
Walking while pushing or pulling a child in
a stroller or an adult in a wheelchair
Carrying a child weighing less than 25 lbs
up a flight of stairs
Child care: handling uncooperative young
children (e.g., chasing, dressing, lifting into
car seat), or handling several young
children at one time
Bathing and dressing an adult

Vigorously playing with children—running
longer distances or playing strenuous games
with children
Racewalking or jogging while pushing a
stroller designed for sport use
Carrying an adult or a child weighing 25 lbs
or more up a flight of stairs
Standing or walking while carrying an adult
or a child weighing 50 lbs or more

Animal care: shoveling grain, feeding farm
animals, or grooming animals
Playing with or training animals
Manually milking cows or hooking cows up
to milking machines
Animal care: forking bales of hay or straw,
cleaning a barn or stables, or carrying
animals weighing over 50 lbs
Handling or carrying heavy animal-related
equipment or tack
Home repair: cleaning gutters, caulking,
refinishing furniture, sanding floors with a
power sander, or laying or removing carpet
or tiles
General home construction work: roofing,
painting inside or outside of the house, wall
papering, scraping, plastering, or
remodeling
Home repair or construction: very hard
physical labor, standing or walking while
carrying heavy loads of 50 lbs or more,
taking loads of 25 lbs or more up a flight of
stairs or ladder (e.g., carrying roofing
materials onto the roof), or concrete or
masonry work
Outdoor carpentry, sawing wood with a power Hand-sawing hardwoods saw
Automobile bodywork
Hand washing and waxing a car
Pushing a disabled car
Occupations that require extended periods of
walking, pushing or pulling objects
weighing less than 75 lbs, standing while
lifting objects weighing less than 50 lbs, or
carrying objects of less than 25 lbs up a
flight of stairs
Occupations that require extensive periods of
running, rapid movement, pushing or
pulling objects weighing 75 lbs or more,
standing while lifting heavy objects of 50
lbs or more, walking while carrying heavy
objects of 25 lbs or more
175

Tasks frequently requiring moderate effort
and considerable use of arms, legs, or
occasional total body movements.
For example:
• Briskly walking on a level surface
while carrying a suitcase or load
weighing up to 50 lbs
• Maid service or cleaning services
• Waiting tables or institutional
dishwashing
• Driving or maneuvering heavy
vehicles (e.g., semi-truck, school bus,
tractor, or harvester)—not fully
automated and requiring extensive use
of arms and legs
• Operating heavy power tools (e.g.,
drills and jackhammers)
• Many homebuilding tasks (e.g.
electrical work, plumbing, carpentry,
dry wall, and painting)
• Farming—feeding and grooming
animals, milking cows, shoveling
grain; picking fruit from trees, or
picking vegetables
• Packing boxes for shipping or moving
• Assembly-line work—tasks requiring
movement of the entire body, arms or
legs with moderate effort
• Mail carriers—walking while carrying
a mailbag
• Patient care—bathing, dressing, and
moving patients or physical therapy
Tasks frequently requiring strenuous effort
and extensive total body movements.
For example:
• Running up a flight of stairs while
carrying a suitcase or load weighing
25 lbs or more
• Teaching a class or skill requiring
active and strenuous participation,
such as aerobics or physical education
instructor
• Firefighting
• Masonry and heavy construction work
• Coal mining
• Manually shoveling or digging ditches
• Using heavy nonpowered tools
• Most forestry work
• Farming—forking straw, baling hay,
cleaning barn, or poultry work
• Moving items professionally
• Loading and unloading a truck

* The ratio of exercise metabolic rate. One MET is defined as the energy expenditure for sitting
quietly, which, for the average adult, approximates 3.5 ml of oxygen uptake per kilogram of
body weight per minute (1.2 kcal/min for a 70-kg individual). For example, a 2-MET activity
requires two times the metabolic energy expenditure of sitting quietly.
176

