Loading clinical trials...
Loading clinical trials...
Log2LoseAI: Reinforcement Learning to Create a Framework for Personalizing Financial Incentives
The purpose of this study is to determine the feasibility of providing personalized incentives for dietary self-monitoring and/or interim weight loss to people enrolled in a weight-loss program
In this study, community outpatients will participate in a clinician-facilitated, group-based behavioral weight-loss program for 24 weeks. Dietary self-monitoring data (input by patients via a mobile phone dietary application) and weight data (input by patients via cellular scale) will be collected by a software platform. A reinforcement learning algorithm will use data collected during the trial to predict which participants will respond to a financial incentive. Incentives will be provided to participants predicted to respond, and they will be notified of incentives via text messaging.
Age
18 - No limit years
Sex
ALL
Healthy Volunteers
Yes
University of Utah
Salt Lake City, Utah, United States
Start Date
February 4, 2026
Primary Completion Date
February 28, 2027
Completion Date
February 28, 2027
Last Updated
March 6, 2026
80
ESTIMATED participants
personalized financial incentives
BEHAVIORAL
Lead Sponsor
University of Utah
Collaborators
NCT07423780
NCT07430007
Data Source & Attribution
This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.
Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.
Neither the United States Government nor Clareo Health make any warranties regarding the data. Check ClinicalTrials.gov frequently for updates.
View ClinicalTrials.gov Terms and ConditionsNCT07414043