Using Re-inforcement Learning to Automatically Adapt a Remote Therapy Intervention (RTI) for Reducing Adolescent Violence Involvement

Overview

This study will use a randomized control trial (RCT) design to administer two versions of a multisession remote behavioral intervention for youth seeking Emergency Department care for a violent injury with the goal to reduce their violence involvement and associated negative behaviors and consequences. The study examines two versions of the remote therapy intervention - a standard RTI (S-RTI) and an Artificial Intelligence RTI (AI-RTI). The application of a just-in-time adaptive strategy to address youth violence is an important and novel direction for this research, particularly given the need to understand best practices for delivering behavioral interventions among lower-income populations.

The Specific Aims for the proposed study are to refine the Remote Therapy Intervention (RTI) for delivery using a standardized remote therapy package (S-RTI; 1 ED + 5 remote sessions) based on a piloted RTI and an adaptive RTI that optimizes bi-weekly dose and intervention intensity between four levels of therapy (remote therapy+, remote therapy; automated electronic tailored therapy; none) based on a reinforcement learning (RL) algorithm \[AI-RTI\]. A total of 750 youth (age=14-24) seeking ED care for a violent injury will be enrolled and randomly assigned (stratified by age/gender) to the S-RTI (n=250), AI-RTI (n=300), and a control (EUC; n=200) condition. In addition to the randomized assignment, all youth will take a daily assessment over the course of the intervention timeline. Outcomes will be assessed at 6 and 12 months.

Conditions