Untracked activities. How users of fitness trackers value their generated data

Bachelor Thesis, 2017

68 Pages, Grade: 1,3


Table of Contents

List of Figures

List of Tables

List of Abbreviations

1 Introduction

2 Theoretical Background: Self-Tracking
2.1 Definition of Self-Tracking and Distinction from similar Constructs
2.2 The Present Development of Self-Tracking
2.3 The Process of Self-Tracking
2.4 The Valuation of Self-Tracking Data
2.4.1 The Characteristics of Tracking Data
2.4.2 Curiosity & Social Comparison in the Self-Tracking Context
2.4.3 Data as Achievement & The Task Value

3 Theoretical Background: Psychological Concepts

4 Research Model and Hypothesis Development

5 Research Method
5.1 Data Collection and Evaluation
5.2 Measurements

6 Results

7 Discussion and Conclusion

List of Literature

A Questionnaire (English and German)
В Flyer for the Gyms (German)
C Introduction that refers to the Online Questionnaire (English and German)
D Statistical Figures and Tables

List of Figures

Figure 2.1 : The expectancy-value model

Figure 6.1: Scatterplot (Emotion)

Figure 6.2: Scatterplot (Value)

List of Tables

Table 3.1: Overview: Big Five Personalities

Table 5.1: Value and Loss Variables

Table 5.2: Intensity Variables

Table 5.3: Task Value Variables

Table 5.4: BI I-10 (see Rammstedt, John (2010), p. 210 f.)

Table 5.5: CEI-II (see Kashdan et al. (2009), p. 996 f)

Table 5.6: INCOM (Ability) (see Gibbons, Bunk (1999) p. 142)

Table 6.1: Cronbach's Alpha (VALUE_ALL)

Table 6.2: Cronbach’s Alpha (EMOTION Al l.)

Table 6.3: Correlation (Value & Emotion)

Table 6.4: Correlation (Intensity & Value)

Table 6.5: Correlation (Trait Curiosity & Value)

Table 6.6: Correlation (Social Comparison & Value)

Table 6.7: Correlation (BF-I0 & Value)

Table 6.8: Descriptive Statistics (Emotion)

Table 6.9: Model Summary (Emotion)

Table 6.10: ANO VA (Emotion)

Table 6.11: Coefficients (Emotion)

Table 6.12: Descriptive Statistics (Value)

Table 6.13: Model Summary (Value)

Table 6.14: ANO VA (Value)

Table 6.15: Coefficients (Value)

List of Abbreviations

BFI-10 Big Five Inventory-

Abbildung in dieser Leseprobe nicht enthalten

1 Introduction

People do now keep track of themselves; with the help of modern technology, every imagi­nable parameter of life is quantified. Through this data the user gains exclusive insights into intangible metrics of everyday life. These insights will foster the understanding of the self. The transparency engages millions to count e.g. their steps and their burned calories. A com­munity around the quantification of self emerged; a community of so-called self-trackers. This data simplifies the everyday life. Nowadays, people do not only pursue an activity in order to stay healthy, they track this activity, to gain a rational judgement about their activity. Is this data more than just the simplification of everyday life? However, how do the users value their generated data?

This thesis focusses on the question of how users value their data generated through tracking. Furthermore, it will be investigated what emphasizes the feeling of loss if an activity remains inadvertently untracked?

This bachelor thesis solely focuses on the subjective valuation of data generated through tracking physical activities. To evaluate the subjective value, this work differentiates be­tween activities that the users tracks and activities that accidentally remain unmonitored. This work assumes that this difference between these activities is the added value of tracking data. To evaluate possible factors that emphasize the value of perceived value of data an empirical study is conducted. This study examines the influence of personality traits on the value of data. Furthermore, the users’ level of engagement in social comparison to evaluate his or her abilities and levels of trait curiosity are examined as potential factors that affect the value users give to their data.

This paper begins with providing a theoretical background of the practice of self-tracking and the applied psychological concepts associated with it. A definition of self-tracking and distinction from similar practices classifies this concept in practices of self-monitoring. Fur­thermore, the process of self-tracking is explained. Then this paper presents possible factors that could drive the valuation of data in the self-tracking context. Here, this paper focusses on the characteristics of the generated data and the associated practices of self-reflection. Furthermore, this paper will shed light onto the role of curiosity and social comparison in self-tracking context. Lastly, psychological concepts and their relevance for the thesis will be introduced. Next, the empirical study is presented which terminates with the data analysis and its associated results. This paper ends with a discussion of the results in which possible limitations are mentioned and a recommendation for future papers is given.

