Available via license: CC BY 2.0
Content may be subject to copyright.
Original Paper
Mobile Apps for Weight Management: A Scoping Review
Jordan Rivera1,2*, BSc (Hons); Amy McPherson3,4*, PhD, CPsychol, AFBPsS; Jill Hamilton5,6*, MD; Catherine
Birken1,6*, MSc, MD, FRCPC; Michael Coons7*, PhD, CBSM; Sindoora Iyer1*, BSc; Arnav Agarwal1*, BHSc; Chitra
Lalloo1,2*, PhD; Jennifer Stinson1,2*, RN-EC, CPNP, PhD
1Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
2Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada
3Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
4Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
5Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, ON, Canada
6Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
7Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
*all authors contributed equally
Corresponding Author:
Jennifer Stinson, RN-EC, CPNP, PhD
Child Health Evaluative Sciences
The Hospital for Sick Children
686 Bay Street
Toronto, ON, M5G0A4
Canada
Phone: 1 416 813 7654 ext 304514
Fax: 1 416 813 8501
Email: jennifer.stinson@sickkids.ca
Abstract
Background: Obesity remains a major public health concern. Mobile apps for weight loss/management are found to be effective
for improving health outcomes in adults and adolescents, and are pursued as a cost-effective and scalable intervention for combating
overweight and obesity. In recent years, the commercial market for ‘weight loss apps’ has expanded at rapid pace, yet little is
known regarding the evidence-based quality of these tools for weight control.
Objective: To characterize the inclusion of evidence-based strategies, health care expert involvement, and scientific evaluation
of commercial mobile apps for weight loss/management.
Methods: An electronic search was conducted between July 2014 and July 2015 of the official app stores for four major mobile
operating systems. Three raters independently identified apps with a stated goal of weight loss/management, as well as weight
loss/management apps targeted to pediatric users. All discrepancies regarding selection were resolved through discussion with a
fourth rater. Metadata from all included apps were abstracted into a standard assessment criteria form and the evidence-based
strategies, health care expert involvement, and scientific evaluation of included apps was assessed. Evidence-based strategies
included: self-monitoring, goal-setting, physical activity support, healthy eating support, weight and/or health assessment,
personalized feedback, motivational strategies, and social support.
Results: A total of 393 apps were included in this review. Self-monitoring was most common (139/393, 35.3%), followed by
physical activity support (108/393, 27.5%), weight assessment (100/393, 25.4%), healthy eating support (91/393, 23.2%),
goal-setting (84/393, 21.4%), motivational strategies (28/393, 7.1%), social support (21/393, 5.3%), and personalized feedback
(7/393, 1.8%). Of apps, 0.8% (3/393) underwent scientific evaluation and 0.3% (1/393) reported health care expert involvement.
No apps were comprehensive in the assessment criteria, with the majority of apps meeting less than two criteria.
Conclusions: Commercial mobile apps for weight loss/management lack important evidence-based features, do not involve
health care experts in their development process, and have not undergone rigorous scientific testing. This calls into question the
validity of apps’ claims regarding their effectiveness and safety, at a time when the availability and growth in adoption of these
tools is rapidly increasing. Collaborative efforts between developers, researchers, clinicians, and patients are needed to develop
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 1http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
and test high-quality, evidence-based mobile apps for weight loss/management before they are widely disseminated in commercial
markets.
(JMIR Mhealth Uhealth 2016;4(3):e87) doi: 10.2196/mhealth.5115
KEYWORDS
weight loss; obesity; mobile apps; smartphones; mHealth
Introduction
Background
The prevalence of overweight and obesity worldwide is
predicted to exceed 1.12 billion and 573 million people,
respectively, by 2030 [1]. Excess weight is closely linked to a
myriad of chronic diseases such as hypertension, type 2 diabetes
mellitus, cardiovascular disease, and stroke [2]. The economic
costs of obesity globally are estimated at 0.7% to 2.8% of total
health care expenditures [3] and the burden of mortality is
estimated at 2.8 million deaths annually [4]. Environmental
changes promoting intake of highly caloric, inexpensive, nutrient
dense foods, and larger portion sizes, coupled with decreased
physical activity and increased sedentary behaviors are
significant causative factors for obesity [5]. Accordingly, efforts
to curb obesity have aimed to promote adherence to
evidence-based recommendations for daily exercise, healthy
eating, and associated behavioral determinants of weight [6].
Clinical interventions for obesity have demonstrated variable
efficacy, which has been primarily attributed to fluctuations in
treatment adherence over time [7,8]. Clinical interventions
employ evidence-based strategies primarily based on behavior
change theory to drive permanent lifestyle modifications
necessary for long-term weight control [9]. These strategies
include self-monitoring, goal-setting, healthy eating training,
increasing physical activity, providing personalized and
objective feedback, stress reduction, and problem solving [10].
