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The Spillover Effects of Wearable Technology in Today’s Society
A Thesis Presented for the Business Honors Program and Baccalaureate Honors Program
Norm Brodsky College of Business
Rider University
Sarah Carbonaro
Faculty Advisor: Dr. Anubha Mishra
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Table of Contents
Introduction/ Research Questions……...………………………………………………………….3
Smart Watch.……………………………………………………………………………………....4
Spillover Effects.……………………………………….………………………………………….5
Long Term Goals………………….…………………………………....….……………...6
Healthier Eating Habits ……………………………..……….…………….…....………...7
Overall Wellbeing...……………………...…….………………………………….……………....8
Research Design and Sample....…………………………………………….………………..…....9
Statistical Analysis...………………………...………………………...………….……………...11
Factor Analysis…………………...………………………………....….………………..11
Multiple Regression...………………………………....….……………………………...12
Simple Linear Regression...…………...……………....….……………………………...13
One-Way ANOVA...………………………………....….……………………………….13
Discussion...………………………………....….………..………………..………...…………...14
Implications….……………………………....….………..………………..………...…………...15
Research Limitations....…………..………....….………..………………..………...…………...16
Conclusion....…………..…………………....….………..………………..………...…………...17
Works Cited…………………….…………..……..……..………………..………...………...…18
Appendix A - Factor Analysis....………........….………..………………..………...…………...20
Appendix B - Multiple Regression Analysis.……..……..………………..………...…………...23
Appendix C - Simple Linear Regressions.……..……..…….....…………..………...…………...25
Appendix D - One-Way ANOVA…………..……..……..………………..………...…………...27
Appendix E - Wearable Technology Survey……...……..………………..………...…………...30
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Introduction
During a time where monitoring personal health has become so important, consumers
seek devices that will improve their personal health without needing to leave their personal
bubble. In a matter of days, health and fitness products can be delivered to their doorstep. These
products can range from stationary bikes, to digital kitchen scales, to heart rate monitors. The
device that this paper will specifically focus on is a group of fitness tracking devices that will be
referred to as wearable technology. The question here is how beneficial can the usage of
wearable technology be, and to explore their main spillover effects of improved health and eating
habits.
A spillover effect refers to the impact that one event may have to a seemingly unrelated
event. When looking at wearable technology, it is notable that their users may experience instant
gratification from seeing their steps appear, calories burned, and resting heart rate decreasing on
their wearable devices. This instant-gratification may lead to other health-conscious behaviors
such as exercising more often and eating healthier foods. On top of these beneficial changes,
consumers may find themselves in a healthier state of mental well-being.
Wearable technology on the market can be very similar in terms of what features they
have and what services they provide. Regardless of the brand of the wearable technology, all of
the products have some similar basic features. Wearable technology is beneficial to the consumer
and brings about awareness to self-health monitoring. The purpose of this paper is to investigate
and explore the spillover effects of using wearable technology on consumers. The specific
research questions that I will explore are:
Q1. Does using wearable technology (e.g., smartwatches) help individuals monitor their health
better?
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Q2. Does using wearable technology help individuals engage in other healthy habits?
Q3. Does wearable technology have an overall impact on a person’s overall health, happiness
and well-being?
Smart Watch
For the purpose of this research I investigate smart watches as a wearable technology of
interest. Smart watches are an item of wearable technology that have been on the rise and are
becoming increasingly popular (Dong-Hee and Ki Joon, 527). The first digital wristwatch that
originally appeared in 1972 was the Hamilton Pulsar P1, but the first smartwatch that had the
capability to show more than the date and time was the Seiko Pulsar NL C01. This watch had
user-programmable memory, which meant that users could store a small amount of data on the
watch (Dong-Hee and Ki Joon, 528). Seiko eventually began to evolve their smartwatches, and
throughout the 1980s they released the Data 2000 and RC-1,000. These two models had external
keyboards which could allow for data transfer and entry from a computer through a cable
(Dong-Hee and Ki Joon, 528).
Digital watches eventually evolved into the modern smart watch because manufacturers
and designers mixed smart features with higher computing power. In 2003, Microsoft introduced
it’s SPOT watch which used FM radio broadcasting signals that would deliver information to the
wearable technology (Dong-Hee and Ki Joon, 528). Although the technology that shaped the
smart watches of the past was FM radio broadcast signals, the technology that shaped today’s
smartwatches is Bluetooth technology.
