Effectiveness of eHealth Interventions to Reduce Perinatal Anxiety
Bibliographic record
Abstract
Objective: eHealth interventions have been shown to be effective in improving anxiety among the general population. Despite the effectiveness of eHealth interventions for perinatal depression, a recent review reported mixed results for perinatal anxiety. The review, however, was not focused on anxiety, and studies with various designs were included. The aim of this systematic review is to summarize the evidence specific to anxiety and to conduct a meta-analysis to examine the effectiveness of eHealth interventions in reducing perinatal anxiety. Data Sources: MEDLINE, CINAHL, EMBASE, and PsycINFO were searched beginning with the date that the databases were available through March 2018 using keywords such as perinatal period, web-based interventions, and anxiety. Study Selection: Randomized controlled trials that were conducted during the perinatal period, examined the effectiveness of an eHealth mental health intervention, measured anxiety symptoms or disorders as a primary or secondary outcome, provided data on anxiety levels both pre-intervention and post-intervention, had a comparison group, and were published in English were included. A total of 770 articles were retrieved, and the full texts of 64 articles were reviewed. Five studies met the inclusion criteria, 4 of which fulfilled the quality criteria and were included in the meta-analysis. Data Extraction: Data were extracted using a data extraction form developed for this study. The Cochrane Collaboration's Review Manager software was used to conduct the meta-analysis. Results: The test for heterogeneity (I² = 0%; P = .80) suggested a homogeneous sample. The meta-analysis for the total effect size showed that at post-intervention, the eHealth group had significantly lower anxiety scores than the control group, with a standardized mean difference of -0.41 (95% CI, -0.71 to -0.11; P = .007). Conclusions: eHealth interventions are promising in improving perinatal anxiety. The content of these interventions should account for common comorbid mental health conditions during the perinatal period and provide opportunities to tailor further treatment if necessary.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".