not on strava? did not happen
TRANSCRIPT
Not on Strava? Did not happen.
Identifying the negative effects of self-tracking.
Ivar Q. Reenis
Msc. Marketing Analytics and Data Science
Supervisor: dr. E. de Vries
2nd Supervisor: dr. J. W. Bolderdijk
Date: 14 June 2021
Vismarkt 15a, 9712CA Groningen
S2976765
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ABSTRACT
The rise of technology has made it possible to track everything nowadays. Where self-tracking
started with the scale only, users of self-tracking devices are now able to track their sleep,
heartrate, activities and much more. At this moment, there has been a fairly amount of
research with regards to self-tracking. However, much of these researches were focused on
the positive effects of self-tracking. There is less literature regarding the potential negative
effects with regards to self-tracking. For example, users of self-tracking devices may
experience feelings of dependency, feelings of pressure and feelings of guilt. Therefore, this
thesis will broaden the scope and focus on these negative effects of self-tracking. To be exact,
self-tracking on running apps such as Strava and Runkeeper and the potential negative effects
on its users with regards to enjoyment of the activity. For this thesis, a questionnaire has been
conducted in order to quantitatively measure the negative effects of self-tracking on
enjoyment. After conducting multiple Ordinary Least Squared regressions by using Hayes’
PROCESS analysis modelling tool, this study found that the more you use self-tracking apps
the more you will experience these negative feelings. However, it was expected that these
feelings had a negative effect on the enjoyment of an activity, but this was not the case. It
turned out that these feelings lead to more enjoyment, which could be explained by the level
of involvement regarding the activities. Some people perform better while experiencing
pressure or guilt which can lead to more joy. Furthermore, effortful activities (running) lead
to more enjoyment compared to effortless activities (walking). Taking this all together, this
thesis contributes to the existing literature by demonstrating a different point of view while
looking at self-tracking.
Keywords: self-tracking; technologies; running apps; device
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ACKNOWLEDGMENT I would like to take this chance to thank my thesis supervisor dr. E. de Vries for guiding me
through this process of writing a master’s thesis. Especially in this period where all the study
related things are digital, I am very happy with the way it went. I am grateful for the flexibility
that she showed and her guidance in total. Next to this, I would like to thank my fellow thesis
group members, especially D. Fokkens and S. Toneman for the teamwork on the
questionnaire. I also would like to express my gratitude to the people that conducted the
questionnaire, without them I did not had a thesis either. At last, I would reflect on the fact
that this is, hopefully, the last step towards graduation. This makes it one of my last moments
as a student. I am looking back on one of the best periods of my life, which I will never forget.
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CONTENTS
§1. INTRODUCTION .......................................................................................................................................... 6
§1.Introduction .................................................................................................................................................. 6
§1.2 Theoretical and social relevance ............................................................................................................... 6
§2. THEORETICAL FRAMEWORK ....................................................................................................................... 7
§2.1 Self-tracking technologies ......................................................................................................................... 7
§2.2 Enjoyment .................................................................................................................................................. 9
§2.3 Dependency on self-tracking technologies ............................................................................................... 9
§2.4 Feelings of guilt and feelings of pressure ................................................................................................ 10
§2.5 Type of physical activity .......................................................................................................................... 12
§2.7 Gender ...................................................................................................................................................... 13
§2.8 Conceptual model .................................................................................................................................... 13
§3. METHODOLOGY ........................................................................................................................................ 14
§3.1 Questionnaire .......................................................................................................................................... 14
§3.2 Measuring of variables ............................................................................................................................ 15
§3.3 Data collection ......................................................................................................................................... 16
§3.4 Data analysis............................................................................................................................................ 16
§4. RESULTS.................................................................................................................................................... 17
§4.1 Descriptive statistics ................................................................................................................................ 17
§4.2 Reliability of the instruments .................................................................................................................. 17
§4.3 First insights of the data .......................................................................................................................... 18
§4.4 Dependency on self-tracking ................................................................................................................... 18
§4.4.2 Feelings of guilt ..................................................................................................................................... 20
§4.4.3 Type of physical activity ....................................................................................................................... 20
§4.4.4 Gender ................................................................................................................................................... 21
§4.4.5 Total Model ........................................................................................................................................... 21
§5. DISCUSSION .............................................................................................................................................. 24
§5.1 Main effects ............................................................................................................................................. 24
§6. CONCLUSION ............................................................................................................................................ 25
§6.1 Implications .............................................................................................................................................. 26
§6.2 Limitations and further research ............................................................................................................. 26
REFERENCES ................................................................................................................................................... 27
APPENDICES ................................................................................................................................................... 33
Appendix A: Self-tracking usage ..................................................................................................................... 33
Appendix B: Dependence on self-tracking ...................................................................................................... 34
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Appendix C: Guilt ............................................................................................................................................. 35
Appendix D: Pressure ....................................................................................................................................... 35
Appendix E: Enjoyment .................................................................................................................................... 36
Appendix F: Descriptives .................................................................................................................................. 36
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§1. INTRODUCTION
§1.Introduction
Where self-tracking started with the old-fashioned scale, it now has become so big that it is
almost indispensable in today’s world. Everything is trackable, from sleep till how many hours
you stood up and from heartrates to food intake. However, there is one clear leader
when it comes to self-tracking, which are the fitness apps. Apps like Strava (55 million users)
and Runkeeper (34 million users) are one of the biggest self-tracking apps out there at the
moment. These apps have multiple functions, first and foremost they enable their users to
measure and quantify their activities (Rockmann & Gewald, 2018; Whitson, 2013). In addition,
they are providing their users to socialize and interact with others in its community (Couture,
2021). Self-tracking helps users to make healthier choices, in terms of sleep, food or exercise.
