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ArticleJMIR research protocols2025

LoVE4MUM Mobile App to Prevent Postpartum Depression: Protocol for a Pilot Randomized Controlled Trial.

Siti Sabrina Kamarudin, Idayu Badilla Idris, Shalisah Sharip, Norfazilah Ahmad

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06366035 (Leveraging on Virtual Engagement for Maternal Understanding & Mood-enhancement "LoVE4MUM" Mobile Application- A Protocol for Pilot Randomized Control Trial for Preventing Postpartum Depression), which is not on this map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–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.

NCT06366035 naunknown statusnot on this map

Leveraging on Virtual Engagement for Maternal Understanding & Mood-enhancement "LoVE4MUM" Mobile Application- A Protocol for Pilot Randomized Control Trial for Preventing Postpartum Depression

TypeinterventionalSponsorNational Institutes of Health, Ministry of Health MalaysiaRan2024 to 2024Enrolled64ConditionsPostpartum Depression, Maternal Behavior, mHealth Intervention, Mental Health IssueArmsLoVE4MUM mobile application
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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

Siti Sabrina Kamarudin *Clinical Research Center Hospital Shah Alam, Institute for Clinical Research, National Institute for Health, Ministry of Health Malaysia, Shah Alam, Malaysia.ORCID 0000-0002-6992-5891
Idayu Badilla Idris *Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia.ORCID 0000-0002-7238-2904
Shalisah Sharip *Department of Psychiatry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia.ORCID 0000-0002-2130-739X
Norfazilah Ahmad *Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia.ORCID 0000-0003-4964-8652

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostpartum depression remains a significant concern, posing substantial challenges to maternal well-being, infant health, and the mother-infant bond, particularly in the face of barriers to traditional support and interventions. Previous studies have shown that mobile health (mHealth) interventions offer an accessible means to facilitate early detection and management of mental health issues while at the same time promoting preventive care.

objectiveThis study aims to evaluate the effectiveness of the Leveraging on Virtual Engagement for Maternal Understanding & Mood-enhancement (LoVE4MUM) mobile app, which was developed based on the principles of cognitive behavioral therapy and psychoeducation and serves as an intervention to prevent postpartum depression.

methodsThis single-blinded, pilot randomized controlled trial includes 64 mothers recruited from the postnatal ward and randomized using a 1:1 ratio to receive either postpartum care (treatment as usual) or postpartum care (treatment as usual) plus the self-guided LoVE4MUM mobile app. The primary outcome is the effectiveness of the mobile app at improving postpartum depression. Secondary outcomes are changes in the mental health literacy score and negative automatic thoughts, which are collected using a self-reported questionnaire.

resultsPatient recruitment began on September 1, 2024. As of January 1, 2025, recruitment was successfully completed, with a total of 72 participants enrolled: 36 in the intervention group and 36 in the control group . The final results are anticipated to be available by March 2025, and publication is expected by the end of 2025.

conclusionsBy examining the LoVE4MUM app alongside standard postpartum care, this pilot randomized controlled trial seeks to offer preliminary evidence on the potential of mHealth tools to improve maternal mental health as well as to reduce postpartum depression symptoms. The findings are expected to contribute to the future development of effective, accessible, and scalable interventions for mothers.

trial registrationClinicalTrials.gov NCT06366035; https://clinicaltrials.gov/study/NCT06366035. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63564.

Indexed as

Depression, PostpartumMobile ApplicationsAdultCognitive Behavioral TherapyFemaleHumansMothersPilot ProjectsPregnancyRandomized Controlled Trials as TopicSingle-Blind MethodTelemedicinedepressionengagementmaternalmental health literacymHealth interventionmobile appmobile healthmobile phonepostpartumpostpartum carepostpartum depressionpreventionpreventive careprotocolrandomized controlled trialself-guidedvirtual engagementwell-being

Identifiers

PMID39869891
PMCPMC11811676

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Registered trials

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.