Through the usage of Bluetooth, users could use smartphones and smart watches in
conjunction with one another. The wearable technology was not meant to replace smartphones,
but instead to compliment it. Through Bluetooth connection, smartwatches are able to provide
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“more convenient, faster, and substitutable access to information,” which sets them apart from
other wearable technology (Dong-Hee and Ki Joon, 528). When a user unboxes their smartwatch
for the first time, it comes with instructions on how to connect it to their smartphone via
Bluetooth and further information regarding any applications that might need to be downloaded
onto the smartphone as well.
Smartwatches have a key strength in that they have the ability to give their user
convenient, fast, and immediate access to information. Customers purchase smartwatches
because they are not only utilitarian tools but “personalized, trendy items that reflect individual
identities, emotions, and aesthetic values” (Dong-Hee and Ki Joon, 535). Based on these
features, it can be possible that wearing smart watches can motivate an individual to lead an
overall healthier life by incorporating healthier eating habits and exercising more.
Spillover Effects
We believe that people take cues from wearing wearable technology devices which
motivates them to be more healthy overall. Few key features of wearable technology makes it
unique with respect to the impact it might have on users. For example, a wearable technology
device can be used to self-track. Self-tracking, also known as self-quantifying, is defined as “the
act of collecting data about oneself to change behavior and improve personal outcomes”
(Giddens, Gonzalez, and Leidner, 1). In addition, it can also help users Self-quantify because
these wearable technologies typically come with applications on smartphones and computers to
display the data. These applications track data from wearable technology “such as mood,
physical activities, and sleep patterns” (Giddens et al., 1). It is based on these features that we
believe wearable technology can produce spillover effects.
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Spillover effect is defined as “where the adoption of one behavior causes the adoption of
additional, related behaviors” (Galizzi and Whitmarsh, 1). We want to see if the usage of
wearable technology creates a feedback loop that has a spillover effect on other healthy habits.
According to a study, “the motivation that drives consumers to undertake self-tracking activities:
improving the quality of health and everyday life like better sleep, physical fitness, or prevention
of diseases” (Przegalinska, 65). It is important to also note that the utilization of these wearable
technologies has the benefits of “decreasing stress levels, increasing productivity, enhancing
cognitive abilities, better time management, and better work-life balance” (Przegalinska, 65).
Additionally, users are motivated by wearable technology due to instant gratification. For
the purpose of this thesis, instant gratification is defined as “the habitual use of various
information services [which] reinforces consumers’ expectations of obtaining ‘answers’
immediately” (Nakayama and Wan, 11). In the case of wearable technology, this instant
gratification derives from when a user checks their user data that is accessible through the
display of the device or through the accompanying application. These displays aim to motivate
users to reach their health and fitness goals through tracking various metrics that can be
displayed in real-time.
Long Term Goals
According to a study on wearable technology, any of these trackers can “measure
physical activity in terms of calories burned, record daily activities, track sleep efficiency, and
provide extensive information on these activities to the user” (Przegalinska, 7). This means that
when a user views the data in their application, they can see what they have done and how it has
affected their profile. For example, resting heart rate is a popular metric for fitness level and
overall cardiovascular health. According to Nieca Goldberg, M.D., “Generally, a lower resting
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heart rate indicates more efficient heart function and greater cardiovascular health—and research
has connected a higher resting HR with a higher risk of cardiac events like stroke and heart
attack” (Winderl). Therefore, healthy habits such as regular exercise and healthy eating can
lower resting heart rate. From this information, one can make the assumption that when a user
uses wearable technology, they may feel compelled to exercise more often in order to lower their
resting heart rate. This study explores if users of wearable technology commit themselves to long
term goals of staying fit or looking younger and therefore may engage in more healthy habits
through a spillover effect.
