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

This paper demonstrates how control systems engineering and risk management can be applied to problems in behavioral health through their application to the design and implementation of adaptive interventions. Adaptive interventions represent a promising approach to prevention and treatment of chronic, relapsing disorders, such as alcoholism, cigarette smoking, and drug abuse. The benefits of the proposed approach are presented in the development of risk-based model predictive control (MPC) algorithm for a hypothetical intervention inspired by two real-life programs: Fast Track, an intervention whose long-term goal is the prevention of conduct disorders in at-risk children, and Communities that Care, a risk-based prevention program for substance abuse. The tailoring or controlled variable of the adaptive intervention is a measure of parental functioning in the family of an at-risk child; the MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper has the potential to increase intervention potency and adherence while reducing waste, resulting in more effective interventions than conventional fixed treatment. MPC is particularly meaningful for the problem given some of its favorable properties, such as ease of constraint-handling, and its ability to scale to interventions involving multiple tailoring variables. Several simulations are conducted under conditions of varying disturbance magnitude to demonstrate the effectiveness of the algorithm