+ For an average person, defined here as 70 kilograms or 154 pounds. The activity intensity
levels portrayed in this chart are most applicable to men aged 30 to 50 years and women aged 20
to 40 years. For older individuals, the classification of activity intensity might be higher. For
example, what is moderate intensity to a 40-year-old man might be vigorous for a man in his 70s.
Intensity is a subjective classification.
Data for this chart were available only for adults. Therefore, when children’s games are listed,
the estimated intensity level is for adults participating in children’s activities.
To compute the amount of time needed to accumulate 150 kcal, do the following calculation: 150
kcal divided by the MET level of the activity equals the minutes needed to expend 150 kcal. For
example:
150) 3 METS = 50 minutes of participation. Generally, activities in the moderate-intensity range
require 25–50 minutes to expend a moderate amount of activity, and activities in the vigorous-
intensity range would require less than 25 minutes to achieve a moderate amount of activity.
Each activity listed is categorized as light, moderate, or vigorous on the basis of current
knowledge of the overall level of intensity required for the average person to engage in it, taking
into account brief periods when the level of intensity required for the activity might increase or
decrease considerably.
Persons with disabilities, including motor function limitations (e.g., quadriplegia) may wish to
consult with an exercise physiologist or physical therapist to properly classify the types of
physical activities in which they might participate, including assisted exercise. Certain activities
classified in this listing as moderate might be vigorous for persons who must overcome physical
challenges or disabilities.
177

Note. Almost every occupation requires some mix of light, moderate, or vigorous activities,
depending on the task at hand. To categorize the activity level of your own position, ask
yourself: How many minutes each working day do I spend doing the types of activities described
as light, moderate, or vigorous? To arrive at a total workday caloric expenditure, multiply the
minutes spent doing activities within each intensity level by the kilocalories corresponding to
each level of intensity. Then, add together the total kilocalories spent doing light, moderate, and
vigorous activities to arrive at your total energy expenditure in a typical day.

178

Appendix G: Informed Consent for Research

179

180

181

Appendix H: Information Sheet for Exempt Research

STUDY TITLE: Exploring Adults’ Physical Activity and Digital Technology via Self-
Determination Theory

PRINCIPAL INVESTIGATOR: Emily Robinson Masters

FACULTY ADVISOR: Patricia Tobey, Ph.D.

You are invited to participate in a research study. Your participation is voluntary. This document
explains information about this study. You should ask questions about anything that is unclear to
you.

PURPOSE
The purpose of this study is to examine physical activity (PA), digital technology use, and
engagement in youth organized sports/physical activity. Examined through SDT (Deci & Ryan,
2000), this study aims to evaluate how motivation plays a role in PA in adults with a focus on
sports and technology. The quantitative analysis focuses on differences between adults and
previous participation in sports or PA in their youth and current digital technology use. It also
examines why people are motivated to engage in PA regularly and their subjective vitality.
Furthermore, this study seeks to discover the role of motivation and technology in PA among
adults.

PARTICIPANT INVOLVEMENT
While there are multiple stakeholders among physically active adults, this study focuses on all
adults over age 18 who are physically active or intend to engage in PA. The online survey will be
open to U.S-based English-speaking adults who have access to take the survey. The survey will
begin by asking participants to verify key demographic details about themselves for eligibility in
the study. Once they state that they are over 18 years of age, the survey will invite them to self-
report. Finally, the survey will review why they are motivated to engage in physical activities,
sports, and exercise with the Motives for Physical Activities Measure-Revised (MPAM-R).
Thus, the study will seek the participation of all adults who are active or intend to be active in
PA or exercise.

If you decide to take part, you will be asked to answer the 42-question online survey.

PAYMENT/COMPENSATION FOR PARTICIPATION
Upon completing the survey, participants will have the option to submit their email to be offered
an anonymized raffle for a fifty-dollar gift card to Amazon (bought by the PI). The PI will send
the two fifty-dollar Amazon gift card codes by email to the two winners. Therefore, two surveys
will be needed to create an anonymized raffle for the study. Survey one, the main research study,
will be anonymous, asking for consent before sending respondents to the raffle—survey two will
be the raffle sign-up form. The study will use one of two versions of survey two. Option one, the
182

PI is responsible for randomizing a winner and sending the incentive. On the other, email
addresses will be downloaded into an Excel document for a randomized selection. Since this
method uses two surveys, each respondent will generate two responses. Response one will be the
primary research study, and response two will collect their email if they enter in the drawing for
a fifty-dollar Amazon gift card. Enabling the anonymized responses setting in Qualtrics will be
utilized in both surveys, scrubbing identifying information before saving it in the data
permanently, removing the respondents’ IP address and location data from the survey results.

