Evidence map›Paper›PMID 42456164›Full record

Trial reportJMIR formative research2026

enDigital Postpartum Support for Early Risk Identification Among Postpartum Women: Formative Randomized Evaluation and Exploratory Predictive Modeling Study.

Lisa Marceau, James Matuk, Jennifer Barkin, Diana Pfeil, Ariana Buterbaugh, Danielle C Perry, Madison Soucie, Allison Bryant

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR formative research, 2026. 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

8 authors.

Lisa MarceauJoyuus, LLC, 1800 Mendon Road, E302, Cumberland, RI, 02864, United States, 1 4014284197.ORCID 0000-0001-5002-1588
James MatukDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0002-1720-9092
Jennifer BarkinDepartment of Health Education and Promotion, East Carolina University, Greenville, NC, United States.ORCID 0000-0002-8364-4260
Diana PfeilJoy, LLC, Boulder, CO, United States.ORCID 0009-0008-7708-3533
Ariana ButerbaughBrown University, Providence, RI, United States.ORCID 0009-0002-1420-7507
Danielle C PerryBrown University, Providence, RI, United States.ORCID 0009-0001-2165-3532
Madison SoucieJoy, LLC, Boulder, CO, United States.ORCID 0009-0009-2852-1872
Allison BryantDepartment of Obstetrics and Gynecology, Mass General Brigham, Boston, MA, United States.ORCID 0000-0002-1791-8833

Funding

Joyuus: A Web-based Tool for Postpartum Care Self-care to Address the Complex Needs of Underserved WomenR44MD014923 · NIMHD · ORANGE SQUARE DESIGN, INC. · PI MARCEAU, LISA · 2022 to 2023
$1.7M
NIMHD NIH HHS R44 MD014923
6 · The paper itself

Abstract

Background: The postpartum period represents a critical window for maternal health, yet many individuals lack sustained support and timely identification of physical and mental health risks. Digital health interventions offer a scalable approach to extend care beyond clinical settings. Yet, key elements, including real-world challenges, usability, and effectiveness of such platforms, are insufficiently characterized in this literature. Objective: This study aimed to conduct a formative randomized evaluation to assess the feasibility, engagement, and preliminary signals of impact of the Joyuus platform and to examine the potential for early identification of postpartum health risks. Methods: We conducted a 12-week randomized evaluation with postpartum participants recruited through community-based organizations. Participants were randomized to either the Joyuus intervention or standard postpartum care. Primary and secondary outcomes included the Barkin Index of Maternal Functioning, the Edinburgh Postnatal Depression Scale (EPDS), the State-Trait Anxiety Inventory, and the Connor-Davidson Resilience Scale. Analyses were conducted using an intention-to-treat approach with linear regression models adjusting for baseline values. Engagement metrics (ie, sessions, time on site, and feature use) were captured through in-app analytics. An exploratory predictive model was developed using baseline clinical, behavioral, and demographic variables to identify individuals at risk for postpartum depression. Results: Baseline characteristics were generally balanced across arms, and no statistically significant differences were observed in education, income, or marital status. No statistically significant differences were found between treatment and control groups in the 12-week changes for the primary or secondary outcomes. Mean EPDS scores were 9.7 in the intervention group and 9.3 in the control group. In the sample, 60 (45.5%) of the 132 participants met the criteria for elevated depression (EPDS score ≥11 or a positive response to question 10 on self-harm), indicating a high burden of symptoms within the study population. Engagement with the platform was highest during the first 4 weeks. Among intervention participants, 80% created an account. Participants reported high levels of perceived usefulness, ease of use, and relevance of content. Qualitative analysis of open-ended survey responses highlighted limited awareness of postpartum-specific resources and a preference for simple, accessible information. An exploratory predictive model demonstrated a recall of 0.89 and a precision of 0.73 in identifying individuals at risk for postpartum depression, suggesting the feasibility of early risk identification using integrated data inputs. Conclusions: Joyuus demonstrated feasibility and acceptability but did not produce statistically significant improvements in maternal functioning or maternal health outcomes over 12 weeks. Joyuus identified high rates of depressive symptoms and early engagement patterns, which suggest an opportunity for earlier identification of risk and intervention during the postpartum period. Exploratory modeling results indicate the potential for data-driven approaches to support earlier detection. Future work includes optimizing engagement strategies and expanding validation of predictive detection to improve postpartum surveillance and outcomes.

Indexed as

Postnatal CarePostpartum PeriodAdultDepression, PostpartumFemaleHumansPredictive Learning ModelsRisk Assessmentdigital healthmaternal functioningpostpartum depressionpredictive dataSaaSsofware as a serviceusability

Identifiers

PMID42456164
PMCPMC13372259

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.