The Potential Value of Sleep Hygiene for a Healthy Pregnancy: A Brief Review
Bibliographic record
Abstract
The quality of the intrauterine environment influences maternal-fetal health and also offspring predisposition to obesity and cardiometabolic disease later in life. Several determinants, including but not limited to pregravid obesity and excessive gestational weight gain, alter the developmental milieu, fetal growth, and child obesity risk. However, the role of sleep and its relationship to healthy pregnancy is not fully established. Given the host of psychosocial and physiological complications associated with childhood obesity, targeting the gestational period is purported to be an opportune time for preventive intervention. Many longitudinal studies suggest that short sleep duration is a risk factor for the development of impaired glycemia and obesity. However, there is a dearth of information concerning the role of sleep hygiene and its role in a healthy pregnancy. Reports note disrupted and poorer quality of sleep during gestation and highlight an association between reduced sleep and risk of gestational diabetes mellitus. Given the lack of well-designed human trials assessing the value of sleep and healthy pregnancy outcomes, this review summarizes current evidence which suggests that incorporating sleep recommendations and utilizing time management strategies that encourage a healthful night 's sleep may improve the health of the mom and the baby.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".