2 Theoretical Background: Self-Tracking

2.1 Definition of Self-Tracking and Distinction from similar Constructs

Definition of Self-Tracking

When trying to come up with a definition of self-tracking some authors concentrate solely on the technological aspect of it and attribute the devices the ability to collect user-generated data and provide the user with a visualized reprocessing of information to reflect upon and analyse the data (see Li, Dey, Jodi (2010), p. 2). Others rather define self-tracking as a pro­cess, seeing self-tracking as the practice of users collecting information about their daily habits and feelings. With the aim of reflect upon it in order to regulate their activities in the future.1

Distinction from Lifelogging and Quantified-Self

The concept of self-tracking, which refers among others to lifelogging and quantified self needs a proper distinction from these two terms in order to prevent misconceptions. Lifelog­ging is an extreme form of self-tracking, which focusses on the practice of producing a rec­ord of someone’s entire life or large portions of life by wearing a portable camera and/or other digital devices (see Macmillan Dictionary (2012))- while self-tracking is practiced by monitoring just one parameter or activity over time. Lifelogging is only possible with large quantities of parameters including “[...] all visual information, all audio, all media activity, as well as biological data from sensors on one’s body” (Kelly (2007)). While self-tracking aims for a reflexive interaction with the data, lifelogging focuses on the collecting and doc­umenting aspect of this practice (see Kelly (2007)).

The term Quantified Self, which was initially founded by the WIRED journalist Gary Wolf and Kevin Kelly refers to the community, which evolved among the practices of self-track­ing (see Gimpel, Nißen, Görlitz (2013) p. 4). However, a self-tracker does not automatically relate to the quantifled-self community. Shin pointed out the differences in terms of the behaviour and the motivations between members of the quantified-self community and self­trackers (see Shin, Cheon, Jarrahi (2015), p. 2 f.): The members of this community monitor and analyse a variety of parameters in their lives with the widespread goal of optimising the parameters tracked and thus improve their lives. They dedicate a lot of time to their tracking generate a great amount of personal data to figure out how certain metrics interact. The com­munity is fascinated about data and digital devices and is always seeking for better and more accurate measurement devices.

2.2 The Present Development of Self-Tracking

The Market for Self-Tracking Devices Self-tracking is nowadays a flourishing business with a growing number of companies, who encourage the user to monitor every single aspect of their life. As self-tracking emerged is has a lot of attention and funding from major companies (see Neff, Nafus (2016), p. 108). The funding for digital health companies exceeded 4.1 billion dollar, money provided by funding companies like e.g. Sequoia and Kleiner Perkins as well as corporate investments from e.g. Google and Merck (see Neff, Nafus (2016), p. 108). It is estimated that by the year of 2019 over 42 million fitness-trackers will be sold (see Svanberg (2014)). In a report by ABI Research, it was estimated that the number of tracking devices shipped in the year 2018 will reach up to be 485 Million (see ABI Research (2013)). This number only refers to wearable devices, but nowadays self-tracking can be done on nearly every smartphone avail­able. According to a study by Fox (ท=3.014) 60% of Americans track their weight, diet or exercise routine (see Fox, Duggan (2013)). In the German market, self-tracking devices are not as present as in the US. A recent study (ท=1.011) found out, that 21% of the German population track at least one parameter, mainly about their fitness level (see Dr. Grieger & Cie. (2016)). The growing numbers of self-trackers in the population is reflected by vast amount of downloads of popular tracking apps for android smartphones in the year 2014: e.g. Fitbit (1-5 million users), Runtastic (5 - 10 million users), Runkeeper (10 - 50 million users) (see Ballano Barcena, Wueest, Lau (2014), p. 8).

Social and technological factors that encourage self-tracking The progressive technological development of mobile devices makes it easier for users to gain proper information about themselves. The processing power and miniaturization of pro­cessors and sensors provide improved performance and enables providing more precise in­formation than ever (see Ballano Barcena, Wuest, Lau (2014), p. 5). In addition, the im­proved battery life and the comprehensive communication infrastructure allows the shrink­age of the tracking devices by the size of an e.g. watch that can easily be carried around all day (see Ballano Barcena, Wueest, Lau (2014), p. 5). Besides the technological develop­ment, customers’ social and material need helps making wearables a sort of status symbol (see Neff, Nafus (2016), p. 110). An increased awareness of the own health is promoted by public media. Information about healthy lifestyles, products and services spread around me­dia channels (see Ballano Barcena, Wueest, Lau (2014), p. 5). The omnipresent usage of social media channels provides users with numerous possibilities of sharing favourable con­tent. Through public media channels, the possibility arose to provide the social peers with a favourable image of the self. The sharing of self-tracking data has enables users to underline a favourable image to the public by sharing sportive successes (see Oehrl (2016a), p. 1).

