NCT05056376|CompletedResultsDMC
Loading search results
Sponsor study identifier
Secondary identifiers
interventional
368
1 country
2
Study Start
October 1, 2021
Actual (reported)Primary Completion
December 16, 2024
Actual (reported)Study Completion
December 16, 2024
Actual (reported)The purpose of this research study is to compare the effectiveness of a fully automated digital diabetes prevention program to standard of care human coach-based diabetes prevention programs for promoting clinically meaningful lifestyle changes to reduce the risk of type 2 diabetes in adults with prediabetes.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 16, 2024
Actual (reported)Click on a node to explore related trials.
Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
September 14, 2021
Actual (reported)September 24, 2021
Actual (reported)October 1, 2021
Actual (reported)December 16, 2024
Actual (reported)December 16, 2024
Actual (reported)December 17, 2025
Actual (reported)Achievement of CDC's Benchmark for Type 2 Diabetes Risk Reduction as a Binary Outcome (Yes/no)
The primary outcome was the CDC-defined diabetes risk reduction benchmark at 12 months (based on the 2021 standards), defined as at least one of the following: 1. Weight loss of ≥5%. 2. Weight loss of ≥4% combined with at least 150 weekly minutes of moderate-to-vigorous PA 3. An absolute decrease of ≥0.2 points in A1C (measured in %). The A1C endpoint was applicable only to participants with baseline A1C of 5.7% to 6.4%. Maintaining an A1C \<6.5% throughout the study was also a criterion for the primary outcome.
12 months
Absolute Weight Change
At 12 months
Cost-effectiveness as Assessed by the Markov Model
Participants will receive Sweetch Digital Diabetes Prevention Program consists of a smartphone app and bluetooth-enabled digital body weight scale that syncs with the app.
Participants will attend a total of 16 weekly sessions during months 1 to 6 and 6 sessions during months 7 to 12. These group sessions may be delivered in-person at the local program or remotely using video conferencing. During these sessions, participants will receive information about lifestyle change behaviors focusing on weight loss, physical activity, and nutrition from a trained lifestyle coach.
The Sweetch app is a hyper-personalized mobile digital coach that provides users with tailored recommendations to promote healthy lifestyle behaviors (150 minutes per week of physical activity, weight reduction, and healthy eating habits) to reduce the risk of type 2 diabetes. The Sweetch app uses self-tracking and multiple evidence-based persuasive eCoaching strategies. The Sweetch artificial intelligence algorithm delivers just-in-time support and/or adapt recommendations based on the user's response. For example, push notifications will be sent when the algorithm detects that the user is potentially available and able to act upon the recommendation, based on various parameters including location, previous response, calendar availability, and weather, etc.
Johns Hopkins Hospital
Baltimore, Maryland, 21287, United States
Reading Hospital - Tower Health
Reading, Pennsylvania, 19611, United States
Lalani B, Siew G, Valdez Y, Shehadeh A, Zade D, Riekert K, Mathioudakis N; AI-DPP Study Group. User Engagement and Feature Preferences in an AI-Powered mHealth Intervention for Diabetes Prevention: Secondary Analysis of a Randomized Controlled Trial. JMIR Mhealth Uhealth. 2026 Sep 2;14:e92981. doi: 10.2196/92981.
Mathioudakis N, Lalani B, Abusamaan MS, Alderfer M, Alver D, Dobs A, Kane B, McGready J, Riekert K, Ringham B, Shehadeh A, Vandi F, Wanigatunga AA, Zade D, Maruthur NM; AI-DPP Study Group. An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial. JAMA. 2025 Dec 16;334(23):2079-2089. doi: 10.1001/jama.2025.19563.
This study has limitations. It was unmasked, relied on surrogate outcomes, and DPP delivery may have varied by site. App updates and study visits outside standard DPP exposures may have influenced outcomes. Referrals were not from primary care, and missing data or assumptions about physical activity had minimal sensitivity impacts. COVID-19 altered DPP delivery, and results may not generalize to broader prediabetes populations due to the sample's high activity levels and education.
Nestoras Mathioudakis, MD MHS
Johns Hopkins University
368 participants (actual enrollment reported by the registry).
First Submitted
September 14, 2021
First Posted
September 24, 2021
Study Start
October 1, 2021
Actual (reported)12 months
Change in Hemoglobin A1C
At 12 months
Percentage Change in Weight
At 12 months
Mean Weekly Moderate-to-vigorous Physical Activity (MVPA)
At 12 months
The Human Coach-Based Diabetes Prevention Program will consist of a CDC recognized lifestyle change program. Participants will attend a total of 16 weekly sessions during months 1 to 6 and 6 sessions during months 7 to 12. These group sessions may be delivered in-person at the local program or remotely using video conferencing. During these sessions, participants will receive information about lifestyle change behaviors focusing on weight loss, physical activity, and nutrition from a trained lifestyle coach.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
Contact the study team to confirm eligibility.
Abusamaan MS, Ballreich J, Dobs A, Kane B, Maruthur N, McGready J, Riekert K, Wanigatunga AA, Alderfer M, Alver D, Lalani B, Ringham B, Vandi F, Zade D, Mathioudakis NN. Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial. Trials. 2024 May 16;25(1):325. doi: 10.1186/s13063-024-08177-8.
Primary Completion
December 16, 2024
Actual (reported)Study Completion
December 16, 2024
Actual (reported)Last Updated
December 17, 2025
Results First Posted
December 17, 2025
Record last verified: 2025-12
The Data Coordinating Center (Johns Hopkins University) will prepare a de-identified database that is consistent with HIPAA requirements. The de-identified database will include all study research data and will be stored in a repository at Johns Hopkins University (archive.data.jhu.edu) at a time that will coincide with the online publication of the study primary endpoint paper. The repository will contain documentation designed to facilitate use of the database. The documentation prepared for the repository will include a list and timetable for secondary and exploratory papers planned by the study investigators. Access to the database will be restricted to qualified users. Qualified users will include those investigators who submit valid pre-specified hypotheses and certify that they will not use the data for commercial purposes.
Download the complete ClinicalTrials.gov record imported by Syfrah, including fields not shown in this page. The registry may contain a newer revision.
Source: ClinicalTrials.gov.
JSON downloads include source attribution and retrieval details alongside the unchanged original record or the separately normalized values.
Source retrieved: