a. Read  the chapters for the week from your assigned textbook before attempting  this assignment . You should also read chapter 6 page 120 to121  of  your assigned textbook for more guidance. 

b. Read the posted article above.
c.  Respond to the questions below by reviewing the article and identifying  those elements (state the page number you found the element). If you  indicate you support the researcher use of the element, make sure your  findings are with literature (eg. you can reference your textbook where  it says that element is important in qualitative research). 

Your critique responses should reflect the following:
1.  What type of qualitative approach did the researcher use? Add the page  number from the article where this information was noted. 
2. what  type of sampling method did the researcher use? Is it appropriate for  the study? Add the page number from the article where this information  was noted. 
3. Was the data collection focused on human  experiences?  Add the page number from the article where this  information was noted. 
4. Was issues of protection of human subjects  addressed?  Add the page number from the article where this information  was noted. 
5. Did the researcher describe data saturation?  Add the page number from the article where this information was noted. 
6.  What procedure for collecting data did the researcher use?  Add the  page number from the article where this information was noted. 
7.  What strategies did the researcher use to analyze the data?  Add the  page number from the article where this information was noted. 
8.  Does the researcher address credibility (can you appreciate the truth of  the patient’s experience), auditability (can you follow the  researcher’s thinking, does the research document the research process)  and fittingness are the results meaningful, is analysis strategy  compatible with the purpose of the study) of the data?  Add the page  number from the article where this information was noted where  applicable. 
9.  What is your cosmic question? (This is a question  you ask your peers to respond to based on the chapter discussed in  class this week i.e. Qualitative studies).

Original Paper

User Experiences With a Type 2 Diabetes Coaching App:
Qualitative Study

Shaira Baptista1,2, BSc, PGDipPsyc; Greg Wadley3, PhD; Dominique Bird4, MD; Brian Oldenburg1, PhD; Jane

Speight1,2, PhD; The My Diabetes Coach Research Group5

1Melbourne School of Population and Global Health, Melbourne, Australia
2Australian Centre for Behavioural Research in Diabetes, Melbourne, Australia
3School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
4Centre for Health Services Research, The University of Queensland, Brisbane, Australia
5See Authors’ Contributions section

Corresponding Author:
Shaira Baptista, BSc, PGDipPsyc
Melbourne School of Population and Global Health
207 Bouverie Street Carlton
Melbourne, 3051
Australia
Phone: 61 3 8344 4037
Email: [email protected]

Abstract

Background: Diabetes self-management apps have the potential to improve self-management in people with type 2 diabetes
(T2D). Although efficacy trials provide evidence of health benefits, premature disengagement from apps is common. Therefore,
it is important to understand the factors that influence engagement in real-world settings.

Objective: This study aims to explore users’ real-world experiences with the My Diabetes Coach (MDC) self-management
app.

Methods: We conducted telephone-based interviews with participants who had accessed the MDC self-management app via
their smartphone for up to 12 months. Interviews focused on user characteristics; the context within which the app was used;
barriers and facilitators of app use; and the design, content, and delivery of support within the app.

Results: A total of 19 adults with T2D (8/19, 42% women; mean age 60, SD 14 years) were interviewed. Of the 19 interviewees,
8 (42%) had T2D for <5 years, 42% (n=8) had T2D for 5-10 years, and 16% (n=3) had T2D for >10 years. In total, 2 themes
were constructed from interview data: (1) the moderating effect of diabetes self-management styles on needs, preferences, and
expectations and (2) factors influencing users’ engagement with the app: one size does not fit all.

Conclusions: User characteristics, the context of use, and features of the app interact and influence engagement. Promoting
engagement is vital if diabetes self-management apps are to become a useful complement to clinical care in supporting optimal
self-management.

Trial Registration: Australia New Zealand Clinical Trials Registry CTRN126140012296; URL
https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366925&isReview=true

(JMIR Diabetes 2020;5(3):e16692) doi: 10.2196/16692

KEYWORDS

type 2 diabetes; mobile phone; mobile apps; mHealth; smartphone; self-management

Introduction

Background
By 2045, 693 million people will be living with diabetes, the
majority with type 2 diabetes (T2D) [1]. Diabetes

self-management behaviors, including blood glucose monitoring,
healthy eating, being physically active and taking prescribed
medications, can improve diabetes-related outcomes, reduce
complications, and improve quality of life, but these behaviors
can be difficult to initiate and sustain [2]. Diabetes

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self-management education and ongoing support are critical for
establishing and maintaining self-care routines [3]. However,
the uptake of face-to-face educational programs is low because
of several factors, including difficulty in attending because of
medical, financial, or transport issues; lack of perceived benefits;
and shame and stigma [4-7]. Furthermore, the provision of
ongoing support is difficult because of resource constraints and
issues of reach and scalability [5]. An increasingly common
strategy to address these challenges has been to use smartphone
apps as a means to deliver diabetes education and
self-management support to complement clinical care.

The evidence for the efficacy and acceptability of diabetes
self-management apps is increasingly robust [8-11]. However,
research trials typically focus on overall efficacy, not individual
differences in user experiences, and cannot shed light on factors
that influence engagement [12-14]. This is a gap that needs to
be addressed if apps that demonstrate efficacy in controlled trial
settings are to be translated into effective real-world
interventions [15,16].

The lower engagement, or lack of thereof, with diabetes
self-management apps is often attributed to a mismatch between
what people with T2D want and the functions provided by apps,
loss of motivation, and the difficulty integrating app use into
everyday life [17-22]. Research suggests that multiple factors,
including treatment, attitudes to self-management, and existing
knowledge, influence the needs and preferences of people with
T2D [22]. For example, people with newly diagnosed diabetes
favor apps that educate them about diabetes, whereas those with
more experience of living with and managing diabetes express
frustration with basic education materials and are keen to see
more cutting edge news and links for further reading [23-25].
Those who have been living with diabetes for longer engage
with technology to refine care routines, whereas those less
experienced use diabetes self-management tools to establish
routines, for example by troubleshooting out-of-range blood
glucose readings [20,26]. Finally, those with more experience
are less willing to explore new options, including apps,
especially if the benefits are uncertain, and the effort is
substantial [27]. Unfortunately, participants in these studies
were asked either to give feedback on apps they had not used
before or to use unfamiliar devices. These limitations precluded
an in-depth examination of user experiences over time and in
the context of participants’ everyday lives.

Objectives
Therefore, this study aimed to investigate users’ experiences of
a diabetes self-management app (My Diabetes Coach [MDC])
accessed via personal devices and used in the context of
everyday life over a prolonged period and to understand the
interplay between users’ characteristics, needs, and preferences
and engagement with a diabetes self-management app.

Methods

Design and Ethics
This qualitative study was a substudy of a randomized controlled
trial testing the efficacy of a T2D self-management app MDC.
The trial was conducted from 2014 to 2018 (Australia New
Zealand Clinical Trials Registry ID ACTRN12614001229662)
[28,29]. The University of Melbourne’s human research ethics
committee approved this study (HREC number: 1442433).
In-depth, semistructured interviews were conducted to evaluate
the MDC app in terms of users’ experiences. We used a
qualitative approach to explore subjective perspectives
constructed from the experience of people with T2D using a
self-management app in the context of their everyday lives [30].
This report is consistent with the consolidated criteria for
reporting qualitative research checklist (Multimedia Appendix
1) [31].

Intervention Description
The MDC app was designed to provide education, support, and
feedback on diabetes self-care using weekly sessions or
appointments with an embodied conversational agent Laura
(Figure 1). Laura had human-like characteristics and mimicked
human conversation using interactive voice recognition (IVR)
and a database of prerecorded conversational elements. Laura
conversed with users either via spoken voice or text, using
sophisticated script logic. The app’s script logic was
personalized by incorporating information and targets provided
by users’ health care professionals (eg, blood glucose monitoring
targets). Users were able to respond to Laura by speaking,
inputting text, or touching an option on the screen. The program
was designed to enable responses made in a preceding session
to dictate the direction of the next session with the user, enabling
a high degree of personalization.

The first appointment with Laura was scheduled to suit the user
and thereafter occurred at the same time every week, with some
flexibility, enabling users to complete their appointment up to
48 hours after the planned time. Users could choose a particular
module from those available but were required to complete the
module over a series of sessions before moving to a new one.
Available modules included blood glucose monitoring, nutrition,
physical activity, medication taking, and foot care. The app
applied several gamification elements, including goal setting,
monitoring of progress, feedback, and quizzes [32].

Throughout the trial, users had access to a program coordinator
to assist them with technical difficulties. They were also given
an Accu-Chek Advantage blood glucose monitoring device with
Bluetooth capabilities (Roche Diabetes Care), enabling the
automated upload of glucose data to the MDC app. Finally, the
app had inbuilt links to a website with diabetes resources and
a user guide for the app.

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Figure 1. Embodied conversational agent Laura.

Study Participants and Recruitment
Invitations to participate in the MDC trial were sent by mail to
adults with T2D (in New South Wales, Queensland, Victoria,
and Western Australia) registered with the National Diabetes
Services Scheme (NDSS). Participants were eligible if they
were adults aged 18 years or older, diagnosed with T2D,
registered with the NDSS for <10 years, had access to a smartphone (with an operating system of at least iOS 8.0 for Apple devices or OS 4.2 for Android), and fluent in the English language. The exclusion criteria were as follows: women who were pregnant or planning to become pregnant; individuals reporting severe comorbid conditions that would prevent participation in the trial; and individuals on nonstable doses of diabetes-related medications. Interview participants for the qualitative study were recruited from the intervention arm of the MDC trial, all of whom had accessed the MDC app for up to 12 months. Purposive sampling was used to achieve variation in user characteristics, including age, gender, education, occupation location, duration of T2D, and use of the app (operationalized as the number of completed chats). Data Collection Participants were sent a plain language statement describing the study and were required to provide written consent. Participant characteristics were collected at baseline via a self-report questionnaire, including demographic and clinical details and current health app use. An interview guide was developed to include questions about the user’s self-reported diabetes expertise, how they managed their diabetes, when and how they engaged with the app, and their experiences using it. In-depth semistructured interviews were conducted through telephone (by SB) and recorded using SmartInteraction Suite, a cloud architecture voice recording solution (CTI Group). SB has several years of experience in diabetes-related research, including conducting telephone interviews. She worked as a research assistant on the MDC project and was involved with program development, participant recruitment, and data collection. Many of the participants had previously interacted with her. At the beginning of each interview, SB summarized the research and reasons for her interest in it. The first 2 interviews were analyzed, and changes were made to the interview guide to capture additional information on the context of use and feedback on the timing and delivery of sessions. Data included researcher observations and postinterview notes. Data collection continued until saturation was achieved (19 interviews), as indicated by the recurrence of themes and no new themes emerging. Recordings were stored in a secure cloud-based location and transcribed verbatim by an accredited transcription service with privacy certification. During each interview, SB kept notes of points of interest and used these as prompts. Immediately after each interview, SB prepared a written summary of the interview and relevant observations. These were used to communicate interim findings to the wider research team. When appropriate, additional questions were added to the interview guide, allowing for further exploration of issues raised by participants that were relevant to the research aims. These notes were also used to guide meaningful interpretation of data during data analysis. JMIR Diabetes 2020 | vol. 5 | iss. 3 | e16692 | p. 3http://diabetes.jmir.org/2020/3/e16692/ (page number not for citation purposes) Baptista et alJMIR DIABETES XSL•FO RenderX http://www.w3.org/Style/XSL http://www.renderx.com/ Data Analysis Descriptive statistics were computed for demographic and clinical characteristics and current app use using SPSS version 25 (IBM Corp). Data are presented as mean (SD) or number (percentage). Raw interview data were imported into NVivo 11 (QSR International) for coding and analysis. We followed 6 steps for the thematic analysis with the development of themes guided by a priori objectives identified in the aims: (1) data familiarization, (2) identifying initial codes and developing a coding framework, (3) identifying potential themes, (4) matching themes to the supporting data, (5) defining and naming themes, and (6) extracting relevant themes and producing a description of findings [30,33]. SB and GW coded the data. A constructionist approach, focusing on social conditions (user profiles and context of use) and structural conditions (app features and delivery of content), was used to interpret the data. Results Overview A total of 19 adults with T2D were interviewed (mean age 60 years, SD 14 years; 42% women). Additional participant characteristics are detailed in Table 1. Interview participants were older, more educated, had a lower baseline hemoglobin A1c, and used the app twice as much as those in the intervention arm of the MDC trial. The mean duration of the interviews was 51 min (range 29-79 min). Table 1. Participants’ demographic and clinical characteristics and current app use. MDC interview participants (n=19)MDCa trial (intervention arm) sample (n=93)Characteristics 8 (42)44 (47)Gender (female), n (%) 60 (8)55 (10)Age (years), mean (SD) Education (highest level), n (%) 5 (26)10 (11)Year 10 2 (11)42 (45)Year 12 or apprentice 12 (63)41 (44)Graduate/postgraduate Employment status, n (%) 7 (37)59 (64)Paid employment 11 (58)22 (23)Retired 1 (5)12 (13)Unemployed or other Diabetes duration (years), n (%) 8 (42)43 (46)<5 8 (42)29 (31)>5 to 10

3 (16)7 (8)>10 to 20

0 (0)14 (15)Unknown

6.8 (0.9)7.3 (1.5)Hemoglobin A1c (%), mean (SD)

51 (20)56 (44)Hemoglobin A1c (mmol/mol), mean (SD)

General app use, n (%)

14 (74)69 (74)Multiple times per day

4 (21)23 (25)Once a day

1 (5)1 (1)Less than once a day

36 (17)18 (15)Interactions with the MDC app (number), mean (SD)

aMDC: My Diabetes Coach.

Themes
A total of 2 high-level themes were constructed from the data:
(1) the moderating effect of diabetes self-management styles

on needs, preferences, and expectations and (2) factors
influencing users’ engagement with the app: one size does not
fit all. These comprised several subthemes, as described in the
following sections (summarized in Textbox 1).

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Textbox 1. Interview themes and subthemes.

Moderating effect of diabetes self-management styles on needs, preferences, and expectations

• Self-directed versus externally directed self-management styles

• Group differences in app preferences

Factors influencing users’ engagement with the app: one size does not fit all

• Interaction mode preferences

• Minimizing disruption to everyday life

• Initiating engagement

Theme 1: Moderating Effect of Diabetes
Self-Management Styles on Needs, Preferences, and
Expectations
This theme describes variations in self-management styles and
how these influenced app preferences.

Self-Directed Versus Externally Directed
Self-Management Styles
When asked to describe how they managed their diabetes and
their diabetes knowledge before using the MDC app, participants
expressed very different levels of autonomy, motivation, and
efficacy. Of the 19 participants, 11 described themselves as
having always had an independent, self-directed
self-management style. For example, they were intrinsically
motivated to seek diabetes-related information when they were
first diagnosed, saying:

I’m a bit of a researcher because it’s about my own
health.

They also expressed confidence in their diabetes knowledge
and self-care ability, describing themselves as experts in their
own care and comparing themselves with “other people [with]
diabetes [who] don’t have as much knowledge.” A common
shared characteristic was that they used their smartphones for
“just about everything” and reported previously using health
apps to help them achieve their health goals.

In contrast, the remaining 8 participants expressed a more
externally directed style and did not engage in independent
information seeking. Instead, they preferred to rely on their
health professionals and diabetes organizations for
diabetes-related information. They expressed less confidence
in their diabetes knowledge, describing it as limited to “only
what the doctor has told me.” As they did not seek diabetes
information at diagnosis, they referred to being “very lost in
the beginning, [because] nobody tells you anything.” Although
most participants used computers and tablets, they were not as
comfortable with smartphones, only using them for phone calls
and text messaging: “the mobile, it’s just for [an] emergency.”
Consequently, these participants were less likely to report using
other health apps.

When asked to describe their experiences with the MDC app,
there were clear differences between participants expressing a
self-directed versus externally directed self-management style
in terms of their needs, preferences, and expectations.

Group Differences in App Preferences
The self-directed participants described how support via an app
should ideally account for their existing diabetes expertise and
be presented to enable them to have the final say in their care:

If I can summarize what I look for, it’s not so much
“tell me what the answers and solutions are, but give
me the information, give me the options, I’m making
this decision.” I’m not looking for hand holding.

Consequently, facilitating decision making by enabling easy
tracking of multiple sources of health-related data was a key
consideration. For example:

Track the things that I want to track, daily readings,
weight, blood pressure, record medication [and]
blood test results and probably 15 other things that
are important to me. If you can’t record something,
you can’t control something.

The purpose of tracking was to refine established routines and
identify how specific actions, for example, taking certain
supplements such as Chromium Picolinate 400 mg, related to
actual changes, such as lowering blood glucose levels from 7.1
to 6.5. The other purpose of tracking was to facilitate changes
to self-management, for example:

When I’m making a change in my own practices: to
closely monitor things when I’m increasing my
exercise.

Curated, in-depth information was another vital feature for this
group: “my motivation in using an app is [only] to get
information.” They were interested in exploring a wide range
of topics:

I’m interested in the technology of diabetes care, I’m
interested in stuff all over the place, like reading
about the impact of sugar on muscle.

It was important that the information was reliable, like Cochrane
Reviews and curated, that is, organized in a way that enabled
them to distinguish basic information from in-depth discussion.

Conversely, what was most helpful for participants with a more
externally directed self-management style was not having to
search out and evaluate diabetes information:

The information is provided, you don’t have to go
searching for it, and that’s what’s convenient.

Without this easy access, one participant described how they
“wouldn’t have looked [it] up… because lazy people don’t do

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that.” There were other instances where these participants
described needing additional motivational support. For example,
one person said they “get lazy,” and another said:

I’m one of these people – I go really good at something
for a while, and then I get a bit slack and then I stop
doing stuff.

This may explain why this group appreciated attempts at
gamification and making learning fun, describing novel features
of the app, such as IVR and the relational agent, as being
“exciting,” “more interactive,” “cool and unique,” and increasing
their “interest.” However, those who described a more
autonomous self-management style were less receptive of
attempts at increasing engagement such as gamification (eg,
quizzes), which for them did not “add or detract from the
experience” and were dismissed as examples of “the same
information presented in a different way.”

Perhaps because of their experience using other health apps,
the group expressing more self-directed self-management styles
had higher expectations of the MDC app and were less tolerant
of technical issues:

It has to be reliable because that’s my expectation
now of apps and other things and I can always find
an alternative these days.

They expected flexibility in navigating through the MDC app
in a way that suited them. For example, “a little less linearly,”
with “a higher degree of user control in terms of being able to
investigate down particular information paths and then back
out of them.” They wanted the choice to be able to skip a
particular topic if it was not “relevant” or “to go back over
information” later through increased “searchability” if they
found a topic particularly interesting.

On the other hand, participants from the other group did not
have much experience with using apps and, therefore, were
more forgiving of technical issues, for example, “just teething
problems because it was so new.” However, because this group
tended to limit their smartphone use to phone calls, they
expected to be able to use the MDC app on their tablet device:

I’m one of these people that think a mobile phone is
a mobile phone, and if I want to do anything else I
go to the iPad.

Theme 2: Factors Influencing Users’ Engagement With
the App: One Size Does Not Fit All
This theme describes how participants engaged with the app,
specifically the context, mode, frequency, and duration of
interactions and the factors influencing these choices.

Interaction Mode Preferences: “I Could Read Quicker,
So I Chose to Not Listen”
Participants could choose one of the multiple ways to interact
with the MDC app. First, they could use the built-in IVR
technology to listen to what the embodied conversational agent
Laura said and respond using the microphone. Second, they
could listen to what Laura said but respond by touching one of
the options on the screen. Third, they could choose to ignore or

mute Laura’s voice, read the text on the screen, and respond by
touching an option on the screen.

The novelty of being able to interact with Laura using IVR was
described by some as “exciting” and “more interactive.”
However, most users, regardless of their self-management style,
soon discontinued their use of IVR, choosing instead to read
the text and respond by touching one of the options on the
screen. The primary reasons were that IVR did not offer any
obvious advantages and had some drawbacks. For example,
using IVR as a mode of receiving and responding to messages
within a session took much longer than reading the text and
tapping in a reply:

There was nothing wrong with the pace of her speech,
it was just that I could read quicker, so I chose to not
listen to her.

Technical difficulties were also a hindrance:

She didn’t understand me [laughs]. I found that
frustrating.

The context of use also influenced the choices of users. For
example, many described the IVR function as inconvenient
because of their surroundings, for example, “I was always doing
it in the bedroom in the morning when my husband was still in
bed asleep” or “I didn’t use it, because most of the time I was
on the train.” Some participants also described talking to the
phone as unnatural: “I think it just looked silly, to be talking to
your phone.”

Giving the user a choice to opt out of using IVR and use other
interaction modes was critical. As one participant put it:

If I had to have talked to her, I think I would have
pulled out!

Minimizing Disruption to Everyday Life: “It Wasn’t a
Problem to Find a Half an Hour”
The MDC app required participants to complete a session with
Laura once a week at a time that suited them. A weekly
appointment suited most, as “any more would become a chore”
or “just too much.” The discipline of a regular weekly
appointment was viewed favorably because it increased
commitment:

If I did it my own way, I wouldn’t have done it. I think
an appointment time kept me accountable.

Another positive attribute was that they mimicked offline
appointments, encouraging automaticity:

It was like an appointment with a doctor or going out
for dinner with friends. You knew that at 6:30 Friday,
you had to sit down and talk to Laura.

Another participant said:

Even my grandchildren would say to me, oh grandma,
it’s Thursday, and you’ve got to speak to Laura. I
structured things outside of those times because I
knew that time was taken. I did things around that
time because it was to me a standard appointment.

Those in paid employment appreciated being able to choose a
time that suited them:

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I’m glad I could choose a time that suited me.

They also valued the flexibility of being able to complete chats
within a certain time frame:

[If I missed my time] that was easy to get around,
because you had 24 hours to actually go in and have
the chat with Laura.

On the other hand, those who were retired had a set time every
week and made the chat part of their schedule, with little to no
variation from 1 week to the next “I’m retired now [laughs], so
what else do I do?” or “I’m a creature of habit, and I like things
to be ordered and I like the regularity, [so] I put it in the
calendar.”

For those with busy schedules, the fact that the MDC
intervention was divided into 15 to 30 minute chats, over several
months, was a benefit and compared favorably with face-to-face
diabetes self-management education and support programs:

It wasn’t a problem to find a half an hour. When
you’ve got to go off to some of these diabetes
[education things] it’s four-and-a-half hours! You try
and find four-and-a-half hours when you work a
16-hour day, it just doesn’t work.

Initiating Engagement: “You Need to Get [the App] in
Front of People When They’re in the First Days”
Participants unanimously emphasized the importance of access
to an app supporting self-management immediately after the
diagnosis of T2D as a means to come to terms with their
diagnosis:

You need to get that in front of people when they’re
in the first days, [and thinking] “Whoa, what just
happened to me?!”

Participants suggested that having an “introduction to the basic
stuff, in a fairly accessible manner,” resulted in “the greatest
benefit” and “greatest impact and usefulness.”

Many participants described diabetes education as nonexistent
or insufficient:

Other than being prescribed medication, there was
really nothing to support [self- management]

Others who had access described diabetes education as being
“blunt, didactic stuff, do this, do that, do this,” with no attempt
to account for their personal circumstances.

Insufficient time spent with the health care team was described
as another barrier to receiving comprehensive information and
understanding it:

I think for most people, they’re getting information
[from the app] they wouldn’t otherwise have heard,
unless their diabetes educators are very, very
thorough, and you’re visiting them once a week, and
we don’t do that. They [educators] don’t have the
time for that. Your GPs don’t have the time to go
through that information with you.

In some cases, the lack of education had the effect of delaying
attempts at initiating lifestyle changes and self-management
behaviors:

So, I was able to reject [my diabetes] and lived in a
bit of denial. It took me quite a while to find and
assemble a team of people that I felt could help me.

Participants consistently expressed the view that MDC would
be “useful for someone who was newly diagnosed” to “help
them transition”:

They need to be pointed in the right direction, because
it will take them a while to find it if they’re not pointed
in that direction.

Many also acknowledged the potential role of health care
professionals in facilitating access to and adoption of apps
following diagnosis:

I would see a real benefit in ensuring that people like
GPs, diabetic educators are made very aware of the
app and that they actively engage patients on
diagnosis with the app.

Another said:

The GP should be going, well here’s your blood test
results, download this app and learn what’s happening
and why it’s happening.

Discussion

Principal Findings
This qualitative study investigated users’ experiences of a T2D
self-management app accessed via their own smartphones over
a 9-month period in the context of their everyday lives. We
identified 2 main themes: (1) the moderating effect of diabetes
self-management styles on needs, preferences, and expectations
and (2) factors influencing users’ engagement with the app—one
size does not fit all. We found that the needs, preferences, and
expectations of diabetes self-management apps differed based
on participants’ self-management styles. The broad implication
is that, in addition to previously identified characteristics, such
as age, gender, and socioeconomic status, self-management
styles also influence engagement and need to be investigated
further [16,34,35].

Participants expressing self-directed rather than externally
directed self-management styles were more likely to be proactive
in seeking diabetes-related information and using other health
apps [36,37]. A possible explanation for this finding may be
found in the literature on health consciousness, defined as the
extent to which an individual takes ownership of their own
health condition [38]. Our data are consistent with previous
evidence suggesting that individuals who are more health
conscious may also be more self-directed in their
information-seeking behaviors and more proactive in managing
their health [39].

Our findings corroborate previous research on the benefits of
personalization and tailoring while providing preliminary
evidence on how app preferences can be personalized based on
a specific user characteristic—diabetes self-management style
[21,26,36,40]. For example, participants expressing more
self-directed styles value tools that assist them in making
independent, informed decisions about their own care. This
suggests that to engage these users, messaging within an app

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needs to be presented as volitional choices rather than explicit
directives and needs to acknowledge the user as an expert in
their own care [41]. Additional features that are likely to
improve engagement by this group include in-depth, current,
accurate information on a range of topics related to diabetes
care; the ability to track, link, and interpret multiple sources of
diabetes-related data; and a high level of flexibility in navigating
the app.

Our study also corroborates previous research that shows that
people who present a more externally directed self-management
style may need additional encouragement to sustain engagement.
Previous research suggests that changing attitudes to and
motivations for diabetes self-management may be especially
important for this group [3,42]. As diabetes is a self-managed
condition, successful models of care, especially for those who
are not intrinsically motivated, must focus on strategies that
promote and maintain autonomy [43]. Strategies to improve
engagement in this group could include gamification elements
such as quizzes and features that promote accountability, such
as goal setting and mechanisms that re-engage users, such as
regular feedback [24,32,44,45]. It may also be useful to consider
giving these users more customization choices, for example,
the device they prefer, because many users were more
comfortable with a desktop computer or tablet than with a
smartphone.

Almost invariably, the participants did not use IVR because it
did not provide any additional benefit. Our findings add to
existing research that suggests that features, such as IVR,
although novel and interesting initially, can deter or distract
from the main objective of using an app over time, especially
if they do not improve usability and require additional effort
[46]. The implication is that novel features should be used with
caution because they can be expensive to implement and may
not have the expected benefit. At the very least, users need to
choose to turn off features based on personal preferences.
Optimizing functionality is key because ease of use and
efficiency trump novelty when apps are used in the context of
ongoing, real-world self-management of a chronic condition
[27,44,47].

Many participants described receiving little to no diabetes
education and support following diagnosis, and in some cases,
this delayed engagement in self-management [48]. Making time
for and having access to adequate face-to-face education and
support are often challenging for people with newly diagnosed
T2D [4,6,7]. Our data support previous research demonstrating
that providing diabetes education and self-management support
via an app could be a feasible and acceptable complement to
clinical care [8-10,14]. Equally important is the suggestion that
this support may be more successful in engaging people when
accessed immediately following diagnosis [37,49,50].

Our findings suggest that the proposed contact frequency and
duration (ie, weekly sessions of 15-30 min) was acceptable
(even for those with busy schedules) and enhanced engagement,
potentially through increasing accountability and automaticity
[51]. Enabling users to choose a regular time fitting into their
schedule and some flexibility in altering that time to fit with
competing demands encouraged engagement. However, it was

clear that some limits on how the app was used were considered
beneficial, even necessary, as many described how complete
freedom could result in disengagement. Appointment reminders
were useful, but only to those with a busy schedule because
those who described themselves as less busy (eg, retired)
preferred set appointment times and considered them to be part
of an established routine, for which they did not need a reminder.

Finally, our data suggest that although diabetes self-management
apps may be helpful in initiating and maintaining
self-management behaviors, people with T2D are more likely
to engage with an app when it is endorsed by their health care
professional. There is some evidence to suggest that although
health care professionals think apps may be useful, sourcing
evidence-based, high-quality apps from the thousands available
on the app stores remains a challenge [50,52,53]. Thus,
initiatives are needed to provide health care professionals with
reliable resources that enable them to choose quickly from a
curated selection of evidence-based diabetes self-management
apps while matching them with the individual’s needs.

Strengths and Limitations
A key strength of this study is that it was conducted in the
context of a randomized controlled trial of the MDC app. In
contrast with many previous trials of self-management apps,
participants used the app in the wild, that is, in the context of
their everyday lives via their own familiar devices, addressing
some of the limitations of previous trials. Participants also had
access to the app for up to 9 months, making it possible to
explore their real-world use and changes over time. This was a
significant strength relative to most previous research where
participants only used an app once or for a short period (usually
less than four weeks). The purposive interview sampling strategy
was successful in recruiting participants with a range of
experience, facilitating examination of the interplay between
user characteristics, app preferences, and engagement. One
exception is that expert app users and those expressing a more
autonomous self-management style were overrepresented,
perhaps because these characteristics made them more likely
to want to participate in the interview study. In addition,
interview participants used the app twice as much as those in
the intervention arm of the MDC trial, suggesting that we were
less successful at recruiting less engaged users. We recommend
that future research focuses on identifying the experiences and
needs of users who are less autonomous and less experienced
with technology because they are likely to have different
diabetes education and support needs. Finally, our sample did
not include younger adults with T2D, a burgeoning cohort with
clear unmet needs [54]. Further research is needed to explore
the experiences of such a sample.

Conclusions
Our study is one of the first to investigate the use of a diabetes
self-management app in the wild. Our findings suggest several
ways in which user experiences can be engineered to improve
engagement with T2D self-management education and support
via an app, such as personalizing app features to user
characteristics, recommending a potential optimal time to
intervene, developing resources to assist health professionals
make evidence-based recommendations for diabetes apps, and

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recommend potential frequency and scheduling of the
intervention. Further research investigating interactions between
user characteristics, including self-management autonomy and
engagement, is warranted to determine specific strategies to

improve engagement with T2D self-management apps if diabetes
self-management apps are to become a useful complement to
clinical care.

Acknowledgments
The MDC randomized controlled trial was conducted with funding from a National Health and Medical Research Council
partnership grant (ID1057411), with additional financial and in-kind support provided by Diabetes Australia, Diabetes Queensland,
Diabetes Victoria, Diabetes Western Australia, and Roche Diabetes Care. The development of the MDC app was the result of a
collaboration between the University of Melbourne, Bupa Australia, the Bupa Foundation, and Clevertar. SB is a graduate
researcher supported by a postgraduate scholarship from the National Health and Medical Research Council, Australia, and
Diabetes Australia. JS is supported by core funding from the Australian Centre for Behavioural Research in Diabetes derived
from the collaboration between Diabetes Victoria and Deakin University. The authors would like to thank all the study participants
for volunteering their time, insights, and experiences.

Authors’ Contributions
BO conceived the MDC study and developed the MDC research program, together with DB, JS, and the MDC Research Group
(Emily D Williams, Michaela A Riddell, Paul A Scuffham, and Anthony Russell). SB, JS, and BO developed the interview
schedule. SB conducted the interviews. SB and GW analyzed and interpreted the data with input from JS and BO. SB prepared
the first draft of the manuscript. All authors reviewed and edited the manuscript for critical content and approved the final version.

Conflicts of Interest
BO and DB received some royalty payments for the development of the scripts for the MDC platform.

Multimedia Appendix 1
COREQ Checklist.
[PDF File (Adobe PDF File), 497 KB-Multimedia Appendix 1]

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Abbreviations
IVR: interactive voice recognition
MDC: My Diabetes Coach
NDSS: National Diabetes Services Scheme
T2D: type 2 diabetes

Edited by C Richardson; submitted 31.10.19; peer-reviewed by J Finderup, J Santos; comments to author 05.02.20; revised version
received 08.03.20; accepted 05.05.20; published 17.07.20

Please cite as:
Baptista S, Wadley G, Bird D, Oldenburg B, Speight J, The My Diabetes Coach Research Group
User Experiences With a Type 2 Diabetes Coaching App: Qualitative Study
JMIR Diabetes 2020;5(3):e16692
URL: http://diabetes.jmir.org/2020/3/e16692/
doi: 10.2196/16692
PMID: 32706649

©Shaira Baptista, Greg Wadley, Dominique Bird, Brian Oldenburg, Jane Speight, The My Diabetes Coach Research Group.
Originally published in JMIR Diabetes (http://diabetes.jmir.org), 17.07.2020. This is an open-access article distributed under the
terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Diabetes, is properly cited.
The complete bibliographic information, a link to the original publication on http://diabetes.jmir.org/, as well as this copyright
and license information must be included.

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