Often however, not every kind of content is shared. To maintain the favourable picture, more often that not only sportive activities that are associated with great performance are shared, while defeats or training terminations are not shared by the users (see Oehrl (2016a), p. 1).

2.3 The Process of Self-Tracking

Before tracking physical activities, the user has to decide which device fits his or her purpose of tracking best. For tracking physical activities, a lightweight and portable device is pre­ferred. Two categories meet these needs (see Trickier (2013), p. 197 f): (1) “portable de­vices” e.g. mobile phones are not detached to the body. Their use is on demand and the usery need to manually activate the tracking process. While (2) “wearable devices” e.g. smart- watches, activity-tracker are attached to the users’ body and are meant to be worn perma­nently over a large period of time. These devices track activity passively and automatically, offering the possibility of easily gathering a large quantity of data. A study conducted by Lí examined the usage of personal informatics (see Li, Dey, Forlizzi (2010), p. 1-10). Personal informatics is defined as “ [...] systems as those that help people collect personally relevant information for the purpose of self-reflection and gaining self-knowledge” (see Li, Dey, Forlizzi (2010), p. 2). The study resulted in the “Stage-Based Model of Personal Informatics Systems” to divide the process of self-tracking in five stages (see Li, Dey, Forlizzi (2010), p. 4 ffi): First, every user starts on the “preparation” stage where he asks himself what infor­mation is needed and how it will be generated. In the second stage, the user-driven “collec­tion” stage, people start the actual self-tracking process by collecting data about their actions. The “integration” stage is the third stage of the model. Here, the devices process the collected data and present it in an understandable manner. The fourth stage is associated with the analysis of the data. On the “reflection” stage, the user reflects upon the collected and inte­grated data, to gain insights about themselves in order to act upon this knowledge in the fifth stage the “action” stage. Furthermore, possible barriers that may occur on each stage were analysed. The most relevant barrier for this work occurs in the “collection” stage where the user may not track his or her behaviour. The reason for this could be either user-driven or device-driven e.g. by a malfunction of the tracking-device itself (see Li, Dey, Forlizzi (2010), p. 5). Due to the error by not collecting data, every following stage will not be able to fulfil its properties for the user. Reflection is a crucial practice in the self-tracking context. In the “Reflection” stage Lí differentiates between two types of reflections (see Li, Dey, Forlizzi (2010) p. 6): The term short-term reflection describes the process of users making themselves aware of their current status. While the term long-term reflection describes the process in which the users compare the information over a greater period of time to reveal patterns or interactions of parameters in their long-term data. The user is not able to either reflect on the short-term without the data of its actual activity nor can not the user reflect upon its actions in the long run due to data holes

The information generated by self-tracking practices act as drivers to optimise the activities tracked, achieve a higher degree of self-awareness or to improve certain aspects of life (see Lupton (2014a), p. 3). According to the definition of personal informatics, the main interac­tion occurs is between device and user (see Li, Dey, Forlizzi (2010), p. 1). Therefore, self­tracking is something that is mostly done for private use.

Lupton defines the major mode for self-tracking as “private self-tracking”, where data is collected voluntarily for the user him or herself (see Lupton, (2014b), p. 5 ff). Here, the data is kept either private or exclusively shared within selected social networks (see Lupton (2014b), p. 6). The use for the self-tracker emerges just by communication with the digital self, through the information provided by the device (see Lomborg, Frandsen (2016), p. 1021 f). This private self-tracking helps people to increase their self-awareness ,which in turn helps them to control their actions in a favourable way. In this mode of self-tracking users are mostly driven by curiosity or the interest in the data per se (see Li, Dey, Forlizzi (2010), p. 4).

Another important mode of self-tracking is “communal self-tracking” (see Lupton (2014b), p. 8 f). Most people who track their physical activities see themselves as part of a commu­nity, in which people push each other to goals or share their.2 In these communities the pri­mary purpose of tracking lies in sharing their data or their achievements (see Lupton (2013), p. 28) and comparing their performance with the performance of other peers (see Lomborg, Frandsen (2015), p. 1022).

Since tracking physical activities is mostly goal-driven (see Munson, Consolvo (2012), p. 4 f.) especially fitness-trackers suggest goals to their users, thus encouraging them to keep track of their activities in order to work towards this goal. This guided tracking style corre­spond to the “directive tracking” (see Rooksby et al. 2014), p. 1167). Although they may not work as effective as intended (see Munson, Consolvo (2012), p. 7), achievements are another crucial factor for keeping the users motivate (see Till (2014), p. 451 ff). Tracking physical activities just for the sake of achievement sake another style of self-tracking. Rooskby sum­marizes this style as “collecting rewards” (see Rooksby et al. (2014), p. 1168 f). In their study, his team found out, that for some users the act of collecting rewards had such a big impact, that these users modified their training in certain ways in order to gain specific achievements. Both styles of tracking indicate a high dependence of the users on their de­vices. Since these tracking styles are found mostly in tracking physical activities, in this paper it is assumed, that the tracking of these activities have a higher relevance for the users than tracking other parameters of life.

