Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions

Abstract

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

Fig. 1

General diagram of the dynamical systems model for GWG adaptive interventions. Blue dash line represents the effect of self-regulation, and the pink dash-dot line stands for the output of the HMPC decision policies.

Fig. 2

Simulation responses for the maternal body mass, energy intake and energy expenditure, and the intervention components dosages. Red lines represent the 2009 IOM guidelines applied on a daily basis; the blue solid line represent the case with intervention and self-regulation while the black dashed line represents the case with no intervention.