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Friends in Activity Trackers: Design
Opportunities and Mediator Issues in Health
Products and Services
Yeoreum Lee, Department of Industrial Design, KAIST, Daejeon, Republic of Korea,
yeoreum_lee@kaist.ac.kr
Min Gyeong Kim, Department of Industrial Design, KAIST, Daejeon, Republic of Korea,
kmg6419@kaist.ac.kr
Saeyoung Rho, Department of Industrial Design, KAIST, Daejeon, Republic of Korea,
littlebee6@kaist.ac.kr
Da-jung Kim, Department of Industrial Design, KAIST, Daejeon, Republic of Korea,
dajungkim@kaist.ac.kr
Youn-kyung Lim, Department of Industrial Design, KAIST, Daejeon, Republic of Korea,
younlim@kaist.ac.kr
Abstract
Other people’s reactions, including attention, affection, and reputation, reinforce an
individual’s desirable behaviors. Specifically, this reinforcement has shown effectiveness in
promoting health-related behavioral changes. This is social reinforcement, and the person
who provides it is a mediator. Although products and services that promote health-related
behavior, such as activity trackers, have increased dramatically in the market, little attention
has been given to their social influences, such as social reinforcement from mediators.
Activity trackers collect a log of daily activity from the user and share it with other users
through an application. Naturally, users compare data and compete through the application.
Although users are connected through the activity trackers, the influences differ according to
the different roles of mediators. To reveal the roles and influences of mediators when using
activity trackers, we conducted interviews with 12 participants who use activity trackers to
maintain their health behaviors. We found that the participants classified mediators into
several groups according to their roles and that the participants wanted to have different
qualities in their social interaction with different mediator types. Based on these findings, we
IASDR2015 Interplay | 2-5 November | Brisbane, Australia 2
explored design opportunities and issues regarding the mediators in health promotion
products and services.
Mediator; Social interaction; Activity tracker; Social reinforcement;
Activity trackers are wearable, life-logging devices or applications for capturing, measuring,
tracking, and analyzing data from a user’s daily health activities, including sleeping, eating,
and moving. There has been a recent market rush on various types of activity trackers,
including Nike Fuel Band, Fitbit, and Jawbone UP. Of the activity tracker users Ledger and
McCaffrey (2014) surveyed, more than half no longer used their activity tracker and a third
of those stopped using it within six months of purchase. Since changes in health behavior
take time, it is important to deliver long-term impact on users in designing health products
and services. One strategy that helps users maintain their health behavior changes while
using activity trackers is social interaction, which we explored, focusing on mediators who
provide social reinforcement for health behavior change and their roles, influences,
challenges, and issues.
Social Reinforcement and Mediators
According to Skinner (1953)’s operant conditioning theory, reinforcement increases an
individual’s desirable behaviors, while punishment decreases undesirable ones. Skinner
claimed that reinforcement encourages people to maintain their behavior change for longer
without side effects such as relapse. There are several types of reinforcement according to
the types of reinforcer—the stimuli, events, and situations that increase the behavior. When
the reinforcers are rewards from another person, such as social attention, affection, and
reputation, the result is social reinforcement and that other person is the mediator.
Specifically, the effects of mediators and social reinforcement are displayed in the context of
various health issues, such as weight loss (Pasch et al., 1997). Due to the advancement of
social networking technology and the development of social media, the place where social
interaction occurs has expanded from offline to online (Macvean and Robertson, 2013). As
products and services are connected to a huge ecosystem, multiple potential mediators can
reinforce users in changing their behaviors through various channels (Lee and Lim, 2015).
Related Works
Persuasive Technology in Health Behavior Change
Fogg (2003; 2009) insisted that a computing device using persuasive technology could
create a social relationship between the device and the user. He argued that the device
influences people as a social actor and impacts behavior. Human–computer interaction (HCI)
has many design cases using persuasive technology to change health-related behaviors in
IASDR2015 Interplay | 2-5 November | Brisbane, Australia 3
everyday life (Adams et al., 2014; Macvean and Robertson, 2013). Although those design
cases have shown short-term effectiveness, they barely focus on long-term maintenance.
Such maintenance prevents relapse (Watson and Tharp, 2005) and provides long-term
impacts on users’ health behavior. According to trend reports (Ledger and McCaffrey, 2014;
IDC Health Insights, 2014; Miller, 2013), most wearable products and services fail to drive
long-term use for a majority of users. Reports have pointed out that a strategy to guide
sustained engagement is the key to long-term success in this market and that motivation
through social interaction is a promising strategy. This backs up Skinner (1953)’s arguments
that reinforcement encourages the maintenance of behavior change for a longer term,
without other side effects such as a relapse. Moreover, among the many types of
reinforcement, social reinforcement has proven effective in maintaining various health
behavior changes (Pasch et al., 1997; Watson and Tharp, 2005). Thus, in this study we tried
to observe the social experiences of users regarding social reinforcement, as well as the
method for promoting long-term effects on health behavior change.
Activity Tracker Study in HCI
Papers that deal with activity trackers have become significantly more numerous in recent
years within the HCI community. Most focus on technical improvements, such as sensing
accuracy, and understanding the methods of self-monitoring, such as research on how to
collect, use, and visualize activity data (Choe et al., 2014; Fan et al., 2012). However,
current interaction techniques between an activity tracker and its users do not seem to focus
on inspiring users to take action. Since a user interacts with not only the product but the
system that encompasses the user and various other stakeholders of the system (Forlizzi,
2007), it is important to see the social interactions between the users of activity trackers
through the activity tracker applications. Moreover, given the activity tracker’s goal of
sustaining users’ health behaviors, it is crucial to investigate how other people reinforce the
user to maintain the behaviors. In HCI, several studies address the importance of social
interactions in using activity trackers. Fritz et al. (2014) found that designs matching
appropriate communities to activity tracker users are crucial to sustaining long-term use of
activity trackers. Rooksby et al. (2014) proposed thinking of activity trackers not as self-
monitoring devices but as social tracking devices in which activities such as competition and
kinship can arise. These previous studies focused on social interaction in activity trackers,
but not on how it occurs or should be designed. To explore design opportunities and social
interaction issues specific to social reinforcement through activity trackers, we investigated
the various roles and inferences of mediators for activity trackers by conducting interviews
with 12 activity tracker users.
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Interviews
Participants and Method
We conducted 12 semi-structured interviews (5 male and 7 female) that lasted between 45
minutes to 1 hour per participant, each of whom uses an activity tracker. The average age of
the participants was 25.3 (SD = 3.3, MIN = 20, MAX = 31), and the average duration they
used the activity tracker was 2.4 months (SD = 2.2, MIN = 1, MAX = 8). We recruited
participants by posting on a Facebook wall of a university students’ group and a university’s
online community board. We had two different recruitment conditions for this study (Table
1). First, we recruited and interviewed six participants in Group A, who used their own
activity trackers. Second, we recruited six participants into Group B, who wanted to use
activity trackers. We gave them activity trackers for 4 weeks, giving them brief online or
offline interviews every week that lasted around 10 minutes, followed by one regular
interview at the end of the period. We recruited Group B to see the detailed quality change in
social interactions per week in relation to activity tracker use. Our participants are all South
Korean, and we think this is a good starting region in which to research this subject
regarding social interaction and social influences due to the country’s high collectivism
(Hofstede, 1980). The participants are all university or graduate school students, who are the
main target users of activity trackers. Half of both groups’ participants used Fitbit, and the
others used Jawbone UP.
Table 1: Participants and recruitment conditions
Group
Participants #
Sex,
age
Activity
Tracker
Model
Duration of
using the
activity tracker
A
A1
M, 29
Fitbit
4 months
A2
F, 24
Jawbone
1 month
A3
M, 31
Fitbit
5 months
A4
F, 24
Jawbone
2 months
A5
F, 24
Fitbit
3 months
A6
F, 26
Fitbit
8 months
B
B1
F, 27
Jawbone
1 month
B2
F, 27
Jawbone
1 month
B3
M, 27
Jawbone
1 month
B4
F, 24
Jawbone
1 month
B5
M, 20
Fitbit
1 month
B6
M, 20
Fitbit
1 month
The interviews were aimed at understanding each participant’s social interactions with their
mediators when using the activity tracker. Our interviews had three parts, covering emerging
IASDR2015 Interplay | 2-5 November | Brisbane, Australia 5
mediators in using activity trackers, mediators whose influential qualities changed, and the
mediators who participants want to be with. The interviews’ composition, questions, and
timeline were applied identically to Groups A and B.
Data Analysis
Three different design researchers analyzed our interview data more than three times in total.
All of the interviews were audio recorded, producing 9+ hours of content. In the first
interview, we found 45 different emerging mediators in using activity trackers; the interview
data had been coded according to the mediators in order to view the social interactions
between the mediators and users. Design researchers classified the 45 mediators into three
groups with affinity diagramming. We similarly found several groups in the second and third
sessions. We refer to each participant by participant group and number, sex, and the name of
the application used (e.g., A1-M-Fitbit).
Findings
From the interviews, we found emerging mediators in activity tracker usage, mediators
whose influence qualities changed, and desirable mediators with whom participants wanted
to be connected through their activity trackers.
Emerging Mediators in Using Activity Trackers
New mediators emerged because the activity tracker connects its users and shares their
health data as a new channel. The detailed features that activity trackers provide to users can
differ across tracker brands, but the general features of the activity trackers from our
interviews were similar to each other. An activity tracker and its application collect a log of a
user’s daily activities by logging meals, tracking steps and distances, and recording sleep
trends. From the interviews, the participants perceived the activity tracker and its
application as a persuasive agent (Fogg, 2003).
“This (activity tracker) knows all about me, and based on the analysis, it provides me an
important insight that really fits to me!” (A4-F-Jawbone)
The participants considered the activity tracker itself an important mediator because of its
physical existence and various feedback modes.
“The powernap function is a smart alarm function that analyzes my sleep pattern and
calculates my optimal nap duration. After lunch, I use this function, and it was gently
vibrating and waking me up. It is caring about me.” (B2-F-Jawbone)
“Whenever I reach my goal of 10,000 steps, this [activity tracker] shows LEDs lighting up
and vibrating several times. It feels like this one is my assistant that is always with me and
supports me to reach my goal.” (A1-M-Fitbit)
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In-app users emerged as a new mediator group. Within the applications, users can easily
find and add their friends by using the contact list in their smartphones, their friend lists from
social networking services (SNSs), and online communities. Users’ activity logs can be
shared with other users through their applications; thus, users can compare data with each
other and compete against one another through the applications.
“I usually go to a leaderboard to check my rank. I realized that I am not a physically active
person when I compare myself to other friends in the activity tracker. Well, I do not own a
car; I usually commute by bus. After I bought my activity tracker, I sometimes got off one or
two stations early and tried to walk more. It was really a good strategy to me regarding the
rankings and my own health habits.” (B2-F-Jawbone)
Activity trackers recorded the participants’ physiological and movement data in real time, so
if the participants had similar life patterns and physical conditions, then their competition
would increase. Interestingly, the participants perceived the activity logs of other users with
similar life patterns and physical conditions as a standard, making them important and
influential mediators. This result resonates with our previous study about a running exercise
application (Lee and Lim, 2015).
“My roommate and I are using Fitbit together. We are in the same department and have
similar activity patterns, so generally our total steps are similar. But whenever I walk more
than my roommate, I would taunt him through the application, and we always try to beat
each other.” (B6-M-Fitbit)
We found one more interesting mediator group from the activity tracker: the past activity
logs of the participants themselves. Since the applications accumulated the participants’
daily activity logs, the participants detached their past data from their present data and
reflected their past health habits to the past data. Finally, the participants regarded their past
as a different entity from the present.
“Me in the past is an important source of understanding and planning (health behaviors).
For instance, I was sick several weeks ago, so I did not exercise enough. Usually I reach the
goal, but the average steps for that period were around 3,000 steps. But still, I can reflect my
past context to the current goal setting. So I can gradually increase my goal to 12,000 steps
from 10,000 steps.” (A1-M-Fitbit)
A mediator is traditionally “a social member who dispenses specific contingent
consequences to a person who tries to change his or her behavior” (Watson and Tharp,
2005). Thus, technically, it is hard to see the participant himself as a mediator. However, we
found that the participants communicated with their past data, which reminded them of their
past. This implies that the participants perceived their past as a different subject from their
present and that it influences them to maintain their health behavior changes.
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Mediators Whose Influential Qualities Changed
The participants were influenced by diverse mediator groups before they used the activity
tracker. However, the qualities of influences from several mediator groups changed while the
participants were using the activity trackers.
