title: 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.
authors: Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA.
authors: Yuwen
authors: Y
authors: Dong
authors: Daniel E
authors: DE
authors: Rivera
authors: Danielle S
authors: DS
authors: Downs
authors: Jennifer S
authors: JS
authors: Savage
authors: Diana M
authors: DM
authors: Thomas
authors: Linda M
authors: LM
authors: Collins