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Gamified Optimized Diabetes Management With Artificial Intelligence-Powered Rural Telehealth Intervention (GODART):
Tapan Mehta1, Tejossy John1, Aseel El Zein1
1Department of Family and Community Medicine, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States.
Background:
Type 2 diabetes mellitus (T2DM) is highly prevalent in the United States and represents a significant public health challenge. Telehealth interventions have shown promise for improving T2DM outcomes, but their effectiveness is often limited by disparities in digital literacy and access, especially in rural areas. To address this gap, we propose an innovative, individualized lifestyle modification intervention delivered via phone call to support glycemic control.
Objective:
This paper outlines the protocol for a pilot study designed to assess the feasibility and preliminary effectiveness of an artificial intelligence-assisted intervention for T2DM self-management in rural settings.
Methods:
The study uses the preparation phase of the MOST (Multiphase Optimization Strategy) framework to evaluate two components: (1) automated versus human health coaching and (2) fixed versus adapted gamified financial incentives, based on participants' daily engagement with automated monitoring calls. We aim to enroll 88 adults with T2DM and hemoglobin A1c (HbA1c) levels between 6.5% and 11.5%. Participants receive daily interactive voice response calls tracking diet, physical activity, medication adherence, and blood glucose, and weekly coaching based on randomization. In the fixed-reward arm, participants earn US $0.60 per day; in the adaptive arm, rewards start at US $0.20 and increase weekly, with penalties for missed days. Primary outcomes include feasibility metrics and preliminary changes in HbA1c. Semistructured interviews will assess patient experience.
Results:
This study was funded by the National Institute of Diabetes and Digestive and Kidney Diseases. As of October 2025, we have enrolled and completed data collection for 88 participants. We expect to complete the feasibility analysis by December 2025.
Conclusions:
This pilot and feasibility study evaluates a low-tech, artificial intelligence-assisted T2DM intervention designed to reduce digital barriers and inform a future MOST optimization trial.
Trial Registration:
ClinicalTrials.gov NCT05344859; https://clinicaltrials.gov/study/NCT05344859.
International Registered Report Identifier (Irrid):
DERR1-10.2196/70271.
