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Diabetes: Management and Pharmacotherapy

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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...

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A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

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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.

JMIR Research Protocols

|December 5, 2025

Summary

This study tested an AI-assisted phone intervention to improve type 2 diabetes management in rural areas, finding it feasible and effective for glycemic control.

Keywords:

artificial intelligence digital health lifestyle modification telehealth type 2 diabetes

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Area of Science:

Digital Health
Diabetes Management
Health Disparities

Background:

Type 2 diabetes mellitus (T2DM) poses a significant public health challenge, particularly in rural areas with limited digital access.
Existing telehealth interventions for T2DM often face barriers due to digital literacy and access disparities.
A low-tech, individualized lifestyle modification intervention delivered via phone call is proposed to enhance glycemic control.

Purpose of the Study:

To assess the feasibility and preliminary effectiveness of an AI-assisted intervention for T2DM self-management in rural settings.
To evaluate automated versus human health coaching and fixed versus adaptive financial incentives.
To inform a future Multiphase Optimization Strategy (MOST) trial for T2DM management.

Main Methods:

A pilot study protocol using the MOST framework preparation phase.
Enrollment of 88 adults with T2DM (HbA1c 6.5%-11.5%).
Daily interactive voice response calls for monitoring (diet, activity, medication, glucose) and weekly coaching; comparison of automated vs. human coaching and fixed vs. adaptive financial incentives.

Main Results:

Data collection completed for 88 participants as of October 2025.
Feasibility analysis is expected by December 2025.
Study funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

Conclusions:

The pilot study evaluates a low-tech, AI-assisted T2DM intervention.
The intervention aims to reduce digital barriers in diabetes self-management.
Findings will inform a future MOST optimization trial for T2DM.