Optimizing Behavioral Interventions to Regulate Gestational Weight Gain With Sequential Decision Policies Using Hybrid Model Predictive Control

Abstract

Excessive gestational weight gain is a significant public health concern that has been the recent focus of control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually-tailored and "intensively adaptive" intervention to manage weight gain for pregnant women with overweight or obesity using control engineering approaches. This paper presents how Hybrid Model Predictive Control (HMPC) can be used to assign intervention dosages and consequently generate a prescribed intervention with dosages unique to each individuals needs. A Mixed Logical Dynamical (MLD) model enforces the requirements for categorical (discrete-level) doses of intervention components and their sequential assignment into mixed-integer linear constraints. A comprehensive system model that integrates energy balance and behavior change theory, using data from one HMZ participant, is used to illustrate the workings of the HMPC-based control system for the HMZ intervention. Simulations demonstrate the utility of HMPC as a means for enabling optimized complex interventions in behavioral medicine, and the benefits of a HMPC framework in contrast to conventional interventions relying on "IF-THEN" decision rules.

Keywords: Hybrid Model Predictive Control (HMPC); Mixed Logical Dynamical (MLD) models; behavioral interventions; gestational weight gain; sequential decision policies.

Conflict of interest statement

Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1:

Block diagram depicting the closed-loop intervention for gestational weight gain developed in the Healthy Mom Zone Study. Energy intake and maternal weight changes can be used by a hybrid model predictive control (HMPC) algorithm to determine optimized intervention dosages of intervention components (such as healthy eating and physical activity active learning).

Figure 2:

Path diagram for the Theory of Planned Behavior (TPB) considered in this work.

Figure 3:

Fluid analogy for the closed-loop Healthy Mom Zone intervention.

Figure 4:

Identification results for integrating the models for the intervention delivery dynamics and the TPB model for Participant A.

Figure 5:

Simulation from the integrated model using actual intervention dosages as inputs is compared with measured data for Participant A. Note: GA refers to gestational age.

Figure 6:

Three-Degree-of-Freedom (3 DoF) controller formulation of MPC (Nandola and Rivera, 2013).

Figure 7:

HMPC results comparison with standard IF–THEN rules for Participant A in the HMZ Study. (HMPC: noise-free signals only; Qy = [0 1]; αr = αd = [0.95 0.95]; ω = 40, Type II filter for measured disturbance rejection; fa = 0.01; p = 28, m = 25.

Figure 8:

HMPC results comparison with standard IF–THEN rules for Participant A from HMZ Study. (HMPC: noise-free signals only; Qy = [1 50000]; αr = [0 0]; αd = [0.95 0.95]; ω = 40, Type II filter for measured disturbance rejection; fa = 0.01; p = 28, m = 25.

Figure 9:

HMPC results comparison with standard IF–THEN rules for Participant A from HMZ Study. (HMPC: noise signal included (covariance R = 0.5); Qy = [0 1]; αr = αd = 0.95; ω = 40, Type II filter for measured disturbance rejection; fa = 0.1; p = 28, m = 25.

Figure 10:

HMPC results comparison with HMZ IF–THEN rules for Participant A from HMZ Study. (HMPC: noise-free signals only; Qy = [0 1]; αr = αd = 0.95; Type II filter for measured disturbance rejection with ω = 40; fa = 0.1; p = 28, m = 25.