Abbildung in dieser Leseprobe nicht enthalten

2.4 The Valuation of Self-Tracking Data

2.4.1 The Characteristics of Tracking Data

People engage in the practice of monitoring themselves for a variety of reasons. These rea­sons depend on the parameters being tracked (see Gimpel, Nißen, Görlitz (2013), p. 8 ff). The recent literature provided several insights into the mind of the self-tracker community to understand the motivation behind this practice. The degree of dedication to self-tracking practices differs strongly among who track their activities. Some simply track out of curios­ity in their daily practices (see Li, Dey, Forlizzi (2010), p. 4), while others use self-tracking as a way to outsource their memories (see Lupton, (2014a), p. 6). These people are not mo­tivated by the desire to improve their habits; the rather they have open-ended questions about themselves, which they try to answer by analysis of the personal data (see Whooley, Plod- erer, Gray (2014), p. 154).

Others track their activities in order to work towards a future goal and out of their willingness to improve themselves.3 The metrics provided by self-tracking devices allow users to com­pare their current performance to the performance in the past in order to objectively evaluate if progress was made (see Ballano Barcena, Wueest, Lau, (2014), p. 5) Another important motive for evaluating one’s own progress is comparison with other people (see Lomborg, Frandsen (2015), p. 1022). Comparison, especially upward social comparison, is related to the desire for self-improvement (see Taylor, Neter, Wayment (1995), p. 1281 f), which is another motivator for engagement in self-tracking (see Whooley, Ploderer, Gray (2014), p. 153 f).

A general approach to understanding the motivation behind tracking lies in the nature of the data generated. By transforming physical activities in general understandable metrics like calories burned or meters walked, it stands to reason that self-tracking has the ability to break down complex activities in a simplified and general understandable way. The uncertainty of life is displaced by objective data to act upon (see Wolf (2010)). The objectivity and neu­trality of data is the base for a rational improvement in aspects of life. Variables such as distance, calories and pace are all universal measurements that allow every person to under­stand their performance and relate their performance to the performance of others (see Till (2014), p. 454). Another factor for the joy of self-tracking (especially for the tracking of physical activities) lies in the presentation of the data (see Lomborg, Frandsen, (2016), p. 1022). By enabling visualization of the data self-tracking is a powerful tool for short-term or long-term reflection of the user’s actions and behaviour (see Rooksby et al. (2014), p. 1170), which “[...] thus prolong[s] and augments] the exercise session [...] (Lomborg, Frandsen (2016), p. 1022). Moreover, the representation of the data in an objective form visualized by charts and tables makes the process of self-tracking appear scientific and there­fore is associated “[...] with values such as seriousness, analysis and competent expertise” (Lomborg, Frandsen (2016), p. 1022).

Ruckenstein focusses on this aspect of self-tracking with the aim of self-optimization in introducing the concept of the “data double” (see Ruckenstein (2014), p. 70 f). Ruckenstein defines the “data double” as” “[...] the conversion of human bodies and minds into data flows that can be figuratively reassembled for the purpose of personal reflection and interaction” (Ruckenstein (2014), p. 68). Former theories about data doubles described them as the configuration of individuals in the process of data collection for the purpose of representation of the person (see Haggerty, Ericson (2000), p. 613 f).

The data double is as unique as the individual producing it. It differs by the variety of pa­rameters the user is tracking and by the intensity of tracking them. These doubles never remain in a resting state; they always alternate when more data is added (see Lupton (2014a), p. 6 f). They are open for re-configuration by adding new parameters of the person and therefore they are open for unlimited purposes of interpretation (see Lupton (2014a), p. 6 f). In this process, the self-tracker finds him or herself in a feedback loop. More data is added to produce a more precise data double to reflect upon in order to act upon. The adapted behaviour adds to the data double and will be reflected again (see Lupton (2014a), p. 6 f). This simplified version of the user’s action serves the basic need of self-tracking; the im­proved understanding of oneself.

This interplay between the generation of data through acting and the reflection on this gen­erated data make the individual tracker become a “prosumer” (see Heyen (2016), p. 238 ffi), a hybrid figure who combines the production and the usage of the same good (see Heyen (2016), p. 239). In this context the self-tracker does not refer to existing knowledge to act upon. Instead, he or she rather generates new knowledge, which optimally relates to himself or herself (see Heyen (2016), p. 240).

