Evidence map›Paper›PMID 37270494›Full record

ArticleBMC pregnancy and childbirth2023

Digital health technologies for peripartum depression management among low-socioeconomic populations: perspectives from patients, providers, and social media channels.

Alexandra Zingg, Tavleen Singh, Amy Franklin, Angela Ross, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 8% 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, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  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

7 authors at 1 institution in 1 country.

Alexandra ZinggMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. azingg2@gmail.com.
Tavleen SinghMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Amy FranklinMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Angela RossMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Sudhakar SelvarajFaillace Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA.
Jerrie RefuerzoUT Physician's Women's Center, University of Texas Health Science Center at Houston, Houston, TX, USA.
Sahiti MyneniMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
The University of Texas Health Science Center at Houston · US

Funding

Pragmatics to Reveal Intention in Social Media (PRISM) for Health PromotionR01LM012974 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MYNENI, SAHITI · 2019 to 2022
$1.5M
TMS-EEG investigation of prefrontal cortical excitability in depression and rTMS treatment responseR21MH119441 · NIMH · BAYLOR COLLEGE OF MEDICINE · PI MURPHY, NICHOLAS, SELVARAJ, SUDHAKAR · 2020 to 2021
$435k
NIH HHS 1R01LM012974-01A1NIH HHS 1R21MH119441-01A1NLM NIH HHS R01 LM012974
6 · The paper itself

Abstract

backgroundPeripartum Depression (PPD) affects approximately 10-15% of perinatal women in the U.S., with those of low socioeconomic status (low-SES) more likely to develop symptoms. Multilevel treatment barriers including social stigma and not having appropriate access to mental health resources have played a major role in PPD-related disparities. Emerging advances in digital technologies and analytics provide opportunities to identify and address access barriers, knowledge gaps, and engagement issues. However, most market solutions for PPD prevention and management are produced generically without considering the specialized needs of low-SES populations. In this study, we examine and portray the information and technology needs of low-SES women by considering their unique perspectives and providers' current experiences. We supplement our understanding of women's needs by harvesting online social discourse in PPD-related forums, which we identify as valuable information resources among these populations.

methodsWe conducted (a) 2 focus groups (n = 9), (b) semi-structured interviews with care providers (n = 9) and low SES women (n = 10), and (c) secondary analysis of online messages (n = 1,424). Qualitative data were inductively analyzed using a grounded theory approach.

resultsA total of 134 open concepts resulted from patient interviews, 185 from provider interviews, and 106 from focus groups. These revealed six core themes for PPD management, including "Use of Technology/Features", "Access to Care", and "Pregnancy Education". Our social media analysis revealed six PPD topics of importance in online messages, including "Physical and Mental Health" (n = 725 messages), and "Social Support" (n = 674).

conclusionOur data triangulation allowed us to analyze PPD information and technology needs at different levels of granularity. Differences between patients and providers included a focus from providers on needing better support from administrative staff, as well as better PPD clinical decision support. Our results can inform future research and development efforts to address PPD health disparities.

Indexed as

Depression, PostpartumSocial MediaDepressionDigital TechnologyFemaleHumansPeripartum PeriodPregnancySocioeconomic FactorsDigital healthMental healthMobile healthSocial media

Identifiers

PMID37270494
PMCPMC10239590
OpenAlexW4379206978

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.