Clinical interventions for obesity are rigorous and typically
require face-to-face contact often for over a year [11]. These
programs can be time, cost, and resource intensive, and often
inconvenient for patients to attend, limiting long-term treatment
adherence and weight maintenance [10,11]. Novel, low-cost,
and widely accessible tools are needed to support the practice
of evidence-based strategies for weight control [12], particularly
when patients face significant barriers to accessing clinical
treatments.
To address these concerns, efforts have shifted to emerging
interactive information and communication technologies as a
novel means to support chronic disease self-management with
the potential for low-cost scalability [10-13]. Clinical
intervention strategies can be translated to mobile devices, such
as mobile apps, leveraging the multifunctional capabilities and
widespread use of mobile devices [10,14]. Growing research
supports the ability for mobile devices to deliver effective
intervention strategies for weight loss [13,15-17]. Mobile
devices purposed toward improvements in weight, diet, and
physical activity have demonstrated superior effectiveness on
weight outcomes and behavioral determinants of weight when
compared with standard no-intervention controls as well as to
controls receiving nonmobile device interventions [17]. Mobile
devices can be used to enhance self-efficacy by priming behavior
activation and reducing the burden of behavior change
techniques, such as providing convenient ways to self-monitor,
set and update goals, communicate with supports, and access
personally relevant education and resources efficiently [17].
Health researchers have started to develop and test their own
mobile apps for weight management, with the objective to create
new clinical and research tools that incorporate evidence-based
strategies used in the treatment of obesity. A review of behavior
change techniques in 12 primary trials and five secondary
analyses examining mobile device interventions for weight loss
found that interventions contained a minimum of five
techniques, the most common of which were self-monitoring,
goal-setting, tailored feedback, general health information,
encouragement, prompting practice, and social support [17].
Mobile device interventions with multiple techniques that
differentiated it meaningfully from the comparison treatment
were associated with superior weight and health behavior
outcomes [17].
However, studies that have explored commercial app markets
have found that mobile apps for weight loss typically incorporate
only a minority of the evidence-based strategies used in the
treatment of obesity [11,18-21]. It is likely that the lack of
evidence-based features limits the effectiveness of these tools,
which is concerning given the abundance and availability of
such tools for assisting with the general public’s weight
management needs.
Objective
To our knowledge, no studies have systematically and
comprehensively explored the current commercial mobile app
market to examine evidence-based strategies, health care expert
involvement, and scientific evaluation of weight
loss/management apps since these earlier investigations. The
rapid growth in the number of health and fitness apps, combined
with an increase in adoption of these tools in recent years
underscores the need for an updated assessment of this rapidly
changing market. Prior studies that have examined the inclusion
of evidence-based strategies in commercial mobile apps for
weight loss have mostly confined their search to a limited
sample of apps (eg, <50) [11,18], to a single app store (eg,
iTunes) [18-21], to a single population (eg, children) [20,21],
or require updating (eg, published more than 3 years ago) [19].
The objective of the current study was to conduct an updated
and comprehensive systematic review of weight management
mobile apps across four major commercial app stores to describe
the inclusion of evidence-based strategies for weight control,
health care expert involvement, and scientific evaluation.
Findings from this study will be used to identify the major
overarching strategies relied upon by current weight
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 2http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
management apps in order to provide direction for the
advancement of research and app development.
Methods
App Search
Figure 1 displays the methodology used in this study, which
replicates that used in studies evaluating the functionality and
content of mobile apps for health management [11,18-23]. An
electronic search was conducted between July 2014 and July
2015 of the official app stores for iPhone operating systems
(OS; iTunes), Android (Google Play), BlackBerry OS
(BlackBerry World), and Windows Phone (Windows Store).
Stores were searched separately using the search term ‘weight
loss’ and no restrictions related to store subcategories were
imposed. A secondary search was also performed to identify
apps intended for children and adolescents using the search term
‘weight loss kids’. Results of the searches were not limited by
language and no date of app publication was used to restrict
search results. Because the indexing of apps varied greatly
across app stores, prior to app selection we performed a
calibration exercise in order to validate our ability to detect
weight management apps. This comprised testing our search
term for the ability to produce app results that were known to
meet the inclusion criteria and be currently available in the app
store for download (ie, if a search of ‘weight loss’ produced
results that included a popular, widely known weight loss app
such as Lose It!). In all cases, our search term identified the
apps we expected to find.
Figure 1. Flow diagram of study methods.
Selection of Apps
Apps met study inclusion criteria if the purpose of the app
involved weight loss or weight management and the primary
intended app user was a person seeking to reduce or manage
their weight. Apps appearing in more than 1 app store were
rated independently in order to account for differences in
features supported by different mobile OS. For our secondary
search, the app also needed to state its intended user to be a
child, adolescent, teenager, young person, or youth. Apps were
excluded if they were classified as “e-books” by the respective
app store or were judged by the reviewers as such. Three
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 3http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
investigators performed app selection independently and all
discrepancies regarding selection were resolved through
discussion with a fourth party. There was greater than 95%
agreement between authors across all app stores prior to fourth
author resolution.
Data Abstraction
Metadata from all included apps were abstracted into a standard
Microsoft Excel
spreadsheet. Metadata were collected from the respective app
store where the app was identified. Abstracted metadata
included: app name, developer (individual or organization), and
price. A systematic approach to data abstraction was used.
Specifically, two investigators independently collected data
from each app store and each dataset was then cross-verified
against the other.
Evidence-Based Strategies
Apps’ evidence-based strategies were characterized based on
inclusion/exclusion of app features or informational content
selected through consensus by our team of obesity experts. The
set of features/content used to characterize apps’evidence-based
strategies are supported by public health recommendations
[19-21], widely disseminated clinical interventions [11,24],
research in behavior change theory [9,17,18,25], as well as
systematic reviews and meta-analyses of mobile device
intervention studies [12,13,15-17,26-32]. While this particular
list of features does not address every evidence-based strategy
used in the management of obesity, our goal was to create broad
categories representing the major aspects of evidence-based
treatment in order to describe the overarching evidence-based
quality of the current market for weight loss/management apps.
The following eight evidence-based strategies were examined:
presence of (1) self-monitoring capabilities [11,12,15,
17-19,25,26,28] for weight, meals, nutrition (including protein,
fats, carbohydrates, fiber, and water), physical activity,
cardiometabolic indicators, sleep, mental health indicators,
including mood, thought patterns, cognitions, and stress,
environmental influences, and custom metrics, (2) goal-setting
[11,15,17,18,25] with/without customization, (3) healthy eating
support [11,15,17-19,25,29], including information, education,
and skills development, (4) physical activity support
[11,15,17-19,25,29], including information, education, and skills
development, (5) social support [12,15,17-19,26,30], (6) weight
and/or health assessment [11,17,19], with/without
personalization, (7) motivational strategies [15,17,18], including
prompts, rewards, or gamified design, and (8) personalized
feedback [12,15,17,26,30].
Health Care Expert Involvement and Scientific
Evaluation
Health care expert involvement in app development and stated
involvement in formal scientific evaluation were also examined.
An app was required to reference the involvement of a health
care professional listed in the Ontario Regulated Health
Professions Act [33] and app descriptions as well as publicly
accessible scientific literature databases (ie, National Center for
Biotechnology Information PubMed and Google Scholar) were
searched by app name for any published research related to the
app. For each category, we assessed inclusion as either ‘present’
or ‘absent’. Descriptive statistics were used to summarize the
results of the assessment.
Results
Summary of Search Results
Our first search across all app stores identified a total of 625
apps. Of these apps, 37.6% (235/625) were excluded from
further review based on the a priori inclusion and exclusion
criteria. The primary reason for app exclusion was the inability
to provide any weight management support (ie, the app was
used exclusively as a game). A total of 390 apps were included
in the primary analysis. Our secondary search identified a total
of 187 apps. Of these apps, 184 were excluded based on the a
priori inclusion and exclusion criteria, which also required the
app’s intended user to be a child, adolescent, teenager, young
person, or youth. A total of three apps were included in the
secondary analysis. In total, 393 apps were included in the final
analysis.
Summary of General App Characteristics
Overall, identified apps were most often classified as ‘medical’,
‘lifestyle’, or ‘health and fitness’. The cost of apps also varied,
with approximately 87.3% (343/393) being free to download.
The cost of paid apps ranged from $0.99 to $7.99 CDN.
Assessment of Evidence-Based Strategies
Table 1 shows the frequency of evidence-based strategies across
included apps. The most common strategy was self-monitoring
(139/393, 35.3%), which allowed the user to track targeted
weight-related metrics over time, the majority of which consisted
of weight, energy balance, water intake, and quantity of physical
activity. Few apps included more comprehensive tracking
options such as nutrition, sleep, and cardiometabolic indicators.
No apps allowed for tracking of mental health indicators,
environmental influences, or allowed for the creation of
customized metrics. Within these apps, 10.2% (40/393) could
automatically monitor the user’s physical activity without the
requirement for manual logging. The second most common
strategy was physical activity support (108/393, 27.5%), which
mostly included fitness plans, exercise guides, and tracking of
daily physical activity. Of apps, 25.4% (100/393) included
weight and/or health assessment, which was limited to
assessment of body mass index (BMI). No other types of health
assessment capabilities were found. Of apps, 23.2% (91/393)
provided healthy eating support, most commonly healthy eating
guidelines, meal plans, calorie balance goals, and nutritional
information for specific foods. No apps included skills
development needed for healthy eating such as stress reduction,
emotion regulation, stimulus control, time management, and
problem solving. Of apps, 21.4% (84/393) included goal-setting,
which mainly consisted of weight loss goals, calorie balance
goals, water intake goals, and physical activity goals. No apps
allowed for the creation of customized goals. Of apps, 7.1%
(28/393) possessed motivational strategies including prompts,
gamification, or use of rewards (ie, points for meeting weight
goals). Of apps, 5.3% (21/393) featured a social support
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 4http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
component such as online communication with other users.
Lastly, 1.8% (7/393) of apps provided personalized feedback
to the user, such as through virtual meetings with a health coach
or through notifications.
Health Care Expert Involvement and Scientific
Evaluation
As shown in Table 1, only 0.3% of apps (1/393), Kurbo Health,
stated the involvement of a regulated health care professional
in the app’s development. This app reported involving a medical
advisory board consisting of pediatricians, psychologists, and
psychiatrists. Furthermore, only 0.8% of apps (3/393) were
found to have been part of formal scientific research or have
undergone scientific testing.
Pediatric Focused Weight Management Apps
Our search identified only three pediatric focused apps. Two of
the apps lacked the majority of evidence-based strategies for
weight management. Ideal Weight BMI Adult and Child only
provided assessment of BMI and Choose My Food used only a
gamified design. The third app, Kurbo Health, possessed 8
strategies including self-monitoring, goal-setting, physical
activity support, healthy eating support, social support,
gamification, and personalized feedback delivered via a health
coach (offered as a premium paid-for feature).
Table 1. Evidence-based strategies, health care expert involvement, and scientific testing in apps for weight management.
Total
(N=393)
Blackberry World
(n=100)
Windows Store
(n=100)
Google Play
(n=98)
iTunes
(n=95)
Criteria
N (%)n (%)n (%)n (%)n (%)
139 (35.4%)24 (24.0%)28 (28.0%)40 (40.8%)47 (49.5%)Self-monitoring
40 (10.2%)16 (16.0%)13 (13.0%)2 (2.0%)9 (9.5%)Automatic self-monitoring
84 (21.4%)11 (11.0%)14 (14.0%)30 (30.6%)29 (30.5%)Goal-setting
108 (27.5%)27 (27.0%)25 (25.0%)30 (30.6%)26 (27.4%)Physical activity support
91 (23.2%)21 (21.0%)7 (7.0%)47 (48.0%)16 (16.8%)Healthy eating support
100 (25.4%)31 (31.0%)24 (24.0%)24 (24.5%)21 (22.1%)Weight /health assessment
7 (1.9%)0 (0.0%)4 (4.0%)2 (2.0%)1 (1.1%)Personalized feedback
28 (7.1%)1 (1.0%)11 (11.0%)7 (7.1%)9 (9.5%)Motivational strategies (rewards,
prompts, or gamification)
21 (5.3%)0 (0.0%)10 (10.0%)7 (7.1%)4 (4.0%)Social support
1 (0.3%)0 (0.0%)0 (0.0%)0 (0.0%)1 (1.2%)Health care expert involvement
3 (0.8%)0 (0.0%)2 (2.0%)1 (1.0%)0 (0.0%)Scientific testing
Comprehensiveness of Weight Management Apps for
Assessment Criteria
As shown in Table 2, the relative representation of assessment
criteria including evidence-based strategies, health care expert
involvement, and scientific evaluation per app varied. Roughly
one-third of apps (130/393, 33.1%) did not meet any of the
assessment criteria. Just over one-quarter of apps (103/393,
26.2%) met one criterion. The remaining apps are described as
follows: 16.8% (66/393) met 2 criteria, 14.8% (58/393) met 3
criteria, 5.1% (20/393) met 4 criteria, 2.3% (9/393) met 5
criteria, 0.8% (3/393) met 6 criteria, 0.5% (2/393) met 7 criteria,
and 0.5% (2/393) met 8 criteria. No app met more than 8 of our
assessment criteria. The average number of criteria present in
an app was between 1 and 2. In general, most apps functioned
as either a fitness app or a dieting app.
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 5http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Table 2. Comprehensiveness of apps for assessment criteria.
Total
(N=393)
Windows Store
(n=100)
Blackberry World
(n=100)
Google Play
(n=98)
iTunes
(n=95)
# of criteria met
N (%)n (%)n (%)n (%)n (%)
130 (33.1%)44 (44.0%)38 (38.0%)9 (9.2%)39 (41.1%)0
103 (26.2%)24 (24.0%)25 (25.0%)41 (41.8%)13 (13.7%)1
66 (16.8%)12 (12.0%)22 (22.0%)23 (23.5%)9 (9.5%)2
58 (14.8%)10 (10.0%)14 (14.0%)11 (11.2%)23 (24.2%)3
20 (5.1%)7 (7.0%)1 (1.0%)6 (6.1%)6 (6.3%)4
9 (2.3%)1 (1.0%)0 (0.0%)6 (6.1%)2 (2.1%)5
3 (0.8%)1 (1.0%)0 (0.0%)0 (0.0%)2 (2.1%)6
2 (0.5%)0 (0.0%)0 (0.0%)2 (2.0%)0 (0.0%)7
2 (0.5%)1 (1.0%)0 (0.0%)0 (0.0%)1 (1.1%)8
0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)9
0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)10
Discussion
Principal Findings
This review demonstrates that despite the abundance of mobile
apps for weight management, the evidence-based quality of
these apps remains generally poor. The rapid growth in the
commercial market for weight management apps has outpaced
progress to improve the content and functionality of these tools,
creating an overabundance of weight management apps with
no evidence base being made readily available to the public.
We can summarize the major limitations of current user-focused
apps for weight management, characterized by: (1) simplistic
capabilities that lack high-level personalization to complex user
needs and preferences, (2) a lack of health care expert
involvement during app development, (3) minimal use of
evidence-based strategies for the management of obesity, and
(4) the absence of scientific evaluation of these tools.
Current capabilities for promoting behavior change for weight
management through mobile apps have low fidelity and do not
reflect the individually tailored practices employed in clinical
obesity interventions. Apps tend to possess a singular focus on
either the physical activity or dieting practices for weight loss.
Moreover, apps do not comprehensively address the full range
of cognitive, behavioral, and environmental factors that can
impact a person’s ability to manage their weight over the long
term. These findings are reflected in similar work by numerous
authors [11,18-21] who also report a low level of adherence to
evidence-based practices in mobile apps for weight loss and the
lack of cognitive and behavioral targets related to weight control.
This suggests that limited improvements have been made in the
commercial app market since these studies, despite the growth
in the number of apps available. The lack of evidence-based
strategies employed by the apps we reviewed drastically narrows
the therapeutic scope of these tool for addressing the
multifactorial causes of overweight and obesity. Hence, most
apps may not be suitable for supporting individuals with severe
or complex obesity who have complex medical and
self-management needs. This may be in part due to the general
lack of health care expert involvement during app development
or that the majority of commercial apps are not developed with
the intention for use by clinical populations. Only three apps,
My Fitness Pal, Lost It!, and Fitbit were found to be involved
in formal scientific research. The lack of rigorous scientific
evaluation by most apps calls into question the validity of apps’
claims regarding their therapeutic benefit and safety, as well as
encourages the rapid development and sale of medical apps with
no demonstrated clinical efficacy.
Evidence-based strategies are critical to most clinical weight
loss programs but were largely absent from the mobile apps we
reviewed. Approximately one-third of the apps included
self-monitoring and physical activity support, less than
one-quarter facilitated goal-setting or provided healthy eating
support, less than one-tenth contained motivating components
or provided appropriate social support, and only 1.8% (7/393)
provided personalized feedback to the user. While research has
yet to be conducted into the appropriate and optimal number of
strategies employed by a single app, effective long-term weight
management requires a range of behavioral and lifestyle changes
in order to address the multifactorial etiology of obesity.
Goal-setting and self-monitoring are key strategies derived from
self-regulation theory to enhance self-efficacy and significantly
predict weight loss and behavior change success [17,18]. Apps
with goal setting focused mainly on weight, calorie, or exercise
goals and generally could not be customized to personal
objectives or personalized to user preferences. Most apps with
self-monitoring features focused on tracking activity, meals,
and calories, but important metrics related to nutrition,
cardiovascular health, sleep, mental health, and environmental
influences, as well as custom metrics, were mostly neglected.
Moreover, the self-monitoring capabilities of current mobile
apps are limited by the manual input demands on the user,
requiring that users remember and be consistently motivated to
input multiple types of data frequently in order to be successful.
