eHealth Interventions for Treatment and Prevention of Depression, Anxiety, and Insomnia During Pregnancy: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pregnancy is associated with an increased risk for depression, anxiety, and insomnia. eHealth interventions provide a promising and accessible treatment alternative to face-to-face interventions. OBJECTIVE: The objective of this systematic review and meta-analysis is to determine the effectiveness of eHealth interventions in preventing and treating depression, anxiety, and insomnia during pregnancy. Secondary aims are to identify demographic and intervention moderators of effectiveness. METHODS: A total of 5 databases (PsycINFO, Medline, CINAHL, Embase, and Cochrane) were searched from inception to May 2021. Terms related to eHealth, pregnancy, randomized controlled trials (RCTs), depression, anxiety, and insomnia were included. RCTs and pilot RCTs were included if they reported an eHealth intervention for the prevention or treatment of depression, anxiety, or insomnia in pregnant women. Study screening, data extractions, and quality assessment were conducted independently by 2 reviewers from an 8-member research team (KAS, PRS, Hangsel Sanguino, Roshni Sohail, Jasleen Kaur, Songyang (Mark) Jin, Makayla Freeman, and Beatrice Valmana). Random-effects meta-analyses of pooled effect sizes were conducted to determine the effect of eHealth interventions on prenatal mental health. Meta-regression analyses were conducted to identify potential moderators. RESULTS: In total, 17 studies were included in this review that assessed changes in depression (11/17, 65%), anxiety (10/17, 59%), and insomnia (3/17, 18%). Several studies included both depression and anxiety symptoms as outcomes (7/17, 41%). The results indicated that during pregnancy, eHealth interventions showed small effect sizes for preventing and treating symptoms of anxiety and depression and a moderate effect size for treating symptoms of insomnia. With the exception of intervention type for the outcome of depressive symptoms, where mindfulness interventions outperformed other intervention types, no significant moderators were detected. CONCLUSIONS: eHealth interventions are an accessible and promising resource for treating symptoms of anxiety, depression, and insomnia during pregnancy. However, more research is necessary to identify ways to increase the efficacy of eHealth interventions for this population. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42020205954; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=205954.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".