Evidence map›Paper›PMID 40194282›Full record

Observational studyJMIR formative research2025

Digital Health Platform for Maternal Health: Design, Recruitment Strategies, and Lessons Learned From the PowerMom Observational Cohort Study.

Toluwalase Ajayi, Jacqueline Kueper, Lauren Ariniello, Diana Ho, Felipe Delgado, Matthew Beal, Jill Waalen, Katie Baca Motes, Edward Ramos

Registry-linked trialAbstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03085875 (POWERMOM, A Healthy Pregnancy Research Community), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03085875 recruitingnot on this map

POWERMOM, A Healthy Pregnancy Research Community

TypeobservationalSponsorScripps Translational Science InstituteRan2017 to 2027Enrolled100,000ConditionsPregnancy Related, Weight Change, Body, Health Problems in Pregnancy, Behavior
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Toluwalase AjayiJacobs Center for Health Innovation, Department of Medicine and Pediatrics, University of California, San Diego, La Jolla, CA, United States.ORCID 0000-0001-5739-4918
Jacqueline KueperDigital Trial Center, Scripps Research Translational Institute, La Jolla, CA, United States.ORCID 0000-0002-6690-1552
Lauren ArinielloDigital Trial Center, Scripps Research Translational Institute, La Jolla, CA, United States.ORCID 0000-0002-6005-8824
Diana HoDigital Trial Center, Scripps Research Translational Institute, La Jolla, CA, United States.ORCID 0000-0002-0429-9711
Felipe DelgadoDigital Trial Center, Scripps Research Translational Institute, La Jolla, CA, United States.ORCID 0009-0006-6042-187X
Matthew BealDivision of Preventative Medicine, Department of Family Medicine, University of California, San Diego, La Jolla, United States.ORCID 0009-0003-9295-2884
Jill WaalenDivision of Preventative Medicine, Department of Family Medicine, University of California, San Diego, La Jolla, United States.ORCID 0000-0003-4772-9635
Katie Baca MotesDigital Trial Center, Scripps Research Translational Institute, La Jolla, CA, United States.ORCID 0000-0002-9156-7111
Edward RamosDigital Trial Center, Scripps Research Translational Institute, La Jolla, CA, United States.ORCID 0000-0003-1675-7094

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaternal health research faces challenges in participant recruitment, retention, and data collection, particularly among underrepresented populations. Digital health platforms like PowerMom (Scripps Research) offer scalable solutions, enabling decentralized, real-world data collection. Using innovative recruitment and multimodal techniques, PowerMom engages diverse cohorts to gather longitudinal and episodic data during pregnancy and post partum.

objectiveThis 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.

methodsParticipants were recruited via digital advertisements, pregnancy apps, and the PowerMom Consortium of more than 15 local and national organizations. Data collection included self-reported surveys, wearable devices, and electronic health records. Anomaly detection measures were implemented to address fraudulent enrollment activity. Recruitment trends and descriptive statistics from survey data were analyzed to summarize participant characteristics, assess engagement metrics, and quantify missing data to identify gaps.

resultsOverall, 5617 participants were enrolled from 2021 to 2024, with 69.8% (n=3922) providing demographic data. Of these, 48.5% (2723/5617) were younger than 35 years, 14% (788/5617) identified as Hispanic or Latina, and 13.7% (770/5617) identified as Black or African American. Geographic representation spanned all 50 US states, Puerto Rico, and Guam, with 58.3% (3276/5617) residing in areas with moderate access to maternity care and 16.4% (919/5617) in highly disadvantaged neighborhoods based on the Area Deprivation Index. Enrollment rates increased substantially over the study period, from 55 participants in late 2021 to 3310 in 2024, averaging 99.4 enrollments per week in 2024. Participants completed a total of 17,123 surveys, with 71.8% (4033/5617) completing the Intake Survey and 12.4% (697/5617) completing the Postpartum Survey. Wearable device data were shared by 1168 participants, providing more than 378,000 daily biometric measurements, including activity levels, sleep, and heart rate. Additionally, 96 participants connected their electronic health records, contributing 276 data points such as diagnoses, medications, and laboratory results. Among pregnancy-related characteristics, 28.1% (1578/5617) enrolled during the first trimester, while 15.1% (849/5617) reported information about the completion of their pregnancies during the study period. Among the 913 participants who shared delivery information, 56.1% (n=512) had spontaneous vaginal deliveries and 17.9% (n=163) underwent unplanned cesarean sections.

conclusionsThe PowerMom platform demonstrates the feasibility of using digital tools to recruit and engage diverse populations in maternal health research. Its ability to integrate multimodal data sources showcases its potential to provide comprehensive maternal-fetal health insights. 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.

trial registrationClinicalTrials.gov NCT03085875; https://clinicaltrials.gov/study/NCT03085875.

Indexed as

Maternal HealthPatient SelectionAdultCohort StudiesDigital HealthFemaleHumansMobile ApplicationsPregnancyResearch DesignTelemedicinedecentralized clinical trialsdigital health platformshealth disparitiesmaternal health researchparticipant engagementpregnancy monitoring

Identifiers

PMID40194282
PMCPMC12012398

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.