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2012. Vol.3, Supplement, 1-5
Published Online December 2012 in SciRes (http://www.SciRP.org/journal/ce) DOI:10.4236/ce.2012.38b001
Copyright © 2012 SciRes.
Integrating Health Theories in Health and Fitness Applications for
Sustained Behavior Change: Current State of the Art
Angela Brunstein1, Joerg Brunstein2, Selma Limam Mansar2
1Department of Psychology, Devisi on of Popu lation Health Sciences, Royal College of Surgeons
in Ireland - edical University of Bahrain, Bussaiteen, Bahrain
2Dietrich College of Humanities and Social Sciences, Information Systems Program, Carnegie Mellon
University in Qatar, Doha, Qatar
Received 20 1 2
Two hundre d mill ion peopl e in the US ar e overweig ht or obes e mirroring a worldwi de trend tha t is asso-
ciated with high morbidity and mortality rates. Health and fitness mobile technology applications have
great capacities for support ing dieter s’ life-st yle changes and could p rofit from a nd provide i nput of health
behavior theories. Those theories have been demonstrated with massive clinical evidence to be efficient
for f ostering hea lthy lif estyle cha nges and w eight los s. This research re viewed the 10 0 most p opular mo-
bil e technolog y appl ications from iTunes App St ore’s Hea lth and Fit ness ca tegory in r espect coverage of
health behavior theories’ concepts und chose 14 of those for a complete analysis. Applications provide
good sup port for at hletes’ work outs and have great potential to be extended to s erve overweight users as
well. Missing features could be easily implemented given the current state of technology. These devel-
opments look promising for tackling sustained weight loss in many mobile technology users .
Keywords: Health and Fitness Applications; Heal th Behavior Theories; Mobile Technology Applications
Overweight users of health applications face complex daily
challenges when trying to change their lifestyle. Not used to
low calorie food and not ready yet for advanced exercise, they
have to integrate new procedures into already established daily
routines. Often there are knowledge gaps to be filled. They may
be overwhelmed by the scale of the project, lack motivation,
and may need support for coming back on track after relapses.
When using health and fitness applications for supporting these
aims, overweight users need to know with confidence why they
are doing what they are doing and what the health benefits from
tho se exer cises will b e.
The i nterp lay of all these factors is covered in intensively re-
searched health behavior theories. Mobile applications can
nicely complement those theories because of their computing
power, multimedia and communication capacities, and their
capacities for tracking daily activities of patients who are seen
by their healthcare provider only on weekly or monthly basis.
Health behavior theories use detailed information about a per-
son for predicting the likelihood of initiation and maintenance
of health b ehavio rs. Health applications can support the process
of behavior change by providing in time, user- and stage-spe-
cific advic e and encouragement.
Curren t health and fitness appli cations use alread y successful
smartphone capabilities for supporting goal oriented behavior
and it does not take effort much to integrate the remaining
components of health behavior theories. The benefit of doing so
fitness ap plication s are well adapted to athl etes who experien ce
workout an d exercise as rewardin g by the mselves. The y are not
well adapted for the potentially 200 million overweight and
obese smartphone users in the US. The majority of those might
have never seen a gym from the inside. When combining the
strengths of health behavior theories and mobile phone tech-
nologies, both can greatly profit. Mobile technology can pro-
vide much more precise input to health behavior theories than
from participants’ self-evaluatio n. Health app lications develop-
ers could win huge numbers of new customers.
For this research, we have evaluated 14 mobile technology
applications from the Health and Fitness category of the iTunes
App Store in respect to already covered components of health
behavior theories. The conclusion of our work will indicate
there is already good coverage for some of those components
and first implementations and great potential for the remaining
components. In this paper, we describe the prevalence of obes-
ity and b r iefly review evidence th at mobile tech nology has been
successfu ll y used t o tackle t hat epidemic obes ity pro bl em. Then
we will briefly sketch the health behavior theories and our me-
thod and analysis of reviewed health applications. Finally we
will review results using 5K Runner as an example und review
suggestions for further development.
Obesity Prev alen ce
According to World Health Organization figures (WHO,
2010), in 2008, 63% of deaths worldwide were caused by non-
communicable diseases, including cardiovascular diseases, can-
cers, diabetes, and lung diseases. These diseases are strongly
associated with health risk behaviors, such as smoking, unheal-
thy diet, insufficient physical activity, and alcohol abuse (WHO,
2010). Between 1980 and 2008, worldwide obesity has almost
doubled (WHO, 2010). In the American WHO regions, 62%
were overweight. In other regions, prevalence statistics are
similar. For example, in Bahrain diabetes type 2 prevalence was
15% in 2010 with 61% of Bahraini males and 67% of Bahraini
A. BR UNS TEIN ET AL.
Copyright © 2012 SciRes.
females bei ng o ver weight or obese (Sh aw et al., 201 0) . Ther e is
evidence t hat habi tual ener gy imb alance of on ly a few kcal can
result in a slow, but substantial gradual weight gain (e.g., Mo-
zaffarian et al., 2011). At the same time, modest life-style
changes can reverse that trend and result in sustainable weight
loss (e.g., Hall, 2012).