Providing personalized feedback to the individual on their
progress as well as facilitating social support from peers are
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 6http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
strategies routinely performed in intensive behavioral
interventions to improve long-term success in goal-setting and
self-monitoring [17]. These important features are mostly absent
from current mobile apps, which do not possess the functional
capabilities to deliver this complex level of interaction and
communication. Furthermore, the limited amount of content
and features dedicated to healthy eating is concerning given the
pivotal role nutritional factors play in obesity management.
Limitations
The search methods used in this review were modeled after
those previously conducted in this area [11,18-21] and in the
management of other chronic diseases [22]. Our search protocol
was intended to mimic the search experience of a general user
who would most likely follow a similar strategy when choosing
apps for their own weight management. Although we attempted
a comprehensive abstraction of data related to app content and
functionality, reviewers did not download apps onto a
smartphone device for thorough review. Rather, information
was gathered from the app store and from associated websites.
Hence, the data presented should not be interpreted to reflect
the accuracy of any particular feature (ie, accuracy of energy
expenditure measurement), as this was not the aim of the present
study. These findings reflect the general knowledge of app
developers regarding practices for effective weight management,
as well as the evidence-based quality of current commercial
weight loss apps. These findings provide an overview of the
current state of the mobile weight loss app market in order to
guide the future direction of research and development of these
tools. Moreover, the findings reported are very broad
characterizations of current app features meant for weight
management, yet the specific strategies relied upon, such as
specific nutritional content offered by the app, is not described.
Future research will be required to determine the accuracy of
particular app features and to characterize the specific types of
strategies employed within each of the reported criteria
categories.
Future Directions
Our findings contribute to the growing work into the
development and evaluation of mobile- and Internet-based tools
for overweight and obesity management. The challenges to
sustaining clinic-based interventions in the long-term, in addition
to the near-ubiquity and multifunctional capabilities of today’s
smartphone devices position mobile apps as a potential
translational platform for the widespread delivery of weight
management interventions. Numerous studies now support the
ability for mobile phones to facilitate weight loss and promote
associated healthy behaviors [12,13,15-17,26-31], as well as
reduce obesity-related comorbidities [32]. While these benefits
have been observed in clinical trials involving
researcher-developed apps, limited data exist evaluating the
quality of commercial mobile apps and their inclusion of
evidence-based strategies for weight loss/management. Our
findings demonstrate that while significant progress has been
made in the development and accessibility of mobile apps for
weight loss, considerable improvements are needed before these
tools can truly be considered evidence-based.
Future efforts by both researchers and commercial developers
should aim to address the limitations discussed. More stringent
standards for the provision of medical apps should be established
and incorporated into the process of submission to an app store.
More comprehensive use of evidence-based strategies used in
routine behavioral counselling for weight loss should be
integrated into apps’ functionality and content. This is not a
straightforward objective because many of these strategies would
require complex, intelligent interaction with the device (eg,
such as providing tailored feedback) and would also need to be
adapted to the usability constraints of a mobile device interface
(eg, screen size). However, the potential outcome of these efforts
would be that mHealth interventions would become personalized
to the user’s needs for managing their health (eg, tailored to the
patient’s lifestyle), rather than providing generic and
homogenous support to every user. In addition, health care
experts need to become more integral to the development and
distribution of medical apps. The concerns of any medical
treatment, such as safety and efficacy, must be equally
considered to those more typically focused on by app
developers, namely the user interface and keeping the user
engaged. Lastly, there is a need for rigorous evaluation and
refinement of these apps using high-quality feasibility testing
and multicenter randomized controlled trials.
Conclusions
The overall conclusions advanced in this review are that despite
the high accessibility of mobile apps for weight management,
the quality of their content and functionality remains poor.
Efforts to address these problems must involve health care
experts during the app development process, a more
comprehensive grounding in evidence-based practice, improved
personalization and tailored feedback, and evaluation and
refinement through scientific trials. The potential for mobile
apps to improve health outcomes in the management of chronic
diseases presents a real opportunity for widespread,
cost-effective delivery of health care. Future research is urgently
needed to develop comprehensive, evidence-based, and
clinically-informed weight management mobile tools toward
these aims.
Acknowledgments
This research project is supported by Dr. Jennifer Stinson’s Peter Lougheed Canadian Institutes of Health Research New Investigator
Award.
Conflicts of Interest
None declared.
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 7http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
References
1. Kelly T, Yang W, Chen C, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond)
2008;32:1431-1437. [doi: 10.1038/ijo.2008.102] [Medline: 18607383]
2. Speiser PW, Rudolf MCJ, Anhalt H, Camacho-Hubner C, Chiarelli F, Eliakim A, Obesity Consensus Working Group.
Childhood obesity. J Clin Endocrinol Metab 2005;90:1871-1887. [doi: 10.1210/jc.2004-1389] [Medline: 15598688]
3. Withrow D, Alter DA. The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obes
Rev 2011;12:131-141. [doi: 10.1111/j.1467-789X.2009.00712.x] [Medline: 20122135]
4. World Health Organization. Fact Sheet No 311. 2016. Obesity and Overweight URL: http://www.who.int/mediacentre/
factsheets/fs311/en/index.html [accessed 2016-06-21] [WebCite Cache ID 6b39dAqqM]
5. Han JC, Lawlor DA, Kimm SYS. Childhood obesity. Lancet 2010;375:1737-1748 [FREE Full text] [doi:
10.1016/S0140-6736(10)60171-7] [Medline: 20451244]
6. Bray GA. Medical consequences of obesity. J Clin Endocrinol Metab 2004;89:2583-2589. [doi: 10.1210/jc.2004-0535]
[Medline: 15181027]
7. Del CP, Chandler-Laney PC, Casazza K, Gower BA, Hunter GR. Effect of dietary adherence with or without exercise on
weight loss: a mechanistic approach to a global problem. J Clin Endocrinol Metab 2009;94:1602-1607 [FREE Full text]
[doi: 10.1210/jc.2008-1057] [Medline: 19258409]
8. DeLany JP, Kelley DE, Hames KC, Jakicic JM, Goodpaster BH. Effect of physical activity on weight loss, energy expenditure,
and energy intake during diet induced weight loss. Obesity (Silver Spring) 2014;22:363-370. [doi: 10.1002/oby.20525]
[Medline: 23804562]
9. Van DB, Lindley EM. Cognitive and behavioral approaches in the treatment of obesity. Med Clin North Am 2011;95:971-988.
[doi: 10.1016/j.mcna.2011.06.008] [Medline: 21855703]
10. Gilmore LA, Duhé AF, Frost EA, Redman LM. The technology boom: a new era in obesity management. J Diabetes Sci
Technol 2014;8:596-608 [FREE Full text] [doi: 10.1177/1932296814525189] [Medline: 24876625]
11. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev
Med 2013;45:576-582. [doi: 10.1016/j.amepre.2013.04.025] [Medline: 24139770]
12. Aguilar-Martínez A, Solé-Sedeño JM, Mancebo-Moreno G, Medina FX, Carreras-Collado R, Saigí-Rubió F. Use of mobile
phones as a tool for weight loss: a systematic review. J Telemed Telecare 2014;20:339-349. [doi:
10.1177/1357633X14537777] [Medline: 24875928]
13. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J
Cardiovasc Nurs 2013;28:320-329 [FREE Full text] [doi: 10.1097/JCN.0b013e318250a3e7] [Medline: 22635061]
14. Smith A. Smartphone ownership - 2013 update. Pew Research Center 2013:1-12 [FREE Full text]
15. Khokhar B, Jones J, Ronksley PE, Armstrong MJ, Caird J, Rabi D. Effectiveness of mobile electronic devices in weight
loss among overweight and obese populations: a systematic review and meta-analysis. BMC Obes 2014;1:22 [FREE Full
text] [doi: 10.1186/s40608-014-0022-4] [Medline: 26217509]
16. Liu F, Kong X, Cao J, Chen S, Li C, Huang J, et al. Mobile phone intervention and weight loss among overweight and
obese adults: a meta-analysis of randomized controlled trials. Am J Epidemiol 2015;181:337-348. [doi: 10.1093/aje/kwu260]
[Medline: 25673817]
17. Lyzwinski LN. A systematic review and meta-analysis of mobile devices and weight loss with an intervention content
analysis. J Pers Med 2014;4:311-385 [FREE Full text] [doi: 10.3390/jpm4030311] [Medline: 25563356]
18. Azar KMJ, Lesser LI, Laing BY, Stephens J, Aurora MS, Burke LE, et al. Mobile applications for weight management:
theory-based content analysis. Am J Prev Med 2013;45:583-589. [doi: 10.1016/j.amepre.2013.07.005] [Medline: 24139771]
19. Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! But does it adhere to evidence-informed
practices? Transl Behav Med 2011;1:523-529 [FREE Full text] [doi: 10.1007/s13142-011-0076-5] [Medline: 24073074]
20. Schoffman DE, Turner-McGrievy G, Jones SJ, Wilcox S. Mobile apps for pediatric obesity prevention and treatment,
healthy eating, and physical activity promotion: just fun and games? Transl Behav Med 2013;3:320-325 [FREE Full text]
[doi: 10.1007/s13142-013-0206-3] [Medline: 24073184]
21. Wearing JR, Nollen N, Befort C, Davis AM, Agemy CK. iPhone app adherence to expert-recommended guidelines for
pediatric obesity prevention. Child Obes 2014;10:132-144 [FREE Full text] [doi: 10.1089/chi.2013.0084] [Medline:
24655230]
22. Lalloo C, Jibb LA, Rivera J, Agarwal A, Stinson JN. “There's a pain app for that”: review of patient-targeted smartphone
applications for pain management. Clin J Pain 2015;31:557-563. [doi: 10.1097/AJP.0000000000000171] [Medline:
25370138]
23. Knight E, Stuckey MI, Prapavessis H, Petrella RJ. Public health guidelines for physical activity: is there an app for that?
