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Downs DS, Pauley AM, Rivera DE, Savage JS, Moore AM, Shao D, Chow SM, Lagoa C, Pauli JM, Khan O, Kunselman A. Healthy Mom Zone Adaptive Intervention With a Novel Control System and Digital Platform to Manage Gestational Weight Gain in Pregnant Women With Overweight or Obesity: Study Design and Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2025; 14:e66637. [PMID: 40080809 PMCID: PMC11950706 DOI: 10.2196/66637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 12/23/2024] [Accepted: 02/12/2025] [Indexed: 03/15/2025] Open
Abstract
BACKGROUND Regulating gestational weight gain (GWG) in pregnant women with overweight or obesity is difficult, particularly because of the narrow range of recommended GWG for optimal health outcomes. Given that many pregnant women show excessive GWG and considering the lack of a "gold standard" intervention to manage GWG, there is a timely need for effective and efficient approaches to regulate GWG. We have enhanced the Healthy Mom Zone (HMZ) 2.0 intervention with a novel digital platform, automated dosage changes, and personalized strategies to regulate GWG, and our pilot study demonstrated successful recruitment, compliance, and utility of our new control system and digital platform. OBJECTIVE The goal of this paper is to describe the study protocol for a randomized controlled optimization trial to examine the efficacy of the enhanced HMZ 2.0 intervention with the new automated control system and digital platform to regulate GWG and influence secondary maternal and infant outcomes while collecting implementation data to inform future scalability. METHODS This is an efficacy study using a randomized controlled trial design. HMZ 2.0 is a multidosage, theoretically based, and individually tailored adaptive intervention that is delivered through a novel digital platform with an automated link of participant data to a new model-based predictive control algorithm to predict GWG. Our new control system computes individual dosage changes and produces personalized physical activity (PA) and energy intake (EI) strategies to deliver just-in-time dosage change recommendations to regulate GWG. Participants are 144 pregnant women with overweight or obesity randomized to an intervention (n=72) or attention control (n=72) group, stratified by prepregnancy BMI (<29.9 vs ≥30 kg/m2), and they will participate from approximately 8 to 36 weeks of gestation. The sample size is based on GWG (primary outcome) and informed by our feasibility trial showing a 21% reduction in GWG in the intervention group compared to the control group, with 3% dropout. Secondary outcomes include PA, EI, sedentary and sleep behaviors, social cognitive determinants, adverse pregnancy and delivery outcomes, infant birth weight, and implementation outcomes. Analyses will include descriptive statistics, time series and fixed effects meta-analytic approaches, and mixed effects models. RESULTS Recruitment started in April 2024, and enrollment will continue through May 2027. The primary (GWG) and secondary (eg, maternal and infant health) outcome results will be analyzed, posted on ClinicalTrials.gov, and published after January 2028. CONCLUSIONS Examining the efficacy of the novel HMZ 2.0 intervention in terms of GWG and secondary outcomes expands the boundaries of current GWG interventions and has high clinical and public health impact. There is excellent potential to further refine HMZ 2.0 to scale-up use of the novel digital platform by clinicians as an adjunct treatment in prenatal care to regulate GWG in all pregnant women. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/66637.
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Affiliation(s)
- Danielle Symons Downs
- Department of Kinesiology, Pennsylvania State University, University Park, PA, United States
- Department of Obstetrics and Gynecology, College of Medicine, Pennsylvania State University, Hershey, PA, United States
| | - Abigail M Pauley
- Department of Kinesiology, Pennsylvania State University, University Park, PA, United States
| | - Daniel E Rivera
- School of Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, United States
| | - Jennifer S Savage
- Department of Nutrition, Center for Childhood Obesity Research, Pennsylvania State University, University Park, PA, United States
| | - Amy M Moore
- Department of Nutrition, Center for Childhood Obesity Research, Pennsylvania State University, University Park, PA, United States
| | - Danying Shao
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, United States
| | - Sy-Miin Chow
- Human Development and Family Studies, Quantitative Developmental Systems Methodology Core, Pennsylvania State University, University Park, PA, United States
| | - Constantino Lagoa
- College of Engineering, School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA, United States
| | - Jaimey M Pauli
- Division of Maternal Fetal Medicine, College of Medicine, Pennsylvania State University, Hershey, PA, United States
| | - Owais Khan
- School of Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, United States
| | - Allen Kunselman
- Department of Public Health Services, Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA, United States
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Baller D, Thomas DM, Cummiskey K, Bredlau C, Schwartz N, Orzechowski K, Miller RC, Odibo A, Shah R, Salafia CM. Gestational growth trajectories derived from a dynamic fetal-placental scaling law. J R Soc Interface 2019; 16:20190417. [PMID: 31662073 DOI: 10.1098/rsif.2019.0417] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Fetal trajectories characterizing growth rates in utero have relied primarily on goodness of fit rather than mechanistic properties exhibited in utero. Here, we use a validated fetal-placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal-placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events in utero.
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Affiliation(s)
- Daniel Baller
- Department of Mathematical Sciences, United States Military Academy, West Point, NY 10996, USA
| | - Diana M Thomas
- Department of Mathematical Sciences, United States Military Academy, West Point, NY 10996, USA
| | - Kevin Cummiskey
- Department of Mathematical Sciences, United States Military Academy, West Point, NY 10996, USA
| | - Carl Bredlau
- Department of Computer Science, Montclair State University, Montclair, NJ 07043, USA
| | - Nadav Schwartz
- Division of Maternal Fetal Medicine, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | | | - Richard C Miller
- Department of Obstetrics and Gynecology, St Barnabas Medical Center, Livingston, NJ 07039, USA
| | - Anthony Odibo
- Division of Maternal Fetal Medicine, University of South Florida, Tampa, FL 33620, USA
| | - Ruchit Shah
- Placental Analytics, New Rochelle, NY 10538, USA
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