Reinforcement Learning (RL), a type of machine learning, holds promise for addressing the limitations of previous approaches to implementing JITAIs. Adaptive RL applications work by updating information about expected rewards (i.e., proximal outcomes) based on the results of sequentially randomized trials. In the context of mHealth, random delivery of intervention messages is referred to as a micro-randomized trial. To realize the potential of adaptive interventions to reduce health disparities in cancer prevention and control, mHealth interventionists first need a user-friendly RL tool that enables use of digital health participant data to continually adapt decision rules guiding highly tailored intervention delivery. This research team has developed a user-friendly, web-based application (AdaptRL) that reads in and analyzes user data (e.g., from Fitbit) in real-time, uses RL to efficiently conduct micro-randomized trials, and creates a JITAI tailored to optimize weight loss for each participant. The objective of this study is to test the feasibility of using the AdaptRL tool in a pilot weight loss study.
The purpose of this pilot study is to conduct a 12-week pilot feasibility study testing usability of the adapt learning tool (AdaptRL)in a weight loss intervention (ADAPT study). Building upon a previous just-in-time adaptive intervention (JITAI), a reinforcement learning tool will generate decision rules regarding which behavior change techniques, in which contexts, are most efficacious for promoting weight loss in a sample of 20 adults.