“We are a couple in the same campus, so we usually have lunch and dinner together, go for
a walk together, and go to bed around the same time, since we always communicate through
an instant messaging application at night. So my boyfriend has always influenced my health
behaviors. However, after we started using the activity tracker, we could share each other’s
health data and see more detailed contexts for ours, for instance, the calories that we burn
by walking together. It is good that we could share something new and special.” (B4-F-
Jawbone)
As seen from the interview above, significant others share many parts of their daily life
activities. With activity trackers, significant others can share their daily activity log data
through the application. This new type of shared data helped participants to be aware of their
health, and several participants tried to plan for healthier dating, such as by exercising
together.
“My girlfriend tried to go on a diet. When my girlfriend did running exercises, I could leave
comments through the activity tracker application, but it felt like it was not enough. I want to
encourage her, so we went for a run together for our date weekend.”(B3-M-Jawbone)
“Usually, we go out for a meal. After logging the meal, we realized that we greatly exceeded
the calorie recommendations for the day. So we are going to eat healthy food.” (B5-M-
Fitbit)
According to our previous research, emotional bonds between people with intimate relations
were reflected in the usage of a running exercise application (Lee and Lim, 2015). Intimate
in-app users do not directly help improve the participants’ health, but they help provide
emotional support to the participants, such as a sense of relief and motivation. However, we
found that significant others reinforce each other as caregivers. Activity tracking is more
pervasive and continuous than running, since participants have to decide to exercise, while
activity trackers collect everyday activities. For this reason, the sense of togetherness more
directly helped the participants to sustain their health behaviors with their significant others
in activity tracking.
Exercise friends practically and directly reinforce participants’ health behaviors.
“One of my friends in my badminton club uses the activity tracker, and we are friends in the
app. I was surprised about his all-day activity, and it motivated me. The competition became
more systematic and accurate, since we record our every step.” (A5-F-Fitbit)
Exercise friends interact with each other in the real world; however, they can see quantitative
statistical records for competition when they use an activity tracker. The activity tracker
records the user’s history of physical activities. Exercise friends can compare their data and
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compete against each other, even when they are geographically far from each other.
Moreover, the activity tracker measured the all-day activities of the participants, and the
competition became more continuous and pervasive than other types of exercise.
“Actually, competition with other app friends does not motivate me that much. I move a lot
more than my friend, who works in an office at the city. Once or twice a week, I have time to
play soccer with my teammates, but maybe the friend at work does not have enough time for
it. Well, I think he will have his goals and I have my goals that reflect our own living
contexts. Of course, if one of my soccer teammates uses the tracker, then it will be a really
tough competition between us.” (A1-M-Fitbit)
The participants perceived that other users from the activity tracker could have different
goals, depending on their individual contexts. Moreover, the participants know that each user
may have different major usages in the activity tracker, since the trackers support multi-
functional health activities, including meal, physical activity, and sleep trend logging. We
found that exercise friends could be a specific type of group when using an activity tracker.
Their health interests are similar, but they can have different goals according to their
environment and health condition. However, if this health interest-based group interacts with
each other as mediators through the activity tracker, the influence of social reinforcement
can be more effective than that of any other mediator groups, which may have different
health interests.
From the interviews, we found that several participants wanted to show their achievements
to the public and used social media as a showcase.
“I used to share my daily mood, photos, diaries, and other trivial events in my life through
Facebook (social media). Recently, I found that I can export my activity records from the
activity tracker to Facebook! I shared my first 10,000 steps to my Facebook friends. They
cheered me on enthusiastically, and I was really proud of myself. After that, from time to
time, I have updated my achievements through Facebook.” (A4-F-Jawbone)
Interestingly, in social media, the participants tried to make ideal presentations of themselves and
gain emotional support, such as attention and reputation, from their social media friends. As a
result, social media may bias the views of the participants’ health behavior change, although
reinforcement through social media has advantages, including huge human resources and
delivering immediate reactions from mediators.
Mediators with Whom Users Want To Be Connected
The participants desired to be connected with several mediator groups that were not
connected to the participants at the time of the interview. We tried to investigate the qualities
of the desirable mediator groups throughout the interview sessions. The participants
categorized their activity friends into several groups based on their roles. Interestingly, they
wanted to interact with their mediator groups differently based on the mediators’ roles. The
first role of a mediator group is to provide emotional support to the participants.
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“I want to be connected with my sister in the U.S. Because of jet lag and our reserved
characteristics, we do not communicate often like other sisters. But we know we adore each
other very much. If she uses this, then I will see what she eats and feel a sense of connection
with her.” (B1-F-Jawbone)
“I want to report my data to my parents in my hometown. In particular, my mother calls me
and worries about my health very much. She said I should eat healthy food and exercise. But
I think she hardly believes me, so it will be good if this tracker sends my health reports to my
mother and makes her feel relieved. Well, but if I have unhealthy data, then I will hide it from
my mother. She must be worried so much.” (B3-M-Jawbone)
The participants perceived significant others, family members, and best friends as the
mediator groups who provide emotional support, such as a sense of relief, togetherness, and
engagement, through the new form of communication provided by activity tracker usage.
Emotional support did not directly help the participants to maintain their health behavior
change, but it indirectly motivates them. We found that the participants wanted different
interactions according to their relationships with mediators in relation to emotional support.
In the case of significant others, the value of togetherness was stronger than the desire to be
healthy. Thus, significant others want to share their activities, such as going on walks in the
evening, and curate this data as commemorative activities between the couple. The
participants also wanted to report their health-related data to their parents and share their
data with their siblings. However, the participants wanted to show “good” and “healthy” data
to relieve their parents’ worry. For best friends, the participants wanted to use data from the
activity tracker as a communication channel, through which they could share their context.
The interactions between best friends resembled those among significant others. However,
the mediator group of best friends is more appropriate for competition than the mediator
group of significant others.
The influence from mediators who give practical support helped the participants maintain
their health behavior changes. Interestingly, the participants classified practical support
groups based on the specific roles that the mediators could provide to the participants. Also,
the participants interacted differently according to the different mediators.
“I want to subscribe to Miranda Kerr’s activity logging and meal logging. It will motivate
me to move more, and she may let her fans know about her know-how in healthy habits.”
(B2-F-Jawbone)
“I play baseball at my university as my hobby. I want to add the health data of the senior
player in my club as an activity tracker friend. He is my role model, and he teaches me how
to be a better baseball player. If we share data, I can see his meal loggings or activity
loggings and he can check mine and give some feedback to me during training [in the real
world].” (B5-M-Fitbit)
“If my personal health trainer knows my data from the activity tracker, he can suggest some
exercises that fit me. He also can reflect my data onto my lesson at the gym; for instance, if I
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did not sleep well, I can do more stretching exercises than weight training. I saw on TV that
one soccer team coach is using [an] activity tracker as a managing channel for his team. It
is something like that.” (A3-M-Fitbit)
We found that the participants want to be connected with celebrities who have ideal health
conditions, role models, health trainers, and other health behavior experts. This group
can be categorized according to impact level. Celebrities with ideal health conditions inspire
and motivate participants. Role models are similar to celebrities but have high chance of
meeting in the real world. Role models can motivate participants and trigger their health
behaviors into action. Health trainers are tutors who analyze the participant and give health
suggestions, such as on exercising methods.
As seen in the interviews, mediators could reinforce the participants’ health behaviors
according to their type. Moreover, the mediators’ diverse roles influenced participants’ health
behaviors in different ways, and the participants wanted different interactions based on their
mediators’ roles.
Discussion: Design Opportunities and Issues
Our findings show how various mediators influence participants’ health behavior based on
mediators’ roles. These findings suggest several opportunities that can aid the designers of
new health products and services that deliver long-term impacts on users.
Design Opportunity 1: Myself as a Mediator
Several new mediators emerged when the participants started to use activity trackers, but the
most interesting among them were the participants’ past selves. We found that when people
want to change their behavior, their past selves are separated from themselves at that
moment and influence their behavior change. This is slightly dissimilar to the traditional
concept of a mediator. However, we expect that this concept, myself as a mediator, can be a
new design source for health products and services. Many studies on quantified self (Choe et
al., 2014; Fan et al., 2012) consider health behavior change as intrapersonal and focus on
individual user characteristics. However, this time-transcending interaction has yielded a
new perspective on the quantified self and self-monitoring in health behavior change, with
design implications.
Design Opportunity 2: From Curating Mutual Data to Achieving Goals Together
In the interviews, significant others valued having mutual data and communicating with each
other using new types of data, such as steps and sleeping trends. The participants wanted to
record and curate mutual events with their significant others, such as jogging: they jogged
together to share their emotions through the application of the activity tracker, for example.
As a result, the activity tracker acted as a new communication channel between them. As we
observed in this study, participants in romantic relationships can be powerful mediators to
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each other by reinforcing health. Several other researchers have found that when users are
able to share their goals or compete for goals with other people, they are more committed to
achieving these goals (Cialdini, 2001; Ledger and McCaffrey, 2014). Therefore, significant
others could reinforce the participants’ health behavior changes effectively and affectively in
the longer term. For instance, one implication could be a design opportunity in which
significant others build a mutual goal and achieve it together. This mutual goal and its
achievement should be curated as a health event just for the couple, while carefully
delivering the couples’ health-related data for a new means of communication.
Design Opportunity 3: Rich Interaction with Role-Based Mediators
In this study, we found that the participants desired diverse interaction methods according to
different mediator roles. First, our interviews show that several mediators who played
different roles existed throughout the participants’ activity tracker usage, and the participants
wanted to interact with diverse mediators, including preexisting ones. However, the activity
trackers currently in the market search for friends by using a friend list from the user’s SNSs
and phone book or entered email addresses. Therefore, it limits opportunities to meet new
mediators for desired roles. One approach to this challenge may be to establish a set of
channels that enable users to meet desired mediators. For example, when a user focuses on
the desired mediators’ roles and selects a channel like learning, the user can contact and
make an appointment with mediators such as tutors, role models, and health trainers. Second,
from our interviews, we found that the participants wanted to have different social
interactions, such as communication, learning, and competition, depending on the role-based
mediator groups. However, the current activity tracker system mainly provides competition
between in-app users, with insufficient social reinforcement. It is necessary to develop a rich
interaction vocabulary in order to help users experience mediator-dependent interaction.
These two design opportunities may help users sustain their health behavior changes through
rich reinforcement with diverse mediators.
Design Issues: Privacy in Context Data
Activity trackers provide social reinforcement by sharing activity data with diverse
mediators. According to Knijnenburg and Kobsa (2013), people feel more sensitive about
contextual data such as location and app usage than demographic data such as age and
gender. Since activity trackers accumulate users’ physiological and movement data in real-
time, privacy concerns require circumspection when sharing data through the activity
trackers. Excessive disclosure would demotivate users while leading to no meaningful
interactions. Sharing conditions regarding what data are shared, with whom, and in what
form may decide the quality and impact of social reinforcement. Hence, research on the
influence of sharing conditions is necessary to design real-time logging systems, including
displaying context, such as for activity trackers.
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Conclusion
This paper sought to reveal emerging mediators in using activity trackers, mediators whose
influences change, and mediators with whom the participants wanted to be connected,
through interviews with activity tracker users. Then, we suggested several design
opportunities and issues that need consideration in designing health products and services,
specifically for promoting social reinforcement. Although this study was conducted in
homogeneous conditions regarding the participants, we found that various social interactions
had already occurred in using activity trackers. In addition, reflecting on current social
interactions, we revealed that social reinforcement could have a meaningful impact on users’
health behavior changes when using an activity tracker. The market has developed an
immense number of wearable health devices and services. Beyond technical improvements,
a new perspective on the ecosystem’s multiple users and stakeholders is needed to guide
people to healthier lives. Diverse users of activity trackers represent different ages, cultural
differences, and relationships, such as between parent and child and boss and employee. This
diversity will be an important theme in future investigations. Activity trackers may also
show long-term dynamic influences on social reinforcement in future studies.
Acknowledgement
This research was supported by "Dr. M" Project funded by KAIST (N01140095). We would
like to express our sincere thanks to all the participants.
References
Adams, P. et al. (2014). Staccato Social Support in Mobile Health Applications. In Proc. CHI 2014, ACM, 653-
662.
Choe, E.K. et al. (2014). Understanding Quantified-Selfers’ Practices in Collecting and Exploring Personal
Data. In Proc. CHI 2014, ACM, 1143-1152.
Cialdini, R.B. (2001). Influence: Science and Practice (4th ed.). Boston: Allyn & Bacon.
Fan, C. et al. (2012). A Spark of Activity: Exploring Informative Art as Visualization for Physical Activity. In
Proc. UbiComp 2012, ACM, 81-84
Fogg, B. (2003). Persuasive Technology: Using Computers to Change What We Think and Do. Morgan
Kaufmann.
