Body Weight Status and Sleep Disturbances During Pregnancy: Does Adherence to Gestational Weight Gain Guidelines Matter?
Bibliographic record
Abstract
BACKGROUND: To determine the effect of preconception body mass index (BMI) and/or gestational weight gain (GWG) on the occurrence of sleep disorders over the whole pregnancy. METHODS: A sample of 2,366 pregnant women, recruited in their 1st trimester (TM), was evaluated until delivery for their sleeping characteristics, using the Pittsburgh Sleep Quality Index (PSQI). Mixed or multinomial logistic modeling was applied, as appropriate. RESULTS: When compared with TM 1, PSQI score was the lowest at TM 2 (β = -1.03; 95% CI: -1.17 to -0.89) and highest in TM 3 (β = 0.27; 95% CI: 0.10-0.45). Obese (β = 0.48; 95% CI: 0.17-0.78) and overweight women (β = 0.24; 95% CI: 0.00-0.48) exhibited an overall greater PSQI than normal-weight women over the whole pregnancy. When sociodemographic factors were accounted for, the effect size was reduced by 25% among obese women (β = 0.36; 95% CI: 0.06-0.68), but was no more not significant in overweight women (β = 0.06; 95% CI: -0.17 to 0.30). Both obese and overweight women still exhibited, although at a lower extend, a greater PSQI than normal-weight women. Further adjustment for GWG had no effect on these observations. Obese women almost had a four-fold increased risk (OR = 3.89; 95% CI: 2.50-6.06) of being snorers in the three TMs. While GWG had only a minor effect (aOR = 3.83; 95% CI: 2.43-6.03), sociodemographic factors increased the odds of being habitual snorer (aOR = 4.04; 95% CI: 2.57-6.35). CONCLUSIONS: High prepregnancy BMI together with sociodemographic correlates appear as the most critical for sleep disturbances across pregnancy. GWG is of marginal importance, especially regarding snoring. These findings confirm the importance of considering the initial body weight status of women in tailoring sleep hygiene advises irrespective of the recommendations for weight gain during pregnancy. The results also underscore the need to providing as early as possible during a pregnancy, supportive tools to ethnic minorities, multiparous, and women from deprived socioeconomic neighborhoods.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".