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An Optimized Behavioral Intervention for Managing Gestational Weight Gain Using Semi-Physical Modeling and Hybrid
Owais Khan1, Francesco Campregher2,3, Daniel E Rivera1
1Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.
None:
This paper describes an optimized behavioral intervention Healthy Mom Zone (HMZ) for managing gestational weight gain featuring sequential decision-making using Hybrid Model Predictive Control (HMPC). Dynamical models incorporating both behavioral and physiological aspects of the problem are presented and estimated from HMZ participant data via constrained semi-physical modeling. Daily measurements are provided to a controller that ultimately makes judicious (though infrequent) augmentations on categorical dosages of healthy eating and physical activity intervention components. Consequently, an HMPC algorithm is required which must follow a logical sequence of control actions conforming to practical requirements. A case study shows the benefits relative to a conventional "IF-THEN" approach. The computational framework (both modeling and control) serves as the basis for the Healthy Mom Zone 2.0 intervention currently being evaluated in a randomized clinical trial (NIH R01DK134863, NCT05807594) at Penn State University.
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