Content-Length: 113060 | pFad | https://preprints.jmir.org/preprint/70271

JMIR Preprints #70271: Gamified Optimized Diabetes Management with Artificial Intelligence-Powered Rural Telehealth Intervention for Underserved Areas: Protocol of an Optimization Pilot and Feasibility Trial

Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Currently submitted to: JMIR Research Protocols

Date Submitted: Dec 19, 2024
Open Peer Review Period: Dec 24, 2024 - Feb 18, 2025
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Gamified Optimized Diabetes Management with Artificial Intelligence-Powered Rural Telehealth Intervention for Underserved Areas: Protocol of an Optimization Pilot and Feasibility Trial

  • Tapan Mehta; 
  • Tejossy John; 
  • Caroline Cohen; 
  • Aseel El Zein; 
  • Tanjila Nawshin; 
  • Andrea Cherrington; 
  • Tejaswini Subhash Chilke; 
  • Victoria Faught; 
  • Mohanraj Thirumalai

ABSTRACT

Background:

Type 2 diabetes mellitus (T2DM) significantly impacts public health, with approximately 21 million US adults diagnosed. Telehealth interventions show promise for improving T2DM outcomes through digital self-management techniques, but face challenges due to disparities in digital literacy and access, especially in rural areas. There is a need for sustainable T2DM management interventions that require minimal digital literacy and are widely accessible. We propose an innovative, individualized lifestyle modification intervention delivered via standard phone service to control blood glucose levels in individuals with T2DM.

Objective:

This paper outlines the protocol of a pilot study aiming to assess the feasibility of implementing and preliminary effectiveness of an artificial intelligence-assisted individualized lifestyle modification intervention for glycemic control in rural populations delivered via landline telephone service.

Methods:

This study employs a multiphase optimization strategy (MOST) and includes two experimental intervention components: automated vs. human health coaching and adapted vs. fixed gamified reward levels based on daily automated monitoring calls. We aim to recruit 88 patients with diabetes and HbA1C levels 6.5–11.5%. Participants receive daily behavioral monitoring phone calls to evaluate self-management behaviors. Participants also receive either weekly human health coaching or automated AI-driven health coaching for six months. In the fixed-reward arm, participants earn 60 cents per day for answering daily calls, while in the adapted gamified reward arm, rewards start at 20 cents per day and increase weekly, with penalties for missed days. Both arms can earn up to $100.80 over six months. Semi-structured exit interviews will gather patient insights post-trial. Primary outcomes include feasibility measures, HbA1c levels, and lipid profiles.

Results:

We have screened 813 people with diabetes and enrolled 54 participants since the launch of the study. We project that enrollment and analyses to assess feasibility completed in 2025.

Conclusions:

This intervention lays the groundwork for a future optimization trial addressing T2DM management, reaching populations through digital health while requiring minimal digital skills. It has the potential to be a scalable low-cost AI-assisted diabetes management solution that is accessible to rural communities and those with low digital literacy or smartphone access. Clinical Trial: ClinicalTrials.gov Identifier: NCT05344859


 Citation

Please cite as:

Mehta T, John T, Cohen C, Zein AE, Nawshin T, Cherrington A, Chilke TS, Faught V, Thirumalai M

Gamified Optimized Diabetes Management with Artificial Intelligence-Powered Rural Telehealth Intervention for Underserved Areas: Protocol of an Optimization Pilot and Feasibility Trial

JMIR Preprints. 19/12/2024:70271

DOI: 10.2196/preprints.70271

URL: https://preprints.jmir.org/preprint/70271

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.









ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: https://preprints.jmir.org/preprint/70271

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy