Evidence map›Paper›PMID 38510485›Full record

ArticlePsychiatric research and clinical practice2024

Improving the Management of Maternal Mental Health with Digital Health Care.

Constance Guille, Natalie Henrich, Alison K Brinson, Hannah R Jahnke

Open access · goldAbstract read
In one paragraph

Article in Psychiatric research and clinical practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. 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

4 authors at 1 institution in 1 country.

Constance GuilleDepartment of Psychiatry and Behavioral Sciences Medical University of South Carolina Charleston South Carolina USA.
Natalie HenrichMaven Clinic New York New York USA.
Alison K BrinsonMaven Clinic New York New York USA.
Hannah R JahnkeMaven Clinic New York New York USA.
Medical University of South Carolina · US

Funding

From Biological to Social Processes: Interdisciplinary Training in Life Course ResearchT32HD091058 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ROBERT A HUMMER, Amanda L Thompson · 2017 to 2026
$4.3M
NICHD NIH HHS T32 HD091058
6 · The paper itself

Abstract

Objectives: Digital health solutions have the potential to improve maternal mental health care. The objective of this study is to determine if utilization of a digital health platform, Maven, is associated with improved management of mental health among peripartum people with a history of mental health disorders and determine which components of utilization associate with maternal mental health outcomes. Methods: Participants in this retrospective cohort analysis ( Results: Multi-utilization exposure mixture models demonstrated that increasing use of digital resources by one quartile is associated with an increased odds of reporting that Maven helped users manage their mental health (aOR: 12.58 [95% CI: 6.74, 23.48]) and was not associated with self-reported incidence of PPD (aOR: 1.30 [95% CI: 0.52, 3.27]). Care advocate appointments, provider messages, and article reads were positively associated with improved mental health management. Conclusions: Digital health platforms, such as Maven, may play an important role in managing maternal mental health conditions among pregnant and postpartum people at high risk for PPD.

Identifiers

PMID38510485
PMCPMC10948940
OpenAlexW4389133918

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.