Nursing 305 presentation

n preparation for this assignment, you will need to 1) select a disease process to investigate(
Diabetes) and, 2) review the available applications (apps) listed on either Apple Store or the Google Play Store. You may also select applications that you have used in clinical practice or that you have seen your patients use. 
(Glucose buddy diabetes Tracker & DiabetesPro)

Go to 

https://www.apple.com/app-store/

 or to 

https://play.google.com/store/search?q=MEDICAL%20APPS&c=apps

 to search for the most recent app ideas from a laptop device.  If you are on a Smartphone, then go to the Apple store or to the Google Play store to search. You can also type in the disease process that you are focusing on such as “diabetes” in the search box on either the Apple or Google Play store to find patient/client centered apps.

Please review the guidelines for PowerPoint presentations: 

PowerPoint Quick Start // Purdue Writing Lab

PowerPoint Quick Start // Purdue Writing Lab Purdue Writing Lab The Purdue University Online Writing Lab serves writers from around the world and the Purdue University Writing …

The presentation should be between 8-10 slides long.

For this assignment, create a PowerPoint presentation that addresses each of the following:

Title Slide

1. Present a title slide for the presentation. ( 
Tracking Blood Sugar, Medication, and A1C)

Introduction

1. Introduce the context of your presentation.

Patient Care Applications Comparison (1 slide)

1. Prepare a slide that shows a side-by-side comparison of two patient care applications. 1) Select two patient care applications that could be used by patients. They can be mobile or computer-based applications. 2) When providing your discussion on the comparison between the two applications, address the following:

·

Does the app:

· Inform: Provide information in a variety of formats (text, photo, video)

· Instruct: Provide instructions to the user

· Record: Capture user-entered data

· Display: Graphically display user-entered data/output user-entered data

· Guide: Provide guidance based on user-entered information (i.e., recommend a physician consultation or course of treatment)

· Remind/Alert: Provide reminders to the user

· Communicate: Provide communication with the user and/or provide links to social networks.

· Privacy: Provide information on how data is protected.

Provider Care Applications Comparison (1 slide)

1. Prepare a slide that shows a side-by-side comparison of two provider care applications. 1) Select two provider care applications that could be used by healthcare providers (nurses or doctors). They can be mobile or computer-based applications. 2) When providing your discussion on the comparison between the two applications, address the following:

·

Does the app:

· Inform: Provide information in a variety of formats (text, photo, video)
· Instruct: Provide instructions to the user
· Record: Capture user-entered data
· Display: Graphically display user-entered data/output user-entered data
· Guide: Provide guidance based on user-entered information (i.e., recommend a physician consultation or course of treatment)
· Remind/Alert: Provide reminders to the user
· Communicate: Provide communication with the user and/or provide links to social networks.

· Privacy: Provide information on how data is protected?

Review of the Evidence to Support the Use of Medical Applications (2 slides)

1. Using the library, research a minimum of two (2) scholarly articles related to the use of medical applications for patient education and disease management.

2.

3. Explain how the research supports the use of medical applications in practice. Provide a summary that is one paragraph with at least 5-7 sentences. Be sure to appropriately cite your references.

4. Provide two slides that offer a summary of the key points that support the use of medical applications for patient education and disease management.

Conclusion and Summary Slide (1 – 2 slides)

1. Provide a thorough conclusion or summary statement and address each of the following questions:

a. What were your perspectives on the use of medical applications?

b. How has your perspective on the use of medical applications changed as a result of completing this assignment?

APA Citation and Reference Page (1 slide)

1. Use at least two scholarly sources to support your position and plan. Cite all references, provide a reference page, and present the paper in APA format.

RESEARCH ARTICLE

Mobile Health Apps to Facilitate Self-Care: A
Qualitative Study of User Experiences
Kevin Anderson, Oksana Burford, Lynne Emmerton*

School of Pharmacy, Curtin University, Perth, Western Australia, Australia

* lynne.emmerton@curtin.edu.au

Abstract

Objective

Consumers are living longer, creating more pressure on the health system and increasing

their requirement for self-care of chronic conditions. Despite rapidly-increasing numbers of

mobile health applications (‘apps’) for consumers’ self-care, there is a paucity of research

into consumer engagement with electronic self-monitoring. This paper presents a qualita-

tive exploration of how health consumers use apps for health monitoring, their perceived

benefits from use of health apps, and suggestions for improvement of health apps.

Materials and Methods

‘Health app’ was defined as any commercially-available health or fitness app with capacity

for self-monitoring. English-speaking consumers aged 18 years and older using any health

app for self-monitoring were recruited for interview from the metropolitan area of Perth, Aus-

tralia. The semi-structured interview guide comprised questions based on the Technology

Acceptance Model, Health Information Technology Acceptance Model, and the Mobile

Application Rating Scale, and is the only study to do so. These models also facilitated

deductive thematic analysis of interview transcripts. Implicit and explicit responses not

aligned to these models were analyzed inductively.

Results

Twenty-two consumers (15 female, seven male) participated, 13 of whom were aged 26–35

years. Eighteen participants reported on apps used on iPhones. Apps were used to monitor

diabetes, asthma, depression, celiac disease, blood pressure, chronic migraine, pain man-

agement, menstrual cycle irregularity, and fitness. Most were used approximately weekly

for several minutes per session, and prior to meeting initial milestones, with significantly

decreased usage thereafter. Deductive and inductive thematic analysis reduced the data to

four dominant themes: engagement in use of the app; technical functionality of the app;

ease of use and design features; and management of consumers’ data.

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 1 / 21

a11111

OPEN ACCESS

Citation: Anderson K, Burford O, Emmerton L (2016)
Mobile Health Apps to Facilitate Self-Care: A
Qualitative Study of User Experiences. PLoS ONE 11
(5): e0156164. doi:10.1371/journal.pone.0156164

Editor: Peter M.A. van Ooijen, University of
Groningen, University Medical Center Groningen,
NETHERLANDS

Received: January 21, 2016

Accepted: May 10, 2016

Published: May 23, 2016

Copyright: © 2016 Anderson et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.

Data Availability Statement: Limited data can be
made available to researchers who meet the criteria
for access to confidential data. Due to the qualitative
nature of these data, the interview transcripts contain
personal information that potentially identifies
participants and would breach participant
confidentiality if made publicly available. Data
requests may be sent to the corresponding author.

Funding: The authors have no support or funding to
report.

Competing Interests: The authors have declared
that no competing interests exist.

http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0156164&domain=pdf

http://creativecommons.org/licenses/by/4.0/

Conclusions

The semi-structured interviews provided insight into usage, benefits and challenges of

health monitoring using apps. Understanding the range of consumer experiences and

expectations can inform design of health apps to encourage persistence in self-monitoring.

Introduction
The increasing aging population will benefit from 21st Century self-care techniques, easing bur-
den on healthcare by enabling self-monitoring at home, office or other location.[1] In order for
self-care of a chronic condition to be sustained, self-management techniques need to be inte-
grated into one’s life.[2, 3] Due to differences between chronic conditions, there is no agreed
definition of self-care.[4] One commonality is that self-care requires self-monitoring for a con-
sumer to pursue daily decisions to maintain functionality.[4] Self-monitoring can be conducted
by consumers on various levels; examples are self-awareness of symptoms (e.g. shortness of
breath), manual blood pressure readings, and self-maintained electronic databases of blood
glucose measurements in diabetes management. For consumers with reasonable health literacy,
self-monitoring offers greater autonomy, aiming to reduce pressure on health resources.[5–8]

Despite being a relatively new phenomenon, self-monitoring has experienced notable devel-
opments in its practical immersion into one’s lifestyle. Health consumers are increasingly
engaging with mobile health applications (‘apps’)[9] for self-monitoring. However, limited reg-
ulation in the technology marketplace enables insufficiently tested[10, 11] self-monitoring
devices to be launched, with potential for health consumers to ill-advisedly change their self-
care regimens. There are many instances of ‘buggy’ health apps.[10, 11] Indeed, a number of
authors have called for guidelines around electronic self-monitoring to prevent errors or other
incidents.[12, 13] In Australia, the introduction of the Health Market Validation Program[14]
signifies the Victorian State Government’s and technology vendors’ commitment to remote/
home monitoring; feasibility studies are required in other jurisdictions.

Research incorporating Consumer Experience Metrics
One report, in which consumer experience with health apps was a key outcome, describes two
Swiss university randomised pragmatic trials.[15] Both studies explored whether an app-based
intervention was more effective than self-management of chronic pain without an app. The
apps included modules for participants to write diary entries and complete questionnaires dur-
ing the six-month intervention. Consumer experience was measured in terms of adaptability
and pre-post sick leave,[15] with the Chronic Pain Acceptance Questionnaire[16] used to
record sick leave taken by participants.

Consumer experience was also included in a four-week British weight management study
involving seven females and six males.[17] A hybrid website and smartphone app were trialed.
Semi-structured telephone interviews were used to assess the two platforms, with data analyzed
via inductive thematic analysis.[17] Participants noted improvement in self-reported dietary
and physical activity. No confounding factors relating to weight management were acknowl-
edged. Key outcomes relating to goal engagement included motivation, self-efficacy, awareness,
effort and achievement. The researchers encouraged critique of the app, whereby participants
suggested use of barcode scanners and free-text entry boxes.

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 2 / 21

Since self-care transfers most of the responsibility to the consumer, the usability of technol-
ogy for this purpose is imperative. Consequently, self-care technologies need to be adaptable to
technological environments and user preferences.

A growing number of studies have explored the impact of technological interventions on
consumers’ health outcomes. These interventions have included automated reminders (via text
messaging)[18, 19] and internet-based information,[20] and have been assessed using self-
report by participants,[21] with little, if any, external validation. Poor persistence with long-
term self-monitoring is evident in chronic conditions such as asthma.[22] Gamification can be
used to increase engagement through use of rewards for repeat logins within a period of time
and achieved milestones.[23] With many usability features conceived to date, mobile health
app design is constantly evolving;[24] many app development frameworks offer fast, scalable
interfaces to deploy changes to user interfaces seamlessly.

An American health app study reported sociodemographic characteristics of app users,
through a 36-item cross-sectional survey of 1604 English-speaking adults.[25] At least one
health app had been downloaded by 934 of the participants. Data from open-ended questions,
such as effectiveness of the app and reasons for halted use, were thematically analyzed by two
researchers, and revealed Weight Loss, Calorie Tracking, Nutrition, and Physical Activity as
their main themes. While facilitating statistical analysis, large-scale studies are compromised
by their limited ability to probe participants for in-depth responses.

Studies into self-care using mobile apps have predominantly involved custom-designed
apps. Examples are a pre-post intervention for asthma using the Smart Phone Application,[26]
randomised-controlled trials for asthma using the t+ Asthma app[27] and another unnamed
purpose-built app,[28] as well as a diabetes randomised-controlled trial using Glucose Buddy.
[29] In these studies, self-efficacy was the only measurement of consumer experience, while
participants’ engagement with the app was determined via self-report. Engagement does not
necessarily mean long-term commitment to using the app; therefore, combining such data
with usage statistics, such as login time and frequency and accessed features would add value to
these studies. In contrast, mobile app-based obesity management in South Korea[30] applied
the purpose-built obesity-management app constructed with ‘knowledge statements’ from an
expert committee. Other custom-designed apps include an app for self-monitoring and guiding
lifestyle management for breast cancer survivors[31] and PD Dr, a home-based monitoring
assessment system for Parkinson’s disease.[32]

Notable deficiencies collectively demonstrated in these studies are their relatively short fol-
low-up periods and lack of detailed consumer experience findings. Additionally, self-manage-
ment programs have measured select outcomes, rather than a more holistic spectrum of
outcomes relevant to conditions such as diabetes, osteoarthritis and hypertension.[4]

Theoretical Frameworks
The Technology Acceptance Model (TAM), published in 1989, quantifies how consumers
accept technology.[33] It is an extension of the Theory of Reasoned Action,[34] and is used to
predict intended behaviour, adopting a technology-focussed paradigm in decision-making.[35]
The TAM has been applied in qualitative[36] and quantitative[37] studies of health apps to
determine the acceptance of mobile technology amongst physicians and medical students,
respectively, and in health-related studies on topics such as adoption of health apps.[38]

The Health Information Technology Acceptance Model (HITAM) is an evolution of the
third version of the TAM for the health technology field,[39] combining behavioural, personal,
social and IT factors. This model also embraces the Health Belief Model[40] and has been used

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 3 / 21

in asthma studies for investigation of medication compliance.[41, 42] However, no literature
was found in which the HITAM informed research into the use of health apps.

The Mobile Application Rating Scale (MARS) is a validated and reliable scale[43] due to its
internal consistency, inter-rater reliability and comprehensive extraction of 25 papers and
resources in its formation. The MARS is an Australian development from 2015 to assess the
quality of health apps, and is based on four quality scales: engagement, functionality, aesthetics
and information quality. Research applying the MARS in studies involving health apps is
emerging, with MARS noted in an Australian wellbeing evaluation protocol[44] and men-
tioned in an Irish mental health app feasibility study without being used in the study itself.[45]

Other theoretical models and frameworks have been applied in studies of self-care. The
PRECEDE-PROCEED model has been applied in an asthma study in Taiwan[46] to measure
factors such as asthma knowledge and self-efficacy; this model contains elements such as
administrative and financial policies that may not be relevant to exploratory research.[47] Sim-
ilarly, Orem’s Self-Care Model has been applied to asthma to investigate “self-care abilities,
self-care practices, and health outcomes.”[48] However, this model and the PRECEDE-PRO-
CEED model lack consideration of technological factors.