Healthier Eating Habits
Most fitness tracking wearable devices are connected to phone applications that can track
the users’ food intake if users log their calories. Additionally, they track how many calories are
burned daily by combining the user’s basal metabolic rate or BMR, which is the rate at which the
body burns calories at rest to maintain vital body functions. This gives the wearable technology’s
app the ability to display how many calories the user has eaten versus burned. According to an
article published in Post and Courier, “Modern wearable devices and mobile apps allow you to
track your weight, what you eat, and your activity fairly accurately” (Parr, 2). The analysis on
these devices can be used to show what a consumer is really eating, because the applications will
require its user to input the item eaten and “the app calculates calories, nutrients, sugar, salt and
water intake based on standard databases” (Parr, 2). Parr also suggests that when consumers
track what they consume, it helps them to learn about their eating patterns which results in
developing healthier eating habits or meeting health goals. These capabilities of wearable
technology may inspire their users to burn more calories than they consume in a day, or to reduce
their caloric intake which over time would result in weight loss. While the applications are useful
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for the consumers, it is not the application or fitness tracker that helps consumers to develop
healthier eating habits, but it is the dedication and lifestyle changes that are a spillover effect of
utilizing the technology.
Overall Wellbeing
Next we will explore the notion that utilizing wearable technology can lead to increased
self-awareness of overall health. Self-awareness is defined as “monitoring, measuring, and
recording the elements of one’s body and life as a form of self-improvement or self-reflection,”
(Przegalinska, 7). In having this awareness and the data from their wearable technology,
consumers are better able to understand their bodies and the actions they take that affect their
health. The primary spillover effects of using wearable technology lead to changes in well-being,
happiness, and state-of-mind. Well-being is defined as “a population‐based term targeting
positive feelings about oneself and reflecting an inner capacity,” (Barkham et al., 352).
Happiness is defined as “often taken to mean something very close to an extended feeling of
pleasure or an extended good mood or pleasant affect,” (Michalos, 355). State-of-mind is defined
as “a person's emotional state:mood” (Merriam Webster). The definitions of well-being,
happiness, and state-of-mind all impact this thesis’ research because they are all a result of the
three spillovers. These definitions will help to identify the relationships between the previously
stated primary spillover effects, wearable technology, and the consumer that uses it.
Along with the notion that wearable technology is capable of synchronization between
other smart devices to display data, it is also utilized to share data with friends and others.
According to existing research “...wearable fitness devices can affect how a person identifies
himself or herself,” (Giddens et al., 3). Therefore, it is possible to assume that wearable
technology will affect a person’s identity. Identity refers to the construct of “how people behave
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and perform,” (Giddens et al., 3). This is important to this thesis research because how
consumers feel perceived by others may impact their identity in conjunction with wearable
technology.
Research Design and Sample
Data was collected through a survey developed on Qualtrics, a web-based online survey
platform and was distributed electronically via email, various social media platforms, and word
of mouth. The survey was approved by the International Review Board at Rider University. The
survey link recorded anonymous survey responses with no linkage of specific personal data (i.e.
names, email addresses) connecting survey responses to the respondents. The survey was live
between March 28 2021 and April 9 2021. The questions asked on the survey can be found in
Appendix E.
Prior to the public distribution of the survey, a pretest of 5 participants was run to ensure
flow, clarity, and to make suggestions for improvement before distribution. Of 339 participants
who opened the survey link, 98 were excluded from further participation because they did not
meet the screening criteria (i.e. participants had to currently own and use health tracking
wearable technology such as wrist-wearables produced by Fitbit, Garmin, Apple, Google, etc.,
and be at least 18 years of age). If participants answered no to either of the screening questions
they were brought to the end of the survey and deemed unable to participate. Ultimately only 241
respondents were eligible to participate, therefore the sample size will be known as n=241.
We found that the sample was primarily female with 166 female participants, and that the
sample was predominantly caucasian with 183 caucasian participants. The highest age range
represented was from 18-24 with 120 participants, followed by participants ages 35-44 with 36
participants. After that followed the age ranges of 25-34 with 28 participants, and 45-54 with 19
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participants. The oldest age ranges of 65+ were not well represented with under 10 participants
in total.
By generation, the generation with the highest participation was Generation Z
(1997-2013) with 118 participants. This was followed by Generation Y (1981-1996) and
Generation X (1965-1980) with 42 and 43 participants respectively. In the generations of Baby
Boomers (1946-1964) and The Silent Generation (Elderly, 1928-1946) there were 18 and 3
participants respectively.
The majority of the participants selected “completed or currently enrolled in a 4-year
degree” with 122 participants, followed by participants who have completed or are currently
enrolled in a professional degree” with 43 participants and some college with 30 participants.
The other categories of “high school graduate”, “2-year degree”, and “doctorate degree”
combined had lower than 30 participants.
The majority of the annual income of participants was less than $10,000 with 61
participants, followed by $10,000-19,000 with 30 participants, then $100,000-$149,000 with 29
participants, then all other categories had less than 25 participants each.
The most preferred variable of technology was 152 using Apple Watch followed by Fitbit
with 60 participants. The next brands were Garmin and Google with less than 10 participants
together total. Respondents who said “Other” wrote-in brands such as Samsung, Whoop, and
Withings.
In addition, the majority of the sample, 142 individuals had used this wearable
technology for over a year. Within the other categories of: for a year, within the last six months,
and within the last three months, we found similarly equivalent answers with around 30
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respondents for each. We can conclude that these participants were well-versed in how this
technology works.
In a typical 7 day week, the respondents mostly use their wearable technology for 7 days
of the week with 121 respondents. The other respondents use their devices between 5-6 days of
the week with 18 respondents for 5 days and 17 for 6 days respectively. Following those days,
the amount of days and the corresponding amount of respondents decreased. We can conclude
that participants mostly utilize their wearable devices for 5 to 7 days of the week.
On a weekly basis, respondents explained how much exercise that they perform on a
weekly basis. We defined exercise to the respondents as anything that includes intentional
exercise such as brisk walks, home exercises, swimming, team sports, etc. Many participants
reported Moderate Exercise (3-5 days per week) with 86 respondents, then Light Exercise (1-2
days per week) with 76 respondents, followed by Heavy Exercise (6-7 days per week) with 53
respondents. The lowest category was Sedentary (defined as office job) with 26 respondents.
Statistical Analysis
Data was collected through Qualtrics as mentioned in the previous section. We utilized
IBM SPSS Statistics to analyze the data. In the following sections, we will discuss our findings
of factor analysis, multiple regression, simple linear regression and one-way ANOVA.
Factor Analysis
The factor analysis will cover what questions were utilized to create the variables. The
study questionnaire measured six variables: users’ satisfaction with their fitness tracking device,
users’ perception of their self-image since using their fitness tracking device, long-term health
and wellness goals, overall wellbeing, and users’ spillover behavior since they have used
wearable technology. A principal components analysis was run on construct items that measured
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the study variables. The KMO Bartlett’s test for all analysis were statistically significant at p=
0.05. Three items measuring users’ satisfaction with their fitness tracking device all loaded on
the same component, and explained 79.91% of the variance. Four items used to measure
self-image also loaded on the same component (variance = 67.96%). All seven items measured
long-term goals (Variance = 63.04%) and the seven items measuring spillover effect (variance =
52.43%) loaded on their individual components. However, the eight items used to measure
overall wellbeing loaded onto two different components. Six items that loaded on the first
component measured users’ happiness, mood, motivation to work harder, etc. This component
was called users’ overall wellbeing (variance = 49.24%). Two other items measuring healthy
cooking habits and eating at home loaded on its own component and were termed eating habits
(variance = 25.26%). Please see Appendix A for items and item loadings. Based on the factor
analysis, six new variables were created by calculating the mean of all items for each variable.
Multiple Regression
A multiple regression was then run to analyze the effect of users’ 1) Satisfaction with
their wearable technology device, 2) Perception of self-image as a result of using wearable
technology, 3) Setting long term goals, 4) Perception of overall wellbeing since using the
wearable technology, and 5) Eating habits since using the wearable technology, on their spillover
effect on other healthy behavior. The overall model was statistically significant F (5, 218) =
29.63, p = .000, while also showing good predictive power, adj. R square = 0.391. The results
show that satisfaction (b = .190, p = .004), long term goals (b = .342, p = .000), and eating habits
(b = .243, p = .000) positively significantly impacted users’ spillover behavior. In other words,
the more satisfied that users were with their fitness tracking devices, if they had set long term
health and wellness goals and were following healthy eating habits, the more likely they were to
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indulge in the spillover of healthy behavior. The perceived self-image and their perception of
overall wellness did not significantly impact the spillover behavior. With all of the independent
variables in the model, eating habits (std. Beta = .303) had the strongest effect followed by long
term goals (std. beta = .292) and satisfaction (std. Beta = .156). Please see Appendix B for
multiple regression analysis.
Simple Linear Regression
We performed a simple linear regression to measure the effect of one independent
variable on the dependent variable. As an ad hoc test, we ran simple linear regression to see if
each variable, by itself, had any impact on the spillover behavior. Interestingly, each variable, by
itself, had a statistically significant positive impact on the spillover behavior. The two variables
that were not significant in the multiple regression analysis, overall wellbeing and self-image,
also had a strong positive effect on users’ spillover behavior. Please see Appendix C for simple
linear regression results.
One-Way ANOVA
We then ran a one-way ANOVA to check for gender differences for all of the study
variables. None of the variable means were statistically significant for male versus female. It
should be noted that the gender representation in this sample is heavily skewed towards females.
This could be a potential reason for no significant impact. However, some interesting trends were
seen in the data. As seen in Appendix D, males had a higher mean than females on Image, Long
Term Goals, and Spillover, while females had a higher mean than males on Satisfaction,
WellBeing, and Eating Habit.
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Discussion
From our research, we can infer that using wearable technology such as smartwatches
does in fact help individuals monitor their health better because it gives them insights into their
health metrics. This was indicated by the level of user satisfaction from our survey questions that
were geared towards user overall satisfaction with their wearable technology. Our findings show
that satisfaction with their wearable technology, long term goals, and eating habits all positively
and significantly impacted the users’ spillover behavior. Out of all of the independent variables,
healthy habits had the strongest effect, then long term goals, and finally satisfaction.
Using wearable technology does help individuals engage in other healthy habits because
it motivated respondents to lead an overall healthier life. Our findings show that the more
satisfied that the users were with their wearable technology, and if they had set long term health
and fitness goals, they were more likely to experience the spillover of healthy behaviors
(healthier eating habits, long term fitness and health goals) to improve their health.
We also found that while the respondents’ perceived self-image and their perception of
overall wellness was not significant in the multiple regression analysis, they still had a strong
positive effect on the respondents’ spillover behavior when we looked at each independent
variable by themselves. We find that wearable technology does have an overall impact on a
person’s overall health, happiness, and well-being. Our findings show that wearable technology’s
effect on respondent’s perceived self-image and their perception of overall wellness had a strong
positive effect on the respondents’ spillover behavior.
We ran gender differences to see if the study variables were perceived differently by
males versus females. In none of the variables, the gender was not significant at all, but we found
some interesting trends. For example, while this was not statistically significant, the mean of
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satisfaction, well being, and engaging in healthy eating habits were all higher for female
compared to male. Whereas the means for spillover effect, setting long term goals, and the self
image as the result for using wearable technology was higher for males than for females.
These findings are important because they prove the positive effects of utilizing wearable
technology.
Implications
The information that we have collected can help wearable technology businesses such as
smartwatch businesses to understand what consumers look for in their wearable technology and
the companion applications. From this study, it seems that consumers are satisfied with the
current abilities of wearable technology. Additionally, we have learned that long-term goals
impact satisfaction. Consumers would likely appreciate it if more wearable technology had the
ability to set long term goals and to keep consumers more accountable for their healthy actions to
become habits. Business could incorporate this information into their strategic plans if they
aimed to highlight how smartwatches not only track data in real-time but that the usage of their
products leads to longevity of life in the long run due to the healthy spillover effects in their
promotional campaigns. Product packaging could also be improved and marketed to consumers
as a life-changing device by highlighting the positive spillovers as observed in the currently
available literature and the research from this study.
Society’s benefit of wearable technology is that consumers can purchase the ability to
actively work on their wellbeing. Current literature reflects that wearable fitness devices can
affect how a person identifies themselves according to research,“...wearable fitness devices can
affect how a person identifies himself or herself,” (Giddens et al., 3). This study measured
overall wellbeing in the users’ answers regarding their happiness, mood, and motivation to work
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harder. The relationship between wearable technology and the construct of human wellbeing are
positively correlated as utilizing wearable technology, as displayed in this study’s research, has
been shown to positively impact wellbeing.