2.4.2 Curiosity & Social Comparison in the Self-Tracking Context

The user-friendly interaction of self-tracking devices and the entertaining visualisation of data bears potential for the integration of self-tracking habits into the daily routine motivated by curiosity. Besides other reasons (interest in data, affinity to technology, suggestions and trigger events) for many users curiosity in the quantification of daily habits remains a major reason for starting self-tracking.4 For many users who do not see themselves as part of the see Sjöklint, Constantiou, Trier (2015), p. 7); Whooley, Ploderer, Gray (2014), p. 154; Li, Dey, Forlizzi (2010), p. 4 quantified-self community curiosity is one of the prior reasons for integrating tracking pro­cesses into their trainings routines. Different from what one would expect, generally the motivation for engagement in tracking is not related to the users’ prior experience with spor­tive activities (see Shin, Cheon, Jarrahi (2015), p. 2).

Baumgart adopted the idea that curiosity acts as a motivational factor to engage in self­tracking (see Baumgart, Wiewiorra (2016), p. 5 f). In their study the users’ degree of curi­osity to engage in self-tracking was shown to have a positive influence on those parameters tracked, that refer to physical activities as well as to parameters referring general well-being (see Baumgart, Wiewiorra (2016), p. 7 ff). Lots of findings related to self-tracking corre­spond to curiosity, people are curious about their own performance relative to other (see Rooksby et al. (2014), p. 1170 f). The main mode of self-tracking so called “private track­ing” is mainly driven by curiosity (see Lupton (2014b), p. 5 ff.). Self-tracking is one way to for the users to learn more about themselves. Therefore, in this study it is assumed that trait curiosity has an influence on the perceived value from the data generated by tracking phys­ical activities.

Social comparison is one motive for sharing data online especially for tracking physical ac­tivities (see Oehrl (2016a), p. 1). Self-trackers share their data with two main goals: (1) to see their improvement relative to others in the community and (2) to see their improvement relative to their former performance (see Oehrl (2016a), p. 1). Self-improvement and social comparison thus seem to be inseparable practices (see Taylor, Neter, Wayment (1995), p. 1281 f). Self-trackers compare their status quo with their goals or their standards in order to gain knowledge about their present performance (see Gimpel, Nißen, Görlitz (2013), p. 8). To understand their status self-tracker need to compare their status “[...] within a respective environment” (Gimpel, Nißen, Görlitz (2013), p. 8). However, findings from Lomborg sug­gest that self-trackers only engage in comparison with others if they already have a certain expertise in sport (see Lomborg, Frandsen (2016), p. 1022). Here self-trackers “[...] express great joy when indulging themselves with the data in search for [...] competitively oriented comparisons with other users” (Lomborg, Frandsen (2016) p. 1022). The ability of compar­ing one’s performance with the performance of others is a main feature of self-tracking due to the social aspect within self-tracking communities. Runtastic, Strava, Fitbit and Nike+ are all examples of closed communities for people who track their activities with help of apps or devices. People share their experience and communicate with their peers withing the com­munity (see Lomborg, Frandsen (2016), p. 1023 f).

Furthermore, the common metrics of performance allow the rational comparison of the own performance (see Till (2014), p. 454). There are two ways users can compare themselves to others: Aggregated data within the communities allows users to either compare their perfor­mance directly with the individual performance of others (see Lupton (2014a), p. 2), or with the average of all people in their age or gender within a community (see Mämecke (2016) p. 117 f). As there is such a vast amount of data openly accessible, every user is able to choose who to compare to and therefore is able to engage in downward comparison with users who perform worse as well as upward comparison with users who perform better.

The possibilities to compare oneself with the community by the degree of achieved successes in the training suggests that social comparison is an important issue for the self-tracking community. Therefore, in this study it is assumed that the degree an individual is willing to engage in social comparison has an influence on the value given to data generated by track­ing physical activities.

2.4.3 Data as Achievement & The Task Value

Self-tracking devices have the ability to make training progresses transparent to the user. They make it easier to recognize if a certain goal is achieved or not. This holds especially for parameters that are easily controllable for the user e.g. tracking physical activities (see Baumgart, Wiewiorra (2016), p. 4 f). The outcome of tracking physical activities can di­rectly be influenced by the user by engaging in the activities to be tracked. The correspond­ing data that is generated by tracking physical activities is the outcome of the activity itself. The data can only be achieved by engaging in this activity. Achievement is defined as “a result gained by effort” (Merriam-Webster (2017a)) and this holds for data generated by physical activity as well. On the one hand, the data itself can be seen as achievement regard­ing the definition provided above. In an interview among users of self-tracking devices con­ducted by Sjöklint one user confirms this assumption by referring to the data as “[...] my personal achievement” (Sjöklint (2015), p. 173). On the other hand, the data can be analysed or aggregated to generate achievements. Fitness trackers often generate achievements for the users in form of badges users can earn after they have reached a predefined goal in their training. While some of these achievements or goals are preset by the devices, others can be set by the users themselves (see Sjöklint, Constanti ou, Trier (2015), p. 3). As mentioned the achievements generated through tracking can be a single reason for the engagement in track­ing (see Rooksby (2014), p. 1168).

