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

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.