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Thoroughly review/read the article posted Identify the theoretical framework used in the article you chose (post the name of the theory and expla


Thoroughly review/read the article posted Identify the theoretical framework used in the article you chose (post the name of the theory and expla

 

Discussion #1

Activities

For this week’s discussion do the following; 

1. Thoroughly review/read the article posted.

2. Identify the theoretical framework used in the article you chose (post the name of the theory and explain the theory).

3. Go the reference section of the article and Identify/select at least one primary source and one secondary source (articles) that supports the theoretical framework (list only the reference of these articles).

4.  Now, search the SU nursing electronic library database such as  Gale-Infotract on LIRN, CINAHL, or ProQuest to identify the primary and  secondary source articles you  selected.

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.

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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

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