NCT07225426

Personalizing Financial Incentives

Log2LoseAI: Reinforcement Learning to Create a Framework for Personalizing Financial Incentives

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 80 (estimated)
• Sponsor: University of Utah · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

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

Detailed description

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.

Conditions

• Obesity & Overweight

Interventions

Type	Name	Description
BEHAVIORAL	personalized financial incentives	Every week, a reinforcement learning algorithm will process data on weight loss, calorie logging, and incentives earned to predict that their weight loss is positively influenced by incentives.

Timeline

• Start date: 2026-02-04
• Primary completion: 2027-02-28
• Completion: 2027-02-28
• First posted: 2025-11-06
• Last updated: 2026-03-06

Locations

1 site across 1 country: United States