Piloting a Reinforcement Learning Tool for Individually Tailoring Just-in-time Adaptive Interventions

The intervention is testing the feasibility of a reinforcement learning model to pull in participants' behavioral data (calories, activity, and weight) and use this data along with participants' past behavioral goal achievements to deliver the type of message that should be most effective for a given participant at a given time. At each decision point (morning, midday, and evening on a daily basis), the system evaluates which behaviors a participant is eligible to receive a message about (eating, activity, self-weighing), which intervention options a participant is eligible to receive, and then chooses what type of behavioral message a participant should receive. Over time, the model uses participant data and response to interventions to better tailor message choice.