Accepted for/Published in: JMIR Formative Research
Date Submitted: Dec 16, 2024
Open Peer Review Period: Dec 19, 2024 - Jan 20, 2025
Date Accepted: Feb 25, 2025
(closed for review but you can still tweet)
Leveraging a Digital Health Platform for Observational Maternal Health Research: Design, Recruitment Strategies, and Lessons Learned from the PowerMom Study
ABSTRACT
Background:
Maternal health research is often limited by challenges in participant recruitment, retention, and data collection, particularly among underrepresented populations. Digital health platforms provide an opportunity to address these barriers by enabling decentralized, real-world data collection and engaging diverse cohorts across wide geographic areas. The PowerMom platform was designed as a scalable, digital research tool to collect longitudinal and episodic data during pregnancy and postpartum stages, leveraging innovative recruitment strategies and multimodal data collection techniques.
Objective:
This study aimed to evaluate the design, implementation and outcomes of the PowerMom research platform, with a focus on participant recruitment, engagement, and data collection across diverse populations. Secondary objectives included identifying challenges encountered during implementation and deriving lessons to inform future digital maternal health studies.
Methods:
Participants were recruited through digital advertisements, pregnancy apps, and the PowerMom Consortium, a group of over 15 local and national organizations. Data collection included self-reported surveys, wearable devices, and electronic health records (EHR). Anomaly detection measures were implemented to address fraudulent enrollment activity. Recruitment trends and descriptive statistics from survey data were used to summarize participant characteristics and engagement metrics, and missing data were quantified to identify gaps.
Results:
A total of 5,617 participants were enrolled from 2021 to 2024, with 69.8% providing demographic data. Of these, 48.5% were younger than 35 years, 14.0% identified as Hispanic or Latinx, and 13.7% identified as Black/African American. Geographic representation spanned all 50 states, Puerto Rico, and Guam, with 58.3% residing in areas with moderate access to maternity care and 16.4% in highly disadvantaged neighborhoods based on the Area Deprivation Index (ADI). Enrollment rates increased significantly over the study period, from 55 participants in late 2021 to 3,310 in 2024, averaging 99.4 enrollments per week in 2024. Participants completed 17,123 surveys, with 71.8% completing the Intake Survey and 12.4% completing the Postpartum Survey. Wearable device data were shared by 1,168 participants, providing over 378,000 daily biometric measurements, including activity levels, sleep, and heart rate. Additionally, 96 participants connected their EHRs, contributing 276 data points such as diagnoses, medications, and lab results. Among pregnancy-related characteristics, 28.1% of participants enrolled during the first trimester, while 15.1% reported information about completion of their pregnancies during the study. Delivery outcomes showed that 56.1% of these 913 participants who shared delivery information had spontaneous vaginal deliveries, and 17.9% underwent unplanned cesarean sections.
Conclusions:
The PowerMom platform demonstrates the feasibility of leveraging digital tools to recruit and engage diverse populations in maternal health research. Its capacity to integrate multimodal data sources highlights its potential for generating comprehensive insights into maternal and fetal health. Challenges with data completeness and survey attrition underscore the need for sustained participant engagement strategies. These findings offer valuable lessons for scaling digital health platforms and addressing disparities in maternal health research. Clinical Trial: ClinicalTrials.gov ID NCT03085875
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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.