Evidence map›Paper›PMID 40773418›Full record

ArticlePLOS digital health2025

Associations between text communication engagement and maternal-neonatal outcomes in the Mobile WACh NEO Trial.

James Peng, Erica Wetzler, Brenda Wandika, Peninah Kithao, June Moraa, Jenna I Udren, Olivia Schultes, Esther Akinyi, Lusi Osborn, Anna Hedstrom and 6 more

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

James PengDepartment of Biostatistics, University of Washington, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0002-8245-8319
Erica WetzlerDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Brenda WandikaSchool of Nursing, University of Washington, Seattle, Washington, United States of America.
Peninah KithaoDepartment of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya.
June MoraaDepartment of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya.
Jenna I UdrenDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Olivia SchultesDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0002-9838-7631
Esther AkinyiDepartment of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya.
Lusi OsbornDepartment of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya.
Anna HedstromDepartment of Paediatrics-Neonatology, University of Washington, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0002-3269-1892
Barbra A RichardsonDepartment of Biostatistics, University of Washington, Seattle, Washington, United States of America.
Manasi KumarInstitute for Excellence in Health Equity, New York University Grossman School of Medicine, New York, New York, United States of America.
Dalton WamalwaDepartment of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya.
John KinuthiaDepartment of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya.
Keshet RonenDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Jennifer A UngerDepartment of Obstetrics and Gynecology, Warren Alpert Medical School of Brown University and Women and Infants Hospital, Providence, Rhode Island, United States of America.

Funding

Mobile WACh NEO: Mobile Solutions for Neonatal Health and Maternal SupportR01HD098105 · NICHD · WOMEN AND INFANTS HOSPITAL-RHODE ISLAND · PI UNGER, JENNIFER · 2019 to 2023
$2.8M
Leveraging interactive SMS messaging to monitor and support maternal mental health in KenyaK18MH122978 · NIMH · UNIVERSITY OF WASHINGTON · PI RONEN, KESHET · 2020 to 2021
$256k
NICHD NIH HHS R01 HD098105NIMH NIH HHS K18 MH122978
6 · The paper itself

Abstract

Despite a global reduction in neonatal deaths in the last few decades, high neonatal mortality rates persist in low- to middle-income countries. Mobile health interventions offer a promising solution to promote early newborn care (ENC) practices and improve neonatal health. The Mobile WACh NEO randomized controlled trial evaluated the effect of a text messaging communication intervention on neonatal health outcomes in Kenya from 2020 to 2023. Perinatal participants received automated messages from enrollment at 28-36 weeks gestation until six weeks postpartum and could message with a study nurse. This secondary analysis aimed to characterize participant text engagement and examine associations between engagement and maternal-neonatal health outcomes. Among 2,470 intervention participants retained through follow-up, median time in the intervention was 14 weeks. Participants received a median of 58 automated messages (average 0.58 per day), sent a median of 24 messages (average 0.25 per day), and received a median of 14 nurse responses (average 0.14 per day). Younger, more educated, unmarried, unemployed, and first-time mothers sent more messages, while those who had a lower social support score at baseline messaged less. Increased participant messaging was associated with greater increase in neonatal danger sign knowledge from baseline to six-week follow-up (Adj Est: 0.39; 95% CI: 0.09-0.68) and lower odds of early initiation of breastfeeding (aOR: 0.62; 95% CI: 0.45-0.86). Our findings contribute to the understanding of who can benefit from mobile health programs and how these interventions might impact behaviors and outcomes.

Identifiers

PMID40773418
PMCPMC12331090

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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.