Therefore, much is known about the positive effects of self-tracking such as enhanced
performances, improved feelings of autonomy, improvement of competence and enhanced
empowerment (Etkin, 2016; Karapanos et al., 2016; Pettinico & Milne, 2017; Zhang et al.,
2019) . However, less is known about any negative effects with regards to self-tracking. There
are a few scientific papers who are focused on the negative effects of self-tracking but still,
there is a lot to cover. Despite knowing the positive effects, knowing negative effects could be
of more value. Therefore, the central question to be addressed in this study is:
To what extent does self-tracking on running apps have a negative effect on their users?
There is some literature that shows that self-tracking may be associated with negative
psychological consequences (Etkin, 2016; Simpson & Mazzeo, 2017). Furthermore, van Dijk et
al., (2015) focused specifically on unintended side effects of self-tracking. According to them
there might be a relation between self-tracking and feelings of pressure, feelings of guilt and
feelings of dependency. In this research this will be investigated further.
§1.2 Theoretical and social relevance
Having answers to the proposed question is very interesting for self-tracking technologies and
apps. Knowing what the positive effects are is good but knowing what the negative effects are
is even better, because with that knowledge there is a possibility to change certain things in
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order to overturn it into a positive effect. In this way, people who suffered from the negative
effect may adopt the technology again. Furthermore, this thesis will broaden the scope of
research in self-tracking and will add to understanding to what extent these negative effects
are around.
The remainder of this paper has the following structure: Section 2 provides an overview of
current literature, including general information on self-tracking technologies and (possible)
negative effects . Section 3 describes the methodology of the research and Section 4 presents
the results. In Section 5, these results will be discussed, followed by the conclusions of this
research in Section 6.
§2. THEORETICAL FRAMEWORK
§2.1 Self-tracking technologies
According to Whitson (2013) self-tracking is explained as the monitoring of consumers’
everyday lives in order to measure and quantify their activities. Self-tracking technologies are
aiming to increase a user’s self-knowledge and eventually helping a healthy way of living by
facilitating long term behaviour change (Kersten-van Dijk et al., 2017). According to Rooksby
and colleagues (2014) we are using self-tracking devices to find meaning in our everyday lives.
It increases self-awareness and quantifies feedback resulting in relevant insights for its users.
Self-tracking can be used in various ways from monitoring sleep and workouts to checking
heartrates and caloric intake. Nowadays, due to the widespread use of smartphones, it has
become a mass phenomenon with the essential sensors and associated apps to analyse,
combine, evaluate and visualize the data collected (Heyen, 2020). In 2019 there were 6.8
billion people around the globe using apps and by 2023 this number will increase to 7.33
billion. The time dedicated to the smartphone is in 90% due to the use of apps (Angosto et al.,
2020). Especially in the health sector, there are more and more apps to download such as
Runkeeper and Strava. As mentioned earlier, people are using self-trackers to measure and
quantify activities but according to Rockmann and Gewald (2017) there is more. They
distinguished six different affordances which are embedded in activity trackers such as
Runkeeper and Strava. These affordances are: self-monitoring, achievement, guidance for
exercise, social recognition, social comparison and self-presentation. The first three
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affordances are related with the self, where the latter three are more focused on other
people. This is also emphasized by Lomborg & Frandsen (2016) where they conceptualized
self-tracking as a communicative phenomenon with three dimensions: communication with
the system, the self and a communication with the social network. Research on the social
aspect of self-tracking is still limited although there is evidence that these social and
interactive elements are highly valued (Angosto et al., 2020; Couture, 2021; Depper & Howe,
2017).
The literature shows that continued engagement in self-tracking helps users in their efforts to
make healthier choices. For example, tracking calorie intake allows people to be more aware
of their eating habits, and counting steps encourages people to put in extra effort to reach
their daily goal steps (Bravata et al., 2007; Burke et al., 2009). On the contrary, there are
associations between food and fitness trackers and symptoms of eating disorders and for
some people it does more harm than good (Simpson & Mazzeo, 2017). Although Bravata et
al., (2017) found that the use of a pedometer significantly increases physical activities and
decreases the body mass index and blood pressure. It also can enhance performance (Etkin,
2016), improve feelings of autonomy, competence (Karapanos et al., 2016) and
empowerment (Pettinico and Milne, 2017; Zhang et al; 2019). Next to this, it facilitates self-
monitoring and weight-loss (Painter et al., 2017). However, despite its appeal, self-tracking
may have unintended less-desired consequences. Other literature shows that self-tracking
may be associated with negative psychological consequences (Etkin, 2016; Simpson & Mazzeo,
2017). As one of the few van Dijk et al. (2015) looked at the unintended side effects of self-
tracking. They found out that users of self-tracking technology may feel a pressure to
"perform": to find self-knowledge through self-tracking and report progress. If this does not
happen, users may hold themselves accountable, which can lead to feelings of guilt and
inadequacy because of disappointing results (Etkin, 2016; Simpson & Mazzeo, 2017; Zheng,
2021). Next to feelings of pressure and guilt, people can also experience feelings of
dependency towards the self-tracking technology or app (Duus & Cooray; 2015; Fritz et al.,
2014; Ryan et al., 2019). The dependency on self-tracking and feelings of pressure & guilt will
be elaborated more extensively in the next couple of paragraphs. Although there is some
research on unintended and less-desired consequences of self-tracking it still is somewhat
underexposed (Duus et al., 2018; Jin et al., 2020), while at the same time the attrition rates of
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users of fitness and health apps are high (Huang & Ren, 2020). The next two paragraphs will
hypothesize why.
