Evidence map›Paper›PMID 42170221›Full record

ReviewmHealth2026

mHealth apps for maternal mental well-being among pregnant and postpartum women: a systematic review.

Syed Niaz Mohtasim, Faiza Omar Arpita, Istiaq Ahmed, Ashraful Islam, M Ashraful Amin, Beenish Moalla Chaudhry

Abstract readReview
In one paragraph

Review in mHealth, 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

6 authors.

Syed Niaz MohtasimCenter for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.
Faiza Omar ArpitaCenter for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.
Istiaq AhmedCenter for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.
Ashraful IslamCenter for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.
M Ashraful AminCenter for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.
Beenish Moalla ChaudhrySchool of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Maternal mental well-being during and after pregnancy is often overlooked, posing serious long-term risks to mothers and children. This systematic review aims to synthesize research on mobile health (mHealth) applications (apps) designed to support perinatal and postpartum mental well-being, with a focus on their design characteristics, intervention approaches, and reported effectiveness. Methods: We conducted a systematic review following the PRISMA 2020 guidelines. PubMed and Scopus were searched up to August 2025. Studies were included if they reported original research on mHealth apps targeting maternal mental well-being during or after pregnancy with a diagnostic or intervention component for mental health. Review papers, conference abstracts, and studies without an mHealth component were excluded. Results: From 2,127 articles, 15 met the inclusion criteria. These studies, published between 2017 and 2024, evaluated 13 distinct mHealth apps targeting primarily anxiety (12 studies), depression (8 studies), and stress (4 studies). Across all 15 studies, 24 screening methods were reported. Apps delivered interventions including mindfulness and guided meditation (7 studies) and cognitive behavioral therapy (CBT)-based tools and mood tracking (7 studies). Six app feature categories were identified: mental health screening, physical and mental well-being exercises and meditation, health education, visual design elements, healthcare support, and additional support features. Usability and engagement were most commonly evaluated using questionnaires and surveys (4 studies) and the Mobile Application Rating Scale (MARS) (3 studies). Six studies reported positive outcomes for depression symptoms. Common methodological limitations included small sample sizes, high dropout rates, and lack of long-term follow-up, constraining the generalizability of findings. Conclusions: This review demonstrates the potential of mHealth apps as accessible tools for supporting maternal mental well-being during pregnancy and the postpartum period. Clinicians should regard these tools as supplementary rather than standalone interventions until larger-scale efficacy trials are available. App developers are encouraged to design solutions that span both prenatal and postnatal periods, address multiple mental health conditions simultaneously, integrate validated screening methods, and combine health education, therapeutic, and behavioral support within a single platform. Future research should prioritize robust, longitudinal trials with diverse populations and standardized outcome measures to establish the evidence base needed for broader integration of mHealth into perinatal care.

Indexed as

digital healthmental well-beingMobile health (mHealth)postpartumpregnancy

Identifiers

PMID42170221
PMCPMC13187566

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.