Research Limitations
A limitation of this study included limitation in time of the distribution of the study. If the
survey had been distributed for a longer timeframe, then it is possible that the sample size would
have been greater. Another limitation of the study was the difficulties in some participants
completing the survey, as a handful of participants completed most of the questionnaire but then
would not fill out the demographic questions at the end of the survey, likely out of not wanting to
submit personal data. Sample size was also a limitation as there were 339 responses, but only
241 of them were viable making the sample size 241. If there were more responses there could
have been a higher sample size making the data more representative of a larger population.
Additionally, the sample size was predominantly female and caucasian. A more diverse sample
would have been more representative of a larger population. Lastly, one more limitation was that
respondents may have lied about how much they purposefully exercised or how often they ate
healthy foods due to social desirability.
In regard to further research on the topic, if a larger population could be reached with the
questionnaire then a larger viable sample could be produced that would be better indicative of a
larger population. Additionally, if this larger population could be reached and the questionnaire
had additional questions that covered topics such as hours slept, further spillover effects could be
explored.
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Conclusion
Utilizing wearable technology that has the capability to view health metrics in real-time
results in a higher awareness of health and habits. These participants mostly utilize their
wearable technology for 5 to 7 days of the week and have owned their wearable technology for
more than a year, and from this we can conclude that they are well-versed in how this technology
works. The more involved that the respondents were in overall health activities (such as working
out at the gym, swimming, or monitoring their health overall) led to a higher satisfaction with
using wearable technology. In addition, if they made more changes to their lifestyle such as
eating at home more regularly or making more meals at home, this also had an impact on their
satisfaction. Wearable technology from brand to brand can be very similar in terms of what
features it has and what services it provides, so producers of these devices must create innovative
marketing campaigns and packaging to highlight why their product is beneficial to spillover
effects. From the results of the statistical analysis, we can conclude that wearable technology use
is beneficial due to the positive spillover effects and that more consumers should utilize wearable
technology when possible to gain insight into their health.
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Works Cited
Barkham, Michael, et al. "Towards an evidence‐base for student wellbeing and mental health:
Definitions, developmental transitions and data sets." Counselling and Psychotherapy
Research 19.4 (2019): 351-357.
Galizzi, Matteo M., and Lorraine Whitmarsh. "How to measure behavioral spillovers: a
methodological review and checklist." Frontiers in psychology 10 (2019): 342.
Giddens, Laurie, et al. "I track, therefore I Am: exploring the impact of wearable fitness devices
on employee identity and well-being." (2016).
Kim, Ki Joon, and Dong-Hee Shin. “An Acceptance Model for Smart Watches.” Internet
Research, vol. 25, no. 4, Oct. 2015, pp. 527–541. EBSCOhost,
doi:10.1108/IntR-05-2014-0126.
Michalos, Alex C. "Education, happiness and wellbeing." Social Indicators Research 87.3
(2008): 347-366.
Nakayama, Makoto, and Yun Wan. "A quick bite and instant gratification: A simulated Yelp
experiment on consumer review information foraging behavior." Information Processing
& Management 58.1 (2021): 102391.
Parr, Brian B. “HEALTH AND FITNESS: Make Wearable Fitness Technology Work for You.”
Post and Courier, 28 Dec. 2018,
www.postandcourier.com/aikenstandard/lifestyle/health-and-tness-make-wearable-tness-t
ech-work-for- you/article_dfb-ef-eb--eebfe.html.
Przegalinska, Aleksandra. Wearable technologies in organizations: Privacy, efficiency and
autonomy in work. Springer, 2018.
“State of mind.” Merriam-Webster.com Dictionary, Merriam-Webster,
www.merriam-webster.com/dictionary/state%20of%20mind. Accessed 15 Apr. 2021.
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Winderl, Amy. “What Your Resting Heart Rate Can Tell You About Your Fitness.” Self.com, 10
Dec. 2017, www.self.com/story/what-resting-heart-rate-can-tell-about-fitness. Accessed
14 Apr. 2021.
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Appendix A - Factor Analysis
I.
Spillover Effect - Q13 to Q19 (% variance = 52.43%)
II.
Long Term Goal - Q28 to Q34 (% variance = 63.04%)
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III.
Self Image - Q23 to Q26 (% variance = 67.96%)
IV.
Overall Wellbeing: Q36 to Q41 (% variance = 49.24%) and Healthy Eating Habits: Q42
and Q43 (% variance = 25.26%)
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V.
