Exploring Quantified Self Attitudes

Christel De Maeyer

1,2

and Panos Markopoulos

Department Graphical Digital Media, Artevelde University College, Mariakerke, Belgium

Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands

Keywords: Quantified Self, Self-tracking, User Design Typology.

Abstract: In recent years there is a growing optimism that health interventions may become more effective through

the use of self-tracking. Related efforts are hampered by the short-lived compliance to self-tracking

schemes. This paper examines the attitudes and motivations of self-trackers that could guide the design of

self-tracking applications. Based on a questionnaire survey and follow up interviews a set of three personas

of self trackers is proposed, in addition, design requirements are proposed for improving adherence to self-

tracking technologies.

1 INTRODUCTION

Wearable self-tracking technologies are often put

forward as a way to help ensure healthy living and to

enhance active participation of patients in

healthcare. However, sustained use of self-tracking

technologies as part of health intervention programs

remains a major challenge (Fritz, 2014), (Karapanos,

2015). A commercial study shows that ‘more than

half of U.S. consumers who have owned a modern

activity tracker no longer use it, and third of U.S.

consumers who have owned one, stopped using the

device within six months of acquiring it’ (Endeavour

Partners, 2014). Karapanos, argues that in the case

of physical activity trackers ‘the disengagement can

signify two outcomes: failure to integrate exercising

into daily life or a swift adoption of exercising as an

intrinsically motivated practice’ (Karapanos, 2015).

On the other hand, there is a whole population of

users who track themselves for years and where the

activity is ingrained in their daily life (Fritz, 2014).

They are actively engaged in the usage and the

whole experience, evolving from beginners towards

avid personal informatics users who have integrated

self-tracking into the fabric of their daily lives

(Darmour, 2013). Long term self-trackers seem to

value the on-going support and motivation these

technologies trigger for having a durable change in

their lives (Fritz, 2014). It appears that a better

understanding of long-term self trackers could be

valuable in designing technologies to support self-

tracking practices.

Most human computer interaction research on

the topic of how people use wearable technologies

follows the pattern of field testing a certain system

and investigating how people interact and behave

with this technology. Few studies, have examined

such use ‘in the wild’ and engagement with such

applications remains a challenge (Karapanos, 2015),

(Vandenberghe, 2015), (Shih, 2015). For example,

Karapanos et al, built a mobile application ‘Habito’

and had 256 participants use the application

voluntary. As they remark: ‘However out of the 86

users who installed a physical activity tracker that

we deployed on Google Play, only 21 percent of

them used it for more than two weeks’ (Gouveia,

2015), (Karapanos, 2015).

In order to get more in depth information about

the process of the evolution of personal informatics

users and what triggers them to create a long term

relationship with their device and its services, we

conducted a survey among long term self-trackers

and, secondly, held in depth interviews. Based on

these studies we propose Personal Informatics User

Design Personas that gives new perspectives and

insights for future design and development of self-

tracking technologies aimed at supporting behavior

change.

2 RELATED WORK

There is already a large body of research concerning

the use and none use or abandonment of self-

tracking activity (Gulotta, 2016) (Epstein, 2016),

Maeyer, C. and Markopoulos, P.

Exploring Quantiﬁed Self Attitudes.

DOI: 10.5220/0006530802530260

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 253-260

ISBN: 978-989-758-281-3

253

(Schwanda, 2011). The motivations as to why

people are tracking themselves and the impact it has

on human behavior (Lazar, 2015).

2.1 Motivation and Feedback

As Shwanda argues, feedback loops are an important

element in getting motivated or not. As some users

experience it as a successful motivator, mainly when

the system gave positive feedback, but also when the

feedback was a bit harsh. Additionally, they liked

the nudging of the system. While others find it hard

to interpret the information and furthermore also

hurtful when there is negative feedback (Schwanda,

2011). The feedback we receive from those

technologies are very blunt and don’t show any

empathy at all, as Ruckenstein states “In light of

feedback loops, people are approached as computer-

like information processors, or auto-correlating

servomechanisms, a living part of a dataistic

apparatus that allows the reflection and regulation

of specific movements and behavior” (Ruckenstein,

2015, p. 10).

From a health perspective, a study from (Gimpel,

2013) shed light on the motivation of patients who

track their own health. This study proposes a

framework of five motivational factors.

Factor 1: Self-entertainment: motivation because

of the pleasure it brings to the self-tracker, this

motivation lies into the aspect that the user has fun

and enjoyment in using these digital devices.

Factor 2: Self-association: the prospect of being

associated within a community, ‘community

citizenship’. This is less about one’s self, but more

‘how one relates to a community or understanding

her or his individualization within a certain

environment, self-individualizing aspects within the

community. The idea that a self-tracker needs a

counterpart to understand him or herself mainly by

comparison’.

Factor 3: Self-design: motivation by the

possibilities of self-optimization. Self-trackers are

interested in controlling and optimizing their life,

whether they track mood, physical activity or other

tracking aspects of their daily life.

Factor 4: Self-discipline: motivation due to self-

gratification. The self-tracker is more motivated by

the prospect of achieving certain goals, getting

rewarded, or not being penalized and avoiding

negative consequences.

Factor 5: Self-healing: motivation by the

possibilities of self-healing. The self-tracker doesn’t

have a lot of trust in the current health system, has a

sort of rebellion attitude towards health systems.

They want to have a certain independence from

traditional health care systems (Gimpel, 2013).

These perspectives are an important aid in

clustering our results and in defining the personas

we sketch in this paper.

Furthermore, to trigger behavior change one also

needs to look at the potential of ‘Network

Interventions’ (Valente, 2010), using network data

to ‘influence’ or ‘accelerate’ behavior change.

Valentine reviews six classes of methods that can be

used on order to influence and accelerate behavior

change: opinion leaders, groups, leaders matched to

groups, snowball methods, rewiring networks, and

crossing network data with attributes. The method of

groups and leaders matched to groups, are methods

that rose during our in-depth interviews. In addition,

in a study by Schmueli, et al, shows that “trust has

significant more impact on social persuasion than

closeness of ties in determining the amount of

behavior change” within ‘network interventions’

(Shmueli, 2014, p. 14).

2.2 Stages within the Usage of Personal

Informatics

In order to get a longitudinal perspective in the use

of personal informatics, (Li, 2010) proposed a stage-

based framework that illustrates the five stages a

user goes through when using Personal Informatics

systems (preparation, collection, integration,

reflection, and action) and identified the barriers that

they might experience during these stages.

In the preparation phase, the user is thinking

about which devices to use, what they are going to

track, which data they are going to collect. The

barriers in this stage are: giving the devices the right

information on the collecting data, do they have to

switch to other tools and what are they doing with

the previous data that has been collected so far.

The collection phase is the phase where people

are observing different personal information they

gathered, they get insights on their usage of the

collected data, might notice a certain behavior, their

interactions with others. The barriers here are that

people forget about collecting data and do not

review all the information that is available. The data

is not always accurate or objective.

The integration phase, is the phase where the

users integrate their different collected data. The

data might come form different sources, and some

put it all in excel to get a more holistic view of their

collected data. The barriers of this phase, is mainly

the interoperability between all these different tools

of self-tracking. The reflection phase is the phase

where users reflect on their data, today most systems

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give immediate information on the device itself and

the second information source, which usually more

detailed is gathered on a website. With the first

information source (short look), usually creates

immediate awareness with the user on their

behavior. The barriers in this phase, not all users can

give meaning to their data, due because of lack of

time, not the ability to have a holistic view of the

collected data or technical limitations. Finally,

during the action phase users will use the personal

information to change behavior, to set goals or

adjust goals set during the previous process.

(Epstein D. , 2015) proposed a model for lived

informatics for personal informatics, comprising of

three stages, initially starting with the decision to

track and decide on the selection of tools to track.

Choosing or deciding to track oneself could be for

many reasons and include: to improve one’s health,

to improve one’s lifestyle or to find a new life

experience/activity (Choe, 2014). Deciding on the

selection of tools involves comparing devices or

mobile apps such as Runkeeper (running mobile

application) or Human (a physical and calorie

tracker) or decide to wear a wristband such as Fitbit

or Jawbone to name a few options.

Stage two relates to the ‘tracking and acting’

process which is ‘an ongoing process of collecting,

integrating and reflecting’ (Epstein, 2015), (Choe,

2014) notes three activities; ‘collecting, integrating

and reflecting’ which are distinct and dependent

upon data. Self-trackers learn about their behavior

during the process of collecting and monitoring the

data, “the main importance however, is to get

meaningful insights and reflect on data to make

positive change” (Choe, 2014, p. 10).

Stage three relates to the ‘lapsing stage’, which is

associated to individuals/users who choose to stop

self-tracking for a set amount of time or completely.

Based upon recent research the dropout rate is quite

high for several reasons, including: technology

failure, lack of interest, curiosity is gone, or the cost

of tracking in terms of time (Endeavour Partners,

2014), (Fritz, 2014), (Karapanos, 2015), (Shih,

2015) (Epstein, 2016).

Finally, there is ‘the resuming phase’, these can

be short breaks, where a self-tracker has gone on

holiday and forgotten to take their wearable device

or they choose to have a longer break. In the latter,

the self-tracker might start again by reflecting first

on the older data, and then decides later to start

tracking again and collecting more data depending

on the tracking activity (Epstein, 2015).

Both Li and Epstein looked at the usage of

Personal Informatics from a user perspective and the

different phases a user goes using these

technologies. Epstein added and refined the stage

model by adding ‘lapsing’ and ‘resuming’ to the

tracking. Considering the challenge of people not

managing to sustain self-tracking for long, we argue

that resuming has a specific importance for long-

term self-tracking. It helps the user to recollect

previous information of the system that has been

gathered before, to evaluate, to look for confirmation

that they achieved in certain goals they have set.

The user can pick up where they left off, set

different goals, sometimes higher goals, or

sometimes in a different way, in a different routine.

They can compare the past achievements with the

new information when picking up a certain activity

again and working towards their personal best result.

In addition, there is also the notion of the need of

tracking, long-term user, use the past gathered

information to motivate themselves again to start a

new. The need to track themselves to be able to

follow and evaluate their progress in a specific

program or activity they have setup.

Next we will look at the personal informatics

from a sociological lens where researchers have

proposed typologies to define Personal Informatics

users.

2.3 Types of Self-trackers

Taking a sociological perspective Lupton argues that

‘The practices, meanings, discourses and

technologies associated with self-tracking are

inherently and inevitably the product of a broader

social, cultural and political process’, (Lupton,

2016). Lupton underlines the sociological dimension

of self-tracking, distinguishing five types: Private,

Communal, Pushed, Imposed and Exploited. Here

we focus mainly on the private and communal

modes of self-tracking, as the users we interviewed

and surveyed are using the devices or tools by

choice. In a private mode, self-tracking is mainly a

private activity by one’s own choice, where at the

communal mode, one shares tracking results within

a community or others like family, friends and so

forth. The remaining modes are not by choice and

are a main concern in the whole movement of self-

tracking: pushed, imposed or exploited self-tracking.

It is known that the data we gather can also be used

by others, as a surveillance tool or for commercial

reasons (Lupton, 2014). Furthermore, pushed and

imposed modes, are increasingly a concern as

Personal Informatics enters the workplace, and the

insurance space, where it is used as an incentive to

stay healthy or to personalize insurances. These last

three modes are therefore important user design

aspects from a design ethics (Cummings, 2006)

perspective. In all of these modes, a Value Sensitive

Design (Cummings, 2006) should focus on

Exploring Quantiﬁed Self Attitudes

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supporting human values such as well-being, welfare

and human rights, trust, autonomy, data ownership,

privacy, freedom from bias, accountability, for these

tracking technologies.

Selke, categorizes self-tracking technologies in

four basic categories according to usage: First,

monitoring health, monitoring biometric real time

data on one’s body and in doing so creating a

healthy lifestyle. Secondly, human tracking,

concentrates on location tracking, mainly about the

whereabouts of a person. Thirdly, human digital

memory, outsourced memory, a comprehensive

archive that document’s one’s life in detail. Fourth,

surveillance and counter-surveillance, the

relationship between the two, monitoring –

surveillance. For example, in the workplace this

surveillance is becoming a common activity, as well

as counter-surveillance where people broadcast their

lives as an alibi and give complete transparency on

there whereabouts (Selke, 2016).

In other recent work ( (Seshagiri, 2016) suggests

four personas that were matching user profiles in

their research on personal fitness and health in India.

These four personas are based on the needs and

expectations of the user considering their lifestyle

and fitness goals in their lives. The four personas are

divided in age groups, the competitive beginner age

group 20-30, they spent a lot of their time on social

media and look for competitive environments. The

majority of these personas are driven by social

approval and physical appearance. They rely on their

social circles to get motivated to start a fitness

activity. The passive practicioner is in the age group

35-45, and usually settled with family and kids and

start to have minor health problems that drive them

to be more active and engage in fitness programs.

The challenge seeker, are long-time fitness

practicioners, age group is 30-40. Fitness is part of

their daily lives. They change their goals on regular

basis after achieving previous goals. They find

measuring devices less usefull overtime. The active

reviver, is the persona that wants to start fitnessing

again after a long break. Usually they find new

motivation with friends and family to start again on

their fitness activity. They want feedback from the

apps or devices they used based on their previous

usage and results gathered by these apps.

The four personas suggested by (Seshagiri,

2016), were defined from a lifestyle perspective, we

try to bring something new in looking more at the

personalities or attitudes users have in order to come

to our three personas. Additionally, they are defined

based on age groups. As in our proposal age groups

are not a peculiar thing we looked at, although it was

one of the filter questions in our survey, it was not

an argument to define the personas.

3 RESEARCH METHODS

A survey was carried out among users of wearable

self-tracking technology during a period of 4

months, from January through April 2016. The

distribution of the survey was done through social

media, namely Facebook, LinkedIn and e-mail, with

a snowball approach (N=95). We used (Qualtrics) as

an online survey tool. The survey was not posted in

the Quantified Self community because these avid

enthusiasts are probably not representative of the

broader cross section of self-trackers, arguably

forming a distinct sub-culture. As previous described

as a specific group by (Choe, 2014) within the

Quantified Self movement today personal

informatics is used by people with a specific profile,

such as, software engineers, startup founders, data

analyzers, measuring mainly physical activity, food,

weight, sleep and mood and would very likely be

more motivated to participate than the average self-

tracker, potentially skewing results.

Additionally, we held in depth interviews (N=10)

with respondents who had been tracking themselves

for an average (mean) of 4,8 years with a standard

deviation of 4,29, with wearable devices, smart

watches or mobile apps on smartphones as these

were the most used and preferred tracking methods

within the survey outcome. We used the laddering

method (Reynolds, 1988) to structure the interviews

as this method would give insight on tracking

method Attributes (A), Consequences (C) in using a

certain technology and personal value (V)

respondents experience in appropriating Personal

Informatics technology. The interviews were

transcribed and we used the model above to map the

patterns that occurred in the in depth interviews,

which we will discuss in the next section.

4 RESEARCH RESULTS

4.1 Pre-survey

The survey was distributed over a period of 4

months with a snowball sampling approach, which

resulted in 95 responses (which was less than our

expectation). 15 respondents reported to have

stopped tracking in last 12 months and were then

excluded from the survey, those respondents could

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not finish the survey, they only could add the reason

why they stopped tracking themselves. The

remaining sample of 70 participants (49 men, 31

women) who participated in the survey was

relatively skewed with regards to the education

level, 80,2 % having a PhD or Masters degree, 16,3

% enrolled in higher education and only 2,5 %

having a high school degree only. The sample is

rather skewed towards younger ages: 55 % is

between 20-30 years old, 30-40 age group represents

26,3 %, the older age group 40-50 and 50-60

represents 8,8 % and 1,3 % is >60.