CONFIDENTIALITY
The research team members and the University of Southern California Institutional Review
Board (IRB) may access the data. The survey will be distributed through the University of
Southern California Qualtrics Online Survey. Data is not labeled with any personally identifying
information or a code that the research team can link to personal identifying information. The
data will be electronically stored on local computers/laptops, removable drives, University of
Southern California Qualtrics, and University of Southern California Google Drive.

Electronic data will be stored with appropriate safeguards, such as unique usernames/passwords,
and limited to authorized study personnel. Dual factor authentication will be used, if feasible.
Security software (firewall, antivirus, anti-intrusion) will be installed and regularly updated on
all servers, workstations, laptops, and other devices used in the study. All computers with access
to study data will be scanned regularly (for viruses and spyware, etc.), and problems will be
resolved. Data stored on a removable drive will be encrypted and have proper access controls,
and data transfer will be encrypted.

INVESTIGATOR CONTACT INFORMATION
If you have any questions or concerns about the research, please contact Dr. Patricia Tobey
(Faculty Advisor) at tobey@usc.edu, University of Southern California Rossier School of
Education, or Emily Masters (Primary Investigator) at ermaster@usc.edu.

IRB CONTACT INFORMATION
If you have any questions about your rights as a research participant, please contact the
University of Southern California Institutional Review Board at (323) 442-0114 or email
irb@usc.edu.

Abstract (if available)

Abstract One in four adults in the United States does not meet the recommended weekly physical activity (PA) of 150 minutes of moderate-intensity aerobic activity. As a result, the lack of PA is one of the more pressing public health problems of the 21st century. This study aimed to evaluate how motivation plays a role in physical activity (PA) in adults, focusing on participation in organized sports (OS) and digital health technology. Self-determination theory (SDT) is a significant theoretical approach in physical activity motivation research, and this study uses a quantitative approach to look at motivation and PA in adults. The study was based on a survey design, including the Motives for Physical Activities Measure-Revised (MPAM-R) instrument, measuring motivational regulation, and analyses were conducted with a sample of 765 adults. ANOVA analyses, correlations, and post hoc Tukey-Kramer were used to present the differences between all study variables. The results showed significant differences between the motivation and groups regarding age, gender, ethnicity, daily PA, physical limitations, sitting habits, participation in OS or PA in youth, current participation in OS or PA, digital health technology use, and vitality. There were significant differences in motivation, primarily intrinsic and integrated regulation, based on physical activity and digital technology. The findings reinforce the basics of SDT and how the theory supports motivation for PA. Data analyses from the study found that participants valued the motivators of fitness, appearance, and interest/enjoyment relatively higher than those of competence and social. Findings from this study could aid health practitioners, clinicians, researchers, or developers in understanding how the data can support engagement in PA by focusing on motivators.

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Asset Metadata

Creator Masters, Emily Robinson (author)

Core Title Aligning digital technology to support motivation, physical activity, and sports

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2022-05

Publication Date 05/07/2022

Defense Date 04/06/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag AI/ML,artificial intelligence,digital health,digital health technology,machine learning,Motivation,OAI-PMH Harvest,organized sports,physical activity,quantitative survey,self-determination theory,vitality

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Tobey, Patricia (committee chair), Adibe, Bryant (committee member), LeCrom, Carrie (committee member), Seli, Helena (committee member)

Creator Email emilymasters@me.com,ermaster@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111296821

Unique identifier UC111296821

Document Type Dissertation

Format application/pdf (imt)

Rights Masters, Emily Robinson

Type texts

Source 20220509-usctheses-batch-940 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu