Hybrid model predictive control for optimizing gestational weight gain behavioral interventions
Year: 2013
Type: conference-paper
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.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: Dynamical System /Related Engineering Approach /Improving Behavioral Intervention, Control Systems Engineering for Optimizing a Prenatal Weight Gain Intervention, Center for Prevention and Treatment Methodology, Control Engineering Approaches to Adaptive Interventions for Fighting Drug Abuse
OpenAlex ID: W2139707015