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.

Inclusion criteria

• Youth will be eligible if they screen is positive for seeking treatment in the ED for a violent injury, age 14-24, and report ownership of a smartphone with texting capability and internet access.

Exclusion criteria

• Youth will be excluded if they do not understand English, cannot provide informed consent due to mental incompetence, incarceration or medical instability (unstable patients will be recruited if they stabilize within 72 hours), are 14-17 years old and presenting without an accessible parent/guardian, are presenting for suicide attempt/intent, sexual assault, and/or child abuse (due to high intensity of social services needed for such patients during their ED visit).