Efficacy of a Novel Digital Platform to Scale-Up a Personalized Prenatal Weight Gain Intervention Using Control Systems Methodology

PROJECT SUMMARY Pregnant women with overweight/obesity (PW-OW/OB) and high gestational weight gain (GWG) are at increased risk for adverse maternal-infant outcomes. Given this high risk and the rapidly changing landscape of healthcare delivery due to the COVID-19 pandemic, there is a timely need for automated, scalable approaches relying on remote delivery to regulate GWG. Our team addressed this need and developed the Healthy Mom Zone (HMZ) intervention with adapted dosages to regulate GWG. Adaptations give less treatment (e.g., education) to women who can self-regulate GWG within recommendations and more treatment (physical activity/energy intake activities) to women who need more support to regulate GWG. Theory-based components were designed with a multiphase, translational science framework and control systems methodology with the long-term goal to scale- up future use by prenatal clinicians. With R01HL119245, we randomized PW-OW/OB to intervention and control groups and examined feasibility and initial impact of HMZ on GWG. A control system driven by decision rules and a woman’s observed GWG informed when to adapt dosages (GWG within goals, dosage was maintained; GWG above goals, dosage was adapted). Trial feasibility markers showed high compliance, low subject burden and attrition; compliance was better with remove vs. in-person delivery. The HMZ group had 21% lower mean GWG and were more likely to have GWG within goals than controls. Exploratory analyses also showed promise for HMZ to impact secondary maternal-infant outcomes. Given these initial findings, we made refinements to increase efficacy and scalability (e.g., added sleep/eating behavior content, modified all components for remote delivery). We replaced the initial control system with a new automated, model-based predictive control system that forecasts a woman’s future GWG under different possible values of activity/intake behaviors and determinants. We found that it made more efficient decisions to adapt dosages and regulated GWG better than the initial system. With a Penn State seed grant, we built an architecture for a novel digital platform with a web interface that automates the linkage of subject data to the new control system, computes optimized dosage changes across multiple maternal variables, and produces a host of behavior strategies to regulate GWG. The goal of the proposed research is to examine efficacy of the enhanced HMZ 2.0 intervention with new control system/digital platform to regulate GWG and impact maternal-infant outcomes while collecting implementation data to inform future scalability. N=144 PW-OW/OB will be randomized to HMZ 2.0 intervention or attention control groups from ~8-36 weeks gestation. Aim 1 will examine efficacy of HMZ 2.0 on GWG (primary outcome) and activity/intake behaviors and social cognitive determinants between intervention and control groups. Aim 2 will measure pre- to post-intervention differences in secondary maternal sleep/eating behaviors and infant birth weight. Aim 3 will examine impact of implementation markers on HMZ 2.0 efficacy on study outcomes to inform future scalability. Impact of this novel research is an optimized and highly scalable intervention to regulate GWG.