High body mass index and sleep problems during pregnancy: A meta‐analysis and meta‐regression of observational studies
Bibliographic record
Abstract
Despite the well-established correlation of weight and sleeping problems, little is known about the nature of the association. The present study examined whether pregnant women with high body mass index have a risk of developing sleep problems, and identified any covariates that affect this relationship. We systematically searched electronic databases, specialized journals, various clinical trial registries, grey literature databases and the reference list of the identified studies. All observational studies were obtained from inception until 9 August 2020. The Newcastle-Ottawa Scale was adopted to assess the quality of studies. Stata software was used to conduct meta-analysis and meta-regression. Forty-six observational studies involving 2,240,804 participants across 16 countries were included. Quality assessment scores ranged from 4 to 10 (median = 6). Meta-analyses revealed that the risk of sleep apnea, habitual snoring, short sleep duration and poor sleep quality is increased in pregnant women with high body mass index, but not for daytime sleepiness, insomnia or restless legs syndrome. Subgroup differences were detected on body mass index between different regions, nature of population, year of publication, age group and study quality. Random-effects meta-regression analyses showed that year and quality of publication were covariates on the relationships between pre-pregnant body mass index and sleep apnea risk. Our review shows that sleep apnea, habitual snoring, short sleep duration and poor sleep quality are important concerns for pregnant women with high body mass index. Developing screening and targeted interventions is recommended to promote efficacious perinatal care.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.074 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".