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Using Mobile & Personal Sensing Technologies to Support Health Behavior Change in Everyday Life: Lessons Learned


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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.
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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
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.
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.
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... However, the major social relations concern is that the participants are afraid to be treated or seen differently by others. Klasnja et al. [39] proposed that social platforms may have a negative impact on the participation rate. People who are not keeping up or who fall behind might feel stressed and afraid to share their activities and eventually drop out of the program [39]. ...
... Klasnja et al. [39] proposed that social platforms may have a negative impact on the participation rate. People who are not keeping up or who fall behind might feel stressed and afraid to share their activities and eventually drop out of the program [39]. Thus, social support may not be the primary tool due to social friction [39]. ...
... People who are not keeping up or who fall behind might feel stressed and afraid to share their activities and eventually drop out of the program [39]. Thus, social support may not be the primary tool due to social friction [39]. ...
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Background Studies have demonstrated that a culturally and linguistically tailored Diabetes Prevention Program (DPP) can be effective in reducing diabetes risk in Chinese Americans. The purpose of this study was to explore the cultural and linguistic acceptability of the Centers for Disease Control and Prevention’s Prevent T2 curriculum in an online format in the Chinese American community in New York City (NYC). Methods Three focus groups among a total of 24 Chinese Americans with prediabetes and one community advisory board (CAB) meeting with 10 key stakeholders with expertise in diabetes care and lifestyle interventions were conducted. Each focus group lasted approximately 1 to 1.5 h. All groups were moderated by a bilingual moderator in Chinese. The sessions were audiotaped, transcribed and translated to English for analysis. Using Atlas.ti software and open coding techniques, two researchers analyzed transcripts for thematic analysis. Results Five key themes were identified: barriers to behavioral changes, feedback on curriculum content and suggestions, web-based intervention acceptability, web-based intervention feasibility, and web-based intervention implementation and modifications. Participants with prediabetes were found to have high acceptability of web-based DPP interventions. Suggestions for the curriculum included incorporating Chinese American cultural foods and replacing photos of non-Asians with photos of Asians. Barriers included lack of access to the internet, different learning styles and low technology self-efficacy for older adults. Conclusion Although the acceptability of web-based DPP in the Chinese American community in NYC is high, our focus group findings indicated that the major concern is lack of internet access and technical support. Providing support, such as creating an orientation manual for easy online program access for future participants, is important.
... This process of using digital traces to draw inferences about individuals' psychological state is a methodology commonly referred to as digital phenotyping. Similar to other terms, such as personal sensing [6,7], reality mining [8], and personal informatics [9], digital phenotyping was defined by Torous and colleagues [10] as the "moment-by-moment quantification of the individual-level human phenotype in situ using data from smartphones and other personal digital devices." Digital phenotyping studies have gained increasing popularity in mental health research in the past 5 years and are being conducted on an ever-increasing range of psychological disorders (eg, social anxiety [11], depression and bipolar disorder [12], psychosis [13,14], and suicidal or nonsuicidal self-injury, the last for which Torous et al provide a review of the research [15]). ...
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Background Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. Objective We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. Methods The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. Results Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. Conclusions We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. Trial Registration NCT04162574; International Registered Report Identifier (IRRID) DERR1-10.2196/38294
... Researchers have also reported on a link between smartphone addiction and the physical condition of the individual (e.g., daytime sleepiness, energy levels, or physical symptoms potentially caused by excessive use of phones) [4,33,39] and mood [25,27,41,63,84]. Unfortunately, usage tracking systems are prone to false positive signals or fail to present data in context [68,107], both of which make it difcult to accurately describe one's physical condition and mental status. ...
... • Service Provision for Personalized Medicine Seekers -Sensing apps for Personalized Medicine provide accurate health status monitoring and personalized interventions or treatments, which can be concluded as healthcare services provision [18], [194], [195]. In most of the Personalized Medicine cases, participants actively engage in the sensing task for personalized medicine with an expectation to seek and extend personal health benefit [196]. To this end, the detail objective of personalized medicine apps in this step is to provide exact healthcare services (e.g., exercise reminders and user-friendly interface) and keep improving service quality (e.g., optimizing intervention times with algorithms) to guarantee and enhance users' active engagement [197]. ...
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Mobile sensing systems have been widely used as a practical approach to collect behavioral and health-related information from individuals and to provide timely intervention to promote health and well being, such as mental health and chronic care. As the objectives of mobile sensing could be either personalized medicine for individuals or public health for populations , in this work, we review the design of these mobile sensing systems, and propose to categorize the design of these systems in two paradigms—1) personal sensing and 2) crowdsensing paradigms. While both sensing paradigms might incorporate common ubiquitous sensing technologies, such as wearable sensors , mobility monitoring , mobile data offloading , and cloud-based data analytics to collect and process sensing data from individuals, we present two novel taxonomy systems based on the: 1) sensing objectives (e.g., goals of mobile health (mHealth) sensing systems and how technologies achieve the goals) and 2) the sensing systems design and implementation (D&I) (e.g., designs of mHealth sensing systems and how technologies are implemented). With respect to the two paradigms and two taxonomy systems, this work systematically reviews this field. Specifically, we first present technical reviews on the mHealth sensing systems in eight common/popular healthcare issues, ranging from depression and anxiety to COVID-19. By summarizing the mHealth sensing systems, we comprehensively survey the research works using the two taxonomy systems, where we systematically review the sensing objectives and sensing systems D&I while mapping the related research works onto the life-cycles of mHealth Sensing, i.e.: 1) sensing task creation and participation ; 2) (health surveillance and data collection ; and 3) data analysis and knowledge discovery . In addition to summarization, the proposed taxonomy systems also help the potential directions of mobile sensing for health from both personalized medicine and population health perspectives. Finally, we attempt to test and discuss the validity of our scientific approaches to the survey.
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University students have low levels of physical activity and are at risk of mental health disorders. Mobile apps to encourage physical activity can help students, who are frequent smartphone-users, to improve their physical and mental health. Here we report students’ qualitative feedback on a physical activity smartphone app with motivational text messaging. We provide recommendations for the design of future apps. 103 students used the app for 6 weeks in the context of a clinical trial (NCT04440553) and answered open-ended questions before the start of the study and at follow-up. A subsample ( n = 39) provided additional feedback via text message, and a phone interview ( n = 8). Questions focused on the perceived encouragement and support by the app, text messaging content, and recommendations for future applications. We analyzed all transcripts for emerging themes using qualitative coding in Dedoose. The majority of participants were female (69.9%), Asian or Pacific Islander (53.4%), with a mean age of 20.2 years, and 63% had elevated depressive symptoms. 26% felt encouraged or neutral toward the app motivating them to be more physically active. Participants liked messages on physical activity benefits on (mental) health, encouraging them to complete their goal, and feedback on their activity. Participants disliked messages that did not match their motivations for physical activity and their daily context (e.g., time, weekday, stress). Physical activity apps for students should be adapted to their motivations, changing daily context, and mental health issues. Feedback from this sample suggests a key to effectiveness is finding effective ways to personalize digital interventions.
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La Telesalud en el Perú, con su Ley Marco, Norma, Directivas y Lineamientos, permitieron una estrategia de prestación de servicios de salud, mediante el uso de las tecnologías de la información y comunicación (TICs) (1-5). Dichos avances tecnológicos, la innovación y la utilización del conocimiento científico en distintos temas involucrados con la salud, han contribuido a cambios en los diagnósticos, tratamientos y recuperación de pacientes en el mundo y en el Perú. Con ello, ha prevenido que más personas tengan algún deterioro en su salud. Las Tics, son el conjunto de servicios, redes, softwares y dispositivos de hardware que se integran en los sistemas de información interconectados, con el objetivo de gestionar datos e información (5). Como ya dije, el Perú cuenta con un mecanismo regulatorio para la maximización de los sistemas de información en salud. Pero, el principal desafío sigue siendo la interoperabilidad de estos (6,7). El reto a cumplir es entonces, obtener una excelente conectividad entre los establecimientos de salud. La Ley 29904, de Promoción de la Banda Ancha y construcción de la Red Dorsal Nacional de Fibra Óptica (8); permitieron el acceso a servicios de Internet con una mejor calidad de los servicios de telemedicina y telesalud. Lo que menciono Ernesto Gozzer, en su exhaustivo análisis de las principales conclusiones de telesalud hechas en el Perú entre los años 2002 al 2010; fue que la mayor parte de las iniciativas de telesalud en el Perú, fueron relacionadas en telemedicina y telegestión. Los primeros proyectos de telesalud se iniciaron desde el año 2002 (9).
Recently, the importance of exercise has been attracting attention. Exercise has positive effects on the body and mind. However, many people do not get enough exercise, and this trend has not improved over the years. One fundamental challenge why people do not have more opportunities to exercise is that they are too busy with work and household chores. Therefore, to develop an exercise habit, it is necessary to incorporate “exercising” into daily life, which can be done while working or doing housework. In this paper, through the design of a system to promote “doing exercise while working at a desk,” we propose both suggestion and feedback methods that conform to working at a desk and enable users to continue to exercise. As future work, we would like to improve the accuracy of the state judgment and the notification timing of the feedback method, thereby adapting the system to various work situations.
After a cardiac event, patients typically enroll in a cardiac rehabilitation program in a rehabilitation center, where physiotherapists guide them in overcoming their fear to move and increasing physical activity. Effectively changing patients’ health behaviour and bringing the newly formed habits to their home environment remains challenging. At home, patients experience difficulties interpreting exercise targets and monitoring physical activity. To bridge the gap between supervised rehab in the center and regular exercise in daily life, we propose a shared decision making (SDM) approach SharedHeart that supports patients in changing their health behaviour and transferring the knowledge and healthy habits to their homes. We developed 3 applications that support patients and physiotherapists in following a SDM approach: (1) a tablet app to record the patient’s sports preferences, (2) a caregiver dashboard to create and follow up on a patient-tailored exercise plan during and in between SDM encounters, and (3) a mobile app to report and follow up on physical activity at home. In this paper, we present the results of our survey investigating physiotherapists’ application of SDM in their current practice and perceived usefulness of SDM and supporting tools. Next, we discuss our proposed SDM approach on the conceptual level and the guideline-based design of the supporting IT applications. We conclude by highlighting how our approach and tools align with physiotherapists’ needs.KeywordsShared decision makingPhysical activityCardiac rehabilitationPatient empowermenteHealth
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Children and young people with neurodevelopmental disorders seem to be more susceptible to developing obesity and eating disorders. To prevent this, therapeutic programs including nutritional education are, therefore, needed. Serious games (SGs) represent a promising solution to improve adherence to the treatment in different populations, including children/adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). The present paper describes the design and development of a SG to promote a healthy diet and lifestyle. To the best of our knowledge, this is the first SG specifically focused on nutritional education developed for young individuals with ADHD or ASD. The SG is made of four mini-games contextualized within a single narrative frame. Through his/her avatar, the player has to challenge four opponents, one for each educational topic, with the help of a wise character that educates him/her throughout the story. The SG can be experienced with a tablet or a PC, and with the supervision of an adult. A pilot study will be carried out to evaluate the feasibility, engagement, and usability of the SG, involving children with ADHD, ASD, and a group of typically developing peers. Based on the results, some adaptations will be implemented to improve the SG before conducting a larger trial to evaluate the effectiveness of the SG in promoting a healthy diet and lifestyle.KeywordsADHDASDSerious gameNutritional educationHealthy diet
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A sedentary lifestyle is a contributing factor to chronic diseases, and it is often correlated with obesity. To promote an increase in physical activity, we created a social computer game, Fish'n'Steps, which links a player's daily foot step count to the growth and activity of an animated virtual character, a fish in a fish tank. As further encouragement, some of the players' fish tanks in- cluded other players' fish, thereby creating an environment of both cooperation and competition. In a fourteen-week study with nineteen participants, the game served as a catalyst for promoting exercise and for improving game players' at- titudes towards physical activity. Furthermore, although most player's enthusi- asm in the game decreased after the game's first two weeks, analyzing the re- sults using Prochaska's Transtheoretical Model of Behavioral Change suggests that individuals had, by that time, established new routines that led to healthier patterns of physical activity in their daily lives. Lessons learned from this study underscore the value of such games to encourage rather than provide negative reinforcement, especially when individuals are not meeting their own expecta- tions, to foster long-term behavioral change.
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Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays.
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Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity. Author Keywords
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Overweight and obesity are a global epidemic, with over one billion overweight adults worldwide (300+ million of whom are obese). Obesity is linked to several serious health problems and medical conditions. Medical experts agree that physical activity is critical to maintaining fitness, reducing weight, and improving health, yet many people have difficulty increasing and maintaining physical activity in everyday life. Clinical studies have shown that health benefits can occur from simply increasing the number of steps one takes each day and that social support can motivate people to stay active. In this paper, we describe Houston, a prototype mobile phone application for encouraging activity by sharing step count with friends. We also present four design requirements for technologies that encourage physical activity that we derived from a three- week long in situ pilot study that was conducted with women who wanted to increase their physical activity. Author Keywords
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In this paper, we propose design strategies for persuasive technologies that help people who want to change their everyday behaviors. Our strategies use theory and prior work to substantially extend a set of existing design goals. Our extensions specifically account for social characteristics and other tactics that should be supported by persuasive technologies that target long-term discretionary use throughout everyday life. We used these strategies to design and build a system that encourages people to lead a physically active lifestyle. Results from two field studies of the system—a three-week trial and a three-month experiment—have shown that the system was successful at helping people maintain a more physically active lifestyle and validate the usefulness of the strategies.
Lifestyle changes are considered first line treatment in type 2 diabetes, but very few data are available in the "real world" of diabetes units. We aimed to measure the effectiveness of moderate and high intensity interventions on weight loss, metabolic control and insulin use. We report a prospective cohort study, carried out in 822 consecutive subjects with type 2 diabetes, first seen in a 4-year period in a diabetes unit of an academy hospital. Subjects were treated with either a sole prescriptive diet (Diet), or received an additional short-course Elementary Nutritional Education (4 group sessions-ENE) or an intensive Cognitive Behavioural Therapy (12-15 group sessions-CBT). The results were adjusted for the propensity score to be assigned different treatments, derived from logistic regression on the basis of age, gender, BMI, HbA1c, diabetes duration and insulin use at baseline. Main outcome measures were weight loss and weight loss maintenance, metabolic control, and secondary failure to insulin use. Both structured programmes produced a larger weight loss, and the adjusted probability of achieving the 7% weight loss target was increased. Similarly, both programmes favoured metabolic control, irrespective of insulin use. After adjustment for propensity score, both ENE (hazard ratio, 0.48; 95% CI, 0.27-0.84) and CBT (hazard ratio, 0.36; 95% CI, 0.16-0.83) were associated with a reduced risk of de novo insulin treatment. Structured behavioural programmes aimed at lifestyle changes are feasible and effective in the "real world" setting of a diabetes unit for the treatment of type 2 diabetes.
Despite the significant reduction in cardiovascular mortality during the past three decades, atherosclerotic coronary heart disease (CHD) remains the leading cause of death and disability in the United States. Randomized clinical trials in patients with CHD have provided convincing evidence that risk factor modification is beneficial in decreasing all-cause mortality and cardiovascular morbidity and mortality. Multifactorial coronary risk reduction provides the most substantial benefit. Coronary risk reduction is associated with a decrease in cardiovascular-related hospital admissions, a reduced need for myocardial revascularization procedures, and an improved quality of life for the patients so treated. Control of coronary risk factors is an integral component of the optimal care of the patient with CHD.
Less than 50% of persons who participate in cardiac rehabilitation (CR) programs maintain an exercise regimen for as long as 6 months after completion. This study was conducted to identify factors that predict women's exercise following completion of a CR program. In this prospective, descriptive study, a convenience sample of 60 women were recruited at completion of a phase II CR program. Exercise was measured using a heart rate wristwatch monitor over 3 months. Predictor variables collected at the time of the subjects' enrollment were age, body mass index, cardiac functional status, comorbidity, muscle or joint pain, motivation, mood state, social support, self-efficacy, perceived benefits or barriers, and prior exercise. Of women, 25% did not exercise at all following completion of a CR program and only 48% of the subjects were exercising at 3 months. Different predictors were found of the various dimensions of exercise maintenance. Predictors of exercise frequency were comorbidity and instrumental social support. Instrumental social support was the only predictor of exercise persistence. Comorbidity was the only predictor of exercise intensity. The only predictor of the total amount of exercise was benefits or barriers. Interventions aimed at increasing women's exercise should focus on increasing their problem-solving abilities to reduce barriers to exercise and increase social support by family and friends. Because comorbidity was a significant predictor of exercise, women should be encouraged to use exercise techniques that reduce impact on muscles and joints (eg, swimming) or exercising for short periods several times a day.