Based on the evidence that the data generated from tracking physical activities can be seen as an achievement for the users, this study uses the achievement value theory by Eccles (see Eccles et al. (1983), p. 75-146) to gain a deeper insight into the valuation of tracked data for the users. Eccles built an expectancy - value model (see Fig. 2.1) to study students’ achievement performance and the choices taken in an academic context. The aim of this model was to understand the behaviour of students to take a certain course. Her team intro­duced this model for achievement behaviour to understand why students choose a math course (achievement) in school (see Eccles et al. (1983), p. 78). To gain a deeper insight into the students’ motivation behind their behaviour a variety of measures was assessed (see Fig.2.1).

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Figure 2.1: The Expectancy-Value Model

However, this paper focusses only on the part concerning the task value of the model to further operationalise the value of data in a self-tracking context. The part of the model con­cerning task value or the achievement value consists of four components (see Eccles et al. (1983), p. 89 f): (1) attainment value or importance, (2) intrinsic value and (3) utility value or the usefulness of the task. (4) Cost refers to the opportunity costs e.g. the activity, its emotional costs and how much effort it will take to accomplish the desired activity (see Wigfield, Eccles (2000), p. 72 f). Eccles limited his study to only the first three components and defined them in detail (see Eccles et al. (1983), p. 89 f): The attainment value includes a variety of dimensions. Eccles defined it as the “perception of the task’s ability to confirm salient and valued characteristics of the self [... ], to provide a challenge, and to offer a forum for fulfilling achievement, power and social needs. The perceived qualities of the task deter­mine its attainment value through their interaction with an individual’s needs and self-perceptions.” (Eedes et al. (1983), p. 89). The intrinsic value corresponds to the inherent, im­mediate enjoyment tasks generate (see Eedes et al. (1983), p. 90). The utility value of a task is determined by the use of the task for future goals. These might not be directly related to the task itself (see Eedes et al. (1983), p. 89 f). Adopting this concept to the context of self­tracking, the task in this case would be the tracking of physical activities. This procedure is directly linked to the activity itself. Without the engagement of the user in the activity, track­ing would not be possible. This task will produce the achievement, in this context the data. However, for a further understanding of the value of tracking this paper uses the three di­mensions of the task value to provide insight into the value of the data generated. Chapter 5.2 will provide further insights into the methodical generation of the items that correspond to the task value.

3 Theoretical Background: Psychological Concepts

This paper will now focus on three psychological aspects to further understand how they influence the users’ perception of value in data. In this section, a deeper understanding of trait curiosity and its relevance for the users’ valuation of data is provided. The engagement in online communities and the rational perception of data facilitate the users’ desire to com­pare themselves among each others. For a better understanding, it is focussed on the psycho­logical motives that motivate an individual to compare him- or herself with others. Further­more, the influence of the users’ personality on the value of data is examined.

Big Five Personalities

The Big Five also known as the five-factor model of personality, is a universal model de­scribing the human personality and psyche. Based on the lexical hypothesis, an assumption where personality traits correspond to the language spoken (see Asendorpf (2007), p. 153 ff ), nowadays the five-factor model is a highly approved and well-researched model in personality research. The model assumes that human personality can be described within five distinctive factors (see Table 3.1). The table only corresponds to the personality traits and its’ attributes and its adjectives which refer to these personalities. However, the accord­ing personality traits can also be described with converse attributes (see Parks-Leduc, Feld­man, Bardi (2015), p. 4): People who score low in openness tend to be close-minded, shallow and simple. Whereas a low score on agreeableness is associated with egoistical, rude and hostile. A low score on the extraversión trait refers to an introvert, shy and reserved person. Irresponsibility, carelessness and laziness are traits that can be found in people scoring low on consciousness. Neuroticism refers in its trait to an emotionally instable person; therefore, a low score on this trait refers to a self-confident, stable and calm personality. The agreea­bleness trait in the five-factor model refers to the social portion of human behaviour (see McCrae, John (1992), 178 f.). Since self-tracking is a practice that is mostly done self-pur­pose, in order to improve the self or gain self-knowledge through the data, in this paper it is assumed that self-tracking is mostly about the interaction between the own digital self (data double) and the user in order to gain control over the actions (see Ruckenstein, (2014), p. 80). By monitoring one’s own activities users become an insight into how it is their actions result in rational appraisable outcomes (see Lupton (2013), p. 29). Through the data, the users gain power over their action and over themselves, which also refers to the “sensing” experience described by Lomborg (see Lomborg, Frandsen (2015), p. 1022). A low level of agreeableness refers to a more egocentric person and is often associated with personal values that refer to power and achievements (Roccas et al. (2002), p. 769). This focus on values associated with dominance makes sense in the context of self-tracking as here great power over the self can be demonstrated by reaching certain goals.