§2.2 Enjoyment
This thesis examines the possibility of users of running apps getting negatively affected by self-
tracking. The next paragraphs will explain why there is a possible negative relation between
self-tracking and enjoyment. There is little research on self-tracking and its possible negative
effects on enjoyment. Etkin (2016) showed that certain activities through self-tracking became
less enjoyable. Because of the measurements the activities such as walking and reading seem
more like work and therefore reduced their enjoyment. Etkin (2016) only focused on effortless
activities and enjoyment, whereas Jin et al., (2021) focused on effortful activities and
concluded it had a positive effect only on females. This thesis differentiates with the previous
two papers in terms of mediation and moderation variables with regards to enjoyment.
§2.3 Dependency on self-tracking technologies
As mentioned earlier, users may experience a feeling of dependency while using their self-
tracking device or app. There are several ways how this dependency is expressed. First of all,
there are users who feel ‘naked’ when they are not wearing their self-tracker (Duus et al.,
2017; Toner, 2018). Literature says that users who complete an activity when ‘naked’ feel bad
afterwards. Not because they did the activity , but because they did it without the device,
which feels for them as a wasted activity (Fritz et al., 2014b; Toner, 2018). This dependency is
slowly developing in a pain point. ‘Not on Strava, did not happen’ is a common saying
nowadays, which even has its own merchandise. The saying itself fully entails this dependency.
There are also users of self-tracking technologies who feel better when they see what they
accomplished but this feeling does not come from the activity itself, but from the
interpretation of the performance (Zheng, 2021). The foregoing also works the other way
around. Inconsistencies between the expectations of users’ their data and what the data
actually says causes frustration and disappointment which can result in a break in self-tracking
or even termination (Alqahtani et al., 2020b; Epstein et al., 2015; Harrison et al., 2015; Kim et
al., 2016). According to Roberts (2018) this phenomenon is called the Strava Overwrite, where
the digital results undermines the actual memories and feelings of the activity. In the broader
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world of self-tracking it is also a common phenomenon. It is seen as a split narrative with the
experience of the activity on one hand and the actual data on the other hand, and these
narratives can differ from each other. A user then has the choice to either discard their own
memories and perception or discard the hard data. This often leads to a sense of cognitive
dissonance (Alqahtani et al., 2020a; Bode & Kristensen, 2015). This dependency on the data
of self-tracking technologies is not only visible on the activity side of the spectrum but is also
applicable to one of the first self-trackers around; the scale. In a study with participants with
eating disorders one participant said that she does not know how she feels until she knows
the number of the scale, the numbers tell her how she feels (Crawford et al., 2015). Some
people have difficulty receiving negative feedback from the scales and thus stop exposing
themselves to the information (Frie et al., 2020). Following the theory, these dependencies
are influencing users of self-tracking in a negative way. When a user forgets a device it is
described as annoying or irritating because it feels like the activity is not credited as it should.
In some cases, this substantially affects the enjoyment of their users, despite getting exercise
(Alqahtani et al., 2020b; Fritz et al., 2014a). Therefore, hypotheses 1 are proposed as:
H1A: The more you use self-tracking technologies the more you will become dependent on
self-tracking technology
H1B: The more feelings of dependency on self-tracking you experience the lower the
enjoyment of an activity
H1C: The use of self-tracking negatively impacts the enjoyment of the activities by increasing
feelings of dependency on self-tracking meaning that feelings of dependency mediate the
relationship between self-tracking usage and enjoyment of an activity
§2.4 Feelings of guilt and feelings of pressure
A second unintended side effect of self-tracking is that users may come across feelings of guilt
& pressure. Since running apps like Strava and Runkeeper are social communities and
communication tools, these feelings of guilt & pressure get a deeper meaning (Couture, 2021;
Rockmann & Gewald, 2018; Stragier et al., 2016). The reason for this fact is that users get
exposed to feelings of guilt & pressure for their own sake; they want to outperform
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themselves or they feel obliged to meet their daily goals and worry about checking their data
(Duus & Cooray, 2015). Failing to meet goals produces feelings of guilt and discouragement
which also leads to quitting of self-tracking for some individuals (Razon et al., 2019). The type
of guilt or pressure they feel can come from the motivation that they carry; some people feel
guilty when not exercising, others feel pressure or fear of the negative consequences followed
by not exercising (Busch et al., 2020). These types of pressure and guilt comes from the self
but in the case of running apps there is also another point of view; they can see that the
people they follow are active and setting records, in which the other becomes an object of
examination and competition (Gabriels & Coeckelbergh, 2019). It can also support physical
activity because it is supported by others (Ehrlén, 2021).
Next to this, not only self-pressure is present, but the self-tracking device is perceived as a
social actor, which is referrable to an observer . The presence of an observer can bring
advantages as well as drawbacks. Furthermore, people tend to evaluate their own
performance in a more judgmental way because of the presence (Hancı et al., 2019).
Subsequently, a study of Munson & Consolvo (2012) revealed that participants did feel good
when receiving a reminder for an activity they forgot to journal. When these participants
received a reminder when they did not have an activity to journal it resulted in negative
feelings due to guilt. Although feelings of guilt and feelings of pressure are quite similar, in this
thesis they are treated separately. Reason for this is that one might explain the other; feelings
of pressure might lead to feelings of guilt if you cannot comply with the pressure you are
feeling. Furthermore, pressure also has a more positive connotation; pressure has an
optimum, a bit of pressure is good, too much pressure is not. Guilt, on the other hand, is never
good, if you feel guilty it is a negative emotion. According to the literature, these feelings are
caused by self-tracking usage (Couture, 2021; Rockmann & Gewald, 2018; Stragier et al.,
2016). Subsequently, these feelings of pressure and guilt impact the enjoyment of an activity
(Duus & Cooray, 2015; Raxon et al., 2019). Following the reasoning above, this thesis proposes
the following hypotheses:
H2A: The more you use self-tracking technologies the more you feel pressure to perform
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H2B: The more feelings of pressure to perform you experience the lower the enjoyment of an
activity
H2C: The use of self -tracking negatively impacts the enjoyment of the activities by increasing
feelings of pressure to perform meaning that feelings of pressure mediate the relationship
between self-tracking usage and enjoyment of an activity
H3A: The more you use self-tracking technologies the more you feel guilt when missing an
activity
H3B: The more feelings of guilt when missing an activity you experience the lower the
enjoyment of an activity
H3C: The use of self-tracking negatively impacts the enjoyment of the activities by increasing
feelings of guilt meaning that feelings of guilt when missing an activity mediate the
relationship between self-tracking usage and enjoyment of an activity
§2.5 Type of physical activity
Self-tracking has more positive influence on effortful instead of effortless activities. Effortful
activities are associated with task difficulty and subjective tiredness. Next to this, these
activities are more mentally and/or physically demanding (Critchley et al., 2000; Jin et al.,
2021). In terms of the running apps like Runkeeper and Strava an effortless activity would be
walking, and an effortful activity would be running or cycling. Furthermore, an activity for
which a high level of effort is needed is perceived as more special since there are more skill
needed to complete the activity. Especially with the social and interactive elements these
running apps possess (Angosto et al., 2020; Couture, 2021; Depper & Howe, 2017). According
to Jin et al., (2021) by signalling a user’s level of accomplishment through numerical feedback,
self-tracking can positively influence perceived competence, and thus enjoyment in effortful
activities. The level of effort plays also an important role of the self-tracking experience in
terms of social recognition. For some people the activity becomes socially meaningful when
an effort is made (Vorderer et al., 2006). Especially with regards to social platforms as
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Runkeeper and Strava the use of self-tracking positively impacts effortful activities. Hence, this
thesis hypotheses the following:
H4: Effortful activities positively affects the enjoyment of the activity.
§2.7 Gender
Previous research entails that in effortful activities there is a positive impact of self-tracking
on perceived competence, and this impact is likely to be stronger among females than males.
The reason for this is that females are more likely to underestimate their competence, since
females tend to be more modest or less confident. Males, on the other hand, are more likely
than females to overestimate their competence (Buser et al., 2018; Herbert & Stipek, 2005).
Next to a positive effect of self-tracking on perceived competence, also a positive effect on
enjoyment in effortful activities was found (Jin et al., 2020). Following this line of research
gender should have a moderation effect on enjoyment since females are more positively
influenced by self-tracking then man. However, as of yet we do not know if there is a
difference in effect in dependency of self-tracking and feelings of guilt & pressure on self-
tracking in terms of gender. There is some research on the differences of feedback, such as
self-tracking, wherein females and males react in different ways. According to Roberts &
Nolen-Hoeksema (1989) men tend to be more influenced by positive feedback than negative
feedback and are in general less influenced by negative feedback than women. Considering
this, women should be susceptible for the negative effects of self-tracking such as
underperforming, not reaching certain goals and completing an activity realizing it did not get
tracked. Following the available line of research the following hypothesis is proposed:
H5: Being female will negatively affect the enjoyment of self-tracking
§2.8 Conceptual model
Given all of the variables discussed so far, the conceptual model used in this thesis is provided
in Figure 1.
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- + + - - - +
- - +
Figure 1: Conceptual model
§3. METHODOLOGY
For this research a questionnaire has been conducted. In section 2, sufficient literature has
been discussed in order to set up the questionnaire. According to Denscombe (2010) research
method questionnaires is a useful research with regards to reaching big groups of respondents
and obtain a clear set of results. Therefore, this type of method is selected to acquire
consumer preferences quantitatively. From the earlier mentioned research on possible
negative effects on self-tracking, only the research of Simpson & Mazzeo (2017) included
questionnaires but these were related to calorie counting. Hence, using a questionnaire for
this line of research can be beneficial for collecting and translating data from a consumer’s
perspective. The questionnaire enables generalizing certain topics (e.g. feelings of pressure
and feelings of guilt) with regards to the enjoyment of an activity, which can be used for
drawing conclusions.
§3.1 Questionnaire
The goal of the questionnaire was to investigate the presence of (unintended) negative effects
due to self-tracking on the enjoyment of an activity. In order to obtain the information needed,
all the variables explained in section 2 were questioned in the questionnaire in order to gather
information related to that variable. How the different variables were measured will be
Feelings of guilt
Enjoyment from
the activity
Use of self-
tracking
technologies
Feelings of
pressure
Dependency on
self-tracking
technology
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discussed later in this section. There were also some questions which did not have any
relevance for this thesis. This is because the questionnaire is combined with two fellow
students who were investigating the same topic. By combining the questionnaires there was
a higher chance of reaching more respondents, which enhances the scientifical strength of the
thesis.
§3.2 Measuring of variables
Enjoyment. The dependent variable enjoyment was measured by three items on a a 7-point
Likert-type scale ranging from “strongly disagree” to “strongly agree”(Hsiao et al., 2016):
“Using fitness mobile apps is pleasurable,” “I have fun using fitness mobile apps,” and “I find
using fitness mobile apps to be interesting”.
Feelings of guilt. This mediation variable was measured by 5 items on a 7-point Likert-scale,
three of these items will be discussed as an example (Davis et al., 1993). The rest can be found
in Appendix 1.
“It is important to my general well-being not to miss my exercise sessions” “It does upset me
if, for one reason or another, I am unable to exercise”,
“I feel ‘guilty’ that I have somehow ‘let myself down’ when I miss my exercise session”
Feeling of pressure. This mediator variable was measured by 3 items, also on a 7-point Likert-
scale. These items are presented to give an impression about how this variable was measured
(Davis et al., 1993):
“I continue to exercise at times when I feel tired or unwell” , “I continue to exercise even when
I have sustained an exercise-related injury” , “I sometimes turn down an invitation to an
interesting social event because it interferes with my exercise schedule”
Dependency of self-tracking technology. This variable was measured by eight items on a 7-
point Likert-scale with four items regarding all respondents, two items for respondents using
wearables and two items for respondents using applications. To give an example, these are
three items with regards to all respondents: “My daily routines are controlled by my wearable
device or self-tracking application,” “It feels like a wasted physical activity when it’s not
tracked,” and “I hate it when I have performed a physical activity and it has not been tracked”.
