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Delivering self-management support to people with type 2 diabetes mellitus is essential to reduce the health system burden and to empower people with the skills, knowledge, and confidence needed to take an active role in managing their own health.
This study aims to evaluate the adoption, use, and effectiveness of the My Diabetes Coach (MDC) program, an app-based interactive embodied conversational agent,
This randomized controlled trial evaluated both the implementation and effectiveness of the MDC program. Adults with type 2 diabetes in Australia were recruited and randomized to the intervention arm (MDC) or the control arm (usual care). Program use was tracked over 12 months. Coprimary outcomes included changes in glycated hemoglobin (HbA1c) and health-related quality of life (HRQoL). Data were assessed at baseline and at 6 and 12 months, and analyzed using linear mixed-effects regression models.
A total of 187 adults with type 2 diabetes (mean 57 years, SD 10 years; 41.7% women) were recruited and randomly allocated to the intervention (n=93) and control (n=94) arms. MDC program users (92/93 participants) completed 1942 chats with
The MDC program was successfully adopted and used by individuals with type 2 diabetes and significantly improved the users’ HRQoL. These findings suggest the potential for wider implementation of technology-enabled conversation-based programs for supporting diabetes self-management. Future studies should focus on strategies to maintain program usage and HbA1c improvement.
Australia New Zealand Clinical Trials Registry (ACTRN) 12614001229662; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12614001229662
Type 2 diabetes mellitus (T2DM) is a common chronic condition that places a significant burden on individuals and the health care system. The prevalence of diabetes has risen substantially over the past two decades worldwide [
Health coaching programs, incorporating continuous feedback and reinforcement [
Despite the increasing use of mobile apps for health purposes, reviews have found that existing digital health solutions are not generally able to meet the needs of people with diabetes, and more evidence is required before their wider scale-up [
By adapting our team’s previous effective and cost-effective Telephone-Linked Care for Diabetes (TLC diabetes) program [
This trial is a two-arm, open-label, randomized controlled trial with participants recruited between June 2016 and April 2017 in Australia. The trial was registered before recruitment (Australia New Zealand Clinical Trials Registry ID: ACTRN12614001229662). Full ethics approval was granted by the University of Melbourne’s Human Research Ethics Committee (Ethics ID 1442433). Participants provided written informed consent and returned the informed consent forms to the research team via email or fax, including permission for their general practitioners (GPs) to regularly share clinical data with the study team.
Adults (aged ≥18 years) diagnosed with T2DM, registered with the National Diabetes Service Scheme (NDSS) for less than 10 years, with basic English language skills, who had access to an internet-enabled smart device with an up-to-date operating system (at least iOS 8.0 for Apple and 4.2 for Android) were eligible to participate in the study. Participants were ineligible if they were pregnant or planning to become pregnant, had severe comorbid conditions that would compromise their participation, or did not have stable doses of diabetes-related medication over the previous 4 weeks or more.
To assist with recruitment, on behalf of the research team, the NDSS sent invitation letters to registered adults with T2DM living in the Australian states of Queensland, Victoria, and Western Australia. These 3 states comprise 54.5% of all Australians with diagnosed diabetes [
Participants were randomly allocated to the intervention or control arm using a 2x4 block randomization sequence, programmed into a Redcap data management system [
Participants allocated to the intervention arm received access to the MDC program for up to 12 months. The overall program comprises 5 components: the MDC app; a printed user guide; the MDC website; an optional blood glucose meter with Bluetooth capability (Accu-Chek Aviva Connect, Roche Diabetes Care); and a small number of brief, structured interactions with a program coordinator, primarily for technical assistance. The MDC app was adapted from the previous TLC diabetes program [
MDC delivers personalized support, monitoring, and motivational coaching via an embodied conversational agent,
Participants were encouraged to use the app weekly to complete online modules by chatting with
In addition to the MDC app, participants were also encouraged to regularly access the user guide and the MDC website and to join the discussion forums on diabetes self-management topics posted fortnightly by the program coordinator on the website. The program coordinator, supported by a web-based user management portal, assisted participants in dealing with system-generated technical alerts by communicating with participants, their GPs, and Clevertar. The program coordinator also telephoned participants after 1, 4, 8, 12, and 24 weeks of program access to answer questions, to troubleshoot technical issues, and to encourage program use.
Participants in the control arm were encouraged to continue their routine diabetes self-care, including access to health care services, resources accessed via NDSS, and the diabetes not-for-profit organizations in their states. They received a quarterly project newsletter to maintain their interest in the study. Following the 12-month data collection, participants in the control arm received access to the MDC program if they wished.
We used the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework to evaluate the impact of the MDC intervention, which covered the 5 dimensions in terms of reach, effectiveness, adoption, implementation, and maintenance [
Program adoption and use were tracked using the program management portal. The key metrics included the number of completed chats with
Program effectiveness was measured by both clinical and psycho-behavioral outcomes. The coprimary outcomes were changes (12 months compared with baseline) in glycated hemoglobin (HbA1c) and health-related quality of life (HRQoL), which were examined in terms of between-arm differences. The secondary time point of analysis examined the change between baseline and 6 month. HbA1c (reported as % and mmol/mol) was measured through a pathology blood test that each participant’s GP requested. HRQoL was assessed via participants’ completion of the Assessment of Quality of Life (AQoL)-8D scale, which is a 35-item multi-attribute utility instrument covering 8 dimensions focused on independent living, happiness, mental health, coping, relationships, self-worth, pain, and senses [
Selected secondary outcomes reported in this paper include anxiety and depressive symptoms, assessed by the Hospital Anxiety and Depression Scale (HADS) [
Data were collected at 4 time points, including screening, baseline, 6 months, and 12 months post randomization. During the screening telephone calls, the research team contacted participants and recorded sociodemographic characteristics (age, sex, education, employment, and language capability), clinical characteristics (duration of NDSS registration and stabilization of the health condition), and app use variables in the REDcap data collection system [
On the basis of a previous study [
All enrolled participants who completed the baseline assessment were included in the
The main effectiveness analysis followed the intention-to-treat principle [
Of the 697 individuals with T2DM who expressed interest in participating in the study, 187 were recruited, including 62 (33%) from Victoria, 21 (16.5%) from New South Wales, and 21 (16.5%) from Queensland (
The intervention and control arms (n=93 and n=94, respectively) were generally comparable, except that those in the intervention arm were slightly younger (
Enrollment, randomization, and follow-up of study participants.
Baseline characteristics of study participants
Baseline characteristics | Waitlist (n=94) | Intervention (n=93) | Total (N=187) | |||
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Agea, (years), mean (SD) | 58.4 (10.5) | 55.4 (9.7) | 56.9 (10.2) | .04 | |
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.12 | ||||
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Male | 60 (63.8) | 49 (52.7) | 109 (58.3) |
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Female | 34 (36.2) | 44 (47.3) | 78 (41.7) |
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.25 | ||||
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Secondary high school or lower | 29 (30.8) | 25 (26.9) | 54 (18.8) |
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Technical apprenticeship or diploma | 30 (31.9) | 27 (29.0) | 57 (30.5) |
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Bachelor’s degree | 23 (24.5) | 17 (18.3) | 40 (21.4) |
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Postgraduate degree or higher | 12 (12.8) | 24 (25.8) | 36 (19.3) |
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.99 | ||||
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Full time | 43 (45.7) | 45 (48.4) | 88 (47.1) |
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Part time or casual | 16 (17.0) | 14 (15.1) | 30 (16.0) |
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Retired | 21 (22.3) | 21 (22.6) | 42 (22.5) |
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Unemployed or others | 14 (14.9) | 13 (14.0) | 27 (14.4) |
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English as a secondary language, n (%) | 11 (11.7) | 6 (6.5) | 17 (9.1) | .21 | |
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Aboriginal or Torres Strait Islander origin, n (%) | 0 (0.0) | 4 (4.3) | 4 (2.1) | .08 | |
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General app use: frequent (multiple times per day), n (%)b | 67 (71.3) | 69 (74.2) | 136 (72.7) | .38 | |
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Health-related quality of life: Assessment of Quality of Life-8 Dimensions score, mean (SD) | 0.7 (0.2) | 0.7 (0.2) | 0.7 (0.2) | .13 | |
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4.7 (3.3) | 3.3 (3.4) | 4.0 (3.4) | .004 | |
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Cases (or likely cases) of depression, n (%) | 24 (25.5) | 12 (12.9) | 36 (19.3) | .03 |
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5.6 (3.3) | 5.4 (3.8) | 5.5 (3.5) | .67 | |
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Cases (or likely cases) of anxiety, n (%) | 27 (28.7) | 28 (30.1) | 55 (29.4) | .84 |
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30.5 (19.9) | 29.2 (21.4) | 29.9 (20.6) | .67 | |
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Severe diabetes distress (PAID score >40), n (%) | 31 (33.0) | 29 (31.2) | 60 (32.1) | .79 |
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.51 | ||||
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≤1 year | 34 (38.2) | 38 (42.7) | 72 (40.5) |
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1-5 years | 27 (30.3) | 23 (25.8) | 50 (28.1) |
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6-10 years | 28 (31.5) | 28 (31.5) | 56 (31.5) |
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Diabetes medication(s) prescribed | 83 (88.3) | 80 (86.0) | 163 (87.2) | .64 |
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Insulin prescribed | 16 (17.0) | 15 (16.1) | 31 (16.6) | .06 |
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Taking medicines daily as recommended | 71 (75.5) | 74 (79.6) | 145 (77.5) | .33 |
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Smoking |
7 (7.5) | 4 (4.3) | 11 (5.9) | .16 | |
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Self-monitoring of blood glucose (>5 days in past 7 days), n (%)b | 49 (52.1) | 53 (57.0) | 102 (54.5) | .49 | |
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Daily foot checks |
23 (24.5) | 21 (22.6) | 44 (23.5) | .87 | |
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Weight (kg), mean (SD)b | 95.7 (19.0) | 97.1 (22.5) | 96.4 (20.8) | .65 | |
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Glycated hemoglobin (%), mean (SD) | 7.3(1.6) | 7.3(1.5) | 7.3 (1.5) | .86 | |
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Total cholesterol (mmol/L), mean (SD)b | 4.5 (1.3) | 4.6 (1.4) | 4.6 (1.3) | .54 | |
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Systolic blood pressure (mm Hg), mean (SD)b | 130.4 (13.6) | 131.1 (14.6) | 130.7 (14.1) | .72 | |
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Diastolic blood pressure (mm Hg), mean (SD) | 78.5 (9.3) | 78.4 (9.4) | 78.5 (9.3) | .94 | |
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Triglyceride (mmol/L), mean (SD) | 2.0 (1.3) | 1.8 (0.8) | 1.9 (1.1) | .26 | |
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High cholesterol, n (%) | 59 (62.8) | 64 (68.8) | 123 (65.8) | .37 | |
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Hypertension, n (%) | 52 (55.3) | 56 (60.2) | 108 (57.8) | .45 | |
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Arthritis (rheumatoid, osteoarthritis, or other), n (%)b | 34 (36.2) | 22 (23.7) | 56 (30.0) | .11 | |
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Depression/anxiety/nervous disorder, n (%) | 26 (27.7) | 26 (28.0) | 52 (27.8) | .60 | |
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Heart diseases, n (%) | 17 (18.1) | 17 (18.3) | 34 (18.2) | .60 | |
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Diabetes-related eye complications, n (%) | 12 (12.8) | 12 (12.9) | 24 (12.8) | .60 | |
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Lung diseases, n (%) | 11 (11.7) | 13 (14.0) | 24 (12.8) | .53 | |
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Diabetes-related neuropathy, n (%) | 11 (11.7) | 11 (11.8) | 22 (11.8) | .60 | |
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Stomach, duodenal, or gastro-intestinal ulcer, n (%) | 11 (11.7) | 10 (10.8) | 21 (11.2) | .59 | |
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Cancer, n (%) | 6 (6.4) | 8 (8.6) | 14 (7.5) | .50 | |
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Stroke, n (%) | 4 (4.3) | 10 (10.8) | 14 (7.5) | .14 | |
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Peripheral vascular diseases, n (%) | 8 (8.5) | 6 (6.5) | 14 (7.5) | .53 | |
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Kidney disease, n (%) | 5 (5.3) | 6 (6.5) | 11 (5.9) | .57 | |
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Had an appointment with a general practitioner or specialist in the past 12 months, n (%) | 93 (98.9) | 92 (98.9) | 185 (98.9) | .32 | |
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Had an appointment with any other health professional (eg, dietician) in the past 12 months, n (%) | 54 (57.4) | 63 (67.7) | 117 (62.6) | .12 | |
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Admitted to hospital in the past 6 months, n (%) | 21 (22.3) | 12 (12.9) | 33 (17.6) | .10 | |
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Used any other hospital service over the past 6 months that did not involve an admission, n (%) | 20 (21.3) | 21 (22.6) | 41 (21.9) | .80 |
aSignificant difference observed between the intervention and control arms.