Satisfaction with using the wearable technology - Q9 to Q11 (%variance = 79.91%)
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Appendix B - Multiple Regression Analysis
I.
II.
Variables Entered/Removeda
Model Summary
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III.
ANOVAa
IV.
Coefficientsa
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Appendix C - Simple Linear Regressions
I.
II.
III.
Satisfaction
Image
Long Term Goals
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IV.
V.
WellBeing
Eating Habit
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Appendix D - One-Way ANOVA
I.
II.
Mean of Satisfaction
Mean of Image
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III.
Mean of Long Term Goals
IV.
Mean of Wellbeing
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V.
VI.
Mean of Eating Habit
Mean of Spillover
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Appendix E - Wearable Technology Survey
Q1 Thank you for participating in this important research on the use of wearable technology. We
appreciate your time and feedback. In order to participate in this survey, you need to be 18 years
or older and use wearable technology worn on the wrist such as Fitbit, Garmin, Apple, Google,
etc.
The intent of this questionnaire is to assess the use of wearable fitness tracking technology such
as Fitbit, Garmin, Apple, Google, etc. You will be asked to answer questions regarding the use of
the technology and how do you share this information. The information you provide will be
anonymous and will take around 10-15 minutes to complete. If you have questions about the
project at any time, please ask for additional information at Rider University, Sarah Carbonaro,
carbonaros@rider.edu.
Participation is completely voluntary. There will be no penalty if you decide not to participate.
You may withdraw from the project at any time. By clicking the next button you are agreeing to
participate in the survey.
Q2 Are you 18 years or older?
□ Yes (1)
□ No (2)
Skip To: End of Survey If Q2 = No
Q3 Do you currently own and use health tracking wearable technology? Examples of this are
items worn on the wrist produced by Fitbit, Garmin, Apple, Google, etc.
□ Yes (1)
□ No (2)
Skip To: End of Survey If Q3 = No
Q4 Select by clicking the brand of wearable technology you are using
□ Apple Watch (1)
□ Fitbit (2)
□ Garmin (3)
□ Google (4)
□ Other, please specify (5)
Q5 How long have you had your current smartwatch?
□ Recently, within the last week (1)
□ Within the last month (2)
□ Within the last 3 months (3)
□ Within the last 6 months (4)
□ For a year (5)
□ For more than a year (6)
�Carbonaro 31
Q6 In a typical 7 day week, how many days do you use your wearable technology?
0
1
2
3
4
5
6
7
Week days ()
Q7 How much exercise would you say that you get on a weekly basis? Exercise can include
anything that is intentional exercise such as brisk walks, home exercises, swimming, team sports,
etc.
□ Sedentary (office job) (1)
□ Light Exercise (1-2 days per week) (2)
□ Moderate Exercise (3-5 days per week) (3)
□ Heavy Exercise (6-7 days per week) (4)
Q8 Indicate your level of satisfaction of wearable technology with the following questions
Q9 I am satisfied with my decision to use my wearable technology
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q10 I truly enjoy using my wearable technology
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q11 Using my wearable technology has been a good experience
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
�Carbonaro 32
Q12 Do you believe using your wearable technology has influenced your other health
related decisions? In answering the following questions, think of the time since you have
started using your wearable technology and indicate the extent to which it has impacted the
following behaviors:
Q13 Eating healthier
□ Never (1)
□ Sometimes (2)
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q14 Exercise More
□ Never (1)
□ Sometimes (2)
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q15 Join a gym
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q16 Visit doctor's office more regularly
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q17 Engage in other physical activities like swimming, hiking, jogging, cycling, etc.
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
�Carbonaro 33
Q18 Examine your health more frequently
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q19 Take more responsibility for the state of your overall health
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q20 Take more preventative measures for your health
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q21 Overall, become more aware of your health
□ Never (1)
□ Sometimes
□ About half the time (3)
□ Most of the time (4)
□ Always (5)
Q22 Please answer the following questions regarding your perceptions of how others view
your use of your wearable technology
Q23 Using my smart watch enhances the image that others have of me
□ Strongly disagree (1)
□ Somewhat disagree
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
�Carbonaro 34
Q24 People who use wearable technology are admired by others
□ Strongly disagree (1)
□ Somewhat disagree
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q25 My wearable technology helps me show others what I am or would like to be (such as an
athlete, health conscious, etc.)
