Artificial Intelligence-Enabled Mobile Health Intervention (mDiabetes) to Reduce Diabetes Risk Behaviors in Rural...

India faces a dual burden of diabetes and prediabetes. Although mobile health (mHealth) interventions have shown promise in promoting healthy lifestyle changes, most interventions deploy generic, "one-size-fits-all" messages that do not consider individual behavioral patterns, motivational states, or changing needs over time.

This formative evaluation study aimed to assess the effectiveness of an artificial intelligence (AI)-enabled, personalized mHealth messaging intervention (mDiabetes) compared to traditional, nonpersonalized mHealth messaging in promoting engagement with diabetes risk reduction behaviors among adults in Gulbarga, Karnataka, South India.

A quasi-experimental pre-post study was conducted among adults without diabetes (N=1048). Participants were divided into intervention and control groups. The control group received static diabetes prevention messages via WhatsApp, while the intervention group received customized messages twice a week based on individual feedback through reinforcement learning algorithms. Data on demographics, diabetes knowledge, and lifestyle behaviors were collected via home interviews. Chi-square tests and t tests were performed to assess group differences. Intervention effects were evaluated using multivariable logistic regression for binary outcomes and ANCOVA for continuous outcomes. Adjusted odds ratios (aORs) with 95% CIs were reported, and Bonferroni correction was applied for multiple comparisons.

A total of 1048 (96.9%) participants (n=661, 63.1%, female) completed the 6-month follow-up. At endline, no significant between-group differences were observed for primary outcomes. Both groups had similar odds of meeting the physical activity goal (≥30 minutes/day) at endline (aOR 1.0, 95% CI 0.7-1.3, P=.74). Baseline activity (aOR 2.1, 95% CI 1.5-3.1, P<.001) and age >50 years (aOR 3.8, 95% CI 1.6-9.3, P=.003) were significant predictors of endline physical activity, while employment was associated with lower odds of physical activity (aOR 0.2, 95% CI 0.1-0.3, P<.001). Daily fruit intake was modestly higher in the intervention group (aOR 1.4, 95% CI 0.8-2.3, P=.24), and participants aged 26-35 years had higher odds of daily fruit intake (aOR 4.7, 95% CI 1.9-11.8, P=.001), while employment was associated with lower odds (aOR 0.3, 95% CI 0.1-0.8, P=.02). The mean BMI difference at endline was -0.0 kg/m² (95% CI -0.6 to 0.5, P=.95), and baseline BMI was a strong predictor of endline BMI (P<.001). Exploratory behavioral outcomes revealed no significant differences: stair use (aOR 0.9, 95% CI 0.7-1.4, P=.79), walking for chores (aOR 2.4, 95% CI 1.0-6.1, P=.06), helping with household chores (aOR 1.0, 95% CI 0.4-2.3, P=.94), and farm work (aOR 1.3, 95% CI 0.9-1.8, P=.19).

Both AI-enabled and traditional mHealth interventions have similar effectiveness in promoting diabetes prevention behaviors in rural India. Simple, well-designed mHealth interventions delivered through an accessible platform like WhatsApp can achieve meaningful behavior change without the need for complex AI technology. The comparable effectiveness suggests the potential for scalable, cost-effective, equitable diabetes prevention strategies in resource-limited settings.