Using Re-inforcement Learning to Automatically Adapt a Remote Therapy Intervention (RTI) for Reducing Adolescent Violence Involvement
Last updated on July 2021Recruitment
- Recruitment Status
- Not yet recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Criminal Behavior
- Substance Use
- Violence
- Violence in Adolescence
- Type
- Interventional
- Phase
- Not Applicable
- Design
- Allocation: RandomizedIntervention Model: Parallel AssignmentMasking: Single (Outcomes Assessor)Primary Purpose: Treatment
Participation Requirements
- Age
- Between 14 years and 20 years
- Gender
- Both males and females
Description
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 lev...
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=14-20) 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.
Tracking Information
- NCT #
- NCT04850274
- Collaborators
- Not Provided
- Investigators
- Not Provided
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