Review of the literature suggests the TAM, HITAM and MARS are the most relevant frame-
works to qualitatively explore consumers’ experiences of mobile health apps. While no pub-
lished research has applied a combination of these models, integration of the TAM, HITAM
and MARS should improve cross-disciplinary relevance and robustness, and provide theoreti-
cal grounding for exploratory research into the consumer experience with health apps.

The objectives of this study were therefore to 1) qualitatively explore consumers’ experi-
ences with mobile health apps and 2) their perceived benefits from use of health apps, and 3)
formulate suggestions for improvement of health apps.

Materials and Methods
This study explored consumers’ experiences with health apps through semi-structured inter-
views. The Human Research Ethics Committee of Curtin University approved this study
(approval number RDHS-102-15). In accordance with this approval, participants provided
signed informed consent for interview.

Inclusion criteria were minimum 18 years of age (no maximum), residence in the metropol-
itan area of the University (for convenience), conversational fluency in English, and recent (at
least one month’s) experience with any health/fitness mobile app. Any duration of use of the
app(s) was of value, because discontinued use and negative experiences complemented data
from persistent users. It was intended to involve participants of a broad age range to combine
experiences of the tech-savvy younger generation with the older generation.

Including fitness apps enabled participants with chronic conditions such as obesity, diabetes
and high blood pressure to elaborate on their experiences without restricting them to disease-
specific apps. Participants without a chronic condition were included to capture fitness app
usage amongst chronic disease consumers and healthy counterparts.

Guiding the research was the post-positivism worldview where relationships can be reverse-
engineered via tested approaches such as deductive analysis.[49] A reductionist philosophy
was applied to deconstruct implicit and explicit responses into manageable variables. The qual-
itative paradigm was crucial to appreciate, observe and deduce consumers’ experiences. The
use of individual interviews offered privacy, and enabled exploration of each user’s interaction
with their identified health app(s). Semi-structured interviews provided participants freedom
to elaborate on the interview guide (Table 1).

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 4 / 21

Table 1. Interview Guide.

Question Elaboration Questions Theory, study or construct

Which health app(s) have you used? Do you still use that/those app(s)? (If multiple apps) Which of those
apps are still on your device? Which of these do you still use?
Which one(s) would you like to talk about today?

Experience

(If on present device) Please show me how you
use your health app.

How did you set it up? What problems do you recall in setting it up?
(Prompts: user interface, prompts, permissions, language used)

Technological literacy

For approximately how long have you used (did
you use) this app?

How often do/did you use it? (If discontinued) Why did you stop
using the app?

Experience

How did you ‘discover’ this app? (Prompts: health prof recommendation, peer/family
recommendation, self-search)

TAM—subjective norms[50]

On which platform do/did you use this app? (Prompts: iPhone, iPad, Android phone, Android tablet) Descriptors of use

What do/did you like about this app? Does/did the app fulfil your needs? Why or why not? Do/did you
enjoy sessions with your health app? How is/was working with your
app satisfying? Is/was your health app worth recommending to
others?

TAM—usefulness;[50] Mobile
App Rating Scale[43]

How easy is/was using your app? What makes/made the app information clear and understandable?
How do/did you find the font size and representation? How do/did
you add remarks to your readings?

TAM—ease of use;[50]
Acceptance Factors of mobile
apps[51]

Have you sometimes not known (did you
sometimes not know) what to do next with your
app?

Are/were there any parts of the app you don’t use, because they’re
complicated? What app features do/did you find unintuitive? Do/did
you sometimes wonder if you’re using the app the right way? Who
do/would/did you turn to for help using the app (prompts: family,
friends, or online forum)?

Technological literacy;
Acceptance Factors of mobile
apps[51]

Have you found any ‘bugs’ in your health app,
or things it can’t do?

If the app crashes or freezes (crashed or froze), is/was it easy to
restart? Have you ever given up due to technical glitches? Have you
ever contacted the company about any technical glitches?

Limitations of the app;
Acceptance Factors of mobile
apps[51]

How much sight and sound stimulation do/did
you get from your health app?

(Prompts: graphs, things that flash up, reminders about personal
targets, warnings, sound effects/reminders)

Mobile App Rating Scale[43]

What customization features would you like to
see in your health app?

Mobile App Rating Scale[43]

What is your view of information stored on the
cloud?

Do you find it an invasion of privacy?

Describe your Initial user profile setup Was registration via social media e.g. Facebook, Google + an
option?

Is your health app affiliated with a government
health organization?

(Researcher to check later if participant unsure) Mobile App Rating Scale[43]

Does/did your doctor (or other main health care
provider) know you have used this app?

(If yes) How would you describe his/her reaction? Are you
encouraged by a health professional (pharmacist, general
practitioner) to self-reflect on your chronic condition?

Doherty[52] Design and
Evaluation Guidelines

What medical or technical jargon have you
seen in your app which you don’t understand?

Doherty[52] Design and
Evaluation Guidelines

Does your app use technology you are already
familiar with?

Are the dialogue boxes and input fields similar to what you are used
to?

Doherty[52] Design and
Evaluation Guidelines

Do you feel you require a peripheral (plug-in or
Bluetooth) device to operate your app more
effectively?

Yin[53] Usability Risk Level
Evaluation

Do you prefer tactile feedback (vibrations) over
plain text feedback?

Have you noticed anything vibrate when you’ve done something
wrong or you receive a warning?

Yin[53] Usability Risk Level
Evaluation

What features of your app do you think conflict
with each other?

(Prompt: inconsistent shortcuts) Yin[53] Usability Risk Level
Evaluation

Are you satisfied with the time taken to perform
tasks on your app?

(Prompts: time to display graphs, time to synchronize information) Yin[53] Usability Risk Level
Evaluation

What age bracket are you? 18–25; 26–35; 36–45; 46–55; >55 years

Your occupation?

Your highest education? Year 10 (junior high school); Year 12 (senior high school); TAFE (technical college); University

doi:10.1371/journal.pone.0156164.t001

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 5 / 21

Constructs of the TAM,[33] specifically, perceived ease of use and perceived usefulness,
were included in the interview guide. The HITAM included constructs describing personal and
social factors such as motivation, self-reflection, competition and recommendation. Addition-
ally, features of the MARS, such as engagement and aesthetics, were included. Duplicated con-
cepts between the three models were deleted. Questions were adapted to suit this study, with
the interview guide comprising core questions and supplementary questions for clarification
and elaboration. The supplementary questions reflected “acceptance factors of mobile
apps,”[51] collectively capturing all perceivable aspects of consumers’ health app usage. Fol-
lowing independent review of the draft interview guide, the structure was revised to enable par-
ticipants to reflect on more than one app during the interview.

Participants were a convenience sample of residents in the Perth metropolitan area, aged 18
years or older and conversationally fluent in English, who self-reported recent use of any com-
mercially-available health/fitness app with capacity for self-monitoring and data input. Any
duration of use was included, because discontinued use and negative experiences were consid-
ered to provide valuable insights into persistence, and complemented data from persistent
users. No preferential sampling of participants with either negative or positive experiences was
applied.

A multi-faceted recruitment strategy was applied. Participants were recruited via co-opera-
tion with health associations such as Celiac Australia and Diabetes Australia, through their
social media accounts and eNewsletters over a period of four weeks. A local radio station popu-
lar with a mature demographic was also utilized in an attempt to recruit listeners with an inter-
est in self-care. The first author’s affiliation with a technology start-up hub enabled a broadcast
announcement to members to attract participants from different educational backgrounds
who shared a common goal to innovate. A static text advertisement was posted on the Univer-
sity portal, in addition to posters with a Quick Response Code at shared university computer
workspaces and the library.

Interviews were scheduled in quiet locations such as a public library or participant’s office.
A single interviewer (author KA) conducted all interviews in June and July, 2015. The first sev-
eral interviews were used to reflect on the question guide. Interviews were digitally recorded,
supplemented with field notes and post-interview reflections. Data saturation was perceived
through recurring explicit ideas[51] such as motivation, customisation, interconnectivity, data
inaccuracy, convenience and competitiveness, and confirmed during analysis. Literature sug-
gests 20–25 participants ranging generally provides adequate saturation of themes when using
qualitative semi-structured interviews.[54–56] Advertising was halted after four weeks on-
campus and seven weeks off-campus. Audio files were professionally transcribed by an accred-
ited agency with privacy certification. Raw data files were imported to QSR NVivo 10 for open
coding and analysis.

Braun and Clarke’s six-step thematic analysis approach was utilised to capture user experi-
ence themes,[57] addressing Objective 1. The deductive approach[58] was applied to continu-
ally reflect on, and refine, emerging themes. The deductive coding framework was synthesized
using the TAM, HITAM and MARS. Step One involved data familiarization to verify accuracy
of transcriptions. This required the researcher to become intimate with the transcripts by re-
reading them and referring to field notes. As per Step Two from Braun and Clarke, co-authors
confirmed selection of codes and themes, and amendments were made as necessary to reach
consensus. Initial coding was performed in NVivo based on the deductive coding framework,
with miscellaneous responses interpreted inductively into new codes as required. Two authors
matched initial coding to ensure consistency and establish common ground to confirm defini-
tion of the full set of themes. Step Three involved clustering nodes to a common theme(s)
based on coherent patterns. To ensure robustness, data extracts are quoted in the Results

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 6 / 21

section to demonstrate legitimacy of the identified themes.[57] Step Four involved reduction of
themes into most prevalent implicit and explicit ideas.[57] Redundant themes derived from the
three published models were deleted. Step Five involved describing the parameters of, and
naming, the themes, whilst Step Six involved reporting to convey the analysis made. Outcomes
from Steps Four, Five and Six are reported in the Results.

Data are presented based on emergent themes from thematic analysis, exploring how health
consumers use apps for health monitoring (addressing Objective 1). Perceived benefits from
use of health apps (addressing Objective 2) and suggestions for improvement of health apps
(addressing Objective 3) are presented descriptively.

Results

Description of Participants
The most common age bracket of participants was 26–35 years; one participant was over 50
years and another recently turned 18 years old; further participant demographics are provided
in Table 2. Interviews were completed in 20 minutes on average, during which time, most par-
ticipants answered all questions relevant to their experience.

Table 3 displays the types of apps reportedly used by the 22 participants, three of whom did
not report any chronic condition. Nine apps were self-discovered, and two recommended by
friends, four by a family member or partner, four by a healthcare professional and one by infor-
mation from a health association or gym. The remaining two participants were influenced by
multiple sources for different apps: self-discovery then a friend; and partner then a gym. All
participants located their app using the respective app store on their smart device. For com-
mercial reasons, the marketed names of the apps are not reported here. Persistence with each
health app ranged from “a couple of weeks” for a diabetes app to “over two years” for a pain
management app.

The chronic conditions reported by participants included sleep disorders, chronic migraines,
menstrual irregularities, chronic depression, arthritis and Behçet’s disease. A number of partici-
pants reported more than one condition. Although the interviews focused on user experiences
rather than their medical condition(s), participants were keen to share insights into their health
as well as app usage.

One participant presented with the new Apple Watch1, seven participants presented with
Android smartphones, and the remaining participants owned an iPhone 4, 5 or 6.

User Experiences
Four emergent themes are described below, based on deductive analysis with reference to the
TAM, HITAM and MARS. The themes were named Engagement, Functionality, Information
Management, and Ease of Use. Each of the four themes aligned with constructs of one or more
of the three published models.

Engagement
Aligned with the MARS, the Engagement theme covers consumer interaction with their app,
motivation to sustain usage, ability to self-reflect or write notes against readings, and social fac-
tors enabling competition with other users. Apps that can sustain positive behaviors and adapt
to changes in consumer requirements were more likely to be used on a continual basis. This
was particularly noted amongst users of pain, sleep and depression management apps. The fol-
lowing user of a blood pressure-monitoring app demonstrated persistence with his/her app:

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 7 / 21

“I was diagnosed with high blood pressure . . . I’ve now been able to come off the medication,
but I still monitor my blood pressure [documenting the readings into an app] just to make
sure it’s in a healthy range.”

[P6]

Inability to engage with one’s health app can result in declined usage:

“I do have some apps I don’t use often,mainly because they’ve kind of bored me in a way. I’ll
just do an example: one fitness app shows you how to lose weight, but the way it’s describing
it, it’s not what I’m after. It’s one of those free apps I bought that—I thought [the fitness app]

Table 2. Participant Demographics.

Characteristic Subcategory Number

Gender Female 15

Male 7

Age (years) 18–25 4

26–35 13

36–45 2

46–55 2

>55 1

Total Participants Met inclusion criteria 22

Excluded 4

Interview Duration (minutes) Mean interview time 20

Shortest interview 15

Longest interview 41

Smartphone Operating System Android 7

Apple 15

Windows 0

Symbian 0

Linux 0

Main Language English

17

Other 5

Recruitment Source Physical university posters and online staff/student portals 10

National Asthma Council eNewsletter and social media (Facebook) 3

Rare Diseases Australia eNewsletter 2

Celiac Australia eNewsletter 2

Diabetes Australia eNewsletter 1

Perth start-up community (posters and daily notices blog) 4

Pharmacy open 24/7 0

Curtin University Radio 0

Occupation Allied health 3 (podiatrist, psychologist and speech therapist)

University student 5

Other office-based workforce 9

Retail 1

Start-up innovator 4

Highest Education High school 2

University 18

Other 2

doi:10.1371/journal.pone.0156164.t002

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 8 / 21

would be great, but when you actually use it, it’s not the same.”

[P2]

Most participants reduced or stopped using their app when they were familiar with how to
self-manage and did not require constant interaction with their app. This finding was evident
in users of strength training and fitness apps, whereby users who had reached their goal were
not stimulated to achieve further, as well as the following user of a pain monitor:

“I think the migraine one’s probably outlived its usefulness for me, but the back pain one, I
could still go back to that at any time. If I started to need to monitor my pain again in a sys-
tematic way, I’d still go back to it. But I haven’t had back pain that’s needed that.”