□ Strongly disagree (1)
□ Somewhat disagree
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q26 Others who use wearable technology have characteristics which I would like to have.
□ Strongly disagree (1)
□ Somewhat disagree
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q27 Do you believe using your wearable technology can help you achieve these goals?
Q28 Stay healthier longer
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
Q29 Stay fit longer
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
�Carbonaro 35
Q30 Look younger
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
Q31 Have more energy
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
Q32 Project a positive image of yourself
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
Q33 Maintain a healthy body weight
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
Q34 Prevent ill health
□ Extremely Unlikely (1)
□ Somewhat unlikely (2)
□ Somewhat likely (3)
□ Extremely likely (4)
□ Extremely likely (5)
�Carbonaro 36
Q35 Do you believe using your wearable technology does the following:
Q36 Makes you happier
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q37 Improves your mood
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q38 Improves your self image
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q39 Makes you feel inspired
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q40 Motivates you to work harder
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
�Carbonaro 37
Q41 Makes you want to go outside more
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q42 Causes you to cook healthier
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q43 Influences you to eat at home more often
□ Strongly disagree (1)
□ Somewhat disagree (2)
□ Neither agree nor disagree (3)
□ Somewhat agree (4)
□ Strongly agree (5)
Q44 The following questions will ask you some questions about your demographics. We do
not save this data or use it to identify participants. This data is strictly used for aggregate
sampling information.
Q45 Please select your birth year range
□ 1928 – 1946 (1)
□ 1946 – 1964 (2)
□ 1965 – 1980 (3)
□ 1981 – 1996 (4)
□ 1997 – 2013 (5)
Q46 Please select your age range
□ 18 - 24 (1)
□ 25 - 34 (2)
□ 35 - 44 (3)
□ 45 - 54 (4)
□ 55 - 64 (5)
□ 65 - 74 (6)
□ 75 - 84 (7)
□ 85 or older (8)
�Carbonaro 38
Q47 Please select your gender
□ Male (1)
□ Female (2)
□ Other, please specify (3)
Q48 Please select the highest level of education attained
□ Less than high school (1)
□ High school graduate (2)
□ Some college (3)
□ Completed or currently enrolled in a 2-year degree (4)
□ Completed or currently enrolled in a 4-year degree (5)
□ Completed or currently enrolled in a professional degree (6)
□ Completed or currently enrolled in a Doctorate degree (7)
Q49 Please select your annual income
□ Less than $10,000 (1)
□ $10,000 - $19,999 (2)
□ $20,000 - $29,999 (3)
□ $30,000 - $39,999 (4)
□ $40,000 - $49,999 (5)
□ $50,000 - $59,999 (6)
□ $60,000 - $69,999 (7)
□ $70,000 - $79,999 (8)
□ $80,000 - $89,999 (9)
□ $90,000 - $99,999 (10)
□ $100,000 - $149,999 (11)
□ More than $150,000 (12)
Q50 How would you describe yourself?
□ American Indian or Alaska Native (1)
□ Hispanic, Latino, or Spanish origin (2)
□ Asia (3)
□ Black or African American (4)
□ Native American or other Pacific Islander (5)
□ Caucasian (6)
□Other (please specify) (7)
�
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Baccalaureate Honors Program (BHP) Capstones
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
The Spillover Effects of Wearable Technology in Today’s Society
Description
An account of the resource
During a time where monitoring personal health has become so important, consumers seek devices that will improve their personal health without needing to leave their personal bubble. In a matter of days, health and fitness products can be delivered to their doorstep. These products can range from stationary bikes, to digital kitchen scales, to heart rate monitors. The device that this paper will specifically focus on is a group of fitness tracking devices that will be referred to as wearable technology. The question here is how beneficial can the usage of wearable technology be, and to explore their main spillover effects of improved health and eating habits.
Creator
An entity primarily responsible for making the resource
Carbonaro, Sarah
Contributor
An entity responsible for making contributions to the resource
Mishra, Anubha
Publisher
An entity responsible for making the resource available
Rider University
Relation
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Baccalaureate Honors Program
Format
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Adobe Acrobat PDF
Language
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English
Type
The nature or genre of the resource
Capstone