bSome missing values exist: general app use (n=4), years registered with NDSS (n=9), weight (n=8), systolic blood pressure (n=2), diastolic blood pressure (n=2), total cholesterol (n=11), triglyceride (n=13), medication adherence (n=24), self-monitoring of blood glucose (n=6), diagnosed comorbidities (n=1), and health care service utilization (n=1).
cHADS-D: Hospital Anxiety and Depression Scale-Depression score.
dHADS-A: Hospital Anxiety and Depression Scale-Anxiety score.
ePAID: Problem Areas in Diabetes scale.
fNDSS: National Diabetes Service Scheme.
Indicators of program adoption and use among participants in the intervention arm (My Diabetes Coach app).
Indicators of program adoption and use | Intervention arm (n=93) | ||
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Participants who had at least one “appointment” with “Laura” over 12 months | 92 (99) | |
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Participants who had uploaded glucose data into the MDC app | 83 (89) | |
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0-6 | 26 (28) |
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7-24 | 37 (40) |
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25 or more | 30 (32) |
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Total number of chats completed over 12 months | 1942 | |
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Total number of valid chats completed over 12 monthsb | 1641 | |
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Number of chats completed per person | 21.8 (16.7); 1-65 | |
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Number of valid chats completed per personb | 18.4 (15.0); 1-53 | |
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Duration of valid chats per person (in minutes), mean (SD); ranged | ||
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Total duration of chats | 242.7 (212.3); 0-1050 |
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Mean duration of each valid chat | 13.4 (4.8); 3-26.8 |
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Glucose data uploaded, mean (SD); range | ||
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Number of glucose level uploads per person | 181.8 (192.1); 1-966 |
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Clinical alerts (eg, abnormal glucose level) | 297; 13.7 (8.8) | |
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Technical alerts (eg, glucose uploading failed) | 179; 8.3 (6.5) | |
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Posts on the web-based discussion forum | 19; 1.1 |
aMDC: My Diabetes Coach.
bInvalid chats were defined as chats for which participants exited the app before the coach modules were fully completed with the closing remark.
cFor individual-level information, the estimation is based on 92 participants who had records of chat with Laura through the app and 83 participants who had uploaded their glucose levels into the app. Mean (SD) and range of number and duration of chats and glucose data uploads were reported.
dOnly completed chats have been included in the calculation of the total duration of chats. If the users did not exit the app after completing the chats, the duration would be continuously counted. Thus, we truncated the values if the duration of the chats were more than two interquartile ranges above the third quartile of the distribution.
There was a statistically significant between-arm difference at 12 months in the mean change in HRQoL (AQoL-8D utility value: 0.04, 95% CI 0.00 to 0.07;
Effectiveness of the intervention on coprimary outcomes.