Mobile Technology and Obesity
Mobile technologies with their advanced computing and
communication capabilities can promote efforts for lifestyle
changes (e.g., Boulous et al., 2011). There is also increasing
evidence that mobile technologies can be efficiently used for
supporting those aims (e.g., Free et al., 2010; Web et al., 2010).
For example, shoppers provided with food substitution advice
continued buying healthier alternatives after program comple-
tion (Huang et al., 2006). Weekly emails on patients’ stage
and specific goals impacted their eating habits (Patrick et al.,
In 2009, 67% of inhabitants globally had a mobile phone
subscription (Geneva, International communication Union,
2010). In 2011, 34% of US households had wireless phones
only (Blumberg et al., 2012). In Qatar, the mobile phones sub-
scription rate reached 121% during that time (IctQatar Land-
scape, 2009, update 2011). This indicates that patients in need
of changing their lifestyle can be reached and supported via
mobile technology. Indeed, many smart phone application
stores, including iTunes App Store, Google Play, and Amazon
Appsto re, provide a “H ealth and Fitness” category.
When promoting a healthier lifestyle, mobile technologies
should implement prevalent health behavior theories that have
been successfully implemented for obesity and diabetes man-
agement, smoking cessation, and other health related habits in
clinical settings. These include the Health Belief Model (HBM;
Becker & Rosenstock, 1984), Theory of Planned Behavior
(TPB; Ajzen & Fishbein, 1970; Ajzen, 1991), Transtheoretical
Model or Stages of Change Model (TTM; Proch aska & di Cle-
mente, 1984; Prochaska et al., 1994) and the Health Action
Process Approach (HAPA; Schwarzer, 1992; Schwarzer et al.,
2008). Because the HAPA integrates components from the
oth er th eories, we review here briefly the HBM, TPB, and TTM
only. Each of the theories provides a slightly different perspec-
tive on the issue of behavior change.
The Health Belief Model (HBM) focuses on a person’s
health related cognitions for determining their behavior. For
example, when deciding whether to schedule an appointment
for cervical cancer, a woman would consider whether she is
likel y to have that cancer, ho w severe the d isease wou ld b e, and
what her costs and benefits of screening would be.
If her sister would be diagnosed with cancer, that might trig-
ger scheduling an appointment herself (Murray & McMillan,
1993).Mobile health applications could support decision mak-
ing by providing relevant health information and by popping up
reminders, for example for s cheduling exercise.
The Theory of Pl anned Beha vior (TPB) ackno wledges t hat
a person’s health behavior is only indirectly impacted by their
beliefs and be social influence and is mediated by a person’s
intention resulting from those distal factors.
For example, perceived behavioral control based on earlier
experience and social norms predicts whether a person would
start to exercise and maintain that new habit (Armitage, 2005).
Many mobile health applications include options for social
networking or provide role models and social norms. Detailed
feedback on workout performance can foster perceived beha-
The Transtheoretical Model (TTM) suggests five stages
that a person progresses through over time: During the
pre-contemplation stage, a person has not recognized yet that
they need to quit smoking, for example. During the contempla-
tion stage, they start thinking about changing. During the prep-
aration stage, the person plans when to throw away cigarettes
and what to do instead of smoking. During the action stage, the
person implements those plans. Finally, during the maintenance
stage, the person aims to maintain abstinence and integrates
new routines in their life-style. During relapse, a person leaves
that cycle, for example by smoking, and returns thereafter to
any of the five stages (DiClemente et al., 1991). Obviously,
mobile health applications cannot serve users during the
pre-contemplation stage, because those do not even consider
yet changing their lifestyle. During contemplation stage, users
could profit from relevant health information. During the plan-
ning stage, mobile health applications could help to set specific
and achievable goals, maybe supported by healthcare providers’
feedback. During the action phase, there is a challenge to pro-
vide specific and detailed instruction on one side and to enable
users to perform on their own. Otherwise mobile health appli-
cation work like car navigation systems: They tell you the way
to the airport, but they do not introduce you to a new city. This
balance is even more important during the maintenance stage,
when users try to integrate new routines in their daily life. Fi-
nally, lap se and relap se managemen t shou ld encourage us ers to
get back to their aims as fast as possible for limiting potential
For this review, we have chosen the ten components of the
three h ealth behavior t heories that were st raightforward t o ana-
lyze and not overlapping. We evaluated whether applications
addressed perceived costs (1) and health benefits (2) and
whether they provided cues for action (3; HBM). We evaluated
whether applications supported subjective norms or social sup-
port (4) and promoted behavior control (5; TPB). Finally, we
discussed which of the six stages (6 – 10) of the Transtheoreti-
cal Model could be supported. Because people in the
pre-contemplation stage do not yet consider changing, we ex-
cluded that stage. We assume that people might browse for
applications from the contemplation st a ge on.