Abbildung in dieser Leseprobe nicht enthalten

Source: McCrae, John (1992), p. 178 f.

Table 3.1 ะ Overview: Big Five Personalities


Curiosity is defined as the “desire to know” (Merriam-Webster (2017b)). Loewenstein summarized the historical understanding of curiosity into three dimensions: (1) “[...] curi­osity was seen as an intrinsically motivated desire for information” (Loewenstein (1994), p. 76), (2) “[...] curiosity was viewed as a passion, with the motivational intensity implied by the term” (Loewenstein (1994), p. 76) and (3) “[...] curiosity was seen as appetitive” (Loe­wenstein (1994), p. ๆๆ)- as an appetite for information. This appetite stems from the infor­mation gap, defined as the difference between what the individual knows and what remains unknown (see Loewenstein (1994), p. 87 f.). For a further understanding of this information gap Loewenstein introduced the “Informational Reference Point”, defined as the degree of information what one’s want to know (see Loewenstein (1994), p. 87). Curiosity arises in this domain if the “informational reference point” is greater than the current level of knowledge (see Loewenstein (1994), p. 87). It is assumed, that the more an individual learns, the greater the discrepancy between the current status of knowledge and the unknown will be, which will result in a greater degree of curiosity (see Kang et al. (2009), p. 963). How­ever, if the degree of current knowledge approaches the “informational reference point” the curiosity in this topic shrinks (see Kang et al. (2009), p. 963).

Based on the theory of the information gap and the resulting rewarding feeling of high curi­ous information Kang hypnotised that the striatum is linked to the feeling of curiosity (see Kang et al. (2009), p. 964). The striatum is an important part of the brain, which has been associated with the rewarding system (see Wicht (2011)). In their study, Kang found out that provided information that is associated with high curiosity results in a high activity in the areas of the brain, which are associated with rewarding feelings (see Kang et al. (2009), p. 966 f). Based on this study Kang conducted an additional study where the participant were able to either spend tokens to buy information referring to a question that they are curious about or could spend time waiting to get the information (see Kang et al. (2009), p. 970 f). They found out that participants spend more tokens or waited longer for information that they are curious about (see Kang et al. (2009), p. 970 f). It is thus assumed, that the degree of curiosity about certain information has an influence on the value of information that the individual is seeking.

Building on these past findings Marvin further developed the idea behind the value of in­formation as the consequence of curiosity behaviour (see Marvin, Shohamy (2016), p. 266­271). In her study, she operationalized the value of information by the time people waited for information. They found out, that those participants, who showed high curiosity for the information waited a significantly larger period of time for the information to be revealed (see Marvin, Shohamy (2016), p. 269 f.). These findings correspond with the study by Kang.

Social Comparison

Social comparison is a universal tool to acquire self-knowledge (see Gibbons, Buunk (1999), p. 129). The comparison to other is motivated by three factors: (1) ’’Evaluation”: By devel­oping knowledge by comparing the own ability with others or to evaluate the own believes by comparing them with other opinions (see Festinger (1954), p. 117 ff). (2) “Improve­ment”: People compare themselves to others who are better of in upward comparisons in order to improve themselves, (see Taylor, Lobel (1989), p. 572 f). (3) “Enhancement”, to build up their self-esteem, people compare themselves to others who are worse off using downward comparison (see Taylor, Lobel (1989), p. 572 f).

As mentioned before, people engage in self-tracking in order to gain knowledge about them­selves and to improve certain aspects of their own behaviour. Both motives correspond to the act of social comparison. In order to improve themselves, people engage in an upward comparison where they compare themselves with others that are better than they themselves are, which can result in motivation or inspiration (see Taylor, Lobel (1989), p. 572). This upward comparison may result in a competitive situation (see Garcia, Tor, Schiff (2013), p. 635). Competition is a major factor in sports, which is also embedded in self-tracking com­munities, where self-tracker are able to measure their performance on similar tracks with others. Furthermore, data can serve as a self-esteem booster if users engage in downward comparison (see Taylor, Lobel (1989), p. 572 f).

