Optimizing behavioral interventions to regulate gestational weight gain with sequential decision policies using hybrid model predictive control

Penghong Guo, Daniel E. Rivera, Yuwen Dong, Sunil Deshpande, Jennifer S. Savage, Emily E. Hohman, Abigail M. Pauley, Krista S. Leonard, Danielle Symons Downs

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number107721
JournalComputers and Chemical Engineering
Volume160
DOIs
StatePublished - Apr 2022
Externally publishedYes

Keywords

  • Behavioral interventions
  • Gestational weight gain
  • Hybrid model predictive control (HMPC)
  • Mixed logical dynamical (MLD) models
  • Sequential decision policies

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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