Coprimary outcomes and analysis models and Arms | Between arm differences at 6 months (95% CI) | Between arm differences at 12 months (95% CI) | Estimated mean changes between baseline and 6 months (95% CI)a | Estimated mean changes between baseline and 12 months (95% CI)a | ||||||
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Intervention | 0.06 (−0.35 to 0.47) | .78 | −0.04 (−0.45 to 0.36) | .84 | −0.20 (−0.49 to 0.09) | .17 | −0.33 (−0.62 to −0.04) | .03 |
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Control | Reference | N/Ac | Reference | N/A | −0.26 (−0.55 to 0.03) | .08 | −0.28 (−0.57 to 0.00) | .05 |
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Intervention | 0.06 (−0.35 to 0.46) | .79 | −0.04 (−0.44 to 0.37) | .87 | −0.20 (−0.49 to 0.09) | .18 | −0.32 (−0.61 to −0.03) | .03 |
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Control | Reference | N/A | Reference | N/A | −0.25 (−0.54 to 0.03) | .09 | −0.28 (−0.57 to 0.00) | .05 |
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Intervention | −0.05 (−0.47 to 0.37) | .81 | −0.14 (−0.56 to 0.28) | .52 | −0.26 (−0.59 to 0.07) | .12 | −0.40 (−0.73 to −0.06) | .02 |
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Control | Reference | — | Reference | — | −0.21 (−0.47 to 0.05) | .11 | −0.26 (−0.51 to −0.00) | .05 |
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Intervention | 0.05 (0.01 to 0.08) | .006 | 0.04 (0.00 to 0.07) | .039 | 0.04 (0.01 to 0.07) | .002 | 0.04 (0.01 to 0.06) | .007 |
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Control | Reference | N/A | Reference | N/A | −0.01 (−0.03 to 0.02) | .48 | 0.00 (−0.03 to 0.02) | .92 |
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Intervention | 0.05 (0.01 to 0.08) | .005 | 0.03 (0.00 to 0.07) | .047 | 0.04 (0.01 to 0.06) | .002 | 0.03 (0.01 to 0.06) | .009 |
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Control | Reference | N/A | Reference | N/A | −0.01 (−0.03 to 0.01) | .46 | −0.00 (−0.02 to 0.02) | .93 |
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Intervention | 0.06 (0.02 to 0.09) | .002 | 0.06 (0.02 to 0.09) | .003 | 0.05 (0.02 to 0.08) | .001 | 0.05 (0.02 to 0.08) | .001 |
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Control | Reference | N/A | Reference | N/A | −0.01 (−0.03 to 0.01) | .49 | −0.01 (−0.03 to 0.02) | .63 |
aMean changes in outcomes were estimated based on the linear mixed-effect regression model.
bFor HbA1c, the intraclass correlation coefficient (ICC) for the primary model was 0.551 (95% CI 0.465-0.634). For HRQoL, the ICC for the unadjusted model was 0.847 (95% CI 0.806-0.880). Number of participants with valid data at each time point: n for HbA1c (intervention vs control): 93 vs 94 at baseline, 78 vs 78 at 6 months, and 77 vs 79 at 12 months. Number of participants at each time point for HRQoL (intervention vs control): 93 vs 94 at baseline, 67 vs 77 at 6 months, and 60 vs 78 at 12 months.
cN/A: not applicable.
dThe adjusted model adjusted baseline values of variables that were either imbalanced by intervention allocation by chance (baseline age and depression score) or associated with loss to follow-up (baseline AQoL-8D utility value and HADS Anxiety score).
eThe per-protocol analysis considered participants who had completed more than 6 chats with Laura as following the study protocol.
Compared with baseline, the mean estimated HbA1c decreased in both arms at 12 months (intervention arm: mean estimated change: −0.33%, 95% CI −0.62 to −0.04;
There was a dose-response relationship between the number of chats and the change in the HRQoL score (
Although this study was not powered for subgroup analyses, the results did show some statistically significant differences favoring the intervention for HbA1c. There were greater between-arm differences at 6 and 12 months in the mean change in HbA1c for those with higher baseline HbA1c and those registered on the NDSS within the previous year (
This study is among the very few randomized controlled trials that have evaluated the adoption, use, and effectiveness of a mobile app–based, interactive, embodied conversational agent to support diabetes self-management within home settings over a 12-month period. Our study adds new evidence to this emerging field by demonstrating the program use in a home setting and the effectiveness of app-based interactive conversational agents in supporting diabetes self-management. The MDC program was feasible and shown to be effective in improving participants’ HRQoL. Although HbA1c levels reduced during the trial, the between-arm difference was not statistically significant at 6 or 12 months.
Despite the growing number of studies using mobile technologies for diabetes management [
The indicators for program use suggest successful uptake among individuals with T2DM, but maintaining long-term program use still remains a challenge. Interestingly, program exposure (an average of 18
We observed a modest but statistically significant improvement in HRQoL at 6 months, which was maintained at 12 months. This finding is consistent with some previous research, which indicates a small but statistically significant benefit from mobile app–based interventions on HRQoL [
This study has several important strengths. First, study participants were recruited from across Australia, with broad inclusion criteria, and the program was delivered within participants’ home settings. Thus, the sample is broadly representative of Australians with T2DM, and the study findings are likely to be generalizable and scalable. Second, the study followed participants over 12 months, which is a relatively long term compared with many studies of this kind [
There were also some limitations. First, due to the nature of the intervention, we were not able to blind participants or their GPs (who provided clinical measurements) to the study arm allocation. Without being able to blind participants, self-report bias and Hawthorne effects may exist. Second, we observed a higher rate of completed assessments among participants in the control arm than in the intervention arm, possibly due to their interest in gaining program access or because of higher attrition in the intervention arm. Third, due to the relatively small sample size, the subgroup analyses should be interpreted with caution. Although the main analyses were fully powered, the subgroup analysis was underpowered and multiple testing would have increased the likelihood of false positives. Overall, the sample was not dissimilar from previous trials in this field [
To summarize, this study presents findings concerning the effectiveness, adoption, and use of the MDC program, an app-based interactive embodied conversational agent, in supporting individuals with T2DM. Participants had good adoption of the program and completed a significant amount of chats with
Introduction about My Diabetes Coach Program and its five components.
Supplementary tables and figures.
CONSORT-eHEALTH checklist (V1.6.1).
Assessment of Quality of Life-8 Dimensions
Consolidated Standards of Reporting Trials
general practitioner
Hospital Anxiety and Depression Scale
glycated hemoglobin
health-related quality of life
My Diabetes Coach
National Diabetes Service Scheme
National Health and Medical Research Council
type 2 diabetes mellitus
Telephone-Linked Care for Diabetes
The authors thank the study participants for volunteering their time and experience. The authors would also like to acknowledge the following members of the research team and thank them for their contributions: Jillian Zemanek, Fiona Cocker, Anna Scovelle, Suman Shetty, Mandy Cassimatis, Robin Zhou, Phillipa Dalach, Shuai Shao, Ameera Katar (all University of Melbourne), Trish Roderick (Diabetes Queensland), Carolyn Hines (Diabetes Victoria), Deborah Schofield (Diabetes WA), Ornella Care, Judith Ngai, and Audra Millis (Bupa Australia). The authors also appreciate the contributions from Dr Pilvikki Absetz for providing suggestions for revising this manuscript and the support from Diabetes Australia (NDSS) in assisting with participant recruitment. The authors also would like to acknowledge other researchers as part of the My Diabetes Coach Group who contributed to the study, including Professor Mark Harris, Professor Robert H Friedman, Ms Taryn Black, Ms Margaret Brand, Associate Professor Louisa Gordon, Ms Carolyn Hynes, Professor Greg Johnson, Mr Michael Skinner, Dr Marlien Varnfield, Mr Andrew Wagstaff, and Professor Rory Wolfe.
This research was supported by a National Health and Medical Research Council (NHMRC) Partnership Grant (ID1057411), with additional financial and in-kind support provided by Diabetes Australia, Diabetes Queensland, Diabetes Victoria, Diabetes WA, and Roche Diabetes Care. Development of the MDC app was made possible with generous financial and other support from Bupa Australia and the Bupa Foundation and collaboration with Clevertar. EG is supported by the Melbourne Graduate Research Scholarship. JS is supported by core funding from the Australian Centre for Behavioural Research in Diabetes provided by the collaboration between Diabetes Victoria and Deakin University. EG, PS, and BO are supported by the NHMRC Centre of Research Excellence in Digital Technology to Transform Chronic Disease Outcomes (ID 1170937).
BO and DB received some royalty payments for the development of the scripts for the MDC platform.