Hybrid Model Predictive Control for Optimizing Gestational Weight Gain Behavioral Interventions

Abstract

Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.

Figures

Fig. 1

Schematic representation for an “adaptive”/optimized gestational weight gain (GWG) intervention by HMPC.

Fig. 2

Fluid Analogy for the TPB.

Fig. 3

Closed-loop system implemented with self-regulation designed by 2 DoF IMC.

Fig. 4

Simulation responses for the maternal body mass, EI and EE, the intervention components dosages, and the PBC inflows to the two TPB models. 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.