A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

Area of Science:

• Behavioral Health
• Control Engineering
• Risk Management

Background:

• Adaptive interventions are increasingly used for chronic, relapsing disorders like substance abuse, mental illness, and obesity.
• Traditional interventions often lack personalization, leading to suboptimal outcomes.
• Behavioral health interventions can benefit from systematic, data-driven approaches.

Purpose of the Study:

• To apply control engineering and risk management techniques to design adaptive behavioral interventions.
• To develop a risk-based Model Predictive Control (MPC) algorithm for behavioral health.
• To demonstrate the effectiveness of MPC in tailoring interventions for at-risk children.

Main Methods:

• Developed a risk-based Model Predictive Control (MPC) algorithm.
• Modeled identifiable risks, their costs, and mitigation strategies.
• Simulated intervention components like home visits, mentoring, and recreation activities.
• Utilized MPC's constraint-handling and scalability for multiple tailoring variables.

Main Results:

• The MPC algorithm effectively determined intervention component frequency based on risk assessment.
• Simulations demonstrated the algorithm's ability to adapt treatment over time.
• The MPC approach showed potential for increased intervention effectiveness and adherence.
• Systematic risk accounting and adaptation led to reduced waste compared to fixed treatments.

Conclusions:

• Model Predictive Control (MPC) offers a robust framework for adaptive behavioral interventions.
• This approach enhances personalization and efficiency in managing chronic and behavioral health conditions.
• Control engineering principles can significantly improve the design and implementation of behavioral health programs.