A review of android and apple app stores. JMIR Mhealth Uhealth 2015;3:e43 [FREE Full text] [doi: 10.2196/mhealth.4003]
[Medline: 25998158]
24. Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): description of lifestyle
intervention. Diabetes Care 2002;25:2165-2171 [FREE Full text] [Medline: 12453955]
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 8http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
25. Van DB, Lindley EM. Cognitive and behavioral approaches in the treatment of obesity. Endocrinol Metab Clin North Am
2008;37:905-922. [doi: 10.1016/j.ecl.2008.08.003] [Medline: 19026939]
26. Bacigalupo R, Cudd P, Littlewood C, Bissell P, Hawley MS, Buckley WH. Interventions employing mobile technology
for overweight and obesity: an early systematic review of randomized controlled trials. Obes Rev 2013;14:279-291 [FREE
Full text] [doi: 10.1111/obr.12006] [Medline: 23167478]
27. Tang J, Abraham C, Greaves C, Yates T. Self-directed interventions to promote weight loss: a systematic review of reviews.
J Med Internet Res 2014;16:e58 [FREE Full text] [doi: 10.2196/jmir.2857] [Medline: 24554464]
28. Wickham CA, Carbone ET. Who's calling for weight loss? A systematic review of mobile phone weight loss programs for
adolescents. Nutr Rev 2015;73:386-398. [doi: 10.1093/nutrit/nuu018] [Medline: 26011913]
29. Chen J, Wilkosz ME. Efficacy of technology-based interventions for obesity prevention in adolescents: a systematic review.
Adolesc Health Med Ther 2014;5:159-170 [FREE Full text] [doi: 10.2147/AHMT.S39969] [Medline: 25177158]
30. Hutchesson MJ, Rollo ME, Krukowski R, Ells L, Harvey J, Morgan PJ, et al. eHealth interventions for the prevention and
treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev 2015;16:376-392. [doi:
10.1111/obr.12268] [Medline: 25753009]
31. Vesta Ghanavati JC. Effectiveness of cellular phone-based interventions for weight loss in overweight and obese adults: a
systematic review. Orthop Muscul Syst 2013;03:141. [doi: 10.4172/2161-0533.1000141]
32. Widmer RJ, Collins NM, Collins CS, West CP, Lerman LO, Lerman A. Digital health interventions for the prevention of
cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc 2015;90:469-480. [doi:
10.1016/j.mayocp.2014.12.026] [Medline: 25841251]
33. Ontario Ministry of Health. Regulated Health Professions Act, 1991. S.O. 1991, CHAPTER 18. 1991. URL: https://www.
ontario.ca/laws/statute/91r18 [accessed 2016-07-05] [WebCite Cache ID 6imD8Rbd5]
Abbreviations
BMI: body mass index
OS: operating systems
Edited by G Eysenbach; submitted 09.09.15; peer-reviewed by M Allman-Farinelli, F Saigí-Rubió, HA Park, E Knight; comments to
author 06.10.15; revised version received 26.04.16; accepted 19.05.16; published 26.07.16
Please cite as:
Rivera J, McPherson A, Hamilton J, Birken C, Coons M, Iyer S, Agarwal A, Lalloo C, Stinson J
Mobile Apps for Weight Management: A Scoping Review
JMIR Mhealth Uhealth 2016;4(3):e87
URL: http://mhealth.jmir.org/2016/3/e87/
doi: 10.2196/mhealth.5115
PMID: 27460502
©Jordan Rivera, Amy McPherson, Jill Hamilton, Catherine Birken, Michael Coons, Sindoora Iyer, Arnav Agarwal, Chitra Lalloo,
Jennifer Stinson. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 26.07.2016. This is an open-access
article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR
mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on
http://mhealth.jmir.org/, as well as this copyright and license information must be included.
JMIR Mhealth Uhealth 2016 | vol. 4 | iss. 3 | e87 | p. 9http://mhealth.jmir.org/2016/3/e87/ (page number not for citation purposes)
Rivera et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX