LoVE4MUM Mobile App to Prevent Postpartum Depression: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Postpartum 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. OBJECTIVE: This 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. METHODS: This 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. RESULTS: Patient 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. CONCLUSIONS: By 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 REGISTRATION: ClinicalTrials.gov NCT06366035; https://clinicaltrials.gov/study/NCT06366035. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63564.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.098 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".