Smartphone-assisted online brief cognitive behavioral therapy to treat maternal depression: findings of a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To test the efficacy of smartphone-assisted online brief cognitive behavioral therapy (b-CBT) to treat maternal depression compared to online brief CBT plus an active control app. METHODS: A randomized controlled trial was conducted. Assessments were performed at baseline (T0), midpoint (T1, week 4-5), post-treatment (T2, week 8), and follow-up (T3, 2-month postnatal follow-up) by blinded interviewers. The primary outcome was depression measured by the Edinburgh Postnatal Depression Scale (EPDS) at T2. We also assessed anxiety, stress, sleep quality, well-being, physical activity, treatment response, and offspring child behavior problems. RESULTS: Eighty-one participants were randomized to the intervention (n=37) or active control (n=44) groups. Seventy-one participants completed the post-treatment assessment or reported primary outcome data. No differences were found between the intervention and active control groups regarding maternal depression or other mental health outcomes. Overall, we found large within-group effect sizes, with 80% of the total sample responding to treatment. CONCLUSIONS: Our data showed no difference between the groups, suggesting that adding apps to psychotherapy treatment may not enhance treatment effects on prenatal depression. A within-groups analysis showed that most participants with depression responded to treatment; however, future studies are needed to confirm whether this effect is related to factors other than the intervention.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".