Evidence map›Paper›PMID 37285185›Full record

ArticleJMIR formative research2023

Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers.

Deepthi S Varma, Maya Mualem, Amie Goodin, Kelly K Gurka, Tony Soo-Tung Wen, Matthew J Gurka, Kay Roussos-Ross

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
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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

7 authors at 1 institution in 1 country.

Deepthi S Varma *Department of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0003-1163-0377
Maya MualemDepartment of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0009-0009-5458-3639
Amie GoodinPharmaceutical Outcomes and Policy, Center for Drug Evaluation and Safety Consortium for Medical Marijuana Clinical Outcomes Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0002-0020-8720
Kelly K GurkaDepartment of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0003-2779-3234
Tony Soo-Tung WenDivision of Maternal and Fetal Medicine, Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0003-1638-0186
Matthew J GurkaDepartment of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0002-3371-4582
Kay Roussos-RossDepartment of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0003-2283-2441
University of Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUp to 15% of pregnant and postpartum women commonly experience undiagnosed and untreated mental health conditions, such as depression and anxiety, which may result in serious health complications. Mobile health (mHealth) apps related to mental health have been previously used for early diagnosis and intervention but not among pregnant and postpartum women.

objectiveThis study aims to assess the acceptability of using mHealth to monitor and assess perinatal and postpartum depression and anxiety.

methodsFocus group discussions with pregnant and postpartum women (n=20) and individual interviews with health care providers (n=8) were conducted to inform the acceptability of mHealth and determine its utility for assessing perinatal and postpartum mood symptoms. Participants were recruited via purposive sampling from obstetric clinics and the surrounding community. A semistructured interview guide was developed by an epidemiologist with qualitative research training in consultation with an obstetrician. The first author conducted all focus group discussions and provider interviews either in person or via Zoom (Zoom Video Communications, Inc) depending on the COVID-19 protocol that was in place during the study period. All interviews were audio recorded with consent; transcribed; and uploaded for coding to ATLAS.ti 8 (ATLAS.ti Scientific Software Development Gmb H), a qualitative data analysis and retrieval software. Data were analyzed using the deductive content analysis method using a set of a priori codes developed based on the interview guide. Methodological rigor and quality were ensured by adopting a systematic approach during the implementation, data collection, data analysis, and reporting of the data.

resultsAlmost all women and providers had downloaded and used at least 1 health app. The respondents suggested offering short questions in layperson language that could be understood by women of all educational levels and offering no more than 2 to 3 assessments per day at preferred timings decided by the women themselves. They also suggested that the women themselves receive the alerts first, with other options being family members, spouses, or friends if the women themselves did not respond within 24 to 72 hours. Customization and snooze features were strongly endorsed by women and providers to improve acceptability and utility. Women mentioned competing demands on their time during the postpartum period, fatigue, privacy, and the security of mental health data as concerns. Health care professionals highlighted the long-term sustainability of app-based mood assessment and monitoring as an important challenge.

conclusionsThe findings from this study show that mHealth would be acceptable to pregnant and postpartum women for monitoring mood symptoms. This could inform the development of clinically meaningful and inexpensive tools for facilitating the continuous monitoring of, the early diagnosis of, and an early intervention for mood disorders in this vulnerable population.

Indexed as

ecological momentary assessmentEMAmHealthmobile appsmobile healthmobile phoneperinatal mental health

Identifiers

PMID37285185
PMCPMC10285622
OpenAlexW4379600636

What OpenQuestion 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 OpenQuestion graph.