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Original Paper
DiaFit: The Development of a Smart App for Patients with Type
2 Diabetes and Obesity
François Modave1, PhD; Jiang Bian1, PhD; Eric Rosenberg2, MD; Tonatiuh Mendoza1, MS; Zhan Liang1; Ravi
Bhosale1, MBA; Carlos Maeztu1, MA; Camila Rodriguez1; Michelle I Cardel1, PhD, RD
1Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, United States
2Department of Internal Medicine, University of Florida, Gainesville, FL, United States
Corresponding Author:
François Modave, PhD
Department of Health Outcomes and Policy
University of Florida
2004 Mowry road
CTRB 3217
Gainesville, FL, 32610
United States
Phone: 1 3522945984
Fax: 1 3522738703
Email: modavefp@ufl.edu
Abstract
Background: Optimal management of chronic diseases, such as type 2 diabetes (T2D) and obesity, requires patient-provider
communication and proactive self-management from the patient. Mobile apps could be an effective strategy for improving
patient-provider communication and provide resources for self-management to patients themselves.
Objective: The objective of this paper is to describe the development of a mobile tool for patients with T2D and obesity that
utilizes an integrative approach to facilitate patient-centered app development, with patient and physician interfaces. Our
implementation strategy focused on the building of a multidisciplinary team to create a user-friendly and evidence-based app, to
be used by patients in a home setting or at the point-of-care.
Methods: We present the iterative design, development, and testing of DiaFit, an app designed to improve the self-management
of T2D and obesity, using an adapted Agile approach to software implementation. The production team consisted of experts in
mobile health, nutrition sciences, and obesity; software engineers; and clinicians. Additionally, the team included citizen scientists
and clinicians who acted as the de facto software clients for DiaFit and therefore interacted with the production team throughout
the entire app creation, from design to testing.
Results: DiaFit (version 1.0) is an open-source, inclusive iOS app that incorporates nutrition data, physical activity data, and
medication and glucose values, as well as patient-reported outcomes. DiaFit supports the uploading of data from sensory devices
via Bluetooth for physical activity (iOS step counts, FitBit, Apple watch) and glucose monitoring (iHealth glucose meter). The
app provides summary statistics and graphics for step counts, dietary information, and glucose values that can be used by patients
and their providers to make informed health decisions. The DiaFit iOS app was developed in Swift (version 2.2) with a Web
back-end deployed on the Health Insurance Portability and Accountability Act compliant-ready Amazon Web Services cloud
computing platform. DiaFit is publicly available on GitHub to the diabetes community at large, under the GNU General Public
License agreement.
Conclusions: Given the proliferation of health-related apps available to health consumers, it is essential to ensure that apps are
evidence-based and user-oriented, with specific health conditions in mind. To this end, we have used a software development
approach focusing on community and clinical engagement to create DiaFit, an app that assists patients with T2D and obesity to
better manage their health through active communication with their providers and proactive self-management of their diseases.
(JMIR Diabetes 2016;1(2):e5) doi:10.2196/diabetes.6662
KEYWORDS
mHealth; diabetes; obesity; apps
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Introduction
Between 1980 and 2014, the number of Americans diagnosed
with diabetes increased fourfold [1]. Almost 90% of individuals
with type 2 diabetes (T2D) are obese, and the global epidemic
of T2D is largely explained by the dramatic increase in both the
incidence and prevalence of obesity over the past 40 years. The
excess lifetime medical spending for individuals with T2D is
up to US $211,400 [2], and aggregate obesity-related medical
care costs in the United States reached a staggering US $147
billion in 2008.
Poor nutrition, low levels of physical activity, and sedentary
lifestyles contribute greatly to T2D and obesity [3-10]. Current
national estimates show that close to 64% Americans are trying
to lose weight, and that nearly half are actively engaged in a
weight loss program [11]. However, primary care providers are
traditionally not trained to provide expertise pertaining to
physical activity and nutrition, which could aid in weight loss
and improved glucose control. Therefore, it is essential to
develop comprehensive novel approaches that complement
clinical care, to help patients with T2D and obesity manage their
conditions, and to reduce long-term T2D and obesity
complications. Longer-term lifestyle interventions and
behavioral modifications have been found to reduce body weight
and T2D complications, including: self-monitoring of weight,
dietary intake, activity, and blood glucose; and medication
compliance [12]. However, these interventions are often costly
and resource intensive, and lack sustainability components.
Management of T2D and obesity is self-directed, as individuals
need to make day-to-day decisions related to controlling their
chronic diseases [13]. Cost-effective and sustainable
interventions to improve T2D and obesity-related outcomes
could be achieved at a relatively low cost [14], and could save
hundreds of thousands of dollars at the individual level and
hundreds of billions of dollars at a national level [2].
The ubiquitous nature of the Internet and mobile technologies
makes them potential cost-effective and sustainable tools to
improve health knowledge and outcomes for chronic diseases,
such as T2D and obesity. According to the PEW Research
Center, 70% of Americans have access to high-speed Internet
at home [1] and 64% have access to a smartphone [15]. Growing
evidence indicates that digital media (apps in particular) have
the potential to be effective and scalable approaches to deliver
health behavior interventions across the socioeconomic gradient
[2,3,14,16-18]. However, research assessing the quality of
health-related apps suggests that many of these apps lack the
evidence-based standards necessary in health care [4-9]. A
possible explanation for this lack of standards is that most apps
are not necessarily developed with the end-user in mind, and
their implementation is undertaken without patient or expert
(eg, physician) input.
The primary goal of this paper is to present DiaFit, a T2D and
obesity-focused iOS app, and its implementation process,
involving its potential end users. The app was developed to help
patients self-manage T2D and obesity, and allow physicians to
keep track of their patients’ progress. The majority of iOS apps
targeting diabetes lack evidence-based support, functionalities,
and interfacing with devices that support standard wireless
communication protocols (eg, Bluetooth, Bluetooth Low Energy
[BLE], or ANT+) [6,10,11]. This limitation forces patients with
T2D and obesity to use multiple apps to address the various
aspects of their chronic conditions. This scenario is far from
ideal, since each app is designed differently and typically comes
with a learning curve before the app can be used adequately.
Moreover, dealing with multiple apps might prevent the user
from understanding the interactions between nutrition,
medication, and glucose levels. Therefore, we developed DiaFit,
an app that allows a user to store their dietary intake, physical
activity log, medication use, blood glucose values, and general
well-being in one app. DiaFit permits seamless uploads via
Bluetooth when possible. Finally, there is evidence that when
building such mobile apps, patients and physicians should be
involved, in particular for health-related apps that are aimed at
older adults [10]. Therefore, DiaFit was developed in close
collaboration with key stakeholders, including a primary care
physician, citizen scientists, and people with diabetes and/or
obesity; all of whom acted as potential users. Citizen scientists
are defined as lay people who engage in scientific research . In
our project these individuals were paid for their time and their
contributions were considered just as important as those of
traditional scientists. The aim of this paper is to present the
software implementation process of DiaFit, alongside the app
itself.
Methods
Agile Software Development
For the purposes of this paper, the terms client and stakeholder
are used interchangeably, and refer to physicians and citizen
scientists. To ensure that a given software meets the needs of
the client, development requires significant communication
between the development team and the client [12]. When
following a traditional waterfall software development lifecycle
model [13], the initial phase of software production aims at
producing a software requirement specification document [12].
This document describes the software simply, unambiguously,
and entirely, from its architectural design to its simplest
functionalities and behaviors. This approach is not well-suited
for rapid software development [19]. Moreover, this approach
significantly delays the presentation of functional prototypes to
the stakeholders. Therefore, we followed best practices in
software engineering by using an Agile software development
methodology [19]. Using Agile, we followed adaptive planning
and evolutionary development principles, aiming to deliver the
software product early with continuous improvement.
We aimed to develop a fully integrative app that could be used
to help patients manage their T2D and obesity, in a primary
care setting and under the supervision of a primary care provider.
We assembled a key stakeholder team comprised of citizen
scientists (RB, CM, CRD), an internal medicine physician (ER),
researchers in biomedical informatics, nutrition, and obesity
science (FM, MC), and a software development team (TM, ZL).
The citizen scientists were paid volunteers from the University
of Florida Clinical and Translational Science Institute citizen
scientist program. All citizen scientists had a chronic disease;
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either obesity and/or diabetes. The citizen scientists acted as de
facto customers for the DiaFit software, whereas the clinician
had both a customer and consultant role in the project.
We followed an Agile software development methodology [19],
with an emphasis on the following principles of the Agile
manifesto:
1. Continuous and regular delivery of software components to
allow the users to provide feedback early in the process, and to
engage the intended users.
2. A project involving highly motivated individuals.
3. Regular face-to-face meetings.
4. Frequent and close cooperation between the stakeholders and
continuous refinement of the design.
5. Functional software as main metric for progress.
6. Simplicity (ie, make the software as simple as possible, in
collaboration with the stakeholders).
7. Pair-programming, whereby two programmers work together
on a part of the code, with one programmer writing the code
(the driver) and the other programmer assessing for correctness
(the observer).
Requirements and Preference Elicitations
During the initial phase of the project we reviewed the
limitations of existing apps (see Comparison With Prior Work
in the Discussion section) [6,10,11]. We then identified the key
elements necessary for the self-management of T2D and obesity,
and how they could be addressed effectively using a mobile
health (mHealth) approach. This phase led us to a first set of
basic software requirements, resulting from best practices for
T2D and obesity management, and the end-user software
requirement elicitation process. The initial set of requirements
generated is summarized in Table 1.
Table 1. Basic requirements.
Functional RequirementRequirement
DiaFit runs on the following devices running iOS 9.2 or newer: iPhone 5 to iPhone
6s Plus
The patient-user should be able to use application on an iPhone
DiaFit requires username and password for access. Passwords are saved via the
keyed-hashed message authentication code - secure hash algorithm 1 random salted
for each password, and standard encryption protocol
The patient-user should have secure access to their account
DiaFit provides user with access to a large nutrition database for logging dietary
intake
The patient-user should be able to track their eating habits
DiaFit calculates the calorie intake of the user, utilizing the food consumption that
is input by the user, and the nutritional database
The patient-user should be able to measure calorie intake
DiaFit provides a graphical breakdown of the macronutrients consumed by the user,
based on food consumption that is input by the user, and the database information
The patient-user should be able to measure carbohydrates, proteins
and fats
DiaFit supports Fitbit devices, Apple watch, or the iPhone on which the DiaFit app
is installed, and provides the following calorie expenditure: (1) energy requirement
estimate [20], calculated using the data that is input by the user; and (2) physical
activity energy expenditure estimate, calculated utilizing the information gathered
by the device selected by the user
The patient-user should be able to measure calorie expenditure
DiaFit allows the user to track their glucose by inputting their current glucose value,
utilizing any type of glucometer
The patient-user should be able to track blood glucose
DiaFit provides data entry for the user to manually enter any medication and, if
desired, create a reminder based on the time that medicine is taken
The patient-user should be able to keep track of their medication
DiaFit was implemented using iOS, so the software engineering
team chose Apple’s Swift 2 programming language. This
approach allowed for a tight integration with the current (and
future) functionalities of Apple Healthkit (a platform for
collecting data from various health and fitness apps in iOS).
We used a serverless back-end developed with Amazon Web
Services (AWS) Lambda and AWS DynamoDB database. The
use of AWS Lambda allowed the app to have high availability
and scalability without provisioning or managing servers. AWS
also provided Health Insurance Portability and Accountability
Act compliance, as well as Family Education Rights and Privacy
Act and Federal Information Security Management Act
compliance, if needed. We used the United States Department
of Agriculture National Nutrient Database Application Program
Interface (API) [21] to allow the users to search for their food
consumed, and allow the app to calculate and save the nutritional
information. The architecture of DiaFit is described in Figure
1.
Additionally, we are in the process of incorporating the RxNorm
API [22] into DiaFit. This addition will allow the user to access
the RxNorm medication database in order to facilitate and
increase the accuracy of medication entry, versus the current
option of free text input. DiaFit will be made available through
GitHub, under the GNU General Public License agreement,
version 3 [23]. By making DiaFit open source, the diabetes
community at large will have the opportunity to contribute to,
refine, and expand the functionalities of the tool, promoting
cooperation and innovation. Following the Agile software
development methodology, the software team started the
implementation of DiaFit with partial requirements, which was
generated after the initial preference elicitation meeting with
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the physician and citizen scientists. Using Agile allowed us to
present incomplete, albeit functional, versions of DiaFit to the
stakeholders of DiaFit. Therefore, an effort was made to ensure
modularity of the various components of the app.
Additional requirements were identified in subsequent meetings,
referred to as sprints in the Agile methodology, with the
stakeholders of the DiaFit project. We incorporated a validated
measure of well-being (Diabetes-Specific Patient-Reported
Outcome Quality of Life [24]) and patient-physician
communication functionality, through direct secure messaging
in DiaFit. Patient-reported outcomes (PROs) are important
validated measures that can be used to measure the quality of
life of patients [25], and therefore provide a method to track
changes. However, a tradeoff needs to be made between
obtaining data through phone prompts and interfering with
patients’ lives. PROs can only be collected via direct manual
entry of the data in the app, so we worked with citizen scientists
to assess how often PROs should be elicited. Consensus was
reached on PROs being entered by patients in DiaFit 1.0 when
they chose to, rather than have an app prompt requesting the
data from the user. Additional focus groups in a primary care
setting allowed us to answer this question more accurately.
Moreover, the team acknowledged that an essential functionality
of DiaFit should be the ability to seamlessly upload data
pertaining to physical activity and glucose to DiaFit, via
Bluetooth and BLE. These requirements are summarized in
Table 2.
Conceptually, the interactive process between the research team,
software team, and the clients is summarized in Figure 2. The
specifications were obtained through a continuous and highly
interactive process led by the development team, primarily via
face-to-face weekly meetings with the research team, and from
face-to-face monthly meetings with the physician and citizen
scientists. Intermediate DiaFit versions were first presented to
the research team for evaluation and feedback. Release was then
presented to the clients, then tested for usability and
functionality.
Table 2. Additional partial requirements.
Functional RequirementRequirement
DiaFit prompts the user weekly for Short Form (9 questions) Diabetes-Spe-
cific Patient-Reported Outcomes Quality of Life (National Institutes of Health
[NIH] Patient-Reported Outcomes Measurement Information Systems
[PROMIS]) [24], and stores PRO responses
The patient-user should be able to monitor their well-being
DiaFit allows user to synchronize with iPhone Apple step counter, iWatch,
and FitBit devices, and stores steps in a database
The patient-user should be able to track physical activity with a variety
of wearables
DiaFit allows the user to track their glucose by automatically detecting if
glucose data has been saved to Apple Health by a Bluetooth glucometer (ie,
the wireless smart glucose-monitoring system from iHealth)
The patient-user should be able to track blood glucose
DiaFit allows patient-user to give access data logs to physician-userThe patient-user should be able to receive feedback from their physi-
cians
DiaFit provides physician viewThe physician-user should be able to see summary statistics of their
patients
DiaFit provides secure messaging interfaceThe physician-user should be able to send encouragement messages to
patient-user
DiaFit provides secure access to messagesThe patient-user should be able to read physician-user messages
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Figure 1. Architecture of DiaFit. API: Application Program Interface; DB: database; USDA: United States Department of Agriculture.
Figure 2. Agile methodology for DiaFit development.
Results
Dimensions
DiaFit incorporates account information and the following
dimensions: nutrition, physical activity, blood glucose,
medication, and Diabetes-Specific Patient-Reported Outcomes
Quality of Life [24], as well as measures of subjective [26] and
objective socioeconomic status information for research
purposes. For privacy reasons, data entry is entirely voluntary,
and participants may choose to leave fields blank. DiaFit
supports Bluetooth data uploads for physical activity and glucose
monitoring.
Icon, Login, and Account Information
DiaFit’s icon was developed with the larger group of citizen
scientists (Figure 3). The objectives were to have a meaningful
icon for the intended users, as well as an icon that is found
quickly on a phone, to increase the likelihood of app use, and
thus adherence. The login screen is pictured in Figure 4. The
basic demographics that we included in our account information
screen (Figure 5) include gender, age, height, weight, marital
status, and employment status.
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Figure 5. Account information.
Key Functionalities
The key functionalities of DiaFit were the following:
1. Access to a large nutrition database, which includes food
items, calories, and breakdown in macronutrients, sodium, and
fiber (Figures 6 and 7). Although micronutrients are an important
aspect of a healthy diet, our discussions with the citizen
scientists suggested that this would likely lead to information
overload, and may not be not critical to our target end-users.
2. Physical activity tracking and seamless data entry. For the
first version of the DiaFit app, the software team focused on
integration with iPhone activity data, Apple watch, and Fitbit
devices (Figure 8).
3. Glucose monitoring, either through manual input or Bluetooth
seamless upload with iHealth glucose monitors. Another feature
of glucose entry is the possibility to differentiate fasting glucose
versus nonfasting glucose, which can be specified by the user
(Figure 9).
4. Medication use via manual data entry, although DiaFit is
being improved with an RxNorm API (Figure 10).
5. PROs (Figure 11), using an NIH PROMIS short form quality
of life assessment tool. These functionalities are described in
Figures 4-11.
6. Simplicity was also identified as a key, albeit nonfunctional,
requirement of DiaFit. Indeed, with an aging population with
T2D and obesity, it is critical to make the app as simple as
possible [10], which led our design choices. We opted for a
slider menu (Figure 6) to allow for easy navigation through the
various components of DiaFit, and we also opted for limited
nutritional variables (beyond macronutrients) versus other
popular nutrition/physical apps such as myfitnesspal.
Additionally, simple graphic capabilities were added to allow
the user to track changes and see improvement over time. To
continue our development process, our citizen scientists are
undergoing software testing of the app and reporting bugs,
frequency of bugs, and needed user interface changes through
a Web-link.
Physician View
Finally, DiaFit offers a physician view, which allows the
monitoring of patient improvements remotely and
asynchronously (Figure 12). The physician side of the app also
offers basic secure messaging capabilities, allowing a physician
to easily and securely send a text-based message to a patient.
We are also working on adding machine learning-based
automated messaging capabilities to DiaFit, which will be
available in the next release of the app.
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Figure 12. Physician view of DiaFit.
Discussion
Principal Results
The 2 main objectives of the DiaFit project were to (1) develop
an evidence-based app that allows patients with T2D and obesity
to manage their chronic conditions, and (2) ensure that the app
is developed with the end-user in mind by involving them in
the entire development of the product, rather than only in the
testing phase (as is commonly done). To achieve this, we
assembled a team of highly motivated individuals comprised
of biomedical informaticians, nutrition and obesity science
researchers, software engineers engaged in app development,
a primary care physician, and citizen scientists with a strong
interest in mHealth as a tool to address chronic conditions. To
the best of our knowledge, this is the first attempt at assembling
such a diverse team that included all stakeholders in the
development of an app for the management of chronic
conditions. It is important to note that our approach differed
from that of focus groups, and that all team members acted as
de facto collaborators on this project, bringing in a diverse range
of expertise and perspectives. Although we included
evidence-based components in the development of DiaFit, we
cannot yet state that we have successfully created an
evidence-based app because the effectiveness of the app has not
yet been tested. Thus, whether we have succeeded in making
the app evidence-based will need to be tested in future studies,
and currently remains outside the scope of this paper.
The initial meeting with all DiaFit constituents occurred in late
January 2016. The design and implementation phase started in
late March 2016 due to the difficulty of recruiting motivated
iOS developers. Finally, the deployment of the current version
of DiaFit occurred mid-August 2016. The main barrier to
accelerating the development process proved to be scheduling.
Coordinating meetings with several citizen scientists who work
full time, and a physician with long clinical hours, resulted in
the research and development teams deciding to have partial
team meetings to elicit feedback for improvement. However,
the high motivation of all involved parties ensured that deadlines
were met, and deliverables were presented on time to DiaFit
stakeholders.
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Limitations and Lessons Learned
The development of DiaFit presented several challenges.
Primary care physicians are significantly time-constrained.
Therefore, careful planning is necessary to schedule Agile
sprints early at the beginning of the project. We had not
accounted for planning issues adequately at the beginning of
this project, and subsequently lost a significant amount of time
in early stages. Given the necessity for short time periods
between meetings (ie, short Agile sprints), the development
process should clearly lay out bimonthly meetings from the
initial phase of the project, rather than letting development drive
meeting times. However, our initial delays were mitigated by
very strong clinical support for DiaFit. Having a clinician
championing such a project is essential, not only to ensure
sufficient feedback, but also to increase chances of adoption at
later time points. Based on the expertise of our team, we decided
to focus our efforts on iOS development and ignored the large
Android market. With a growing segment of the population
speaking Spanish, we also need to make the app available in
Spanish. We are currently in version 1 of the app, and have not
yet moved on to the staging that incorporates automated
messaging, which would help the patients handle interactions
related to diet, physical activity, and their glucose responses,
which would be beneficial for self-management of T2D or
obesity. Finally, DiaFit has not yet been tested as part of a
pragmatic trial in a primary care setting with patients and
physicians. However, prior work on mHealth strategies for
diabetes management suggests that DiaFit could have a
significant positive impact on patients’ lives [11]. Development
of DiaFit for Android, a Spanish version of DiaFit, and
assessment of the DiaFit in a primary care setting (internal
medicine) are planned as part of our future work.
Comparison with Prior Work
Most apps developed for managing T2D and obesity do not
include all variables that need to be addressed for these chronic
conditions. Such apps typically address one dimension only,
such as glucose monitoring or nutrition tracking, and often omit
key functionalities that facilitate data entry and adherence, such
as Bluetooth compatibility [6,10,11]. Moreover, to the best of
our knowledge, no diabetes-related app attempts to link nutrition,
physical activity, glucose monitoring, and medication use with
PROs, thus missing critical patient feedback for quality of life
with a chronic condition. App creation also lacks patient and
physician involvement [10], and therefore lacks essential
feedback from the targeted users. Finally, very few apps on the
market are available open source, despite several attempts at
democratizing health data, such as the Open mHealth initiative
[27].
Conclusions
Despite the presence of >100,000 health and fitness-related apps
in the Apple store alone, apps tend to be of poor quality with
regards to clinical evidence. Very little effort has been placed
in developing apps while including the potential end-users (eg,
patients and physicians or health care professionals) in the
process. In this paper, we presented the iterative process and
design of the DiaFit process development, an app created to
help patients with T2D and obesity manage their conditions
more effectively. The process was based on the creation of a
team representing all constituents of the DiaFit project, and we
involved them as clients in an Agile software development
project. We believe that this approach will allow academicians
interested in mHealth strategies to close the gap between fun
apps and evidence-based apps, and allow mHealth to reach its
goal of revolutionizing health care by improving scalability of
access. Finally, we hope that providing DiaFit as an open source
solution to diabetes and obesity management will lead the
community to improve and grow its functionalities to better
serve patients.
Acknowledgments
FM was the lead author and was responsible for leading the app development and writing and editing of the manuscript. MC and
ER were responsible for the expert content of the app and the writing and editing of the manuscript. JB was responsible for the
writing and editing of the manuscript. TM was responsible for leading the app development. RB and CM were responsible for
expert content and testing of the app. CR-D was responsible for expert content of the app. This research was supported by grant
NIH/NCATS UL1TR001427.
Conflicts of Interest
None declared.
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JMIR Diabetes 2016 | vol. 1 | iss. 2 | e5 | p.13http://diabetes.jmir.org/2016/2/e5/
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Abbreviations
API: application program interface
AWS: Amazon Web Services
BLE: Bluetooth Low Energy
mHealth: mobile health
NIH: National Institutes of Health
PRO: patient-reported outcome
PROMIS: Patient-Reported Outcomes Measurement Information Systems
T2D: type 2 diabetes
Edited by G Eysenbach; submitted 26.09.16; peer-reviewed by N Tatara, H Vincent; comments to author 25.10.16; revised version
received 29.11.16; accepted 03.12.16; published 13.12.16
Please cite as:
Modave F, Bian J, Rosenberg E, Mendoza T, Liang Z, Bhosale R, Maeztu C, Rodriguez C, Cardel MI
DiaFit: The Development of a Smart App for Patients with Type 2 Diabetes and Obesity
JMIR Diabetes 2016;1(2):e5
URL: http://diabetes.jmir.org/2016/2/e5/
doi:10.2196/diabetes.6662
PMID:
©François Modave, Jiang Bian, Eric Rosenberg, Tonatiuh Mendoza, Zhan Liang, Ravi Bhosale, Carlos Maeztu, Camila Rodriguez,
Michelle I Cardel. Originally published in JMIR Diabetes (http://diabetes.jmir.org), 13.12.2016. 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://diabetes.jmir.org/, as well as this copyright and license information must be included.
JMIR Diabetes 2016 | vol. 1 | iss. 2 | e5 | p.14http://diabetes.jmir.org/2016/2/e5/
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