Fogg, B. (2009). A Behavior Model for Persuasive Design. In Proc. Persuasive 2009, ACM, No. 40.
Forlizzi, J. (2007). The Product Ecology: Understanding Social Product Use and Supporting Design Culture.
International Journal of Design, 2(1), 11-20
Fritz, T. et al. (2014). Persuasive Technology in the Real World: A Study of Long-Term Use of Activity Sensing
Devices for Fitness. In Proc. CHI 2014, ACM, 487-496.
Hofstede, G. (1980). Culture's Consequences: International Differences in Work-Related Values. Sage.
IDC Health Insights. (2014). Vendor Assessment: Consumer Engagement-Fitness Activity Trackers, Improving
Health One Step at a Time, IDC Health Insights (Doc #HI249519), June 2014.
Knijnenburg, B. and Kobsa, A. (2013). Making Decisions about Privacy: Information Disclosure in Context-
Aware Recommender Systems, Transactions on Interactive Intelligent Systems (TiiS), Volume 3 Issue 3.
IASDR2015 Interplay | 2-5 November | Brisbane, Australia 13
Ledger, D. and McCaffrey, D. (2014). Inside Wearables: How The Science Of Human Behavior Change Offers
The Secret To Long-Term Engagement. Endeavour Partners: Cambridge, MA, USA.
Lee, Y. and Lim, Y. (2015). Understanding the Roles and Influences of Mediators from Multiple Social
Channels for Health Behavior Change. In Proc. CSCW 2015, ACM, 1070-1079.
Macvean, A. and Robertson, J. (2013). Understanding Exergame Users' Physical Activity, Motivation and
Behavior over Time. In Proc. CHI 2013, ACM, 1251-1260.
Miller, A. (2013). Fitness Trackers. XRDS: Crossroads, The ACM Magazine for Students, 20, 2 (December
2013), 24-26.
Pasch, L. A. et al. (1997). Social Support in Marriage: An Analysis of Intraindividual and Interpersonal
Components. Sourcebook of Social Support and Personality. New York, Plenum
Rooksby, J. et al. (2014). Personal Tracking as Lived Informatics. In Proc. CHI 2014, ACM, 1163-1172.
Skinner, B. F. (1953). Science and Human Behavior. New York: McMillan, 1953.
Watson, D. L. and Tharp, R. G. (2005). Self-directed Behavior: Self-modification for Personal Adjustment,
(9th Ed.). Belmont, CA: Thomson/Wadsworth, 242-245.
Author Biographies
Yeoreum Lee
Yeoreum Lee is a PhD candidate in the Department of Industrial Design at KAIST. She
received her BS and MS in industrial design from the same university. Lee’s primary
research interests are the intersection of health behavior change and social interaction design
within the HCI field. In particular, Lee is interested in applying social reinforcement as a
source for designing primary health prevention services, and one part of her PhD study was
published at CSCW 2015 as a full paper.
Min Gyeong Kim
Min Gyeong Kim is a master student in the Department of Industrial Design at KAIST. Her
research interests include designing user experience regarding privacy issues in digital
system. Kim received her BS in industrial design from KAIST.
Saeyoung Rho
Saeyoung Rho is MS candidate in the Department of Industrial Design at KAIST. Her
research interests include health behavior change, Quantified-Self, personal informatics, and
wearable devices. Recently she has been developing a new way of representing personal data
in QS services. Rho received her Bachelor's degree in industrial design from KAIST.
Da-jung Kim
Da-jung Kim is a PhD candidate in the Department of Industrial Design at KAIST, where
she holds her BS and MS degrees as well. Her research interests include aesthetics of
interaction, personal informatics, and social interaction design. For her dissertation, she has
IASDR2015 Interplay | 2-5 November | Brisbane, Australia 14
been studying the design of social applications and its impacts on user’s personal and social
lives.
Youn-kyung Lim
Youn-kyung Lim is an associate professor in the Department of Industrial Design at KAIST,
where she leads the Creative Interaction Design (CIxD) lab. Her research interests have been
in the areas of experience-centered design and prototyping in HCI, with a design-oriented
perspective, especially focusing on enabling creative interaction design. Lim received her
PhD in design from the Institute of Design at the Illinois Institute of Technology in Chicago.
