Evidence map›Paper›PMID 39979899›Full record

ArticleBMC health services research2025

Identifying key indicators to develop a novel mobile application for early screening of postpartum depression in developing countries.

Sumaiya Nuha Mustafina, Muhammad Nazrul Islam, Mohammad Ratul Mahjabin, M M Rushadul Mannan, Md Motaharul Islam

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

5 authors.

Sumaiya Nuha MustafinaDepartment of Computer Science and Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh.
Muhammad Nazrul IslamDepartment of Computer Science and Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh. nazrul@cse.mist.ac.bd.
Mohammad Ratul MahjabinDepartment of Computer Science and Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh.
M M Rushadul MannanDepartment of Computer Science and Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh.
Md Motaharul IslamDepartment of Computer Science and Engineering, United International University (UIU), Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postpartum depression (PPD) poses significant risks to both maternal and child health, yet it remains underdiagnosed and undertreated, particularly in developing countries like Bangladesh. Factors such as hormonal changes, lack of communal support and socioeconomic stressors primarily contribute to its prevalence. Despite advancements in research, technological solutions for PPD support are lacking in regions where mental health is deemed negligible. Thus, the objectives of this research are to reveal the underlying factors contributing to PPD among mothers in developing countries and to propose a novel application based on these factors for PPD detection. A Design Science Research (DSR) approach is adopted to achieve these objectives. In this research, firstly 17 factors that could serve as effective indicators for detecting PPD were identified through interviews with 12 key informants, including doctors and patients. Next, for each identified factor, visual and scenario-based questionnaires were designed to facilitate effective screening through user feedback. Then, an Android application, 'PPD Screening App', was developed, which provides users with visual, auditory, and multilingual (in English and Bengali) scenario-based questionnaires for each identified factor, as well as the questionnaires proposed in the Edinburgh Postnatal Depression Scale (EPDS) for PPD detection. Finally, the developed application was evaluated with 45 new mothers in the postpartum period and demonstrated high accuracy (96%) compared to the traditional screening method (84%). The effectiveness of the application was also explored in relation to participants' demographic profiles (age and IT literacy), and participants provided very positive feedback on the usability of the application. This research thus contributes in improving early detection and intervention for PPD, ultimately enhancing maternal and child well-being.

Indexed as

Depression, PostpartumDeveloping CountriesMass ScreeningMobile ApplicationsAdultBangladeshEarly DiagnosisFemaleHumansSurveys and QuestionnairesYoung AdultDesign science researchEPDSMobile applicationPostpartum depressionScenario based questionsUsabilityVisual design

Identifiers

PMID39979899
PMCPMC11844140

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.