An Optimized Behavioral Intervention for Managing Gestational Weight Gain Using Semi-Physical Modeling and Hybrid...

Area of Science:

• Control Engineering
• Behavioral Science
• Maternal Health

Background:

• Gestational weight gain management is crucial for maternal and infant health.
• Existing behavioral interventions often lack personalized, adaptive strategies.
• Sequential decision-making offers a promising framework for optimizing interventions.

Purpose of the Study:

• To describe an optimized behavioral intervention, Healthy Mom Zone (HMZ), for managing gestational weight gain.
• To implement Hybrid Model Predictive Control (HMPC) for sequential decision-making in behavioral interventions.
• To demonstrate the benefits of HMPC-based intervention over conventional approaches.

Main Methods:

• Developed dynamical models integrating behavioral and physiological factors.
• Estimated models from participant data using constrained semi-physical modeling.
• Applied an HMPC algorithm for sequential control of intervention dosages.

Main Results:

• A case study demonstrated the advantages of the HMPC approach compared to "IF-THEN" strategies.
• The computational framework enables judicious, infrequent augmentations of intervention components.
• The developed framework is the foundation for Healthy Mom Zone 2.0.

Conclusions:

• HMPC provides an effective framework for optimizing behavioral interventions like HMZ.
• Personalized, adaptive control can improve gestational weight gain management.
• The Healthy Mom Zone 2.0 intervention is currently under evaluation in a clinical trial.