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Using Mobile & Personal Sensing Technologies to Support
Health Behavior Change in Everyday Life: Lessons Learned
Predrag Klasnja1, Sunny Consolvo1,2, PhD,
David W. McDonald1, PhD, James A. Landay1,2, PhD, & Wanda Pratt1, PhD
1University of Washington, Seattle, WA; 2Intel Research Seattle, Seattle, WA
Abstract
Lifestyle modification is a key facet of the prevention
and management of chronic diseases. Mobile devices
that people already carry provide a promising
platform for facilitating these lifestyle changes. This
paper describes key lessons learned from the
development and evaluation of two mobile systems
for encouraging physical activity. We argue that by
supporting persistent cognitive activation of health
goals, encouraging an extensive range of relevant
healthy behaviors, focusing on long-term patterns of
activity, and facilitating social support as an optional
but not primary motivator, systems can be developed
that effectively motivate behavior change and provide
support when and where people make decisions that
affect their health.
Introduction
Although pharmacological advances have made great
strides in decreasing morbidity and mortality from
chronic diseases, lifestyle modification remains a key
aspect of effective chronic disease management.
Interventions that target lifestyle modification have
been shown to be effective in the prevention and
management of heart disease,1 diabetes2 and obesity.3
Yet, patient compliance with lifestyle modification
remains low. For example, fewer than half of heart
disease patients continue to exercise six months
following the completion of cardiac rehabilitation.4
Numbers are similar for compliance with dietary
recommendations.5 Why is behavior change so
difficult to achieve? Simply put, it is a complex
process. Even a single change, such as increasing
physical activity, likely requires the individual to
restructure her priorities as well as her daily and
social routines, such as finding time for exercise in
the midst of work and family obligations.
Encouraging health-promoting lifestyle change
requires that interventions be integrated into
everyday life, with support available when and where
individuals make decisions that affect their health.
Mobile technologies that individuals routinely carry,
such as mobile phones, may be a particularly
effective platform for delivering such encouragement
as they are likely to be with the individual when she
most needs the support.6,7 Over the past several years,
we have conducted early stage field studies of mobile
technologies designed to encourage physical activity.
In this paper, we describe key lessons learned from
that work in an effort to help others who are
designing systems to support health behavior change.
We conclude with methodological reflections about
how to design such systems so that they smoothly
integrate into users’ everyday lives while effectively
encouraging lifestyle change.
Systems and Field Studies
We have designed two mobile phone-based systems
to encourage regular physical activity: Houston and
UbiFit. Houston8 (Figure 1, left), the first system we
designed, uses a mobile phone application and a
pedometer to encourage users to increase their daily
step count. The phone application provides a journal
where users can review trends of their daily step
counts, add comments to their step counts, receive
small rewards for reaching their daily goal, share
their step counts with ‘fitness buddies,’ and exchange
messages with those buddies. We conducted a three-
week field study of Houston with 13 participants,
comprised of three groups of female friends from
pre-existing social networks. Each participant was
interviewed at the beginning of the study, after the
first week, and at the end of the study.
Figure 1: Houston (left) and UbiFit (right)
AMIA 2009 Symposium Proceedings Page - 338
Based on our experiences with Houston, results from
other persuasive technology research, and behavioral
and social psychological theories,9 we designed
UbiFit10,11 a system that uses a mobile phone and a
sensing device to encourage regular and varied
physical activity. Two of UbiFit’s components run on
the user’s mobile phone: (1) an interactive
application used to journal physical activities, review
activities done on any given day, and track progress
toward a weekly goal, and (2) a glanceable display
that uses an abstract, stylistic representation of the
physical activities the user performs each week,
displayed on her phone’s background screen. The
glanceable display provides weekly goal attainment
status, physical activity behavior, and a subtle,
persistent reminder of her commitment to physical
activity. In our implementation, the display uses a
garden metaphor to represent a week’s worth of
physical activity behavior. The garden blooms with
different types of flowers to represent the different
types of activities the user performs: walking, cardio,
strength, flexibility, and other non-exercise physical
activities (e.g., housework). Upon reaching her
weekly goal, a large butterfly appears. Up to three
smaller butterflies represent goal attainments for the
prior three weeks (Figure 1, right).
In addition to the mobile phone components, UbiFit
uses the Mobile Sensing Platform (MSP),12 a pager-
sized, battery-powered computer that uses a
barometer and three-dimensional accelerometer to
automatically detect the duration and start time of
walking, running, cycling, stair machine, and
elliptical trainer activities. When the MSP is worn on
the waistband and within Bluetooth range of the
phone, these activities are detected automatically. As
they are detected, the activities appear both in the
interactive application and on the glanceable display.
We conducted two field studies of the UbiFit system:
a three-week trial and a three-month experiment. In
the three-week trial,10 12 participants used UbiFit and
provided feedback on the system and the automatic
activity detection. As with the Houston study, each
participant was interviewed at the beginning of the
study, after the first week, and at the end of the study.
Based on the results, we redesigned elements of the
system, including improving the activity detection.
The revised version of UbiFit was evaluated over the
winter holiday season in a three-month experiment
with 28 participants.10 Participants were randomly
assigned to one of three experimental conditions:
interactive application and sensing device only (no
glanceable display), interactive application and
glanceable display only (no sensing device), or
interactive application, glanceable display, and
sensing device (full system). Each participant was
interviewed at the beginning of the study, at the end
of the first month, and at the end of the study.
In the following sections, we discuss key lessons
learned from these two research projects.
Lessons Learned
Four lessons from our work are particularly relevant
to the design of mobile systems for encouraging
health behavior change. These are the importance of
(1) supporting persistent cognitive activation of
health goals, (2) encouraging an extensive range of
relevant healthy behaviors, (3) focusing on longer-
term patterns of activity, and (4) facilitating social
support as an optional but not primary motivator.
Supporting Persistent Activation of Health Goals
An important result of the three-month experiment of
UbiFit was the significant difference in the weekly
activity level between participants who had and did
not have the glanceable display. The former group
maintained their activity levels throughout the study,
even though the winter holiday season is known for
physical inactivity. In contrast, the activity levels of
the latter group decreased significantly. This result is
consistent with the social psychological literature13
on automatic goal activation. This literature finds that
goals can be activated through environmental cues,
and that such “primed” goals can effectively guide
goal-directed behavior. The glanceable display kept
the physical activity goals chronically activated,
enabling participants who had the display to stay
engaged with their commitment to physical activity.
A participant explained:
[The garden] was a constant reminder…whereas if
you didn’t have a [garden], you probably—I wouldn’t
think about [physical activity] as much, you know.
[With the garden] I think about it maybe
subconsciously every time I look at my phone.
Supporting this kind of persistent activation of health
goals can be a powerful means of fostering health
behavior change. Although a number of commercial
mobile phone applications enable users to track their
diet and physical activity, they are likely to yield
results similar to those of our no glanceable display
condition because they do not provide the persistent
visual reminder. UbiFit is, to our knowledge, the only
health application to date to use the phone’s
background screen to provide users with continuous
feedback about a behavior they are trying to change.
In addition, the stylized nature of UbiFit’s display
allowed users to maintain some level of privacy
should their phone be seen by someone else.
AMIA 2009 Symposium Proceedings Page - 339
Future directions. UbiFit’s glanceable display subtly
reminded participants about their commitment to
physical activity and it provided feedback about their
recent activities and goal attainment. Future work
should investigate whether the former is effective
without the latter. Other types of representations, for
example, providing an encouraging message or
drawing from a loved one, could be explored.
Assuming the user strongly associates the
representation with her health goals, it could be
effective at achieving persistent goal activation.
Whether such representations are sufficient on their
own or if explicit, persistent feedback about recent
activities is necessary should be further examined.
Encouraging an extensive range of healthy behaviors
Our work suggests that the system can substantially
influence how the individual engages in health
behavior change. Specifically, the activities that the
system supports or encourages can become the focus
of the user’s efforts, potentially to the exclusion of
other relevant activities.
An example will help clarify this point. Following
health science literature on the effectiveness of
pedometers, Houston attempted to encourage
physical activity by helping users track their daily
step count. Users could add comments to their step
count (e.g., “Went for a bike ride”), but the system
provided no other explicit functionality for tracking
other forms of physical activity. This resulted in an
unintended, negative side-effect. Several participants
realized that the pedometer did not capture cardio
activity well—for example, running three miles
yields a lower step count than walking the same three
miles and cycling yields no steps at all. As a
frustrated participant explained, “my main source of
exercise [rock climbing] doesn’t register.” Similarly,
the pedometer did not distinguish between steps
made while walking on a flat surface or up hills—
although these activities differ in their intensity and
in their ability to help individuals lose weight.
Because the system did not provide proper credit for
these types of exercise, several participants simply
chose not to do them. A participant noted, for
example, that the pedometer did not “care whether
you went up and down hills or whether you walk on
flats, so why kill yourself?” This outcome was not
what we intended. Based on this experience, we
trained UbiFit’s sensing device to detect a wider
range of activities (walking, running, cycling,
elliptical trainer, and stair machine), and we allowed
participants to journal any other physical activity in
the interactive application. As a result, 26 types of
cardio activities were performed by participants in
the three-month experiment, including skiing, cardio
classes, dancing, swimming, and ice skating.
This experience highlights an important lesson that
needs to be considered when designing systems for
the support of health behavior change. Such systems
not only help users track and modify their behavior,
but insofar as the user becomes invested in using the
system, the system also shapes how she thinks about
the behaviors she is trying to change. The type of
credit that the system provides could inadvertently
encourage the user to focus only on activities that the
system supports, potentially at the expense of
activities that might be, from a health perspective,
equivalent or even more important.
Future directions. With Houston, we tracked step
count only; with UbiFit, we tracked and encouraged
the range of relevant physical activities. To continue
this trajectory, the range of healthy behaviors that are
encouraged by the system could be further expanded,
especially when the system is targeting the
prevention and management of chronic diseases. In
the case of heart disease, for example, a patient might
not only need to increase physical activity, but also
change her diet, reduce stress, and stop smoking. Our
findings suggest that an effective system will support
an extensive range of the healthy behaviors within
the relevant areas of lifestyle change.
However, it is unlikely that every user will need to
focus on changing all of those aspects of her life, or
at least not all at the same time. Providing
customization that allows the user (or health care
provider) to select aspects of the system that are
appropriate for the user’s current needs, and adjust
the system as her needs change, may improve the
effectiveness of the system over time.
Focusing on long-term patterns of activity
Behavioral economics claims that individual actions
can have a very different value than the patterns of
those same actions.14 If, while on her morning coffee
run, an individual is deciding between ordering a
black coffee (0 calories) and a caramel frappuccino
(380 calories), the tasty frappuccino might appear
much more appealing. However, if she is deciding
which of those beverages to have every morning of
the week, she may decide that the 2660 calories from
seven frappuccinos are not worth it. The difficulty is
that in the moment that decisions are made,
individuals tend to focus on the current decision, and
not on the pattern that such decisions form over time.
With UbiFit, the week’s worth of activities and
month’s worth of goal attainments represented on the
glanceable display encouraged participants to think
about physical activity not as a one-off choice (e.g.,
AMIA 2009 Symposium Proceedings Page - 340
Do I need to work out today?) but rather in terms of
patterns of behavior (e.g., What did I do last week?
What have I done so far this week? What can I still
do to have an active week?).
Helping participants reflect on a week’s rather than a
day’s worth of activity in the display meant that even
if the participant had a couple of sedentary days, she
would not necessarily be discouraged, as she could
still have a good week. Just as importantly, seeing a
week’s worth of activity helped many realize how
inactive they were—this awareness surprised most
participants—and take concrete steps to be more
active. A participant explained:
I used [the glanceable display] to increase my
awareness of what I was doing…’cause like…after
about two days, you kind of forget, like ‘did I really
do that or am I just dreaming or was that last week?’
Encouraging users to reflect on how each choice they
make forms a pattern of behavior over time can be a
powerful way to encourage health behavior change.
Mobile devices offer an advantage over Web-based
tools as they are often with the user when decisions
are being made. A participant explained:
I liked having [my garden display] be on the
phone…something I have with me…[with] a Web site,
it’s so easy, ‘oh, I didn’t do anything, I’m not going
to click on it.’ It’s so easy to ignore it. But on the
phone, you can’t really ignore it as
easily…Otherwise, it’s just…out of sight, out of mind.
Over time, such tools could help users learn to
conceptualize choices naturally in terms of how they
create patterns, thus helping users internalize one of
the most powerful means of ensuring self-control.14
Future directions. Future systems could further
explore supporting reflections on patterns of
behavior. For example, a system could help the user
realize how other factors affect her healthy and
unhealthy activities (e.g., location or other people).
Similarly, a context-aware mobile tool could learn
over time what challenges the user faces in trying to
lead a healthy lifestyle and proactively provide
support when she most needs it.
Facilitating but not depending on social support
Houston explicitly facilitated social support by
providing features that enabled users to share step
counts, goal progress, and messages with their fitness
buddies. What we discovered in the Houston study,
however, was that social support was a double-edged
sword: participants enjoyed sharing their step count
when they were being active and were motivated by
positive feedback and seeing their buddies do well.
But participants were often not comfortable sharing
when they were less active, and some felt that sharing
introduced too much competition. The effect of social
support on users’ motivation was decidedly mixed.
Based on these findings and similar results from
others,15 we realized that while social support can be
helpful, it should not be the primary strategy used to
motivate health behavior change. With UbiFit, we
designed a system to motivate users to engage in
physical activity without using social support as a
primary motivator. However, sharing regularly took
place anyway. Participants routinely showed their
gardens to family and friends, and for some, family
members helped to encourage physical activity. A
participant explained,
[My daughter] would really encourage me to [be
active] and she would ask me for pink flowers all the
time...She was very excited, and she wanted [me to
get] the butterflies.
Similarly, family members and friends who
participated in the study together often compared
their gardens, somewhat replicating the sharing
functionality explicitly facilitated by Houston. As
with Houston, however, UbiFit participants felt
uncomfortable when someone would ask to see their
garden when they had not been very active.
Future directions. While social support can be a
powerful strategy for encouraging behavior change—
and users engage in it even when the system does not
facilitate it—it can also hamper motivation and even
introduce social friction (if, for example, someone
initially shares her data, then later decides to stop).
Although health behavior change systems could
benefit from facilitating social support, they should
not depend on it. In addition, future work should
explore how to incorporate social support. For
example, while the system should allow users to
determine what to share with whom, an open design
challenge is how to allow the user to adjust these
settings easily as circumstances change. Without such
control, social support can backfire, ultimately
leading to system abandonment.
Discussion & Conclusion
Unlike technologies that individuals have to use—as
part of their jobs, for example—the use of
technologies for health behavior change is often
discretionary. For such systems to be effective and
continue to be used, they must be well designed with
careful consideration given to how they will fit into
everyday life. How functionality such as journaling,
feedback, or social support is designed can make or
break the effectiveness of a system, and even lead to
system abandonment (and, potentially, abandonment
of the behavior the system was trying to encourage).
AMIA 2009 Symposium Proceedings Page - 341
Designing for integration into everyday life while
effectively encouraging health behavior change is
complicated. Important effects of a system’s design
often cannot be anticipated in advance. When a
system is used in the field by individuals from the
target audience, even for a short period of time (e.g.,
a few weeks), important issues surface that cannot
easily be predicted or found during initial system
design or in usability lab evaluations.
In our work, we follow a user-centered design
process, common to the field of human-computer
interaction (HCI). We engage target users early and
often. We employ surveys, as well as beta testing
with the research team and friends and family for
weeks to months. Following beta testing, we employ
short-term field studies (e.g., a few weeks) with small
numbers of participants (e.g., 12-15) who are asked
to use the technology in their everyday lives. We
redesign our systems based on these results prior to
going into the field with more formal, long-term
experiments. In our studies, we combine quantitative
data collection methods with qualitative methods,
where participants are often interviewed in depth
about their experiences with the system. The insights
we have gained from the qualitative methods have
been critical to our developing a system that has
effectively encouraged health behavior change.
Such HCI-style practices could greatly improve the
effectiveness of technologies developed for medical
informatics research. It is through rich qualitative
data and use in the field that subtle, yet critical,
design problems are often revealed, the timely
discovery of which may determine how effective a
system proves to be in clinical trials. (See 16 for a
discussion of the value of such data even in RCTs)
As the prevalence and cost of chronic diseases
continue to rise, the need for lifestyle modification as
a means of prevention and treatment becomes
greater. In this paper, we have argued that carefully
designed mobile interventions can be a powerful way
of fostering health behavior change. By supporting
the persistent activation of health goals, encouraging
an extensive range of relevant healthy behaviors,
focusing on patterns of activity, and facilitating
optional social support, effective systems can be
designed to help people live long, healthy lives.
References
1. Zafari AM, Wenger NK. Secondary prevention
of coronary heart disease. Archives of physical
medicine and rehabilitation. 1998;79(8):1006-17.
2. Forlani G, Lorusso C, Moscatiello S, et al. Are
behavioural approaches feasible and effective in the
treatment of type 2 diabetes? A propensity score
analysis vs. prescriptive diet. Nutrition, metabolism,
and cardiovascular diseases. 2008.
3. Galani C, Schneider H. Prevention and treatment
of obesity with lifestyle interventions: review and
meta-analysis. International journal of public health.
2007;52(6):348-59.
4. Moore SM, Dolansky MA, Ruland CM,
Pashkow FJ, Blackburn GG. Predictors of women's
exercise maintenance after cardiac rehabilitation.
Journal of Cardiopulmonary Rehabilitation.
2003;23(1):40-9.
5. Twardella D, Merx H, Hahmann H, Wüsten B,
Rothenbacher D, Brenner H. Long term adherence to
dietary recommendations after inpatient
rehabilitation: Prospective follow up study of patients
with coronary heart disease Heart. 2006;92:635-40.
6. Fogg BJ. Mobile persuasion: 20 perspectives of
the future of behavior change. Palo Alto, CA:
Stanford Captology Media; 2007.
7. Tufano JT, Karras BT. Mobile eHealth
interventions for obesity: A timely opportunity to
leverage convergence tools. Journal of Medical
Internet Research. 2005;7(5):e58.
8. Consolvo S, Everitt KM, Smith I, Landay JA.
Design requirements for technologies that encourage
physical activity. CHI 2006; 2006. p. 457-66.
9. Consolvo S, McDonald DW, Landay JA.
Theory-driven design strategies for technologies that
support behavior change in everyday life. CHI 2009.
10. Consolvo S, Klasnja P, McDonald DW, et al.
Flowers or a robot army? Encouraging awareness &
activity with personal, mobile displays. UbiComp
'08; 2008. p. 54-63.
11. Consolvo S, McDonald DW, Toscos T, et al.
Activity sensing in the wild: a field trial of UbiFit
Garden. CHI '08; 2008. p. 1797-806.
12. Choudhury T, Borriello G, Consolvo S, et al.
The Mobile Sensing Platform: An Embedded
Activity Recognition System. Pervasive Computing,
IEEE. 2008;7(2):32 - 41.
13. Fitzsimons GM, Bargh JA. Automatic self-
regulation. In: Vohs KD, Baumeister RF, editors.
Handbook of self-regulation: Research, theory, and
applications. New York: Guilford; 2004. p. 151-70.
14. Rachlin H. The science of self-control.
Cambridge, MA: Harvard University Press; 2000.
15. Lin JL, Mamykina L, Lindtner S, Delajoux G,
Strub HB. Fish'n'Steps: Encouraging physical activity
with an interactive computer game. Ubicomp 2006;
2006: Springer; 2006. p. 261-78.
16. Hurling R, Catt M, De Boni M, et al. Using
internet and mobile phone technology to deliver an
automated physical activity program: Randomized
controlled trial. Journal of Medical Internet Research.
2007;9:e7.
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