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Type of physical activity. This variable was measured by 1 item with three options: “What
physical activities do you track with the application(s)?”. The respondents had three options;
walking, running or ‘other, namely..’. Where walking is considered an effortless activity,
running and other activities, on the other hand are considered as effortful activities.
Gender. This variables was measured by 1 item containing three options: “Male”, “Female”
and “other”. Neglecting the before so logical binary option of gender can result in a more
positive reaction. Using a non-binary question in a general-population questionnaire could be
applied without facing significant adverse reactions from respondents (Medeiros et al., 2020).
§3.3 Data collection
As mentioned earlier, the data was collected by means of a questionnaire. This was done in a
program called Qualtrics, which is a survey builder, also supported by the Rijksuniversiteit of
Groningen. When the questionnaire was built, it was distributed by using social media
platforms such as LinkedIn, Instagram and Facebook in order to get as much respondents as
possible. Since many people are exercising and are using self-tracking devices/apps it was
fairly easy to reach out to respondents. Together with two other researchers, Fokkens and
Toneman, who are also doing research in this field, we reached 568 respondents.
§3.4 Data analysis
The analysis of the data will be performed by an Ordinary Least Squares regression (OLS). Since
most of the data is measured on a 7-point Likert-scale we can assume these variables as
continuous. Therefore, an OLS can be applied. The OLS will be performed by a path analysis
modelling tool called PROCESS. This is an extension with can be used on coding programs such
as RStudio and SPSS. It is written by Hayes (2018) and it allows you to estimate direct and
indirect effects in single and multiple mediator or/and moderator models. Interaction effects
are also included in this tool. This makes it an all-embracing tool which enables a researcher
to do extensive analyses.
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§4. RESULTS
In this section, the results of the questionnaire analysed with the process macro will be
discussed.
§4.1 Descriptive statistics
In total 568 participants started the questionnaire, of which 205 did not use any type of self-
tracking technology or app. From the remaining 363 participants, 19 participants who wrongly
answered the control question (if you read this question, please select the disagree option)
were deleted, assuming that everyone who did not fill in the right answer was a serious
respondent. From this selection, 90 participants were removed because they did not finish the
questionnaire. Therefore, the final dataset consists of 273 respondents. From these 273
respondents, 132(48.35%) are male and 141(51,65%) are female, which makes it a fairly even
gender distribution. The respondents have an average age of 25.47 years (SD= 7.11) and
around 65% has a bachelor’s (n=100) or master’s degree (n=78). These social demographics
do not correspond with the Dutch population in general, which has to be taken into account
when the results are generalized. Further descriptive statistics can be seen in Appendix F.
§4.2 Reliability of the instruments
The different items within all instruments have been validated in order to see which items can
be combined. This validation is done by performing Cronbach’s alpha tests. The results of
these tests can be seen in the following outcomes:
Instrument Number of items Cronbach’s alpha
Enjoyment (DV)
Dependency
3
5
0.77
0.81
Guilt 5 0.64
Pressure 3 0.52 (low)
Table 1: Cronbach’s alpha’s
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According to Cronbach’s alpha’s rule of thumb every value below 0.6 is classified as
unacceptable. This means that the a-value of Pressure (0.52) is considered as not reliable.
Taking this into account, the items of the instrument Pressure will be used together and
separately (“I often feel pressure to reach my targets”) to account for any differences.
§4.3 First insights of the data
Before analysing the data, some first insights of the variables are presented in Table 2 in order
to see how the data is classified. Next to this, it will be investigated if there is any sign of
correlation.
Variable Min Max Median Mean SD
Enjoyment 2.00 7.00 6.00 5.68 0.77
Dependency 1.00 6.60 3.40 3.42 1.38
Guilt
Pressure
1.80
1.00
6.80
6.33
4.80
3.33
4.79
3.38
1.01
1.16
Table 2: Descriptives of variables
To prevent biased results, multiple correlation matrices were performed, and it can be
concluded that all correlation scores are between 0.09 and 0.53, except for one score (0.67).
Since this is the only outlier with regards to correlation, we can assume that there is no
multicollinearity between the variables.
§4.4 Dependency on self-tracking
In order to check whether dependency has a mediating effect on the enjoyment of the activity
two analyses were performed; a regression analysis and a process macro for multi-categorical
mediation with bias-corrected bootstrapping and 5,000 resamples (Model 4; Hayes 2018). In
both models the usage of self-tracking was used as the independent variable, enjoyment as
the dependent variable, and dependency on self-tracking as a mediator. As hypothesised in
H1A, the use of self-tracking leads to self-tracking dependency (B = .21, SE = .03, t(271) = 6.38,
p < .001, R² = .13). Next to this, the regression analysis presents a significant positive
relationship between dependency on self-tracking and enjoyment (B = .17, SE = .03, t(271) =
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5.14, p < .001, R² = .12), which is not in line with 1B because a negative relation was expected.
The bootstrapped mediation analysis showed a significant index of mediation since the
confidence interval of the indirect effect did not include zero. Hence, this analysis showed that
the effect of self-tracking on enjoyment from an activity was indeed mediated by feelings of
dependency (95% CI: [.0609, .1710]; completely standardized indirect effect .11). However, a
negative relation was expected, instead it turned out to be a positive relation. This is not in
line with 1C since a negative relation was hypothesised.
§4.4.1 Feelings of pressure
The analyses of the variable feelings of pressure was also done by performing a regression
analysis and model 4 of the process macro analysis for multi-categorical mediation with bias-
corrected bootstrapping and 5,000 resamples (Model 4; Hayes 2018). Again, the usage of self-
tracking was used as the independent variable, enjoyment as the dependent variable, and
feelings of pressure as a mediator. On the contrary of hypothesised in H2A, the use of self-
tracking does not significantly lead to more feelings of pressure (B = .04, SE = .03, t(271) = 1.46,
p > .05, R² = .01). Also, the regression analysis does not present a significant relationship
between feelings of pressure and enjoyment (B = .05, SE = .04, t(271) = 1.17, p > .05, R² = .04).
Although this relationship is insignificant, it is a positive relationship which is in contrast with
the expected relationship in 2B. The bootstrapped mediation analysis did not show a
significant index of mediation as the 95% confidence interval of the indirect effect did include
zero. (95% CI: [-.0026, .0100]; completely standardized indirect effect .0062). Considering this,
the effect of self-tracking on the enjoyment of an activity was not mediated by feelings of
pressure (95% CI: [-.0026, .0100]; completely standardized indirect effect .0062). However, as
mentioned earlier the variable feelings of pressure turned out to have a relatively low
Cronbach’s alpha of 0.52 which could be an explanation for the insignificant results. Therefore,
the same analysis was performed for a single item from the feelings of pressure variable.
Namely the question: ‘I often feel pressure to reach my targets’ which resulted in the following
outcomes: In line with hypothesis 2a the use of self-tracking does significantly lead to more
feelings of pressure (B = .21, SE = .04, t(271) = 4.77, p < .001, R² = .08). Next to this, the
regression analysis presents a significant positive relationship between feelings of pressure
and enjoyment (B = .06, SE = .03, t(271) = 2.22, p < .05, R² = .06), which is not in line with 2B
because a negative relation was expected. Lastly, The bootstrapped mediation analysis
20
showed a significant index of mediation as the 95% confidence interval of the indirect effect
did not include zero (95% CI: [.0013, .0255]; completely standardized indirect effect .0123).
Considering this, the effect of self-tracking on the enjoyment of an activity was mediated by
feelings of pressure. Following hypothesis 2C, there is indeed a mediating effect, but it is a
positive relation opposed to a negative relation.
§4.4.2 Feelings of guilt
The third variable, feelings of guilt, contains hypotheses 3A till 3B. Also for this variable a
regression analysis and process macro for multi-categorical mediation with bias-corrected
bootstrapping and 5,000 resamples (Model 4; Hayes 2018) was performed. Again, usage of
self-tracking was used as the independent variable, enjoyment as the dependent variable, and
feelings of guilt as a mediator. Hypothesis 3A should be accepted as the regression analysis
shows that the use of self-tracking leads to feelings of guilt (B = .05, SE = .03, t(271) = 2.05, p
< .05, R² = .02). Although the outcomes of the analysis regarding hypothesis 3B are significant
(B = .16, SE = .04, t(271) = 3.55, p < .001, R² = .08), it should be rejected since the expected
relationship is the opposite. Meaning that the more feelings of guilt you experience the higher
the enjoyment of the activity. In the discussion section it will be discussed why the expected
relation might be the opposite. At last, The bootstrapped mediation analysis showed a
significant index of mediation as the 95% confidence interval of the indirect effect did not
include zero (95% CI: [.0003, .0603]; completely standardized indirect effect .0259). Hence,
the effect of self-tracking on the enjoyment of an activity was mediated by feelings of guilt.
Although hypothesis 3C expected a mediating effect, it cannot be accepted since the sign of
the relationship is the other way around.
§4.4.3 Type of physical activity
In order to see whether the type of physical activity has an effect on the enjoyment of the
activity a double moderation analysis was performed (Model 2: Hayes 2018). This resulted in
a significant main effect for walking (B = -.78, SE = .36, t(271) = -2.18, p < .05, R² = .08) with a
negative coefficient meaning that walking has a negative effect on the enjoyment of the
activity. Running also shows a significant negative relationship (B = -.82, SE = .35, t(271) = -
2.32, p < .05, R² = .08). However, when looking at the interaction effects with regards to
21
walking (B = -.15, SE = .10, t(271) = -1.42, p > .03, R² = .08) and running (B = .25, SE = .11, t(271)
= 2.24, p < .05, R² = .08) we can see a difference. There is a significant interaction effect
between the use of self-tracking and running with regards to the enjoyment of the activity.
Therefore, this analysis support hypothesis 4. Hence, effortful activities positively affects the
enjoyment of an activity.
§4.4.4 Gender
Another moderation analysis (Model 1; Hayes 2018) was performed in order to see whether
gender has a moderating effect on the use of self-tracking and the enjoyment of the activity.
Both main effects did not show any significance (p=0.89). This was also the case with both
interaction effects regarding males and females (p=.19). Hence, hypothesis 5 can be rejected.
Although there is no significant interaction effect, there is reason to believe that there is a
different effect between the two genders, since females had a negative effect on enjoyment
and males a positive effect. Next to this , a double moderation model (Model 3; Hayes 2018)
was executed in order to find out if males and females have enjoyed their activity different
when the activity is effortful. No significant effect was found (p=.32), although there is a
different coefficient sign for both genders.
§4.4.5 Total Model
The analyses that were discussed earlier in this chapter described parts of the full model
presented in 2.8. Now, in order to test the total model a moderated mediation analysis was
performed (Model 14 & 17; Hayes) using the continuous measures of pressure, guilt and
dependency as mediators and type of physical activity and gender as moderators. This gives
the following models:
𝐸𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 = 𝛽0 + 𝛽1𝑆𝑇𝑢𝑠𝑎𝑔𝑒 + 𝐵2𝑆𝑇𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑦 + 𝐵3𝐺𝑢𝑖𝑙𝑡 + 𝐵4𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 +
𝐵5(𝐺𝑒𝑛𝑑𝑒𝑟 𝑥 𝑆𝑇𝑢𝑠𝑎𝑔𝑒)
𝐸𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 = 𝛽0 + 𝛽1𝑆𝑇𝑢𝑠𝑎𝑔𝑒 + 𝐵2𝑆𝑇𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑦 + 𝐵3𝐺𝑢𝑖𝑙𝑡 + 𝐵4𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 +
𝐵5(𝑃𝐴 𝑥 𝑆𝑇𝑢𝑠𝑎𝑔𝑒)
22
The moderators are used separately in the total model, since the process macro analysis only
offers space for two moderators (running vs walking & male vs female). When analysing all
the mediators together with gender as a moderator, guilt (B = .18, SE = .07, t(271) = 2.73, p <
.001, R² = .17) and dependency (B = .17, SE = .05, t(271) = 2.73, p < .05, R² = .17) show a
significant positive main effect with regards to the enjoyment of an activity. Also, being a male
has a marginally significant main effect (B = .85, SE = .44, t(271) = 1.91, p = 0.06, R² = .17) on
enjoyment. The interaction effects, however, are not significant. There is no evidence for a
moderated mediation effect since the index shows an insignificant effect [-.0214, .0050] on all
mediating variables. When applying Model 17 to the total model for determining the results
with type of activity as a moderator there a no significant results, except for an interaction
effect between the usage of apps and running (effortful activity) on the enjoyment of the
activity (B = .39, SE = .17 t(271) = 2.31, p < .05, R² = .22). This implies that an effortful activity
leads to more enjoyment according to the users of running apps. There is no sign of moderated
mediation because the index did include zeros for all mediation variables.
Table 3 shows an overview of the hypotheses and whether they have been rejected or
accepted.
Hypothesis Result
H1A: The more you use self-tracking technologies
the more you will become dependent on self-
tracking technology
H1B: The more feelings of dependency you
experience the lower the enjoyment
H1C: The use of self-tracking negatively impacts
the enjoyment of the activities by increasing
feelings of dependency meaning that feelings of
dependency mediate the relationship between
self-tracking usage and enjoyment
Accepted
Rejected
Rejected
23
H2A: The more you use self-tracking technologies
the more you feel pressure
H2B: The more feelings of pressure you
experience the lower the enjoyment
H2C: The use of self -tracking negatively impacts
the enjoyment of the activities by increasing
feelings of pressure meaning that feelings of
pressure mediate the relationship between self-
tracking usage and enjoyment
Accepted
Rejected
Rejected
H3A: The more you use self-tracking technologies
the more you feel guilt
H3B: The more feelings of guilt you experience
the lower the enjoyment
H3C: The use of self -tracking negatively impacts
the enjoyment of the activities by increasing
feelings of guilt meaning that feelings of guilt
mediate the relationship between self-tracking
usage and enjoyment
Accepted
Rejected
Rejected
H4: Effortful activities positively affects the
enjoyment of the activity.
Accepted
H5: Being female will negatively affect the
enjoyment of self-tracking in effortful activities
Rejected
Table 3: Overview hypotheses
24
§5. DISCUSSION
This section discusses how the results from section 4 can be interpreted and contributed to
the literature. First, the effects of parts of the model will be compared and discussed. The
unexpected outcomes are discussed in order to try to comprehend how it turned out that
certain way. At last, the full model will be discussed and evaluated.
§5.1 Main effects
When looking at the first three hypotheses a certain pattern can be found. All H1A, H2A and
H3A are accepted. People who use self-tracking apps more often are susceptible for more
feelings of dependency towards the self-tracking app. Furthermore they experience more
feelings of pressure and feelings of guilt. This is in line with the expectations from section 2
and with the earlier described literature (Etkin, 2016; Kersten-van Dijk et al., 2017; Simpson &
Mazzeo, 2017). However, the following B’s and C’s for all three hypotheses are rejected. For
the variables with regards to feelings of guilt and feelings of dependency the hypotheses B
and C are all significant. For the variable regarding feelings of pressure these are only
significant when taking the one item that asked directly after pressure as mentioned in 4.4.1.
It was expected that these negative feelings resulted in less enjoyment of the activity, but
based on the results of the survey, it resulted in more enjoyment towards the activity. One of
the reasons that might explain this unexpected outcome is that users of self-tracking apps,
who do experience feelings of pressure, guilt and dependency might be more involved
regarding their activities. They might need these feelings in order to perform at their best,
resulting in more enjoyable activities. Some people need an object of examination and
competition in order to get the most out of their workouts (Gabriels & Coeckelbergh, 2019).
Nevertheless, it is established that self-tracking usage causes feelings of guilt, dependency and
pressure, which on their turn are mediating the effect of self-tracking on enjoyment. Hence,
as seen in the analyses these variables are positively influencing enjoyment. One can say that
these feelings are needed for some users in order to get the most joy out of their activities.
This might also be one of the reasons that the fourth hypothesis was accepted. Effortful
activities are contributing to the enjoyment of an activity. As the literature prescribes an
25
activity becomes more meaningful when an effort is made, which in light of this research is
running instead of walking (Vorderer et al., 2006).
According to the literature, there was reason to believe that being female might had a
moderating effect on the enjoyment of an activity, especially when this activity was effortful.
However, this effect was not found after analysing the data. A possible explanation can be
that some of the literature regarding gender and its effect is outdated. The research of Roberts
& Nolen-Hoeksema (1989) might not be applicable to the technological era we live in now
instead of the zeitgeist then.
