=Paper=
{{Paper
|id=Vol-1582/14Evangelos
|storemode=property
|title=Designing for Different Stages in Behavior Change
|pdfUrl=https://ceur-ws.org/Vol-1582/14Evangelos.pdf
|volume=Vol-1582
|authors=Evangelos Karapanos
|dblpUrl=https://dblp.org/rec/conf/persuasive/Karapanos16
}}
==Designing for Different Stages in Behavior Change==
https://ceur-ws.org/Vol-1582/14Evangelos.pdf
Designing for Different Stages in Behavior Change
Evangelos Karapanos
Cyprus University of Technology, Limassol, Cyprus
evangelos.karapanos@cut.ac.cy
Abstract. The behavior change process is a dynamic journey with different in-
formational and motivational needs across its different stages; yet current tech-
nologies for behavior change are static. In our recent deployment of Habito, an
activity tracking mobile app, we found individuals ‘readiness’ to behavior
change (or the stage of behavior change they were in) to be a strong predictor of
adoption. Individuals in the contemplation and preparation stages had an adop-
tion rate of 56%, whereas individuals in precontemplation, action or mainte-
nance stages had an adoption rate of only 20%. In this position paper we argue
for behavior change technologies that are tailored to the different stages of be-
havior change.
Keywords. Persuasive technologies, stages of behavior change, user engage-
ment.
1 Introduction
Despite their initial promise, physical activity trackers are failing to sustain users’
engagement in the long run [1]. Shih et al. [2] found 50% of users who adopted a
Fitbit to abandon it within the first two weeks of use. Similarly, we found [3] 62% of
the users who downloaded an activity tracking mobile app to stop using it within the
first two weeks, while in an online survey, one third of owners of activity trackers
self-reported that they discarded them within six months after the purchase [4].
The question arises: is this a sign of activity trackers’ failure to instill behavior
change, or is this a positive sign in the sense that the tracker enabled the swift adop-
tion of exercising by users as an intrinsically motivated practice, and exercising was
no longer required (see [3])?
In a longitudinal field study of Habito [3], an activity tracking mobile app, we set
to explore how individuals adopt and engage with activity trackers. Our study showed
that things often do not go as we designers expect them to. For instance, contrary to
conventional wisdom in the quantified-self community that behavior change is the
result of deep knowledge about one’s own behaviors, we found that people rarely
look back at their past performance data and may not have deep knowledge about
their own behaviors. Instead, we found the use of the tracker to be dominated by
glances: brief, 5-sec sessions where users call the app to check how much they have
walked so far without any further interaction. But activity trackers are not designed
with glanceable interaction in mind.
Copyright © by the paper’s authors. Copying permitted for private and academic
purposes.
In: R. Orji, M. Reisinger, M. Busch, A. Dijkstra, A. Stibe, M. Tscheligi (eds.): Pro-
ceedings of the Personalization in Persuasive Technology Workshop, Persuasive
Technology 2016, Salzburg, Austria, 05-04-2016, published at http://ceur-ws.org
58 Designing for Different Stages in Behavior Change
Figure 1. By deploying Habito [3] on Google Play we were able to monitor its
usage over a 10-month period by 256 users who installed it on their own volition.
Habito employs three design strategies to promote behavior change: goal setting,
contextualizing physical activity and textual feedback that keeps updating.
Similarly, one of the most common design strategies in activity trackers is ‘goal
setting’ - a user sets his or her own walking goal (e.g., 8 km per day) and feedback is
provided as to how far he or she is from accomplishing the goal. But, while goal set-
ting is a theoretically and empirically grounded strategy one could bring to design, it
assumes that people self-set their own goals. Our study found that only 30% of users
set their own goal, while 80% of users who did so, never updated the goal again
(while updating one’s goal would be expected in the process of behavior change).
Perhaps most interestingly, we found that current physical activity trackers work
only for people that are in the intermediary stages of behavior change: those that have
the motivation to change their behaviors but have no developed plans for doing so.
Individuals in the contemplation and preparation stages, who have the intention but
not yet the means (i.e. motivation, strategies) to change, had an adoption rate of 56%
(with adoption being defined as use that extends beyond the first two weeks), whereas
individuals in precontemplation, action or maintenance stages had an adoption rate of
only 20%.
Yet, these individuals (in the intermediary stages of behavior change) are only
about 43% of the population that are likely to purchase an activity tracker, or down-
load an app on their smartphones (based on our sample [4]). So, there is a significant
population of users for whom we currently fail to address their needs. To remediate
this situation, we need to ask new questions, such as, how can trackers instill initial
Designing for Different Stages in Behavior Change 59
motivation for behavior change rather than merely supporting the process of it? Indi-
viduals in the precontemplation stage are often unaware of the extent of their inactivi-
ty [5]. As a result, initial experiences are marked by dismay as individuals realize
their low activity levels. Rather than confronting users with this “truth”, one could ask
how trackers could increase individuals’ perceptions of self-efficacy and competence
and support them in the gradual increase of physical activity.
A second challenge is detecting the stage of behavior change individuals are in
from behavioral cues. In doing so, one should bear into account that transitions across
stages are not always unidirectional. Individuals often relapse to prior stages of be-
havior change. When this occurs, some individuals “feel like failures – embarrassed,
ashamed and guilty” [6]. Detecting those transitions is as critical as detecting the
stage an individual is currently in. Future work should thus embrace behavior change
as a dynamic journey, should seek to understand the experiential side of behavior
change, and to design strategies that support individuals across the full spectrum of
their journey.
2 References
1. Karapanos, E. (2015). Sustaining user engagement with behavior-change tools. Interac-
tions, 22(4), 48-52.
2. Shih, P. C., Han, K., Poole, E. S., Rosson, M. B., & Carroll, J. M. (2015). Use and Adop-
tion Challenges of Wearable Activity Trackers. In Proceedings of iConference’15.
3. Gouveia, R., Karapanos, E., & Hassenzahl, M. (2015). How do we engage with activity
trackers?: a longitudinal study of Habito. In Ubicomp’15 (pp. 1305-1316). ACM.
4. Ledger, D. and McCaffrey, D. (2014) How the Science of Human Behavior Change Offers
the Secret to Long- Term Engagement. Retrieved from http://endeavourpartners.net/white-
papers/, December 2014.
5. Karapanos, E., Gouveia, R., Hassenzahl, M., & Forlizzi, J. (2015) “It's not that hard to
walk more”: Peoples' experiences with wearable activity trackers. M-ITI Technical Report.
6. Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people
change: applications to addictive behaviors. American psychologist, 47(9), 1102.