The evaluation process of social comparison with the aim to gaining knowledge about the self relatively to others is based on two concepts: abilities and opinions (see Festinger (1954), p. 117 f). Abilities in this context refer to the performance of a person, which depends on a certain ability. Festinger mentions the problem to define criterion for comparing the own performance to others (see Festinger (1954), p. 117). Self-tracking solves this problem with its understandable metrics for performance in sport. The understanding of the performance visualized by self-tracking data is universal (see Till (2014), p. 454). These tangible metrics and the database of past performances of other users help to gain knowledge about the self and answer the related question “How am I doing?”. Opinions on the other hand, refer to the persons’ intangible cognition about situations, which will be not relevant in the study since self-trackers use tangible data to compare themselves with others (see Festinger (1954), p. 117).

4 Research Model and Hypothesis Development

This paper examines the influence personality traits has on the perceived value of tracking data from monitoring physical activities. This study solely focuses on the tracking of physi­cal activities since in this context the user is directly involved in the activity and can take complete influence on the outcome (see Baumgart, Wiewiorra (2016), p. 4 f). Therefore, the involvement is assumed to be greater in these kinds of activities in contrast to when other parameters are tracked. The research question refers to the valuation of physical activates and the perceived loss by not tracking these activities. To examine the value this paper dif­ferentiates between activities that were tracked and activities that remained untracked. Since both activities remain the same, in this study it is assumed that for the person engaging in self-tracking the main difference between these activities lies in the information generated through tracking and therefore the added value users feel. This paper focuses on curiosity and social comparison as drivers for the valuation of data. Theoretical evidence for this was provided in chapter 2.4, 3.2 and 3.3. Furthermore, this paper examines the influence of per­sonality traits on the valuation of data; chapter 2.4 and 3.1 provided the corresponding the­oretical constructs.

The loss perceived when not tracking one’s own activities is an emotional result of the sub­jective value participants give to their data. The higher the perceived value of the data is, the higher must be the perceived feeling of loss of these data.

Hypothesis 1: The subjective value of tracking data has a positive effect on the per­ceivedfeeling of loss due to accidentally not tracking the activity.

The intensity of self-tracking has an influence on the cost users willing to accept for self­tracking devices (see Baumgart, Wiewiorra (2016), p. 10 f). Intensity plays an important role for the valuation of self-tracking devices and the motivational factors influence the in­tensity (see Gimpel, Nißen, Görlitz (2013), p. 10 f). In this paper it is assumed that the level of intensity takes influences on the valuation of self-tracking data.

Hypothesis 2: The level of intensity has a positive effect on the perceived subjective value of data generated through tracking ofphysical activities.

A main incentive for engaging in self-tracking lies in users’ natural curiosity, which makes them want to gain more knowledge about the themselves.5 Therefore, it is assumed that the global degree of the user’s curiosity has an influence on the value given to the data.

Hypothesis 3: The level of trait curiosity has a positive effect on the perceived sub­jective value of data generated through tracking ofphysical activities.

The communal aspect of tracking in groups, where there is a high level of transparency for personal data, encourages users to engage in comparison to learn about the relative perfor­mance (see Lomborg, Frandsen (2016), p. 1022). The neutrality and objectivity of data in form of universal metrics can act as a common ground to compare the one’s own perfor­mance to others. Therefore, it is assumed that the degree of users’ desire to enter in social comparison has an impact on the value attributed to data.

Hypothesis 4: The degree of participation in social comparison has a positive effect on the perceived subjective value of data generated through tracking physical activ­ities.

Within the scope of the five-factor model, the trait of agreeableness is positively associated with personality traits like being social, altruistic and kind (McCrae, John (1992), 178 f). Furthermore, this trait is negatively associated with egocentrism (see Parks-Leduc, Feldman, Bardi (2015), p. 4) characterized by a person’s valuing of power and achievement goals (Roccas et al. (2002), p. 769. Therefore, this work expects a negative influence for the trait of agreeableness on the valuation of self-tracking data.

Hypothesis 5: The level of agreeableness has a negative effect on the perceived sub­jective value of data generated through self-tracking devices.

see Baumgart, Wiewiorra (2016), p. 10; Shin, Cheon & Jarrahi (2015), p. 2; Li, Dey & Forlizzi (2010), p.4


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Untracked activities. How users of fitness trackers value their generated data
University of Frankfurt (Main)
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Wearables, Consumer Experience, Empirische Arbeit, Empirical, Self-Tracking, Quantified Self, Lifelogging, Apple Watch, Fitbit, Fitness-Tracker, Tracker, Fitness, Bachelorthesis, Curiosity, Social Comparison, Big Five Personalities, BFI-10, CEI-II, INCOM, Neugierde, Sozialer Vergleich, Big Five, Runtastic, Psychology, Psychologie, Expectancy Value Model, Value of Data, Feeling of Loss
Quote paper
Peter Andres (Author), 2017, Untracked activities. How users of fitness trackers value their generated data, Munich, GRIN Verlag, https://www.grin.com/document/368021


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