At last, the variables were combined and presented in a total model. In this model, only some
main effects were significant except for one interaction effect regarding the effortful activity
running. It can be concluded that these variables combined together are not presenting the
same outcomes as they did in the partly models. Knowing this, the mediator variables can best
be treated independently with regards to enjoyment. Coming back, to the central question of
these thesis: To what extent does self-tracking on running apps have a negative effect on their
users? It can be concluded that self-tracking does lead to negative feelings such as guilt,
pressure and dependency. Moreover, this does not mean that it has to affects users in a
negative way. Based on the results, these feelings eventually lead to a higher enjoyment of
the activity.
§6. CONCLUSION
The main objective of this thesis has been to research potential negative effects with regards
to self-tracking. Since there is little literature where possible negative effects of self-tracking
are discussed, this paper broadens the scope with self-tracking at the centre of it.
A questionnaire was used in order to research the topic by collecting data from people with
self-tracking experience. By doing this, insights from more than 500 respondents were
generated. Based on these insights, it can be concluded that the use of self-tracking leads to
more feelings of guilt, pressure and dependency, which seems like a negative relationship.
However, these feelings provide more enjoyment. This is a surprising finding which was not
expected. In a research after motivational profiles for physical activity and links with
26
enjoyment Guérin and Fortier (2012) found that self-determined motives still lead to
enjoyment of a physical activity despite feelings of guilt or shame. Next to this, there are more
studies that found a positive effect instead of a negative effect (Edmunds et al., 2008;
Ntoumanis, 2002). Considering this, the surprising effect could be explained. Although, these
studies did not study the effects of self-tracking, I think it is applicable to the results of this
thesis. Especially for feelings of guilt and feelings of pressure since these feelings can be drivers
for better performances and therefore more feelings of enjoyment. Apparently do feelings of
dependency not have a negative effect on enjoyment. A possible explanation could be that
feelings of dependency does not impact the activity itself which makes it more like a peripheral
issue.
§6.1 Implications
Compared to the positive effects of self-tracking there is little to none research after the
negative effects of self-tracking. Therefore, I think it is sensible to do more research in this
field. A possible explanation for these results can be that the respondents who participated in
this study are more sportive than the average person. Hence, they might be enjoying feelings
of guilt, pressure and dependency more than others.
§6.2 Limitations and further research
The findings from this paper have limited generalizability because the sample of the
conducted questionnaire does not correspond with the Dutch population in general. As
mentioned earlier, around 65% of the respondents have a bachelor’s or master’s degree. In
reality, around 34% of the Dutch population has a bachelor’s or master’s degree according to
a survey of the Central Bureau of Statistics. This is an example for the correspondence of the
sample used and the Dutch population in general. It could be that people who are lower
educated are more susceptible for the negative effects of self-tracking. Considering this, it is
important to mention that the findings of this thesis have limited generalizability. Next to this,
this paper looked into self-tracking in a quantitative way, while it is also interesting to research
this in a qualitative manner. By doing this, you are able to find out why self-tracking may or
may not have negative consequences on their users. Using interviews can be a good way to
get to know more about self-tracking and its effects. The results from this thesis show that
27
running apps have an important role with regards to the enjoyment of their users. Future
research can investigate more between two groups, one group that uses self-tracking and one
group that does not. Which group enjoys their workouts the most?
28
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APPENDICES
Appendix A: Self-tracking usage
Do you use a mobile application to track physical activity or exercising? (E.g., Apple Health
App, Google Fit, StepsApp, Strava, Runkeeper, etc.)
Yes, namely… (please name all of them)
No
How often do you use the mobile self-tracking application?
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o Daily (day & night)
o Daily (day time only)
o Only when exercising
o Twice a week
o 3-5 times a week
o Over 5 times a week
o Other, please specify
What physical activities do you track with the application(s)?
o Walking (counting steps)
o Running (tracking distance & speed)
o Other, namely …
Do you use any type of a wearable self-tracking device to track physical activity or exercising?
(E.g., Fitbit, Apple Watch, Garmin Smartwatch, etc.)
o Yes, namely…
o No
How often do you use the wearable device?
o Daily (day & night)
o Daily (day time only)
o Only when exercising
o Twice a week
o 3-5 times a week
o Over 5 times a week
o Other, please specify
What physical activities do you track with the wearable device?
o Walking (counting steps)
o Running (tracking distance & speed)
o Other, namely …
Appendix B: Dependence on self-tracking
35
For all respondents:
- It feels like a wasted physical activity when it’s not tracked.
- I hate it when I have performed a physical activity and it has not been tracked.
- My daily routines are controlled by my wearable device or self-tracking application.
For respondents using wearables:
- If I forget my device at home, I would go back to get it.
- It feels like something is missing when I am not wearing my wearable device.
For respondents using applications:
- If I forget my telephone at home, I would go back to be able to track my physical
activity.
- It feels like something is missing when I am not tracking my physical activity (e.g.,
tracking your steps).
Appendix C: Guilt
- It is important to my general well-being not to miss my exercise sessions
- It does upset me, for one reason or another, when I am unable to exercise
- If I miss an exercise session, or several sessions, I try to make
them up by putting in more time when I get back.
- I have a set routine for my exercise sessions, e.g., the same time of day, the same location, the same number of laps, particular exercises, and so on.
- I feel ‘guilty’ that I have somehow ‘let myself down’ when I miss
my exercise session.
Appendix D: Pressure
- I continue to exercise at times when I feel tired and unwell.
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- I continue to exercise even when I have sustained an exercise-
related injury.
- I sometimes turn down an invitation to an interesting social
event because it interferes with my physical activity schedule
- I often feel pressure to reach my targets (e.g., reaching the daily
10.000 steps)
Appendix E: Enjoyment
- Using fitness mobile apps is pleasurable
- I have fun using fitness mobile apps
- I find using fitness mobile apps to be interesting
Appendix F: Descriptives
Age
37
App Usage
38
Gender
39
Employment status
Education level