The influence of sleep quality on weight retention in the postpartum period
Bibliographic record
Abstract
Poor sleep in the postpartum is often treated as an unavoidable consequence of childbirth. This study aims to compare objective and subjective measures of sleep, explore the relationship between sleep and postpartum weight retention (PPWR), and investigate factors that may contribute to sleep quality in the postpartum period. In this cross-sectional cohort, PPWR, sleep quality (Pittsburgh Sleep Quality Index (PSQI)), and objective sleep and physical activity (accelerometry) were assessed in 109 women 0–52 weeks postpartum. Anthropometric and demographic data were collected. Gestational weight gain (GWG) was classified as inadequate, appropriate, or excessive according to Institute of Medicine guidelines. Average GWG (33.7 lbs) and PPWR (5.39 lbs) were not different between “good” (PSQI < 6) and “bad” (PSQI ≥ 6) sleepers. Following adjustment, mothers with excessive GWG who were “bad” sleepers had 5.26 higher odds of PPWR ≥ 10 lbs compared with all other combinations of GWG and PSQI. PSQI was not correlated with total sleep time (accelerometer-derived). Light activity and moderate-to-vigorous physical activity (MVPA) were associated with reduced odds of being a “bad” sleeper. The influence of GWG on PPWR was modified by postpartum sleep quality. Both light activity and meeting the MVPA guidelines in the postpartum were associated with higher sleep quality. Novelty Subjectively rated poor sleep may represent the number of awakenings and wake after sleep onset in postpartum women. Poor postpartum sleep quality increases excessive postpartum weight retention in women with excessive GWG. Women doing light-to-vigorous physical activity in the postpartum are less likely to experience poor sleep quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".