Study design
The study was conducted among outpatients with type 2 diabetes at two sites: Hoan My Cuu Long Hospital (Can Tho, Vietnam) from March to July 2023, and Nguyen Tri Phuong Hospital (Ho Chi Minh City, Vietnam) from May to September 2023. The follow-up period was three months, which was considered sufficient to detect meaningful changes in both diabetes knowledge and HbA1c levels.
This study utilized a multicenter, open-label, quasi-experimental comparative design to evaluate the effectiveness of diaB, a digital Diabetes Self-Management Education and Support (DSMES) program for individuals with type 2 diabetes in Vietnam. Participants were allocated to either the intervention or control group based on their eligibility and willingness to participate. The primary objectives were:
1.
To assess the effectiveness of the diaB program in improving diabetes-related knowledge, measured using a modified version of the Michigan Diabetes Knowledge Test (MDKT).
2.
To evaluate the program’s impact on glycemic control and other key clinical outcomes.
Participants
Diagnostic criteria
Type 2 diabetes diagnosis followed ADA 2022 criteria6: fasting plasma glucose ≥ 7.0 mmol/L (126 mg/dL), 2-h plasma glucose ≥ 11.1 mmol/L (200 mg/dL) during a standard 75-g oral glucose tolerance test, HbA1c ≥ 6.5% (48 mmol/mol), or random plasma glucose ≥ 11.1 mmol/L with classic symptoms of hyperglycemia or hyperglycemic crisis. In the absence of unequivocal hyperglycemia, diagnosis required two abnormal test results from the same sample or from two separate samples.
Inclusion criteria
(1) aged ≥ 18 years; (2) newly or previously diagnosed with type 2 diabetes (per ADA 2022); (3) no initiation or intensification of antidiabetic therapy within the preceding 3 months; (4) ownership of an internet-connected smartphone (intervention group only); (5) provision of written informed consent; and (6) willingness to return for a 3-month follow-up visit.
Exclusion criteria
Pregnancy or plans to become pregnant; use within the last 3 months of agents that acutely alter glycemia (e.g., systemic glucocorticoids, thyroid hormones); severe acute illness; communication barriers due to significant visual or auditory impairments; psychiatric disorders or ongoing psychotropic medication treatment; alcohol or psychostimulant abuse; and inability to use a smartphone (for the intervention group).
Group assignment
Participants were allocated to the intervention group (n = 81) or the usual-care control group (n = 82). Individuals who declined participation in, or did not meet inclusion criteria for the digital program were enrolled in the control group (Fig. 1). Control participants received routine endocrine clinic care and lifestyle counseling but did not attend intervention sessions and did not install the diaB application.
Flow diagram of participants: recruitment, intervention and follow-up.
Intervention
Participants in the intervention group received a ten-week digital DSMES program (diaB) grounded in the ADA DSMES framework7 and informed by self-determination theory18. The diaB mobile application delivered 26 structured lessons (video or text) covering diabetes knowledge, nutrition, home-based physical activity, and behavior change. Videos were concise (5–12 min) and culturally adapted with Vietnamese examples. Participants attended twice-weekly Zoom sessions led by diabetologists, nutritionists, psychologists, and physical coaches. Between sessions, health coaches moderated group chats via local instant-messaging platform Zalo to reinforce learning and address barriers. Participants were encouraged to record home blood-glucose readings in the app. Coaches and clinicians reviewed entries and provided individualized feedback (diet, activity, and medication-use advice within routine care). All participants continued routine endocrine-clinic follow-up.
Process evaluation and app usage monitoring
App analytics automatically logged lesson completion status, video watch time, and quiz attempts. Attendance for live sessions and coaching meetings was recorded from Zoom rosters and coach logs. The study team reviewed engagement dashboards weekly to identify participants below predefined adherence thresholds (< 60% lesson completion) for targeted outreach.
Data collection
At the study’s outset, baseline characteristics—including age, sex, diabetes duration, smoking status, weight, height, BMI, waist circumference, blood pressure, comorbidities (hypertension, dyslipidemia), and the use of antidiabetic medications—were recorded. Simultaneously, various biological parameters were collected, encompassing HbA1c, fasting blood glucose (FBG), creatinine, estimated glomerular filtration rate (eGFR), and lipid profile.
In conjunction with these measurements, diabetes knowledge, as an educational outcome was evaluated during the inclusion visit using the modified Michigan University Diabetes Knowledge Test (MDKT), a widely employed tool featuring 23 items19. The first 14 items gauged general knowledge, while the remaining 9 items assessed knowledge regarding insulin use. The original questionnaire underwent translation into Vietnamese and cultural adaptation. This Vietnamese version had been previously utilized in a study conducted in Vietnam and demonstrated good validation5. Each correct response in the multiple-choice questions earned one point. Participants scoring ≥ 12 points were categorized as having sufficient diabetes knowledge (passing the test), while those scoring < 12 points were considered test failures. All the baseline variables mentioned above were once again reviewed three months after the initiation of the study.
For the assessment of the primary outcome in the study, which involved a significant change in diabetes knowledge measured by the MDKT questionnaire, the sample size was determined using the following formula:
$$n = 2\left( {Z_{{1 – \frac{\alpha }{2}}} + Z_{1 – \beta } } \right)^{2} S_{\rho }^{2} \delta_{2}$$
The significance level (α) was set at 5%, and the statistical power at 90%. Other variables in the formula were derived from a previous study conducted in Vietnam, which evaluated the change in diabetes knowledge after direct group-intervention sessions5. The calculated minimum required sample size was 37 patients for each group, accounting for a 20% estimated drop in participants. Consequently, the final minimum sample size needed was 44 patients for each group.
Outcomes
Primary clinical outcome was change in HbA1c from baseline to 12 weeks. Secondary outcomes included changes in fasting plasma glucose, lipid profile (LDL-C, HDL-C, triglycerides, total cholesterol), weight, BMI, waist circumference, and diabetes knowledge (MDKT). HbA1c was assayed in hospital laboratories accredited to national standards (NGSP-aligned methods).
Ethical considerations
This study was approved by the Ethics Committees of Hoan My Cuu Long Hospital and Nguyen Tri Phuong Hospital. All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants before inclusion in the study.
Statistical analysis
Descriptive statistics were presented as mean (standard deviation) or median (interquartile range) for continuous variables, and as counts (percentages) for categorical variables, as appropriate. Group comparisons of categorical variables were performed using the chi-square test or Fisher’s exact test when expected cell counts were low. For continuous variables, Student’s t-test was used for normally distributed data, while the Mann–Whitney U test was applied for non-normal distributions. Within-group comparisons (baseline vs. 3 months) were conducted using paired t-tests for normally distributed variables and Wilcoxon signed-rank tests for non-normal data. The McNemar test was used for paired categorical variables.
To assess the independent effect of the intervention on the primary outcome (change in HbA1c from baseline to 3 months), a multivariable linear regression model was used, adjusting for potential confounders including age, sex, hospital site, baseline BMI, diabetes duration, and baseline knowledge score. To further address selection bias due to the non-randomized design, we applied disease risk score (DRS) adjustment. The DRS was constructed using a linear regression model predicting HbA1c change from baseline covariates (age, sex, hospital site, baseline BMI, diabetes duration, and knowledge score) and included as a covariate in the final regression model. The application of a disease risk score (DRS) for a continuous endpoint (ΔHbA1c) follows the prognostic score framework, where the score represents the conditional mean outcome under usual care rather than a probability. Accordingly, linear regression is the natural modeling choice, serving as the continuous analog to logistic/probit models used when outcomes are binary. This approach is supported by prognostic score theory20, methodological work demonstrating efficiency gains with linear adjustment for continuous outcomes21 regulatory guidance endorsing prognostic covariate adjustment in clinical trials22, and empirical applications of DRS in observational research23. In line with this evidence, we estimated the DRS via ordinary least squares from baseline covariates (age, sex, site, baseline BMI, diabetes duration, baseline knowledge score) and included it as a covariate in the final regression model.
All statistical tests were two-sided with a significance level of 0.05. Data entry was performed using Microsoft Excel 2016, and statistical analyses were conducted using Stata/MP version 14.0 (StataCorp LLC).