Patient engagement, acceptability, and preference of artificial intelligence versus human coaching for diabetes prevention

Artificial intelligence-powered lifestyle interventions may expand access to behavioral change support, but patient experience remains critical to real-world uptake and effectiveness. In a phase 3 randomized clinical trial of adults with prediabetes and overweight or obesity ( N = 368), we compared a fully automated AI-driven diabetes prevention program (DPP) with a CDC-recognized human-coach-based DPP. Participants assigned to the AI-led DPP initiated the intervention sooner (median 11 vs 26 days; p < 0.001) and demonstrated higher and more evenly distributed engagement over 12 months. Acceptability ratings were consistently higher for the human-led DPP across all domains, with the largest differences in satisfaction ( p < 0.001). Preference responses also favored human coaching, with 46.5% of AI-assigned participants indicating they would have preferred a human coach compared with 31.5% preferring a fully automated program in the human-led group ( p = 0.012). These findings illustrate a trade-off between scalability and perceived quality of experience. Clinical trial registration: ClinicalTrials.gov NCT05056376 (registered September 24, 2021).

Authors

Institutions

Publication Details

Journal
npj Digital Medicine
Published
2026-08-03
DOI
https://doi.org/10.1038/s41746-026-03063-w
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Patient engagement, acceptability, and preference of artificial intelligence versus human coaching for diabetes prevention

DANIEL ZADE, Adrian Dobs, Kristin Riekert, Cyd Eaton et al.
npj Digital Medicine
Artificial Intelligence in Healthcare and Education
article

Patient engagement, acceptability, and preference of artificial intelligence versus human coaching for diabetes prevention

DANIEL ZADE, Adrian Dobs, Kristin Riekert, Cyd Eaton, Nisa Maruthur, Benjamin Lalani, Nestoras Mathioudakis
article en

Abstract

Artificial intelligence-powered lifestyle interventions may expand access to behavioral change support, but patient experience remains critical to real-world uptake and effectiveness. In a phase 3 randomized clinical trial of adults with prediabetes and overweight or obesity ( N = 368), we compared a fully automated AI-driven diabetes prevention program (DPP) with a CDC-recognized human-coach-based DPP. Participants assigned to the AI-led DPP initiated the intervention sooner (median 11 vs 26 days; p < 0.001) and demonstrated higher and more evenly distributed engagement over 12 months. Acceptability ratings were consistently higher for the human-led DPP across all domains, with the largest differences in satisfaction ( p < 0.001). Preference responses also favored human coaching, with 46.5% of AI-assigned participants indicating they would have preferred a human coach compared with 31.5% preferring a fully automated program in the human-led group ( p = 0.012). These findings illustrate a trade-off between scalability and perceived quality of experience. Clinical trial registration: ClinicalTrials.gov NCT05056376 (registered September 24, 2021).

npj Digital Medicine
University of Maryland, Baltimore (US), Boston Medical Center (US), University of Maryland Medical Center (US), Harvard University (US), Johns Hopkins University (US), Johns Hopkins Medicine (US), University of Maryland Medical Center Midtown Campus (US)
Johns Hopkins University, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences
Good health and well-being
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.