Impact of sleep on postpartum health outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of postpartum sleep interventions and postpartum sleep on maternal health outcomes. DESIGN: Systematic review with random-effects meta-analysis. Online databases were searched on 12 January 2024. STUDY ELIGIBILITY CRITERIA: Studies of all designs (except case studies and reviews) in all languages were eligible if they contained information on the population (individuals up to 1-year post partum), sleep interventions/exposures including (type, duration, frequency, alone or in combination with other components), comparator (control or different duration, frequency or type of sleep intervention) and outcomes: mental health, cardio-metabolic, postpartum weight retention (PPWR), low back pain and pelvic girdle pain, breastfeeding and urinary incontinence. RESULTS: 60 studies (n=20 684) from 14 countries were included. 'High' certainty of evidence showed that sleep interventions were associated with a greater decrease in depressive symptom severity compared with no intervention (five randomised controlled trials; n=992; standardised mean difference -0.27, 95% CI -0.40 to -0.14; small effect). Sleep interventions had no impact on the odds of developing depression ('moderate' certainty of evidence) or anxiety or anxiety symptom severity ('low' certainty of evidence). Additionally, 'low' certainty of evidence demonstrated no effect on cardiometabolic outcomes (systolic blood pressure, diastolic blood pressure, mean arterial pressure), anthropometric measures (maternal weight, body mass index) or prevalence of exclusive breastfeeding. 'Low' certainty of evidence from observational studies found that high-quality sleep reduces the odds of developing anxiety and reduces the severity of depression and anxiety symptoms. 'Low' and 'very low' certainty of evidence from observational studies found that shorter sleep duration is associated with greater PPWR. CONCLUSIONS: Postpartum sleep interventions reduced the severity of depression symptoms.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.047 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".