Control engineering examines how to transform the behavior of systems over time from undesirable conditions to desirable ones. Cruise control in automobiles, the home thermostat, and the insulin pump are just some examples of control systems at work in our daily lives. The last half-century has seen the extensive application of control engineering concepts to physical systems; however, control engineering has yet to substantially impact the field of behavioral health. An increasing interest by government and community agencies for developing comprehensive systems solutions to the prevention and treatment of chronic disorders (among them drug and alcohol abuse, HIV/AIDS, cancer, mental health, diabetes, obesity, and cardiovascular health) has created new opportunities for novel approaches to these important public health problems that rely on control engineering principles.

The presentation will provide a perspective on the role that control engineering principles can play in behavioral health by examining the problem of adaptive, time-varying interventions. Adaptive interventions systematically individualize therapy through the use of decision rules that determine intervention dosages and forms of treatment by relying on measurements of tailoring variables over time. Adaptive interventions constitute a form of feedback control system in the context of behavioral health. Consequently, drawing from control engineering has the potential to significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions.

The conceptual framework underlying this work comes from Collins et al. (2004) and Rivera et al. (2007). A simulation study of a hypothetical adaptive intervention inspired by the Fast Track program for prevention of conduct disorders in at-risk children is presented. The results of explicit decision rules (similar to those proposed by Collins et al. (2004)) are compared to both Proportional-Integral-Derivative (PID)-type and Model Predictive Control (MPC)-based decision policies designed on the basis of model-based engineering control principles. In light of this analysis and simulation study, a series of systems technologies that will impact future research on this problem are presented.

[1] Collins, L.M., S.A. Murphy and K.L. Bierman (2004). “A conceptual framework for adaptive preventive interventions,” Prevention Science, 5 (3), 185-196.

[2] Rivera, D.E., M.D. Pew, and L.M. Collins (2007). “Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction,” Drug and Alcohol Dependence, Special Issue on Adaptive Treatment Strategies, 88, Supplement 2, pgs. S31-S40.