A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control
Physical inactivity is a major contributor to morbidity and mortality worldwide. Many physical activity behavioral interventions at present have shown limited success addressing the problem from a long term perspective that includes maintenance. This paper proposes the design of a decision algorithm for a mobile and wireless health (mHealth) adaptive intervention that is based on control engineering concepts. The design process relies on a behavioral dynamical model based on Social Cognitive Theory (SCT) with a controller formulation based on hybrid model predictive control (HMPC) being used to implement the decision scheme. The discrete and logical features of HMPC coincide naturally with the categorical nature of the intervention components and the logical decisions that are particular to an intervention for physical activity. The intervention incorporates an online controller reconfiguration mode that applies changes in the penalty weights to accomplish the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the controller using a hypothetical model for physical activity interventions, under realistic conditions.
2016 American Control Conference (ACC)