[P8]

The same participant reported ‘outgrowing’ two pain-management apps:

“So they’ve [migraine and pain tracking app apps have] sort of exceeded their usefulness now,
but initially they were very helpful.Well, initially I was using them to track migraine symp-
toms and to track the effects of medication. But now I know what most of my triggers are, and
I know what medication works. I guess for me to use it again, it would have to offer something
different. So maybe alternative management strategies to what I’m already doing.”

[P8]

Convenience was found to be the main factor why participants engage with health apps, as
exemplified by a participant who used a smartwatch app for weight management:

“I really want to have a more active lifestyle . . . Being able to just look at [the smartwatch] on
the fly and going, ‘Right, if it just means that I have to go move that little bit more, or I have to
exercise that little bit more’, I will do it, because you have a real-time gauge of how well you’ve
done for the day. So that gets me going because the perceived barrier of just getting the thing
done is a lot lower.”

[P7]

Table 3. Types of Health Apps used by Participants.

Type of App Used by Android Participant
Number

Used by iOS Participant Number Number of
Participants

Blood pressure monitoring app (1 type) P6 1

Diabetes monitoring app (2 types) P2, P17, P20 3

Migraine management app (2 types) P5, P8 2

Menstrual cycle monitoring (4 types) P1, P22 P6, P4 4

Anxiety management app (1 type) P13 1

Calorie management and weight loss monitoring
app (5 types)

P1 P2, P3, P16, P20 5

Celiac disease management app (1 type) P11 1

Sleep monitoring app (4 types) P14 P6, P13, P21, 4

Pain management app (2 types) P8 1

Cycling app (2 types) P12 1

Fitness App (22 types) P8, P9, P11, P14, P18, P22 P2, P3, P7, P9, P10, P15, P16, P17, P19,
P20, P21

17

Other (saliva analysis kit) P16 1

doi:10.1371/journal.pone.0156164.t003

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With one exception [P8], all reported increased engagement when describing the social or
competitive angle of their app by all participants. This phenomenon was noted for fitness
trackers over other health monitors. Examples were:

“Yes, thankfully [I am socially competitive]. Bragging rights, unfortunately, count.”

[P9]

“Whenever we do a weekend challenge, you always have a look at what the other person’s
doing and [their] competitive side. I just want to beat the other people I see on there, so [using
the app] is quite a good motivator.”

[P10]

Having to purchase an app was expected to increase users’ engagement and persistence
with the app. Two cases relating to this concept were of note. Participant 5 had experienced
migraines for over 20 years, and used a migraine app for one year after recommendation from
her neurologist who suggested using a migraine diary. Participant 16 was an app developer
who used an app for weight management. Neither expressed concern paying for health apps:

“Usually apps are not expensive, they’re usually under $5. So if you found something really
good, you would definitely pay.”

[P5]

“I’m prepared to pay for applications. As well as being in the software industry, I understand
that it’s people’s livelihoods are attached to this. I use some free applications, but I often will
pay for the upgraded or the purchased option.”

[P16]

Functionality
Aligned with the MARS and HITAM, Functionality encompasses guidance provided by the
app developers, aesthetics, annoying features, layout, navigation and tactile feedback. While
Functionality and Engagement are subjective concepts, Functionality relates to more tangible
physical features. One consumer found color-coding in outputs a useful function:

“These pink patches are REM [rapid eye movement] sleep. The green, which is the light sleep,
is basically stage two sleep. The blue is your stage three, four sleep.”

[P13]

Use of tactile, visual and sound feedback was divided amongst participants, based on con-
sumer preference and task performed:

“Yeah [I haven’t disabled the auditory alerts].My running app will ping every so often . . . say-
ing a friend has completed a run, or it’s time for me to do a run, or something along those
lines. [My app with a wristband device] sends me a little alert if I’m close to my goals, if I’ve
got 2,000 more steps to go. [The auditory alert] doesn’t really bother me. I just tune out.”

[P9]

“I would find that annoying, yeah [push notification suggesting exercise]. I’d turn it off.”
[P10]

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App functionality is dependent on the environment in which it is used. For example, a par-
ticipant using a cycling app did not use any tactile or sound feedback:

“I usually keep it [the smartphone with the cycling app] on my bike while I’m riding, so I can
see the speed, and the time, and distance and things. I don’t think I use any sound or anything
like that.”

[P12]

Reminders to upgrade app versions for greater functionality were deemed annoying:

“With [the weight management app], they always ask you to upgrade to Pro, so you get more
advice and stuff, but that’s really annoying.”

[P1]

When asked about peripheral devices to synchronize with a diabetes app, a participant
responded:

“That would be very helpful, yes.”
[P2]

Despite well-received navigation and layout features, the physical requirements for apps to
measure sleep duration and quality were inconvenient:

“You have to put it [the phone] under your sheet, on the mattress, or under your pillow, and I
think I just always had that consciousness that my phone was there and I had to remember to
turn [the app] on before I went to sleep and turn it off again when I woke up, and it just
wasn’t really contributing to good sleep hygiene.”

[P6]

Some participants indicated inclination towards customizing app features to suit their
requirements:

“I would love . . . to be able to record reps, and sets, and weights and things like that [if their
running app were more customisable].”

[P3]

Information Management
Information Management is aligned with the HITAM, and describes reliability, privacy to
third parties, data security at rest and in transit, and quality and quantity of data. Without
acceptable information management processes, health apps would lack the ability to compute
readings, analyse data accurately, reject false or faulty entries and securely manage data. Data
security appeared highly valued by participants, but was generally dependent on the type of
data. For example, self-documenting height and weight did not raise any concern, although
concerns were raised around access to those data by health insurers. One participant [P8], who
used a sleep management app, expressed some concerns about potential access to stored data.
Another [P13] had created a separate account for services used to preserve privacy. A user of a
menstrual cycle tracker [P4] was not comfortable with the prospect of her data accessed by
third parties, while another was less concerned:

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“I don’t think about [data security], to be honest. This is going to sound terrible—maybe I’m
just really naive . . . I don’t know, it doesn’t really concern me. Probably, it should.”

[P22]

Similarly, the following participant did not have any significant concerns about data security
relating to a fitness tracking app:

“Not in this instance [no concerns about cloud storage]. I think there’s been a lot of hoo-ha
about it. And this is a company; I’ve been with Apple for a long time. They’ve done a good job
making consumers feel that their data is safe . . . for Apple, because the data is just used for
the benefit of the consumer. It’s not otherwise; I have no qualms about it.”

[P7]

Counterbalanced against privacy issues, there was some gravitation towards apps inter-
connected with consumers’ healthcare services, as one participant with chronic migraine
explained:

“I think it’s not so much the app, but it’s where the app can go. . . If it’s just an app in isolation,
it doesn’t have as much power [compared to] if it’s something that you can feed into informa-
tion that you need somewhere else.”

[P5]

Apps interconnected with each other were also of interest:

“Yes, that would work. I don’t mind that. [if her diabetes app was connected to an insurance
provider].”

[P17]

The ability to send blood pressure readings to general practitioners for analysis was highly
valued by a participant:

“What I really liked about the blood pressure app is that it’s really easy for me to export my
results and email them . . . and I’ve actually done that before for the doctor.He said, ‘How are
you tracking with your blood pressure?’ I’ve just been over [to the clinic].While I’m sitting
there and the doctor’s in clinic, [I] just email him a PDF straight away of my results. He’s able
to save that on his computer, so it’s quite handy for him too.”

[P6]

Glitches in accuracy were reported in some apps:

“I was actually overseas back in April, and for some reason, [the wearable device] keeps on
syncing back to the time when I was Turkey . . . which is a bit inaccurate.”

[P2]

Additionally, the ubiquitous nature of self-care apps captured all forms of movement, at
times leading to glitches:

“I went go-karting a while ago and [the app] thought I did like a hundred flights of stairs and
thousands and thousands of steps in the hour I was driving around . . . I know it’s never going

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to be exact, but if it’s within a few hundred steps, then that’s fine.”
[P10]

The following consumer was familiar with environments instigating inaccurate heart rate
readings, and was able to rectify the issue:

“Sometimes [the heart rate app] gives numbers that are definitely not right, and then I’m like,
“Okay, the lighting was too low” and discard that. I’ve noticed when . . . you’re really cold, or if
the florescent lighting is coming on a funny angle that [the phone’s camera] will sometimes
not register that there’s too little lighting, or that the situation isn’t going to give a good [heart
rate] reading. So I tend to do it [measure heart rate using the app] twice rather than once.”

[P13]

Some participants were particularly keen on statistics, and utilized their data in a more
sophisticated way than others who merely glanced at their graphs and charts:

“I think I’m the sort of person that I like to see the data around whatever problem I’ve got, just
to help me understand it and monitor it. So I’m always really interested in seeing the statistic.”

[P6]

“For me, the major interest was the ability to export my data and consume it, and interpret it,
and analyse it in a set of third-party tools. . . “I use some of our heavier statistical analysis
tools from work against the number of times I go running and get some insight there.”

[P16]

The same participant [P16] particularly valued using existing phone hardware to measure
heart rate and blood pressure:

“So this technology is a really interesting use of the phone. Obviously, the camera flash, and
the camera, and the light weren’t intended for that use [heart rate, blood pressure using the
smartphone’s flash and camera]. I quite like that an entrepreneur somewhere has seen that
these pieces of technology can be used to create something different . . . I would be interested
more in things like blood pressure and even . . . blood glucose levels, and some of the measure-
ments which I suspect are probably useful for people with diabetes and what have you.”

[P16]

Ease of Use
Ease of Use is aligned with the TAM, and includes concepts such as automation, convenience,
fun and health literacy suitable to cater a range of consumers. Recurring patterns among the 22
participants included the desire to use the app, particularly until consumers had reached their
self-management goal. Various app features were appreciated by consumers, for example:

“The audio cues [telling me my duration and distance on my running app . . . I really like
them.”

[P3]

Automation of in-app functions reduces time to perform tasks and was appreciated by all
participants:

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“I use . . . [certain health apps] because they’re connected to wearables, so I don’t have to do a
lot of the data collection. It does it automatically for me and then feeds me back the informa-
tion.”

[P9]

Convenience was appreciated by one user in self-managing diabetes:

“This one’s quite good because it helps you out with planning. It also has information on how
you can upload what your blood sugar levels are like. And it even shows you meal-time goals,
it shows you how much juice you can have, how many starchy foods, how much protein, but
even suggests what you should have every day, which is helpful, and it shows you what you
can do, because you may think, ‘Oh, well, eight pieces of vegetables is a lot,’ but when you look
into it, it’s not that much, really.”

[P2]

Perceived Benefits. The analysis in this section represents the benefits of using a health
app to facilitate self-care, analysed inductively from coded interview data.

Perceived benefits from usage of health apps included greater self-awareness of one’s condi-
tion, easier integration of self-management in daily life, ability to send data to allied health pro-
fessionals without repeated visits, the ability to view historical data without visiting a doctor,
social motivation to improve fitness, and desire to customise app features to suit individual
needs. Participants also expressed greater control of their condition, in this case, menstrual
problems:

“I decided just to search and find out whether there was an appropriate app just to make life a
little bit easier. . .my specialist had told me to keep track of any symptoms and the length of
my cycle, so I just found [the menstrual cycle tracker] myself online, and found that to be an
easy tool to use.”

[P6]

Suggestions for Improvement. Suggestions for improvement were identified deductively
from responses, and were not constructs of the TAM, HITAM or MARS. Suggestions included:

“[The diabetes app] could remind you when to do your blood sugar, say, before your meal or
two hours after your meal. . . would be very helpful, because that’s another thing I get amnesia
on, is forgetting about [when to take blood sugar readings]. A beep would help me, but being
at work. . . I’ll turn my phone on silent—a vibration would be helpful just to remind you.”

[P2]

“Maybe if I could leave the features I don’t use [in this menstrual cycle tracker] behind, since
I’m not trying to get pregnant, so just get rid of these fertile days.”

[P4]

“[Receiving benefits for sharing migraine management tips with other app users] would be
amazing . . . if I could have just pressed a button and sent it in [my migraine action plan from
the app to the doctor’s email], that would have been ideal.”

[P5]

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The aforementioned limitation about fitness-tracking apps not recognizing certain activities
was also mentioned by another participant, who suggested:

“I guess being able to track different styles of exercise, so not just running and cardio-based
activities, but if it could somehow track better movement with the bodyweight exercises or
high-intensity exercises, which aren’t as cardio-based.”

[P3]

Furthermore, the same participant [P3] gravitated towards more interconnectivity of raw
data fromMedicare and data from multiple apps aggregated in one graph. Suggestions for
improvement included appropriate use of gamification techniques throughout the app.

Discussion

Principal Findings
Data from this limited sample of health app users suggest self-management by health consum-
ers with chronic conditions can be enhanced via use of mobile applications. This is the first-
known research to combine these models, benefits of which include chronic condition-specific
dimensions such as targeting health and information technology literacy, as well as functional-
ity, engagement and information management. Additionally, more depth identifying usability
issues when exploring consumer interaction with self-management goals via health apps was
encountered when combining these three models. While the TAM and HITAM were not
developed specifically for mobile apps, combining it with the MARS enabled a targeted, mobile
health app focus and backing from more established technology acceptance constructs. Com-
bining the TAM and HITAM with the more-recently-published MARS also provides an
updated framework to assess health app usability. As confirmed by one study, health behavior
is too complex and multi-faceted for one model to cover comprehensively,[39] which is why
relevant constructs from TAM, HITAM and MARS were combined.

Similar qualitative studies include user perception of an oral health app.[59] However, user
responses in that study were gathered via an online survey with no follow-up questions.
Another health app study measured spirometry readings from adolescents with asthma and
had no qualitative component.[60] This is the first study to explore self-care consumer experi-
ences with health apps amongst adults. Our study covers a broader range of health apps, and
more depth in exploring consumers’ experiences.

Randomised-controlled trials have reported clinical impact of health apps on outcomes
such as self-efficacy, but have not focused on consumer interaction and engagement. No con-
trolled trials have been published exploring consumer engagement with health apps. Adopting
a qualitative approach has enabled insight into consumers’ experiences with health apps across
a range of health conditions and with sufficient depth to understand motivators, desired fea-
tures and issues relating to persistence.

The MARS was designed to provide quality star ratings for health apps.[43] This research
has aligned the ‘Engagement’ theme from the MARS in the context of health apps. ‘Functional-
ity’, concerning the operability of apps, is aligned with the MARS and HITAM, the HITAM
introducing concepts such as health beliefs. ‘Information Management’ was aligned from the
HITAM, while ‘Ease of Use’ was aligned from the TAM and relates to personalization of the
user experience. This research provides novel insight from combined models to describe the
experiences of users of health apps. User experience design considers user experience, includ-
ing usability and perceived enjoyment of the product.[33, 61]

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This study has established that self-management of a chronic condition using an app
requires constant stimulation to accommodate changing user requirements and changes in
wellbeing. Additionally, this study determined consumers with chronic conditions such as dia-
betes, depression, weight and sleep management issues are often recommended fitness apps.
There were participants with a chronic condition who only used a fitness app and no disease-
specific app. During the interview, no participant expressed confusion using an app to the
point additional training or information such as on YouTube1 was required.

The benefits of gamification in health apps have been reported,[23, 62] but there has been
only one chronic disease clinical trial using gamification, amongst minors.[60] Gamification in
a health context does not necessarily ‘trivialize’ health management.[23] However, gamification
involving inter-personal competition would be most suited to fitness trackers, whilst gamifica-
tion for health apps would be most suited to intra-personal competition. Some apps were iden-
tified by participants as incorporating elements of gamification,[62] and provided those
participants dynamic opportunities to engage with their health apps, such as receiving badges,
passing levels and animated learning.[23]

Some health apps are designed for novelty or entertainment purposes, such as those provid-
ing blood pressure readings via touching the screen;[63] the accuracy of such outputs for medi-
cal monitoring is questionable. All participants presented apps designed for the intended
medical or health purpose. Research suggests a paucity of evidence-based apps.[43] Restricting
our inclusion criteria to evidence-based apps would have been inefficient, since research in this
area is regularly updated.

Apps used via smartwatches and mobile telephones should offer more convenience than
those requiring a tablet device or personal computer. This was confirmed by the participant
who presented with an Apple Watch1 for convenient use of the health app of choice. As con-
sumer uptake of smartwatches and other smart wearables increases, unique functionality with
apps will emerge.

The present data cannot conclusively support the correlation between “willingness to pay”
and “user experience”, although the correlation has been reported elsewhere in a study of
mobile apps.[61]

Actual health benefits from engagement with self-monitoring can only be determined
through clinical trial of an app. Nevertheless, perceived benefits from self-documentation can
improve a consumer’s engagement with a health app, and provided the measurements are valid
and reliable, this practice would presumably improve self-management. Positive oral hygiene
self-management have been reported as a result of engagement with a health app.[16]

Some participants indicated their health professionals (dietician, psychologist or general
practitioner) are already receiving consumers’ self-reported data electronically. How health
professionals use these data requires further investigation, specifically, whether they cross-
check consumer-reported clinical readings with their own, or consider trends in consumer-
reported data.

Participants tended to reduce usage of their app when they reached their goals and no new
self-management techniques were offered. For app engagement to be sustained after reaching a
goal or for usage to become habitual, regular intervals of engagement are recommended.
Rewards for chronic conditions involve intra-personal competition and involve different met-
rics to fitness apps employing more persistent and active inter-personal competition. Fitness
apps can be used by consumers with chronic conditions such as diabetes and depression as
part of a self-management program. At present, there is no research exemplifying long-term
impact of reward-based engagement for mobile health apps.

Health monitoring devices are steadily increasing in market availability, with biometric-
based innovations reducing the need for manual data input by consumers and providing more

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advanced, ubiquitous features.[25] Partnerships between health researchers and start-up com-
munities, known for their agile coding methods, could help develop health apps conformant
with the themes identified in this research: Engagement, Functionality, Information Manage-
ment and Ease of Use.

Strengths and Limitations
As explained previously, strengths of this study include combining the TAM, HITAM and
MARS in a single study, which has not been attempted before, providing greater breadth in the
deductive analytical framework than with the use of a single model. Additionally, using the
post-positivism paradigm supports the concept of ever-changing consumer user requirements
by viewing “knowledge as conjectural.”[64]

Limitations in this study include not referring participants to suitable apps based on their
insight, and not scheduling a follow-up interview to gauge a change in their user experience.
As such, these data represent a point-in-time measurement, and longitudinal research would
better gauge individuals’ changes in self-monitoring patterns. This study was limited to a pre-
dominantly tertiary-educated Australian perspective; apps marketed internationally may
incorporate different user experience metrics. This study did not quantify participants’ experi-
ences, which would be of greater use and relevance when a single app is studied. It is unknown
whether male and female users of health apps differ in their usage and expectations of these
apps. The present sample comprised mostly female participants, possibly due to the recruit-
ment methods.

This study is unable to correlate user experiences with credibility of health app. It may be
possible for users to report positive experiences with an app that lacks an evidence base; con-
versely, an evidence-based app might be poorly designed, with low levels of engagement or
usability. There are minimum design guidelines for the Apple App Store1[65] and similar
guidelines for Google’s Play Store1,[66] although these were not assessed in our study.

Our research has revealed a range of apps used by consumers with a particular health condi-
tion, and use of multiple health apps. It would not be feasible to focus the study on one app;
this would also limit the generalizability of the findings.

This study deliberately included a broad range of users of a variety of health apps, and it is
not feasible to draw correlations or associations between groups of participants. Because some
consumers used more than one app to manage their condition, any attempt to document the
outcomes from use of a particular app could be confounded, and would rely on self-report.
Evaluation of the clinical contribution of apps to health care requires careful experimental
design and control of environmental influences on self-management of the medical condition
of interest.

Participants discussed the app with which they are most familiar (most engaged), as this
would highlight any frustrations they had encountered with programming bugs and limita-
tions. However, participants were welcome to discuss other health/fitness apps with which they
had experience. In the interests of keeping participants engaged in the interview, and ensuring
currency and validity of the data, it was not considered worthwhile for participants to discuss
all health/fitness apps they recalled using.

Further Research
Future research may focus on users of apps for a particular health condition (e.g. asthma), with
longitudinal monitoring of their engagement with a selected app(s) and changes in user experi-
ences. Usage of apps incorporating gamification is an area requiring supplementary research,

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 17 / 21

to enable researchers to gauge whether artificial intelligence has been designed in an intuitive
and compatible way with regard to consumers’ health objectives.

The concept of competitive wellbeing also warrants consideration, with social Application
Programming Interfaces linking health data to social media and other services to increase moti-
vation and competitive spirit, and to assist users to achieve health goals.[67] Chronic condi-
tions require persistent self-management and longitudinal monitoring, and health apps should
deliver a customized solution for the user’s condition.[68] Moreover, sustained use of apps can
be optimised by further insights into how consumers use apps.[69]

Conclusion
This study explored consumers’ interactions with health apps through semi-structured inter-
views, uncovering a wide variety of users with a degree of commonality in their user experi-
ences. User experiences have been described via four themes: Engagement, Functionality,
Information Management and Ease of Use. These themes describe concepts such as motiva-
tion, customization, interconnectivity, data inaccuracy, convenience and competitiveness, and
suggest how health apps can benefit by ‘growing’ with the user and adapting to changing oper-
ating environments.

Acknowledgments
The researchers thank the participants in the study and the health bodies who helped with
recruitment.

Author Contributions
Conceived and designed the experiments: KA LE OB. Performed the experiments: KA. Ana-
lyzed the data: KA LE OB. Wrote the paper: KA LE OB.

References
1. Wiederhold BK, Riva G, Graffigna G. Ensuring the best care for our increasing aging population: health

engagement and positive technology can help patients achieve a more active role in future healthcare.
Cyberpsychol Behav Soc Netw. 2013; 16(6):411–12. doi: 10.1089/cyber.2013.1520 PMID: 23751102

2. Lorig KR, Sobel DS, Stewart AL, Brown BW Jr, Bandura A, Ritter P, et al. Evidence suggesting that a
chronic disease self-management program can improve health status while reducing hospitalization: a
randomized trial. Med Care. 1999; 37(1):5–14. PMID: 10413387

3. Holman H, Lorig K. Patient self-management: a key to effectiveness and efficiency in care of chronic
disease. Public Health Rep. 2004; 119(3):239. PMID: 15158102

4. Chodosh J, Morton SC, Mojica W, Maglione M, Suttorp MJ, Hilton L, et al. Meta-analysis: chronic dis-
ease self-management programs for older adults. Ann Intern Med. 2005; 143(6):427–38. PMID:
16172441

5. Williams MV, Baker DW, Honig EG, Lee TM, Nowlan A. Inadequate literacy is a barrier to asthma
knowledge and self-care. Chest. 1998; 114(4):1008–15. PMID: 9792569.

6. Baker DW, Gazmararian JA, Williams MV, Scott T, Parker RM, Green D, et al. Functional health literacy
and the risk of hospital admission among Medicare managed care enrollees. Am J Public Health. 2002;
92(8):1278–83. PMID: 12144984.

7. Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. J
Gen Intern Med. 1998; 13(12):791–8. PMID: 9844076.

8. Williams MV, Parker RM, Baker DW, Parikh NS, Pitkin K, Coates WC, et al. Inadequate functional
health literacy among patients at two public hospitals. JAMA. 1995; 274(21):1677–82. PMID: 7474271.

9. Gill PS, Kamath A, Gill TS. Distraction: an assessment of smartphone usage in health care work set-
tings. Risk Manag Healthc Policy. 2012; 5(1):105–14.

10. Dumas RA. Health App completely buggy? [Internet]. c2014. Available: https://discussions.apple.com/
thread/6680914. Accessed 2 November 2015.

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 18 / 21

http://dx.doi.org/10.1089/cyber.2013.1520

http://www.ncbi.nlm.nih.gov/pubmed/23751102

http://www.ncbi.nlm.nih.gov/pubmed/10413387

http://www.ncbi.nlm.nih.gov/pubmed/15158102

http://www.ncbi.nlm.nih.gov/pubmed/16172441

http://www.ncbi.nlm.nih.gov/pubmed/9792569

http://www.ncbi.nlm.nih.gov/pubmed/12144984

http://www.ncbi.nlm.nih.gov/pubmed/9844076

http://www.ncbi.nlm.nih.gov/pubmed/7474271

https://discussions.apple.com/thread/6680914

https://discussions.apple.com/thread/6680914

11. Thomas O. Apple’s health app is an embarrassment [Internet]. c2014. Available: http://readwrite.com/
2014/10/02/apple-health-app. Accessed 2 November 2015.

12. Finkelstein EA, Chay J, Bajpai S. The economic burden of self-reported and undiagnosed cardiovascu-
lar diseases and diabetes on Indonesian households. PLoS ONE. 2014; 9(6):e99572. PMID:
24915510; PubMed Central PMCID: PMC4051736. doi: 10.1371/journal.pone.0099572

13. Miller KM, Beck RW, Bergenstal RM, Goland RS, Haller MJ, McGill JB, et al. Evidence of a strong asso-
ciation between frequency of self-monitoring of blood glucose and hemoglobin A1c levels in T1D
exchange clinic registry participants. Diabetes Care. 2013; 36(7):2009–14. doi: 10.2337/dc12-1770
PMID: 23378621.

14. Host T, Person C, Lewis P. Health Market Validation Program (Health MVP) call for proposal applica-
tion form for SMEs. [Internet]. c2014. Available: http://www.business.vic.gov.au/grants-and-assistance/
programs/health-market-validation-program. Accessed 20 February 2015.

15. Blödt S, Pach D, Roll S, Witt CM. Effectiveness of app-based relaxation for patients with chronic low
back pain (RelaxBack) and chronic neck pain (RelaxNeck): study protocol for two randomized prag-
matic trials. Trials. 2014; 15(1):490–99. doi: 10.1186/1745-6215-15-490

16. Nilges P, Köster B, Schmidt CO. Pain acceptance—concept and validation of a German version of the
chronic pain acceptance questionnaire. Schmerz. 2007; 21(1):57–8. PMID: 17111168

17. Morrison LG, Hargood C, Lin SX, Dennison L, Joseph J, Hughes S, et al. Understanding usage of a
hybrid website and smartphone app for weight management: a mixed-methods study. J Med Internet
Res. 2014; 16(10):e201. doi: 10.2196/jmir.3579 PMC4259922. PMID: 25355131

18. Cooper S, Foster K, Naughton F, Leonardi-Bee J, Sutton S, Ussher M, et al. Pilot study to evaluate a
tailored text message intervention for pregnant smokers (MiQuit): study protocol for a randomised con-
trolled trial. Trials. 2015; 16(1):s13063-014-0546-4. doi: 10.1186/s13063-014-0546-4 PMID: 25622639;
PubMed Central PMCID: PMCPmc4318454.

19. Haug S, Castro RP, Filler A, Kowatsch T, Fleisch E, Schaub MP. Efficacy of an Internet and SMS-
based integrated smoking cessation and alcohol intervention for smoking cessation in young people:
study protocol of a two-arm cluster randomised controlled trial. BMC Public Health. 2014; 14(1):1140–
48. doi: 10.1186/1471-2458-14-1140 PMID: 25369857; PubMed Central PMCID: PMCPmc4228117.

20. Proudfoot J, Clarke J, Birch M, Whitton AE, Parker G, Manicavasagar V, et al. Impact of a mobile phone
and web program on symptom and functional outcomes for people with mild-to-moderate depression,
anxiety and stress: a randomised controlled trial. BMC Psychiatry. 2013; 13(1):312–24. doi: 10.1186/
1471-244X-13-312

21. Eyles H, McLean R, Neal B, Doughty R, Jiang Y, Mhurchu C. Using mobile technology to support
lower-salt food choices for people with cardiovascular disease: protocol for the SaltSwitch randomized
controlled trial. BMC Public Health. 2014; 14(1):950–8. doi: 10.1186/1471-2458-14-950 PMID:
25217039.

22. Hasford J, Uricher J, Tauscher M, Bramlage P, Virchow JC. Persistence with asthma treatment is low in
Germany especially for controller medication—a population based study of 483051 patients. Allergy.
2010; 65(3):347–54. PMID: 19712117

23. Zichermann G. Gamification by design: implementing gamemechanics in web and mobile apps. Cun-
ningham C, editor. Sebastopol: O’Reilly Media; 2011.

24. Pandey A, Hasan S, Dubey D, Sarangi S. Smartphone apps as a source of cancer information: chang-
ing trends in health information-seeking behavior. J Cancer Educ. 2013; 28(1):138–42. doi: 10.1007/
s13187-012-0446-9 PMID: 23275239

25. Krebs P D D. Health app use among USmobile phone owners: a national survey. JMIR mHealth
uHealth. 2015; 3(4):e101. doi: 10.2196/mhealth.4924 PMID: 26537656

26. Licskai CJ, Sands T, Ferrone M. Development and pilot testing of a mobile health solution for asthma
self-management: Asthma action plan smartphone application pilot study. Can Respir J. 2013; 20
(4):301–6. PMID: 23936890

27. Ryan D, Price D, Musgrave SD, Malhotra S, Lee AJ, Ayansina D, et al. Clinical and cost effectiveness
of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial. BMJ.
2012; 296(6614):e1756. doi: 10.1136/bmj.e1756

28. Liu WT, Huang CD, Wang CH, Lee KY, Lin SM, Kuo HP. A mobile telephone-based interactive self-
care system improves asthma control. Eur Respir J. 2011; 37(2):310–7. doi: 10.1183/09031936.
00000810 PMID: 20562122.

29. Kirwan M, Vandelanotte C, Fenning A, Duncan M. Diabetes self-management smartphone application
for adults with type 1 diabetes: randomized controlled trial. J Med Internet Res. 2013; 15(11):e235. doi:
10.2196/jmir.2588 PMID: 24225149.

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 19 / 21

http://readwrite.com/2014/10/02/apple-health-app

http://readwrite.com/2014/10/02/apple-health-app

http://www.ncbi.nlm.nih.gov/pubmed/24915510

http://dx.doi.org/10.1371/journal.pone.0099572

http://dx.doi.org/10.2337/dc12-1770

http://www.ncbi.nlm.nih.gov/pubmed/23378621

http://www.business.vic.gov.au/grants-and-assistance/programs/health-market-validation-program

http://www.business.vic.gov.au/grants-and-assistance/programs/health-market-validation-program

http://dx.doi.org/10.1186/1745-6215-15-490

http://www.ncbi.nlm.nih.gov/pubmed/17111168

http://dx.doi.org/10.2196/jmir.3579

http://www.ncbi.nlm.nih.gov/pubmed/25355131

http://dx.doi.org/10.1186/s13063-014-0546-4

http://www.ncbi.nlm.nih.gov/pubmed/25622639

http://dx.doi.org/10.1186/1471-2458-14-1140

http://www.ncbi.nlm.nih.gov/pubmed/25369857

http://dx.doi.org/10.1186/1471-244X-13-312

http://dx.doi.org/10.1186/1471-244X-13-312

http://dx.doi.org/10.1186/1471-2458-14-950

http://www.ncbi.nlm.nih.gov/pubmed/25217039

http://www.ncbi.nlm.nih.gov/pubmed/19712117

http://dx.doi.org/10.1007/s13187-012-0446-9

http://dx.doi.org/10.1007/s13187-012-0446-9

http://www.ncbi.nlm.nih.gov/pubmed/23275239

http://dx.doi.org/10.2196/mhealth.4924

http://www.ncbi.nlm.nih.gov/pubmed/26537656

http://www.ncbi.nlm.nih.gov/pubmed/23936890

http://dx.doi.org/10.1136/bmj.e1756

http://dx.doi.org/10.1183/09031936.00000810

http://dx.doi.org/10.1183/09031936.00000810

http://www.ncbi.nlm.nih.gov/pubmed/20562122

http://dx.doi.org/10.2196/jmir.2588

http://www.ncbi.nlm.nih.gov/pubmed/24225149

30. Jeon E, Park H. Development of a smartphone application for clinical-guideline-based obesity manage-
ment. Healthc Inform Res. 2015; 21(1):10–20. doi: 10.4258/hir.2015.21.1.10 PMID: 25705553

31. McCarroll ML, Armbruster S, Pohle-Krauza RJ, Lyzen AM, Min S, Nash DW, et al. Feasibility of a life-
style intervention for overweight/obese endometrial and breast cancer survivors using an interactive
mobile application. Gynecol Oncol. 2015; 137(3):508–15. doi: 10.1016/j.ygyno.2014.12.025
WOS:000355779000025. PMID: 25681782

32. Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based parkinson’s disease assessment sys-
tem for home-based monitoring. JMIR mHealth uHealth. 2015; 3(1).

33. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technol-
ogy. MIS Quart. 1989; 13(3):319–40.

34. Madden TJ, Ellen PS, Ajzen I. A comparison of the theory of planned behavior and the theory of rea-
soned action. Pers Soc Psychol B. 1992; 18(1):3–9. doi: 10.1177/0146167292181001WOS:
A1992HC29000001.

35. Fishbein M, Ajzen I, Albarracin D, Hornik RC. Prediction and change of health behavior: Applying the
reasoned action approach. Mahwah: Lawrence Erlbaum Associates; 2007.

36. Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care
Res Rev. 2007; 64(6):650–52. PMID: 17717378

37. Briz-Ponce L, García-Peñalvo F. An empirical assessment of a technology acceptance model for apps
in medical education. J Med Syst. 2015; 39(11):1–5. doi: 10.1007/s10916-015-0352-x

38. Cho J, Quinlan MM, Park D, Noh GY. Determinants of adoption of smartphone health apps among col-
lege students. Am J Health Behav. 2014; 38(6):860–70. Epub 2014/09/11. doi: 10.5993/ajhb.38.6.8
PMID: 25207512.

39. Kim J, Park H-A. Development of a health information technology acceptance model using consumers’
health behavior intention. J Med Internet Res. 2012; 14(5):e133. doi: 10.2196/jmir.2143 PMID:
23026508

40. Janz N, Becker M. The health belief model: a decade later. Health Educ Behav. 1984; 11(1):1–47.

41. Becker MH, Radius SM, Rosenstock IM, Drachman RH, Schuberth KC, Teets KC. Compliance with a
medical regimen for asthma: a test of the health belief model. Public Health Rep. 1978; 93(3):268–77.
PMID: 652949

42. Cormier DJ, Ferreira DC, Vise KM, Cahalin LP. A pilot study of childhood health behaviour and asthma
using the health belief model. J Cardiopulm Rehabil. 2006; 26(4):250–74. doi: 10.1097/00008483-
200607000-00015

43. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a
new tool for assessing the quality of health mobile apps. JMIR mHealth uHealth. 2015; 3(1):e27. PMID:
25760773

44. Antezana G, Bidargaddi N, Blake V, Schrader G, Kaambwa B, Quinn S, et al. Development of an online
well-being intervention for young people: an evaluation protocol. JMIR research protocols. 2015; 4(2):
e48. doi: 10.2196/resprot.4098 PMID: 25929201

45. Kenny R, Dooley B, Fitzgerald A. Feasibility of “CopeSmart”: a telemental health app for adolescents.
JMIR mHealth uHealth. 2015; 2(3):e22. http://doi.org/10.2196/mental.4370.

46. Chiang LC, Huang JL, Yeh KW, Lu CM. Effects of a self-management asthma educational program in
Taiwan based on PRECEDE-PROCEEDmodel for parents with asthmatic children. J Asthma. 2004;
41(2):205–15. PMID: 15115173.

47. Green L. The PRECEDE-PROCEEDmodel of health program planning & evaluation [Internet]. c2014.
Available: http://lgreen.net/precede.htm. Accessed 11 December 2014.

48. Velsor-Friedrich B, Pigott T, Srof B. A practitioner-based asthma intervention program with African
American inner-city school children. J Pediatr Health Care. 2005; 19(3):163–71. PMID: 15867832

49. Hesse-Biber SN, Leavy P. The Practice of Qualitative Research: SAGE Publications; 2010.

50. Holden RJ, Karsh B-T. The technology acceptance model: its past and its future in health care. J
Biomed Inf. 2010; 43(1):159–72.

51. Scheibe M, Reichelt J, Bellmann M, Kirch W. Acceptance factors of mobile apps for diabetes by
patients aged 50 or older: a qualitative study. Med 20. 2015; 4(1):e1. Epub 2015/03/04. PMID:
25733033.

52. Doherty G, Coyle D, Matthews M. Design and evaluation guidelines for mental health technologies.
Interact Comput. 2010; 22(4):243–52.

53. Jin BS, Ji YG. Usability risk level evaluation for physical user interface of mobile phone. HCC Sys Ind.
2010; 61(4):350–63.

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 20 / 21

http://dx.doi.org/10.4258/hir.2015.21.1.10

http://www.ncbi.nlm.nih.gov/pubmed/25705553

http://dx.doi.org/10.1016/j.ygyno.2014.12.025

http://www.ncbi.nlm.nih.gov/pubmed/25681782

http://dx.doi.org/10.1177/0146167292181001

http://www.ncbi.nlm.nih.gov/pubmed/17717378

http://dx.doi.org/10.1007/s10916-015-0352-x

http://dx.doi.org/10.5993/ajhb.38.6.8

http://www.ncbi.nlm.nih.gov/pubmed/25207512

http://dx.doi.org/10.2196/jmir.2143

http://www.ncbi.nlm.nih.gov/pubmed/23026508

http://www.ncbi.nlm.nih.gov/pubmed/652949

http://dx.doi.org/10.1097/00008483-200607000-00015

http://dx.doi.org/10.1097/00008483-200607000-00015

http://www.ncbi.nlm.nih.gov/pubmed/25760773

http://dx.doi.org/10.2196/resprot.4098

http://www.ncbi.nlm.nih.gov/pubmed/25929201

http://doi.org/10.2196/mental.4370

http://www.ncbi.nlm.nih.gov/pubmed/15115173

http://lgreen.net/precede.htm

http://www.ncbi.nlm.nih.gov/pubmed/15867832

http://www.ncbi.nlm.nih.gov/pubmed/25733033

54. Charmaz K. Constructing grounded theory: a practical guide through qualitative analysis / Kathy Char-
maz. London: London: SAGE Publications; 2006.

55. Radcliffe C, Lester H. Perceived stress during undergraduate medical training: a qualitative study. Med
Educ. 2003; 37(1):32–8. PMID: 12535113

56. Green J, Thorogood N. Qualitative methods for health research. 2nd ed. Los Angeles: SAGE; 2009.

57. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006; 3(2):77–101.

58. Fereday J, Muir-Cochrane E. Demonstrating rigor using thematic analysis: A hybrid approach of induc-
tive and deductive coding and theme development. Int J Qual Methods. 2008; 5(1):80–92.

59. Underwood B, Birdsall J, Kay E. The use of a mobile app to motivate evidence-based oral hygiene
behaviour. Br Dent J. 2015; 219(4):7. http://dx.doi.org/10.1038/sj.bdj.2015.660. 1707792818.

60. Elias P, Rajan NO, McArthur K, Dacso CC. InSpire to promote lung assessment in youth: evolving the
self-management paradigms of young people with asthma. Med 20. 2013; 2(1):e1. Epub 2013/01/01.
PMID: 25075232; PubMed Central PMCID: PMCPmc4084766.

61. Hebly P. Willingness to pay for mobile apps [Dissertation]. Rotterdam (Holland): Erasmus University
Rotterdam; 2012.

62. Lister C, West JH, Cannon B, Sax T, Brodegard D. Just a fad? Gamification in health and fitness apps.
JMIR Serious Games. 2014; 2(2):e9. doi: 10.2196/games.3413 PMID: 25654660.

63. Dolan B. The rise of the seemingly serious but “just for entertainment purposes” medical app [Internet].
2014. Available: http://mobihealthnews.com/35444/the-rise-of-the-seemingly-serious-but-just-for-
entertainment-purposes-medical-app. Accessed 20 January 2016.

64. Phillips DC, Burbules NC. Postpositivism and educational research. Lanham: Rowman & Littlefield
Publishers; 2000.

65. Apple. App Store review guidelines [Internet]. 2015. Available: https://developer.apple.com/app-store/
review/guidelines/. Accessed 12 April 2016.

66. Android. Launch checklist [Internet]. 2016. Available: http://developer.android.com/distribute/tools/
launch-checklist.html. Accessed 12 April 2016.

67. OptumHealth. OptumHealth debuts OptumizeMe fitness app to help Microsoft(R) Windows Phone 7
users connect and compete for better health [Internet]. c2010. Available: http://www.businesswire.com/
news/home/20101115005281/en/OptumHealth-Debuts-OptumizeMe-Fitness-App-Microsoft%C2%
AE-Windows. Accessed 17 January 2015.

68. Anderson K, Emmerton LM. The contribution of mobile health applications to self-management by con-
sumers: review of published evidence. Aust Health Rev. 2015;In Press. doi: 10.1071/AH15162 PMID:
26681206.

69. Korhonen I, Parkka J, Van Gils M. Health monitoring in the home of the future. IEEE Eng Med Biol Mag.
2003; 22(3):66–73. PMID: 12845821

Health App User Experiences

PLOS ONE | DOI:10.1371/journal.pone.0156164 May 23, 2016 21 / 21

http://www.ncbi.nlm.nih.gov/pubmed/12535113

http://dx.doi.org/10.1038/sj.bdj.2015.660

http://www.ncbi.nlm.nih.gov/pubmed/25075232

http://dx.doi.org/10.2196/games.3413

http://www.ncbi.nlm.nih.gov/pubmed/25654660

http://mobihealthnews.com/35444/the-rise-of-the-seemingly-serious-but-just-for-entertainment-purposes-medical-app

http://mobihealthnews.com/35444/the-rise-of-the-seemingly-serious-but-just-for-entertainment-purposes-medical-app

https://developer.apple.com/app-store/review/guidelines/

https://developer.apple.com/app-store/review/guidelines/

http://developer.android.com/distribute/tools/launch-checklist.html

http://developer.android.com/distribute/tools/launch-checklist.html

http://www.businesswire.com/news/home/20101115005281/en/OptumHealth-Debuts-OptumizeMe-Fitness-App-Microsoft%C2%AE-Windows

http://www.businesswire.com/news/home/20101115005281/en/OptumHealth-Debuts-OptumizeMe-Fitness-App-Microsoft%C2%AE-Windows

http://www.businesswire.com/news/home/20101115005281/en/OptumHealth-Debuts-OptumizeMe-Fitness-App-Microsoft%C2%AE-Windows

http://dx.doi.org/10.1071/AH15162

http://www.ncbi.nlm.nih.gov/pubmed/26681206

http://www.ncbi.nlm.nih.gov/pubmed/12845821

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individual use.

Mobile applications for emerging adults transitioning to
independent diabetes monitoring
Jennifer Schindler-Ruwisch and Abby Peter

s

Egan School of Nursing and Health Studies, Public Health, Fairfield University, Fairfield, CT, USA

ABSTRACT
Access to high-quality mhealth tools for diabetes management is critical. The
purpose was to systematically review mobile apps for features relevant to
helping emerging adults manage their diabetes as they transition to inde-
pendent diabetes monitoring. Mobile apps were reviewed for relevance to
emerging adults, aged 18–25, living with diabetes. The GooglePlay store was
systematically searched to identify diabetes management mobile tools. Of
the 29 apps, only one app had any features relevant to emerging adults. In
total, 20 apps had a feature to share a copy of diet or blood sugar logs with
a family member or provider. Only 9 apps had any interactivity other than
tracking. While most apps had graphics, only 5 were deemed high quality.
Just one app met all three included Mobile Application Rating Scale (MARS)
criteria. This review serves as a starting point to guide educators and
patients, especially to aid continuity of care when in-person support is not
feasible. Ongoing review of new apps with improved functionality and
effectiveness studies of the apps’ impact on emerging adults’ diabetes
management is imperative.

KEYWORDS
mhealth; diabetes
management; emerging
adults; mobile apps; diabetes
technology

Introduction

The transition of emerging adults to more independent diabetes management, with less active parental
supervision and support, can be challenging. This transition can lead to emotional distress, decreased
blood glucose monitoring, and ultimately poorer health outcomes.1 To enhance a sense of indepen-
dence, Babler and Strickland recommend more effective communication strategies that allow for
improved self-monitoring.1 Mobile applications (apps), are a potential avenue to improve commu-
nication and effective diabetes management as emerging adults are transitioning to independent
monitoring.

In the past decade, there has been tremendous growth in the field of mobile health (mhealth),
particularly for diabetes self-management. Mobile phone applications (apps) designed for diabetes
self-management have a broad range of features, which can include options for logging blood
glucose (sometimes directly from a glucose monitor), logging diet and exercise, assisting with
insulin dosing and medication adherence, and sharing the data with others, including one’s
provider.2 Additionally, evaluations of these mhealth tools for diabetes management have demon-
strated efficacy in several contexts and trials.2 However, few reviews of diabetes apps have high-
lighted the specific need and usefulness of these apps among emerging adults, many of whom are
millennials, and represent some of the highest frequency users of mobile apps.3 An estimated 95% of
teens have smartphone access, and this is consistent across socio-economic groups, genders and
racial/ethnic groups in the U.S (see Figure 1 for details on internet usage among this demographic).4

Further, in a nationally representative survey of 1,337 young people aged 14–22, 64% reported using

CONTACT Jennifer Schindler-Ruwisch jschindler-ruwisch@fairfield.edu Marion Peckham Egan School of Nursing and Health
Studies, Fairfield University, Fairfield, CT 06824-5195, USA

INFORMATICS FOR HEALTH AND SOCIAL CARE
2021, VOL. 46, NO. 1, 56–67
https://doi.org/10.1080/17538157.2020.1837839

© 2020 Taylor & Francis Group, LLC

http://www.tandfonline.com

https://crossmark.crossref.org/dialog/?doi=10.1080/17538157.2020.1837839&domain=pdf&date_stamp=2021-01-07

a health-related mobile app.5 Smartphone usage has increased from 73% of teens in 2014–2015 to
95% of teens in 2018.4

Literature review

In a study of 1,682 social media users living with diabetes, 1,179 (70%) utilized diabetes self-
management apps.6 Among app users specifically, there were significant increases in self-reported
blood glucose, diet, and physical activity monitoring, as well as cumulative self-care compared to non-
app users.6 While the mean age of respondents in this survey was 39 years, and the majority of the
sample was under age 40, there was no specific breakdown to highlight the utility of these applications
for diabetes self-management among emerging adults.

(a)

(b)

45%

44%

11%

Internet usage among U.S teens (%)

Almost constantly Several times daily Less often

93

97

94 94

95

93 93

97

50

55

60

65

70

75

80

85

90

95

100

% US teens with a smartphone by demographic category

Figure 1. (a) Internet Usage by U.S. Teens (%); adapted from data from Pew Research Center.4, 1b. Smartphone Usage by U.S. Teen
Demographic Groups (%); adapted from data from Pew Research Center.4

INFORMATICS FOR HEALTH AND SOCIAL CARE 57

Despite the potential utility of mobile apps for diabetes management, an international study of
usability, found that many app users felt these mobile tools often were not user-friendly and lacked
needed engagement-related features.7 Adult respondents surveyed (N = 217) indicated that younger
adults were significantly more likely to utilize apps for diabetes management and that the most utilized
features included trackers for blood glucose, blood sugar and calories; with the most useful features
being blood glucose and calorie monitoring. Participants did note that they wished apps had improved
functionalities including actionable reminders, and consolidated, customized options, reliable and
current information, and simplicity of use.

Similarly, survey data from 746 adults across China living with diabetes utilizing the Unified
Theory of Acceptance and Use of Technology found that intentions to use diabetes management
mobile apps were most related to performance expectancy (perceived usefulness of the app) and social
influence of providers, family members, or important others (subjective norms).8 These results suggest
that if diabetes self-management apps are perceived to be useful and recommended by key people, that
their uptake and intended use may improve. While there exists a myriad of diabetes self-management
apps available for download by interested patients, many are not meant to be stand-alone tools, but
rather a complement to in-person clinical interactions.9 Health practitioners can act as key players in
helping refer patients to relevant and credible mobile apps to allow for increased engagement and
monitoring in a variety of settings.9

When looking at adolescents specifically, apps designed for efficiency, and with relevant social
outreach were most appreciated among a small focus group and highlight the importance of apps
specific to the targeted needs of the younger population living with diabetes.10 Understanding features
most relevant to a younger population, including use of color and ease of navigation, easy data entry
(not manual), and capability to share with parents, may be unique to this population, but highly
relevant in encouraging app use and self-monitoring. A related qualitative study of young people (aged
15–23) living with diabetes in Denmark looked at their utilization of a mobile app (Young with
Diabetes) designed by and for young people.11 Again, youth highlighted the importance of sharing
features, that allowed communication with peers, parents, and health care providers to assist with
diabetes self-monitoring. Youth appreciated having chat rooms within the mobile app, having all of
their self-management data in one location, and simple (informal) ways to connect with health
providers in between appointments, which added to collective support.11

Features that may be relevant to diabetes self-management are not necessarily helpful to emerging
adults transitioning to more independent diabetes management (i.e., students leaving home, going to
school etc.). For example, in one analysis, not specific to emerging adults, the authors reviewed 201
diabetes self-management apps and found that of 15 desired functions, none covered all the necessary
features.12 While about half of the reviewed apps had functions surrounding medication, self-
management, and diabetes education, only 25% had the ability to share data with one’s provider or
provide notifications, and only 14% had the option to work with a local device. The authors concluded
that the diabetes apps were largely informational and served basic tracking functions rather than
providing the comprehensive tools and features needed for self-management. Further, they posited
that a standard baseline set of criteria should be used to develop apps to prevent repetitive apps that
lack core features.

A summary of systematic reviews on diabetes app efficacy (n = 6 for Type 1 diabetes; n = 5 for Type
2 diabetes) found that several apps, supplemented by provider support, may help improve HbA1C
levels for both of these types of diabetes but, usability (i.e., navigation by users) was mixed among
adults.13 A meta-analysis of efficacy studies (randomized controlled trials, specifically) on diabetes
mobile apps included 18 studies that covered Type 1 (n = 5), Type 2 (n = 11) and pre-diabetes (n = 2).
The results of the meta-analysis14 on a key diabetes outcome (HbA1c) found statistically significant
improvement for those with Type 2 diabetes (both in the long and short term). Outcomes for the other
diabetes types were inconclusive and not specific to any particular age group.14

Another systematic review of literature focused on mobile applications with integrated (patient-
provider) communication features for diabetes management found that few methodologically rigorous

58 J. SCHINDLER-RUWISCH AND A. PETERS

studies are available on this topic and likewise that few studies highlight apps with integrated features
to communicate directly with health providers,15 which is of special utility to younger users. Of the
available and relevant studies, three highlighted the significant impact of integrated app-based
provider communication. However, in most apps uncovered, if health provider feedback was provided
within the app, it was typically an automated reply, which has limited utility.

Several diabetes apps have been evaluated systematically. For example, the SocialDiabetes App was
analyzed to see if the frequency of app usage affected patient outcomes among a group of adults (over
18) who had diabetes for at least 1 year.16 While results were slightly better for users with Type 1 versus
Type 2 diabetes, there were significant improvements in blood glucose across all of the groups studied,
regardless of app usage frequency.16 The SocialDiabetes platform includes remote monitoring, insulin
dosing, HbA1c estimation tools, diet tracking, and carbohydrate calculators, reminders, and the ability
to connect with one’s provider.16 The authors suggest that while app use can be continuous and
consistent, the usage of such tools may promote diabetes self-management beyond the frequency of
use of the app itself.16

A content analysis of “best diabetes apps” in 2017 used Google to come up with 4 papers in which
the author discussed 26 apps.17 The identified apps (of which only half were completely free) were then
reviewed for medication, blood glucose and weight management functions, and exercise/diet features.
In total, most had blood glucose monitoring features (67%) and diet tracking features (79%), but fewer
had the remaining features. The authors ask, as noted above, whether apps with just a few diabetes
features truly offer sufficient diabetes self-management support. Further, none of these apps specifi-
cally consider the population of emerging adults, who may require additional familial monitoring and
data-sharing capabilities.

The Association of Diabetes Care and Education Specialists (ADCES) also helps promote quality
app selection through their program Danatech. Danatech provides a library of enhanced consumer
reviews including the Association of Diabetes Care and Education Specialists’ customized reviews
highlighting information such as literacy level, interactivity, and information sharing.18 While a very
useful repository of vetted mobile resources, this subscription platform does not explicitly highlight
specific functionalities or app features that may further assist emerging adults and their families in
selecting the most appropriate app for those transitioning to independent diabetes monitoring,
including shared data monitoring.

The results of this literature review are summarized in Table 1, and demonstrate very few studies on
diabetes apps relevant to emerging adults. The purpose of this paper was to systematically review
mobile apps that may have features relevant to helping emerging adults, individuals aged 18–25,
manage their diabetes as they transition to independent diabetes monitoring. An independent review
of apps from the GooglePlay store was undertaken to better elucidate key features that may have
particular relevance to this population, such as sharing and dual-monitoring capabilities with parents
and caregivers. In addition, criteria for the evaluation of, and reporting on, mobile apps were reviewed
and adapted from a variety of sources.19–22 In particular, several elements of the Mobile App Rating
Scale (MARS), which was recently devised to help better assess mhealth app quality,23 were employed
to enhance application evaluation.

Materials and methods

The Google Play store was systematically searched (from a location in the U.S.) for apps with the
following search criteria, “diabetes AND management OR log OR tracker,” with at least 4-stars and
free to download to capture high-quality and accessible diabetes management apps. Next, app landing
pages were reviewed to ensure they met the following inclusion criteria: 1) English language, 2) specific
to diabetes (any type), 3) includes a tracking mechanism or log for blood sugar, and 4) and a way to
track diet (for insulin dosing). Whether each app met the inclusion criteria was determined based on
the app store landing page, which included a description and screenshots of the application.

INFORMATICS FOR HEALTH AND SOCIAL CARE 59

Two independent reviewers, using a common codebook, reviewed all application landing pages and
associated screenshots, between June–September 2019 to ensure there were none erroneously
excluded. Any errors or questionable apps were marked, discussed, and reconciled based on codebook
definitions. Of the final included applications, each independent reviewer cataloged the following
information for half of the apps and then systematically and thoroughly checked the others work for
confirmation: app category (i.e., Medical, Health & Fitness), author/creator, country of origin/lan-
guages offered (other than English), version, year created/updated, GooglePlay rating (stars and by
how many people), download/install frequency, group target/diabetes type (if specified), and several
additional features adapted from the Mobile Application Rating Scale (MARS).23 Since the apps were
not individually downloaded, not all the MARS questions were applicable, but generally, the main
content areas were reviewed for engagement (interactivity, customization), esthetics/graphics (and
level of quality), and information/credibility/accuracy (sourcing). For example, if the app had any
interactivity (games, responses, customization) other than basic tracking and reminders, the app was
given credit for interactivity. Esthetics and graphics quality were rated as low/moderate/high quality
depending on the type of imaging (clip art versus photographs) and the use of icons/symbols and color
contrast. Finally, the source was noted for each app (if any) and categorized by credibility (i.e.,
government organization or nonprofit versus independent developer). All MARS items relevant to
these categories are summarized in Table 2. If the app was in any way specific to emerging adults, this
was noted, and if the app had sharing ability to link data to family, friends, providers or a device, this
was also cataloged, as this is relevant for communication and transitioning to independent monitor-
ing. Finally, the type of blood sugar and diet tracker were noted (i.e., graphically, manual entry,
database of foods/bar code) as well as any referrals to additional support or resources. In August 2020,
all included apps were re-reviewed for currency and any notable updates are highlighted in
Appendix 1.

Resu

lts

In total, 189 unique apps were found in the GooglePlay store using the search criteria. Three apps were
excluded as they were no longer available 2 months after initial data collection, nine apps were
excluded because they were not in English, 12 included apps did offer translations or international
versions), 94 apps were excluded because they had no blood sugar tracker, 127 apps were excluded
because they had no diet tracker, and three apps were not specific to diabetes (not mutually exclusive).
A total of 86 apps were excluded for multiple reasons (most frequently, missing both a diet and blood
sugar tracking function). Further, nine apps were excluded because they had a “freemium” or paid
version of the app that was necessary to access all the inclusion criteria. In total, 157 apps were
excluded (see Figure 2), and 29 apps (see Appendix 1 for a complete list of apps) were included for
further review. The two independent coders had 95% agreement in codes after dual-review.

Table 3 includes descriptive characteristics of the apps with their categorization in the app store,
the year the app was last downloaded, rating (out of 5 stars) and download frequency. As indicated,
most included apps (n = 20) fell into the “Medical” GooglePlay category, but several were categorized
as “Health & Fitness.” The majority of apps (n = 17) were last updated in 2019, but several were
updated years earlier (2013–2018). Most apps received a 4-star rating (n = 24). Two apps received
a perfect 5-star rating, but the rating is based on number of reviews and these two apps had only 9 and
13 reviews, respectively. Other included apps had anywhere between 12 and 29,205 reviewers rating
the app. While only apps above a 4-star rating were originally searched, three apps dropped below the
4-star rating during the review period (two apps to 3.9 stars and one to 3.6 stars). Finally, the
GooglePlay store categorizes the install frequency to estimate user number in categories as depicted
in Table 1. As shown, 6 apps were downloaded 1000 times or less, 10 apps 5,000–50,000 times, and 13
apps were downloaded over 100,000 times. One app was even downloaded over 1,000,000 times
(mySugr).

60 J. SCHINDLER-RUWISCH AND A. PETERS

Most the apps were relevant for individuals with any type of diabetes (pre-diabetes, Type 1, Type 2,
or gestational diabetes), although some apps specified they were targeted to Type 1 and Type 2
diabetics only (N = 2). One app was targeted specifically for Type 2 or prediabetes only, and one
app was designed specifically for Type 1 diabetes. Of the 29 apps, only one app had any features that
were specifically relevant to emerging adults. This one app, Dario, had a “hypo alert” feature, which
was specifically designed for parents and caregivers with diabetic children, to automatically send a GPS
location via text-message to four emergency contacts when a low blood sugar level is recorded.
Additionally, 20 of the 29 apps had some type of sharing feature that a user could download or
directly share a copy of their logs (diet, blood sugar, etc) with a family member, provider, or friend.
Several apps had options to directly e-mail a provider, while others required a user to download a PDF
or CSV file that they could share. Only 12 of the included apps linked directly with an accompanying
glucose, insulin, or related medical device to track and automatically populate data. Of these 12 apps,
several connected via Bluetooth, and others were only compatible with certain device systems.

The type of diet and blood sugar logs available were varied in terms of features and capabilities. For
example, 23 apps had some type of blood sugar graphing function, whether the graph came directly
from device data or was based on manually entered blood sugar levels. For the diet trackers, many
required manual entry (N = 20), several had the option of selecting foods from a pre-populated food
database (N = 7), and a few had barcode scanning capabilities to detect and log food items (N = 2).
Additionally, a few apps had the ability to upload your own images of the food, in addition to a manual
entry (N = 3). Several devices had additional features such as linking with fitness trackers, syncing over
multiple devices, personal coaching, analysis of data trends, bolus calculators, and reminders to take/
record readings.

An abbreviated version of the MARS scale was used to look at the app features related to
engagement, esthetics/graphics, and information/credibility accuracy. For engagement, only nine
apps had any interactivity (i.e., games, responses, customization) other than tracking (trends and
reminders are not included here). Further, while most apps had graphics of some kind, only five were
deemed high quality with clear modern images and color-coded icons. Six had basic icons or clip art,
nine had basic icons and some color contrast, three had color contrast, and six had no/limited color
contrast. Finally, most of the app creators were individuals or private companies and the included

189 unique apps in Google Play
3 no longer available

186 apps total

Excluded apps:
9 apps non-English
94 no blood sugar tracker
127 no diet tracker
3 not specific to diabetes
9 diabetes features not free

86 apps excluded for
multiple reasons:

72 no blood sugar or diet tracker

2 non-English/no diet tracker

7 non-English/no diet tracker/
no blood sugar tracker

157 apps in total exclude

d

29 included apps

1 no blood sugar/diet tracker/freemium
4 no diet tracker/freemium

Figure 2. Exclusion Criteria for GooglePlay apps.

INFORMATICS FOR HEALTH AND SOCIAL CARE 61

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62 J. SCHINDLER-RUWISCH AND A. PETERS

information and related accuracy of app content are largely unknown. Of the remaining, one app
creator stated they partnered with the American Diabetes Association (ADA) and one claimed to
follow ADA guidelines. Another creator stated that their app was designed based on the Diabetes
Prevention Program (DPP) study. One app was developed by people living with diabetes and three
were designed or led by a diabetes/health group or hospital system. Several apps carried other
endorsements that may improve their public credibility, such as being featured in ADA’s Forecast
magazine or being highlighted on Healthline’s Best app list. Otherwise, few apps had real evidence-
based backing, sourcing, or other visible credibility.

Based on the overview of included characteristics and features, apps that met at least one of the
MARS criteria and have either, a way to share data with caregivers/providers, or link directly to
a device are depicted in Figure 3 with additional and relevant features. Four apps met two of three

Table 3. Descriptive Characteristics of Included Mobile
Apps (n = 29).

App Feature Frequency n(%)

Category
Medical
Health & Fitness

20 (69.0)
9 (31.0)

Last Updated Year
2013
2016
2017
2018
2019

1 (3.4)
1 (3.4)

5 (17.2)
5 (17.2)

17 (58.6)
Rating (out of 5)

5 stars
4 stars
3 stars

2 (6.9)
24 (82.8)
3 (10.3)

Download Frequency/Installs
50+
100+
500+
1000+
5000+
10,000+
50,000+
100,000+
500,000+
1,000,000+

1 (3.4)
2 (6.9)
1 (3.4)
2 (6.9)
1 (3.4)

5 (17.2)
4 (13.8)
9 (31.0)
3 (10.3)
1 (3.4)

Table 2. MARS Criteria Utilized for Included Mobile Apps.

MARS Category MARS Questions Utilized Original MARS Questions23

Engagement/Interactivity Does the app have any interactivity (games,
responses, customization?) other than basic
tracking functionality (trends and reminders
not included)?

Interactivity: Does it allow user input, provide
feedback, contain prompts (reminders, sharing
options, notifications, etc.)? Note: these
functions need to be customizable and not
overwhelming in order to be perfect.a

Esthetics/Graphics Are there graphics in the app? Quality low or high
(icons, color contrast, clip art versus
photograph or quality stock image, busy or
clear, graphing features)?

Graphics: How high is the quality/resolution of
graphics used for buttons/icons/

menus/content?b

Information/Credibility/
Accuracy

Is there a source for the content? Is it credible/
accurate?

Credibility: Does the app come from a legitimate
source (specified in app store description or
within the app itself)?c

aPart of 5-item engagement scale
bPart of 3-item esthetics scale
cPart of a 7-item information scale

INFORMATICS FOR HEALTH AND SOCIAL CARE 63

measured MARS criteria (mySugr, One Drop – Diabetes Management, Glucocare, and Habits
Diabetes Coach) and one app (Glucocare) met all three MARS criteria. As illustrated, only half of
the top ten apps highlighted have a known, credible source for the content within (Dario, Glucocare,
Habits Diabetes Coach, Diabetes Plus and DIABNEXT). All but three of the top 10 apps link directly
with a device for insulin monitoring (Sugar Sense, BG Monitor Diabetes, Habits Diabetes Coach).
While several apps engage users through a mixture of challenges, tips, discussion boards, community
blogs, media, and coaching, three of the top 10 apps are only offer one-way interactivity (Dario, BG
Monitor Diabetes, Diabetes Plus). All of the top 10 apps have the ability to share data with a caregiver
or provider, an important feature for emerging adults, but as noted, only one app has any additional
functionality (GPS locator) relevant to the emerging adult population, specifically (Dario). As shown
in Figure 3, visually, not one app scored positively on all relevant features.

Discussion

Mobile apps for diabetes self-management are a promising tool for people living with diabetes and
diabetes educators. Apps can supplement in-person care and help bridge gaps between routine
provider visits and medical care. Navigating the landscape of available diabetes apps can be over-
whelming based on the sheer quantity and variable quality of the apps. The final list of suggested apps
described herein differs from apps listed in common searches like “Healthline’s Best Diabetes Apps of
2019.”24 While many of the apps included in such lists were reviewed in this content analysis (not all were
freely available), only three of Healthline’s 13 made our top ten list (MySugr, BG Monitor Diabetes, Sugar
Sense). These three apps had great variability in available sourcing, credibility, engagement, quality
graphics, and the ability to link with a medical device. One app (mySugr) was also reviewed in previous

Figure 3. Key Features of Selected Apps (n = 10).

64 J. SCHINDLER-RUWISCH AND A. PETERS

research, and found to be most popular among a large sample of people living with both Type 1 and Type
2 diabetes who indicating that tracking blood sugar and diet were the most useful features of an app.6

This study also found many other apps that overlapped with the apps discovered in this review,
highlighting that these apps are not just available, but commonly used by individuals living with diabetes.
This review serves as a starting point to guide educators and people living with diabetes to higher quality
apps and to understand the limitations inherent to the apps available.

There are limitations to the methodology described herein, mainly that apps were not individually
downloaded due to device constraints. However, the review of app content using the landing page is
consistent with the strategy used in similar content analyses12,17 and representative of the information
a user would have at their disposal prior to downloading an app. Further, only free apps were reviewed to
understand which apps would be accessible to all users, but it is possible that apps with paid features or
upfront costs may have additional features and functionality. Including only free apps can be useful for
widespread distribution to various populations who are looking for no-cost supplemental options for
diabetes self-monitoring. Likewise, the app store search was limited to highly-rated apps to help ensure
the top user rated results were included in this review, indicating that there are likely many additional
apps with even fewer features and functions. The GooglePlay store was chosen for this content analysis
because Google has the majority of the market share worldwide in this mobile arena,25 and because the
iTunes app store cannot be searched with multiple Boolean operators as is typically necessary in a content
analysis.20 Additional apps may be available in the iTunes store that were not included here. Users with
varying mobile technology (i.e., Apple versus Android devices) may not be able to access all of the apps
included in this review. It should also be noted that the GooglePlay store does not check the quality of
apps in advance of posting them,20 this burden falls on the user to evaluate. Further, research indicates
the need for constant review and re-assessment of mobile apps and features due to the constantly
evolving mobile landscape, updating of features, and changing mobile offerings.20 While the mobile apps
included herein were first vetted in 2019, the authors re-vetted the included apps in 2020. While most
apps remain current, the authors suggest another formal app analysis is undertaken in two to three years
to highlight any additional changes in the app marketplace and improved technologies that may be
available. The authors would also like to note that health literacy of the included apps was not assessed as
part of this review, and this is something important to consider when disseminating mobile apps to
a variety of audiences, so could also be part of a future review.

Conclusions

Few apps exist specifically for the emerging adult population, with features that enable family and
caregivers to help support independent diabetes monitoring in a variety of settings. In total, only one app
was found with any features that were specifically relevant to emerging adults. Further, almost 70% of
included apps has some type of data-sharing feature, which has potential utility for emerging adults, but
which could benefit from more integrated communication with parents or providers directly rather than
limited options to download data files or send an e-mail. Manual entry was a common way to update app
information, but as noted in the literature,10 younger people prefer more automated entry and technol-
ogy exists to allow blood glucose data directly from a device, Bluetooth functionality for merging device
data, and scanning of foods or databases for relevant caloric or meal content. Only one app met all three
MARS criteria for usability, including features of particular relevance to emerging adults such as
engagement and esthetic features.7,10,11 Further, few apps had credible backing or evidence-based
documentation, highlighted a critical concern for ensuring emerging adults have access to the most
reputable and dependable diabetes management software and further documenting a need from the
literature for more evidence-based review of mobile applications.15

Ongoing review of new and emerging apps with improved functionality, evidence-based content,
and app usability, specifically for emerging adults, are needed. Further, additional effectiveness studies
that review the impact that these tools can have on diabetes management and outcomes are also

INFORMATICS FOR HEALTH AND SOCIAL CARE 65

warranted. Additional app development or supplemental functionality specific to the emerging adult
population should be considered by developers and health professionals.

Acknowledgments

The authors would like to acknowledge Sally Gerard and Christa Esposito for their review and support.

Declaration of conflicting interests

The Authors declare that there is no conflict of interest.

References

1. Babler EK, Strickland CJ. Moving the journey towards independence: adolescents transitioning to successful
diabetes self-management. J Pediatr Nurs. 2015;30(5):648–60. doi:10.1016/j.pedn.2015.06.005.Moving.

2. Istepanian RSH, Al-anzi TM. m-Health interventions for diabetes remote monitoring and self management:
clinical and compliance issues. mhealth. 2018;4(4):3–5. doi:10.21037/mhealth.2018.01.02.

3. Panko R. Mobile app usage statistics 2018. The Manifest. Published 2018. [accessed 2020 Feb 29]. https://
themanifest.com/app-development/mobile-app-usage-statistics-2018.

4. Anderson M, Jiang J. Teens, Social Media, and Technology 2018. Pew Research Center: Internet & Technology.
Published 2018 May 31. [accessed 2020 Feb 29]. https://www.pewinternet.org/2018/05/31/teens-social-media-
technology-2018/

5. Rideout BV, Fox S Digital health practices, social media use, and mental well-being among teens and young
adults in the U.S. articles, abstracts, and reports. 2018; 1093. https://digitalcommons.psjhealth.org/cgi/viewcon
tent.cgi?article=2092&context=publications

6. Kebede MM, Pischke CR. Popular diabetes apps and the impact of diabetes app use on self-care behaviour:
a survey among the digital community of persons with diabetes on social media. Front Endocrinol. 2019;10
(135):1–14. doi:10.3389/fendo.2019.00135.

7. Adu MD, Malabu UH, Malau-aduli AEO, Malau-aduli BS. Users’ preferences and design recommendations to
promote engagements with mobile apps for diabetes self- management: multi-national perspectives. PLoS ONE.
2018;13(12):1–21. doi:10.1371/journal.pone.0208942.

8. Zhang Y, Liu C, Luo S, Xie Y, Liu F. Factors influencing patients’ intentions to use diabetes management apps
based on an extended unified theory of acceptance and use of technology model: web-based survey. JMIR.
2019;21(8):1–17. doi:10.2196/15023.

9. Hale K, Capra S, Bauer JA. Framework to assist health professionals in recommending high-quality apps for
supporting chronic disease self-management: illustrative assessment of type 2 diabetes apps. JMIR Mhealth
Uhealth. 2015;3(3):1–12. doi:10.2196/mhealth.4532.

10. Ledderer L, Møller A, Fage-butler A. Adolescents’ participation in their healthcare: a sociomaterial investigation
of a diabetes app. Digit Health. 2019;5:1–13. doi:10.1177/2055207619845448.

11. Husted GR, Weis J, Teilmann G. Exploring the influence of a smartphone app (young with diabetes) on young
people’s self-management: qualitative study. JMIR Mhealth Uhealth. 2018;6(2):e43. doi:10.2196/mhealth.8876.

12. Brahmbhatt R, Niakan S, Saha N, Tewari A, Pirani A, Keshavjee N, Mugambi D, Alavi N. Diabetes mHealth apps:
designing for greater uptake. Building Capacity Health Inf Future. 2017;4–8. doi:10.3233/978-1-61499-742-9-49.

13. Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden KB, Nussbaum K, Weiskopf N, Guise J-M, Helfand M,
et al. Rapid evidence review of mobile applications for self-management of diabetes. J Gen Intern Med. 2018;33
(7):1167–76. doi:10.1007/s11606-018-4410-1.

14. Wu X, Guo X, Zhang Z. The efficacy of mobile phone apps for lifestyle modification in diabetes: systematic review
and meta-analysis. JMU. 2019;7(1):1–13. doi:10.2196/12297.

15. Holmen H, Health P, Wahl AK, Cvancarova M. Tailored communication within mobile apps for diabetes
self-management: asystematic review. JMIR. 2017;19(6):e227. doi:10.2196/jmir.7045.

16. Vehi J, Isern JR, Parcerisas A, Calm R, Contreras I. Impact of use frequency of a mobile diabetes management app
on blood glucose control: evaluation study. JMU. 2019;7(3):1–9. doi:10.2196/11933.

17. Jimenez G, Lum E, Car J. Examining diabetes management apps recommended from a google search: content
analysis. JMU. 2019;7(1):e11848. doi:10.2196/11848.

18. Danatech. What is danatech anyway? Danatech.org. Published 2020 Feb. [accessed 2020 Feb 29]. https://
danatech.org/about-danatech/.

19. Bindhim NF, Hawkey A, Trevena L, Wales NS, Wales NS. A systematic review of quality assessment methods for
smartphone health apps. Telemed J E Health. 2015;21(2):97–105. doi:10.1089/tmj.2014.0088.

66 J. SCHINDLER-RUWISCH AND A. PETERS

https://doi.org/10.1016/j.pedn.2015.06.005.Moving

https://doi.org/10.21037/mhealth.2018.01.02

https://themanifest.com/app-development/mobile-app-usage-statistics-2018

https://themanifest.com/app-development/mobile-app-usage-statistics-2018

https://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/

https://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/

https://digitalcommons.psjhealth.org/cgi/viewcontent.cgi?article=2092%26context=publications

https://digitalcommons.psjhealth.org/cgi/viewcontent.cgi?article=2092%26context=publications

https://doi.org/10.3389/fendo.2019.00135

https://doi.org/10.1371/journal.pone.0208942

https://doi.org/10.2196/15023

https://doi.org/10.2196/mhealth.4532

https://doi.org/10.1177/2055207619845448

https://doi.org/10.2196/mhealth.8876

https://doi.org/10.3233/978-1-61499-742-9-49

https://doi.org/10.1007/s11606-018-4410-1

https://doi.org/10.2196/12297

https://doi.org/10.2196/jmir.7045

https://doi.org/10.2196/11933

https://doi.org/10.2196/11848

https://danatech.org/about-danatech/

https://danatech.org/about-danatech/

https://doi.org/10.1089/tmj.2014.0088

20. Grundy QH, Wang Z, Bero LA. Challenges in assessing mobile health app quality: systematic review of prevalent
and innovative methods. Am J Prev Med. 2016;51(6):1051–59. doi:10.1016/j.amepre.2016.07.009.

21. Heminger C, Schindler- Ruwisch JM, Abroms LC. Smoking cessation support for pregnant women: role of
mobile technology. Subst Abuse Rehabil. 2016;7:15–26.

22. Schindler-Ruwisch JM, Roess A, Robert RC, Napolitano MA, Chiang S. Social support for breastfeeding in the era
of mHealth: a content analysis. J Hum Lact. 2018;34(3):543–55. doi:10.1177/0890334418773302.

23. Stoyanov SR, Psych M, Hides L, Kavanagh DJ, Wilson H. Development and validation of the user version of the
mobile application rating scale (uMARS). JMU. 2016;4(2):e72. doi:10.2196/mhealth.5849.

24. Doyle A. The best diabetes apps of 2019. Healthline. Published 2019. [accessed 2020 February 29]. https://www.
healthline.com/health/diabetes/top-iphone-android-apps.

25. IDC Research Inc. Smartphone OS market share, 2015 Q2. Published 2015. [accessed 2019 August 1]. www.idc.
com/prodserv/smartphone-os-market-share.jsp.

Appendix 1.

List of Included/Reviewed Apps

mySugr- https://play.google.com/store/apps/details?id=com.mysugr.android.companion
Diabetes:M- https://play.google.com/store/apps/details?id = com.mydiabetes
Dario: Diabetes Management Simplified- https://play.google.com/store/apps/details?id = com.labstyle.darioandroid
ForDiabietes: diabetes self-management app- https://play.google.com/store/apps/details?id = gr.tessera.

fordiabetesapp
Glucose buddy diabetes tracker- https://play.google.com/store/apps/details?id = com.skyhealth.glucosebuddyfree
Diabetes, blood pressure, health tracker app1- https://www.amazon.com/Justin-Taylor-Diabetes-Health-Tracker/dp/

B00WKU1D2W
One Drop- Diabetes Management- https://play.google.com/store/apps/details?id = today.onedrop.android
Sugar sense- Diabetes app2- https://diabetespedia.com/mobile-apps/sugar-sense/
A1C Blood sugar calculator tracker diabetes app- https://play.google.com/store/apps/details?id = com.procyoncon

sult.a1cconvert
Glooko- Track Diabetes Data- https://play.google.com/store/apps/details?id = com.glooko.logbook
OnTrack diabetes- https://play.google.com/store/apps/details?id = com.gexperts.ontrack
Diabetes Connect- https://play.google.com/store/apps/details?id = com.squaremed.diabetesconnect.android
Cornerstones4care diabetes app- https://play.google.com/store/apps/details?id = com.glooko.novo
BG monitor diabetes- https://play.google.com/store/apps/details?id = com.wonggordon.bgmonitor
Glucocare- A diabetes management app- https://play.google.com/store/apps/details?id = com.ihealth.iglucopro
Habits Diabetes coach- https://play.google.com/store/apps/details?id = com.janacare.habits
Diaguard: Diabetes Diary- https://play.google.com/store/apps/details?id = com.faltenreich.diaguard
GLog: Glucose logbook for diabetics- https://play.google.com/store/apps/details?id = com.stelladomus.glog
Gluci-chek- https://play.google.com/store/apps/details?id = com.roche.glucichek
Diabetic’s logbook- https://play.google.com/store/apps/details?id = com torkalina
Diabetes plus- https://play.google.com/store/apps/details?id = com.squaremed.diabetesplus.typ1
DSM Diabetes self management- https://play.google.com/store/apps/details?id = com.ionicframework.dms689197
Glimp- https://play.google.com/store/apps/details?id = it.ct.glicemia
Dottli: Diabetes made simple- https://play.google.com/store/apps/details?id = com.modz.app
ManageAm App- https://play.google.com/store/apps/details?id = com.app.shei.manageam
Betes- your diabetes diary- https://play.google.com/store/apps/details?id = com.dev.matte.sugarlog
GluQUO: Control your Diabetes3- https://apps.apple.com/app/gluquo/id1187195552
Kyorr- https://play.google.com/store/apps/details?id = com.istrategyweb.kyorr
DIABNEXT make your Diabetes management easy- https://play.google.com/store/apps/details?id = com.diabnext.

diabnext
1As of August 2020, available for download on Amazon.com for Android devices under the name “Diabetes Health

Tracker”
2As of August 2020, not available in U.S. store, but still available internationally
3As of August 2020, only available on iTunes store

INFORMATICS FOR HEALTH AND SOCIAL CARE 67

https://doi.org/10.1016/j.amepre.2016.07.009

https://doi.org/10.1177/0890334418773302

https://doi.org/10.2196/mhealth.5849

https://www.healthline.com/health/diabetes/top-iphone-android-apps

https://www.healthline.com/health/diabetes/top-iphone-android-apps

http://www.idc.com/prodserv/smartphone-os-market-share.jsp

http://www.idc.com/prodserv/smartphone-os-market-share.jsp

https://play.google.com/store/apps/details?id=com.mysugr.android.companion

https://play.google.com/store/apps/details?id=com.mydiabetes

https://play.google.com/store/apps/details?id=com.labstyle.darioandroid

https://play.google.com/store/apps/details?id=gr.tessera.fordiabetesapp

https://play.google.com/store/apps/details?id=gr.tessera.fordiabetesapp

https://play.google.com/store/apps/details?id=com.skyhealth.glucosebuddyfree

https://play.google.com/store/apps/details?id=today.onedrop.android

https://diabetespedia.com/mobile-apps/sugar-sense/

https://play.google.com/store/apps/details?id=com.procyonconsult.a1cconvert

https://play.google.com/store/apps/details?id=com.procyonconsult.a1cconvert

https://play.google.com/store/apps/details?id=com.glooko.logbook

https://play.google.com/store/apps/details?id=com.gexperts.ontrack

https://play.google.com/store/apps/details?id=com.squaremed.diabetesconnect.android

https://play.google.com/store/apps/details?id=com.glooko.novo

https://play.google.com/store/apps/details?id=com.wonggordon.bgmonitor

https://play.google.com/store/apps/details?id=com.ihealth.iglucopro

https://play.google.com/store/apps/details?id=com.faltenreich.diaguard

https://play.google.com/store/apps/details?id=com.stelladomus.glog

https://play.google.com/store/apps/details?id=com.roche.glucichek

https://play.google.com/store/apps/details?id=com torkalina

https://play.google.com/store/apps/details?id=com.squaremed.diabetesplus.typ1

https://play.google.com/store/apps/details?id=com.ionicframework.dms689197

https://play.google.com/store/apps/details?id=it.ct.glicemia

https://play.google.com/store/apps/details?id=com.modz.app

https://play.google.com/store/apps/details?id=com.app.shei.manageam

https://play.google.com/store/apps/details?id=com.dev.matte.sugarlog

https://apps.apple.com/app/gluquo/id1187195552

https://play.google.com/store/apps/details?id=com.istrategyweb.kyorr

https://play.google.com/store/apps/details?id=com.diabnext.diabnext

https://play.google.com/store/apps/details?id=com.diabnext.diabnext

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• Abstract

Introduction

Literature review

Materials and methods

• Results

Discussion

Conclusions

Acknowledgments

Declaration of conflicting interests

References

• Appendix 1.List of Included/Reviewed Apps