We have focused on ITune’s What’s Hot section of most
downloaded applications for IPhones and IPads from the
Health and Fitness category. Most applications offered in that
store are also offered for other platforms. From that section, we
selected the top 100 applications (US store, state of September
1, 2012) and successively reduced the number of applications to
14 (see Table 1) by excluding applications that were not of-
fered in English, were offered for free, did not cover health
style related diet or physical activity behaviors, or had several
very similar ap pl icat io ns fro m the same p ub lish er. We exclu ded
free applications to reduce redundancy because most of those
had paid siblings with very similar features. In addition, users
demonstr ate some co mmitment t o chan ging their lifestyle when
paying some money for downloading an application.
From the original list of 100, 59 applications were for free,
the remaining applications cost between $0.99 and $5.99. The
A. BR UNS TEIN ET AL.
Copyright © 2012 SciRes.
Tabl e 1.
Analyzed applications, publisher, short description and analyzed components: cues to action (HBM3), subjective norm (TPB 4), behavior control
(TPB 5), contemplation phase (TTM 6), preparation phase (TTM 7), action phase (TTM 8), maintenance phase (TTM 9), relapse phase (TTM 10).
Application Publisher Description HBM 3 TPB 4 TPB 5 TTM 6 TTM 7 TTM 8 TTM 9 TTM 10
Heavy Duty Apps
8 week running program
aiming for 5K
C25K TM - 5K Trainer
Zen Labs LLC
8 weeks running progra m
aiming for 5K
workout program and tra cker
workout organizer and tracker
for distanc e based s po r t s
Food Substituti on
Calculator Lean Bodies
Consulting provision of hea lthier food
alternatives 0 1 0 1 1 0 0 0
Gorilla Workout: Athletic
workout program and tracker
MapMyRIDE+ GPS Cyc
biking and running tra cker
Mountain bike PRO
GPS based b ik e c omputer
myWOD - All in one
WOD Log for Cros sFit Jimmy Tangeman workout or ganizer and trac ker 1 1 1 0 1 1 1 0
Points Calculator Plus
Seal Fitness Challenge
Jgo Labs LLC
fitness pro g r a m
Walkmeter GPS Walking
GPS-based fitness computer
Workout Hero TM
Storeboughtmil k Inc.
workout program and tra cker
Six to Start
running game and audio
same range holds for the final selection. The final 14 applica-
tions required between 3.2 MB and 322 MB space on the de-
vice (M = 45 MB, SD = 84), and were ranked by customers
with at least four stars (M = 4.7, SD = 0.15).
As an illustration, 5K Runner provides a 3 days for 8 weeks
running program for beginners with the final aim to master
5,000 m. As most other applications, 5K Runner does not link
to relevant health information for the HBM. Under the Learn
tab, th e Help & Tips section informs about health requirements,
what is needed to start, and about the best way to warm up. It
also suggests to “consult a doctor before beginning”.
Fo r the TP B, 5K Runner provides several cu es for perceived
behavioral control: While running, the application provides
visual and auditory feedback on progress and a halfway notifi-
cation (TPB5). It awards different badges after completing a
workout and automatically tracks and records training sessions.
However, i t does not track dist ance or physiol ogical p arameters
or contributes to changing personal attitudes toward running.
For implementing subjective norms (TPB 4), there is an option
to use social networking tools to broadcast accomplishments,
but no role models or options to team up for running.
For the TTM, 5K Runner provides support for the prepara-
tion phase (TTM 7) under its learn tab, help & tips section. By
providing detailed instruction, when to walk and when to run, it
guides the action phase (TTM 8) for 3 days a week. This might
also contribute to the beginning of the maintenance phase
(TTM 9) because it is tailored for 8 weeks. Still, there is no
instruction on how to maintain performance once a user has
reached the 5K goal. For relapse stage (TTM 10), there is a
message in the help & tips section, but not a complete relapse
In total, ten applications had options for social networking
(TPB 4), for sending emails or getting applause and encou-
ragement from friends. This kind of social support is as good as
your friends are. Workout Hero TM provided a list with heroes,
benchmark girls/boys, etc. as an implementation of subjective
norms. Daily Workouts and other exercise programs provide
videos with demonstrations on how to perform an exercise.
These can contribute to subjective norms (TPB 4) and to beha-
vior al c on tro l (TP B 5, N = 11). What is missing in all programs
we evaluated is an interface for the healthcare provider that
could either feed performance data back to the healthcare pro-
vider or for enabling guidance for the patient. There was also
no relationship between provided instruction and relevant
health information (HBM 1 and 2). For example, the Food
Substitution Calculator might suggest substituting bread with
green beans, but would fail to explain how this serves health
Contemplation and relapse phases (TTM 6, N =7, and TTM
10, N = 5) were neglect ed by most applications. Zombies, run!
starts the first time with a little story explaining the scenario. In
contrast, most of the workout programs start immediately with
choosing an exercise (TTM 8, N = 13). In case of a relapse
(TTM 10), SEAL Fitness Challenge ranks the user back to
‘wannabe’ cancelin g out all accomplish ments made so far. This
might trigger motivational issues for users when attempting to
get back on track. In contrast, 5K Runner provides encourage-
ment for “I h ad a reall y bad run” saying t hat even Mich ael Jor-
dan had occasionally bad days under its Help & Tips section.
A. BR UNS TEIN ET AL.
Copyright © 2012 SciRes.
This kind of (re-)lapse management is especially important
because there is evidence that, for example, dieters in preload
studies tend to loose control and overate after consuming a high
calorie milkshake in the morning, the ‘what-the-hell effect’
(Herman & Mack, 1975). It seems many dieters think in
all-or-none terms and do not have a backup plan in case of
lapse. While the SEAL Fitness Challenge supports that kind of
thinking, the 5K Runner invites for reframing the problem and
getting back to track. The milkshake in the morning spoils to-
day’s diet. Whatever comes after that, could spoil the whole last
Many reviewed applications did also miss to address the
maintenance phase (TTM 9, N =7). Once a C25K TM - 5K
Trainer Pro user has reached the magical 5,000 meters, how
shall they keep up performance and fitness?
Most health and fitness applications on iTunes’ What’s Hot?
list provide instruction and tracking how to perform
health -related behavior, but are only loosely associated with
detailed health information. They also cover some, but not all
components of health behavior theory. This makes it impossible
to predict success for adopting healthier behaviors using those
applications based on these theories. Technology has suffi-
ciently advanced to implement the theories. They can take the
burdens from patients tracking their performance and can pro-
vide much more precise measures, for example for physical
activity than pati ents could app r oximate.
To return to our 5K Runner example, the application could
integrate HBM components more, by taking, for example, Body
Mass Index and other variables as input and tailoring the run-
ning program accordingly. Alternatively it could allow the
Health Care Provider (HCP) to do so and send alerts to the
HCP if, for example, the blood glucose levels are too low. More
general, 5K Runner could display health benefits as motivating
pop-up messages after completing the day’s program. For the
cues to actions component of the HBM, 5K Runner could
push-up automatic reminders or suggest to ‘book exercise in
your calendar’ or to keep the running shoes in a prominent
location as a reminder to run next ti me. All th ese can con tr ib ute
to having users come back and complete their running program.
For the TPB, 5K Runner could provide benchmark or role
models for implementing subjective norms (TPB 4) or simulate
an workout competition. For behavioral control (TPB 5), the
application could integrate distance or physiological measures
tracking and not just reference to GPS programs or runner fo-
rums as external sources.
For the TTM, 5K Runner could support users to make
achievable and measurable plans for performance outside of a
running session (TTM 7 and 8). It could suggest days for run-
ning and remind users to run. The application could keep send-
ing those reminders after completing the program (TTM 9) to
keep up high performance and fitness. For managing relapses, it
probably takes more than a message in the help & tips section.
Again, health care provider could contribute toward winning
users b ack after a phase of relapse.
In general, for getting the best from both worlds, we would
wis h for integrating user’s motives and aims for tailoring per-
sonalized advice that is integrated in a treatment plan for that
person. It would be good to implement physiological bench-
marks for aler tin g p atien ts an d heal th care p rovi der, for exa mple
if a person has too high heart rates during workout. This is as-
sociated with an interface for the healthcare provider allowing
for monitoring, guidance and feedback. Health and fitness ap-
plications should not only build performance, but also support
building planning competence, support the perceived
cost/benefits analysis, and provide long-term motivational
support and relapse management. For implementing those,
developers can greatly profit from mathematical modeling ef-
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