Optimized behavioral interventions: What does system identification and control engineering have to offer?

The last decade has witnessed an increasing interest in applying systems science concepts for problems in behavioral health, and using these to inform the design, analysis, and implementation of optimized interventions. How can system identification and control engineering impact interventions for chronic, relapsing disorders such as drug abuse, cigarette smoking and obesity? The paper addresses this question by focusing on the problem of time-varying "adaptive" interventions. In an adaptive intervention, dosages of intervention components are assigned based on the assessed values of tailoring variables that reflect some outcome measure (e.g., number of cigarettes smoked, parental function) or adherence (e.g, days abstinent). Because time-varying adaptive interventions constitute closed-loop dynamical systems, they are correspondlngly amenable to control engineering solutions. System identification is enabled by intensive longitudinal data (ILD) that can be obtained in the field via ecological momentary assessment (EMA); this creates the availability of rapidly sampled, continuous-time assessments from which dynamical system behavior can be discerned and modeled. How can system identification and control be applied in this broad setting is demonstrated with a number of illustrative problems: dynamic modeling and hybrid model predictive control of low-dose naltrexone as treatment for fibromyalgia, a chronic pain condition; modeling of a smoking cessation intervention involving bupropion and counseling; constructing a dynamic model of an intervention for preventing excessive weight gain during pregnancy, and Model-on-Demand Model Predictive Control in a hypothetical intervention based on the Fast Track program for assigning the frequency of home counseling visits to families with at-risk children.