Prevention of postpartum depression via a digital acceptance and commitment therapy-based intervention: protocol for a pilot usability study
Bibliographic record
Abstract
Introduction: The perinatal period - encompassing pregnancy and the first months after childbirth - is a time of increased psychological vulnerability. It is often associated with high levels of anxiety, stress and depression. Access to psychological support is frequently limited by stigma, geographical barriers, and a shortage of services. Digital health interventions offer promising solutions to overcome these obstacles. Methods: This study evaluates the acceptability, feasibility, and user experience of REA, a virtual coach based on Acceptance and Commitment Therapy (ACT), to promote psychological well-being and prevent postpartum depression (PPD). Fifty pregnant women (25-30 weeks of gestation) will be recruited. The 8-week intervention delivers psychoeducational content via text, audio, and video, and collects steps, sleep, and heart rate via smartwatches for triangulation with self-reported measures. User Experience (UX) and User Engagement (UE) will be assessed with the System Usability Scale (SUS), the User Engagement Scale-Short Form (UES-SF), the Italian Chatbot Usability Scale, version B (ITA BUS B), and the User Version of the Mobile Application Rating Scale (uMARS), alongside semi-structured interviews. Psychological outcomes will be assessed pre-post with the two Whooley Questions, the Center for Epidemiological Studies Depression Scale (CES-D), the Multidimensional Psychological Flexibility Inventory (MPFI), and the 12-Item Short Form Health Survey (SF-12). Expected results: The intervention is expected to demonstrate high levels of user satisfaction and engagement (SUS ≥ 68, UES-SF ≥ 3,5/5; ITA BUS B ≥ 44/55 (≈4,0/5); uMARS ≥ 4,0/5), resulting in improvements in psychological flexibility, perceived well-being, and overall quality of life, recognizing that preventive efficacy will be evaluated in subsequent studies with controlled designs and postpartum outcome measures. Discussion: REA represents a scalable and accessible tool to support perinatal mental health, offering an innovative approach to the early prevention of postpartum distress. Trial registration: This study was approved by the ethics committee of the APSS (Provincial Health Services Authority) under number 12090 (May 15, 2025).
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How this classification was reachedexpand
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".