Out of the 70 respondents, 15 have stopped as

mentioned in the previous paragraph, in the last 12

months for a variety of reasons (see table 4):

• They track when things are going well

• Practical issues such as losing the tracker or

forgetting to charge it

• The effort and time required

• Not wanting to share in social media

anymore

• Obtrusiveness of the technology

We were particularly interested in the reasons

why self-trackers track themselves, what they track

and which tools or devices are they using to track

themselves ‘Out of curiosity’ is the major reason

why this group of respondents starts tracking

themselves 36,2%. The second reason is leisure

30,5% and third is health reasons 21%. The most

popular tracking activities are exercise and steps

55,9%, followed by sleep 14,5% and heart rate

13,8%. Mobile apps on smartphones are the major

tool for tracking themselves 42,3%. Followed by

wearable tech such as Fitbit, BodyMedia, Jawbone

and so forth 32% and the smart watches 16,5%

Approved medical devices and spreadsheets are a

minority use 9,3 % for tracking oneself.

Respondents (91,5 %) claimed that the wearable

devices created awareness. In addition, the

respondents (89,8 %) agreed that the data generated

by the wearable devices gives awareness.

Furthermore, it was stated that the wearable devices

are helping in creating habits (81,3%). Results also

showed that the wearable devices helped in

maintaining habits (77,9%).

We run a Pearson correlation test to test whether

there is more engagement at a certain age in creating

and maintaining habits. Our results show a

significant positive relationship between age and

creating habits [r(52)=.29; p<.0.038] and

maintaining habits [r(52)=.627; p=.000].

Within our survey we want to get different

insights on how users feel while using these

technologies. As we seen in related work, emotions

and motivation are connected and users feel

sometimes controlled, guilty and can become

stressed about using self-tracking. Feelings of being

controlled can be seen both in a positive or negative

light (Epstein, 2016), (Schwanda, 2011). When

using these digital devices to monitor one’s health,

users feel more in control of their own health and

see a lot of benefits in using these digital devices in

achieving their goals and getting useful insights on

their behavior. Other users experience self-tracking

as extra pressure, leading to more anxiety, failure

and even self-hatred (Lupton, 2012).

50,9% feels controlled, 13,8% feels stressed,

50,6% feels motivated by the device. There is a

significant negative linear relation between the

feeling (motivation and stress) that is induced by the

device. The more motivation the less stress. [1(52) =

r =- 0,33, p = < 0,016].

Most respondents don’t share their data on social

media 64,4%; 13,6% share their data with friends

and family, 10,2% share on social media while 6,8%

share in specific interest groups related do their

tracking activity. This finding is inline with (Fritz,

2014), (Epstein D. J., 2015) regarding the sharing

data and social effects, where respondents mostly

share in specific communities related to their

tracking activity. One could connect this to the

theory of ‘Network Interventions’ (Valente, 2010),

where Valente

The results of which tools and what the preferred

tracking topics are with self-tracking users, are in

line with the findings of Choe, et al even though

they surveyed and analyzed a different target group,

the quantified self meet-ups. A new finding within

our survey is that users are not eager to share their

data on social media, but more likely in the special

interest groups and communities within the service

they use. In the results we also see a decline of

usage after 6 months, also this is inline with findings

from Fritz et al (Fritz, 2014), (Gouveia, 2015)

(Karapanos, 2015)

4.2 In-depth Interview

In-depth interviews were conducted with

respondents who filled in the survey to clarify the

above statements and survey results, which we will

discuss in the next section. We divided the in depth

interviews in three levels. First we asked about the

attributes of the products they use. Second, we

followed up regarding the consequences of using a

specific technology and the tracking activity in

general and, third, the values the technology brings

Exploring Quantiﬁed Self Attitudes

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for the users. The results are summarized along these

themes.

Mobile apps on smartphones are experienced as

a major convenience for self-tracking. E.g., about

wearable devices they commented ‘Always on me, I

never forget it, is just tracking things’.

Regarding the use of the products we notice the

distinction between active and passive tracking.

Respondents mentioned that the less they have to log

(passive tracking) the more they like it, just wearing

the device that tracks things as opposed to logging

food or mood tracking, where respondents have to

actually put information in to the application, find

that this is too much work and energy to sustain for

longer periods.

Users stated that using apps on smartphones and

wearable technology are a convenient way to

measure themselves. They tend to track themselves

in periods. Some track themselves continuously by

just wearing the device. This is less so if they use

apps on smartphones, smartphones are not always on

the body and tend to be forgotten occasionally.

Most respondents set goals for themselves. We

can make two distinctions within the goal setting,

the daily goals and the goals that run over a certain

amount of time, depending on the tracking activity.

The daily goals could have the possibility to

integrate in a daily life schedule, contributing

towards a fundamental behavior change, progressing

from extrinsic to internalized motivation and

transformation (Deci, 2000). Examples are sleeping

for at least 8 hours, having a 30-minute activity a

day, walking at least 10000 steps in a day and so

forth. These goals are usually set by the system

rather than by the user. On the other hand, longer

term goals are associated with higher aims, like

running a Marathon for example. Once such a goal

is achieved people stop tracking for a while, but will

reinitiate the activity when they have another life-

goal of that kind. In this process the track-record of

past achievements is an important asset for the user.

Other users track their weight. Goals related to this

take are longer term and require a more

encompassing behavior change to which the user

needs to adapt to.

The respondents who work on physical activity

or exercise love the competition element that some

apps support by connecting with friends and

stimulating each other to go an extra mile.

Especially Strava (Strava, sd) seem to be doing great

work in that perspective: Users can share maps,

routes, and so forth. Such tracking services share

their results within the service but not on social

media, thus creating the feeling of belonging to a

group.

The respondents create some kind of a

dependency upon the tools they use. They spoke of

‘first’ and ‘second’ information that is gathered by

the device or application. First information is

immediately visible on the device or application and

the second information is the dashboard online

where they have a more holistic and view spanning

over time, by week, by month, by six months of their

gathered data. The latter is not viewed often; the

frequency is tied to the activity tracked. Most

respondents would like to see more aggregation of

their data, they experience a data silo effect between

the different services and tools they use and miss

context around their data gathered by different

applications and wearable.

4.2.1 Values

The respondents mentioned that their general goal is

to create a better lifestyle “being healthy aware”

and to be mindful of their lifestyle. Using these

technologies helps them create habits (“we don’t

think about it anymore”) and routines that are

consciously planned and thought about. In most

cases tracking gives confirmation and makes things

‘official’ip4: “if I didn't measure it I didn’t do it”. In

addition, they give the user a track record an

evolution of a certain aspect of their life which

motivates them to continue or resume if necessary:

ip4: “My track record of the past is important to

start again I know I can do this!” For some a

technology dependency is formed, an outsourced

memory that helps to look back:ip3: “I bike more, I

feel much better, things are going easier, I have less

pain in my back which makes me feel better”. In

general, they see it as a digital buddy, ingrained in

their daily life: ip4 “I feel smarter I think this is an

important word. I don’t think about it anymore it is

just there, part of my life”. A few respondents

mentioned feelings of being confronted with the

data. If things are not going well, they tend to ignore

it ip3 “If I would look at the past, I might get

worried, it might not be a good thing”. Or another

participant, ip7: “If things are going well it is ok, but

if I let go, I don’t want to see it. I don’t want a

negative confrontation”.

Using the laddering method, we were able to

cluster self-trackers based on the consequences and

values identified as above. As mentioned before to

build the clusters of self-trackers personas, we

looked at their social behavior, sharing the data, and

why they are sharing data and how and where. How

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they build their self-tracking attitudes, being more

interested in competition for example, setting

different goals to achieve, while in the third cluster

we looked at how they implement the technology in

their daily life, as a sort of digital mirror, trying out

different technologies, and more acting as lead-users

(Von Hippel, 1986).

4.3 Social Self-tracker

Even though personal informatics users in our

research didn’t like to share their data on social

media, they do like to share it within the community

of the application or the device and its platform that

they use. They don’t really have a community

feeling, but do have a group feeling. Social trackers

stimulate each other to do certain activities and to go

for mutual goals. Long-term trackers help early

trackers and stimulate them by liking or commenting

on their status within the group: ip8: “I advise the

use of applications to friends or colleagues and

endorse them in a positive way, because I know how

difficult it is”. The ‘communal mode’ of Lupton’s

Self-tracking mode is referring to this accent of self-

tracking, however it is not mentioning the stimuli

users get from being within a group having a

common interest, trying to achieve common goals.

(Lupton, 2016). While studies of (Valente, 2010)and

(Shmueli, 2014) show the potenital of groups,

leaders of groups to influence or accelerate behavior

change. From the health perspective and motivation

factors, the ‘self-associated’ self-tracker is part of

the social self-tracker. ‘The idea that a self-tracker

needs a counterpart to understand him or herself

mainly by comparison.’(Gimpel, 2013)

4.4 Achiever Self-tracker

The achiever’s main goal is to achieve in the goals

they set for themselves. They enjoy keeping a track-

record to stimulate themselves to start new goals and

to pursue in achieve them. They might track

different aspects of their body to get more context,

however not so much in detail then the next

proposed persona, the immersive type or avid self-

tracker. They are focused on their tracking activity

and also explore different solutions and they might

use them simultaneously. They have a big desire to

compete with others, so they also have interest in

sharing within the application they use. By tracking

themselves they get confirmation The achiever self-

tracker, has elements of the self-discipline self-

tracker, the user is looking for self-gratification,

getting rewarded for achieving goals, and avoiding

negative results (Gimpel, 2013). Additionally, there

are similarities toward the Competitive and

Challenge seeker personas proposed by (Seshagiri,

2016).

4.5 Avid Self-tracker

The avid self-trackers tries to create a digital mirror

of themselves, they are completely engaged and

immersed with self-tracking. It has become part of

their daily life - a norm in their life. They are

building a ‘Human Digital Memory’ (Selke, 2016)

there is an embodiment of the technology. They love

the data and they love analyzing it: ip1:‘It is

important to keep the data stream’. They use custom

tools to get complete overviews or they build their

own overviews ip1:‘every year I start a new file in

Excel, it is like gathering photos’. These self-

trackers are also recognizable in the self-design

factor motivation: ‘interested in controlling their life,

optimize their life, whether they track mood,

physical activity or other tracking activities’

(Gimpel, 2013). Their main goal is to stay aware

about their behavior and keeping up a healthy

lifestyle on constant basis.

5 CONCLUSIONS

We presented a survey and a follow up interview

study that examined the triggers, motivation and

ability that long-term trackers experience whilst

practicing self-tracking. The survey suggests that

mobile apps and wearable devices are the most used

tracking methods. The top three tracking topics are

physical exercise, steps and sleep.

Self-trackers use such tools to create and

maintain habits. Self-tracking users don’t like to

share their data on social media, preferring closed

communities related to the tracking service. Some

people track periodically (in bursts) rather than

continuously as we have seen in the consequences.

They use their self-tracking track record to get up to

speed again. The main goal of these self-trackers is

to stay aware about their lifestyle and health. In

general, respondents aim for a better state of well-

being, a better lifestyle. For some the data collected

gives meaning to their life.

We proposed a user typology for self-trackers.

Characterized by different usage patterns and values,

in addition, we based our user typology on existing

typologies form other domains, from a sociological,

health, lifestyle and usage perspective. Within these

typologies, there is overlap and we find similarities

Exploring Quantiﬁed Self Attitudes

259

in their characteristics. Different types of self-

trackers need to be approached differently by

designers, supporting a different flow in the usage

and feedback loops. For example, within Strava or

Runkeeper users have more detailed data analytics

over time and can receive personal coaching when

they choose for a premium model (paid subscription

fee). These extra features, detailed data analytics,

customized feedback loops, creating group feelings,

peer pressure approach by peer endorsing methods

might create more engagement with the user and is

subject for further research.

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