Associations Between Sleep Duration Patterns and Overweight/Obesity at Age 6
Bibliographic record
Abstract
OBJECTIVE: To investigate whether longitudinal sleep duration patterns during early childhood is a risk factor of overweight or obesity at school entry while controlling for a variety of obesogenic environmental factors. DESIGN, SETTING, AND PARTICIPANTS: This is a prospective cohort study (March-December 1998 to December 2004) of a representative sample of infants born in 1997-1998 in the Canadian province of Quebec. Body mass index (BMI) was measured at ages 2.5 and 6 years. Sleep duration was reported yearly from 2.5 to 6 years of age by their mothers. Prenatal, postnatal (5 and 29 months), and lifestyle (6 y) potentially confounding factors for excess weight were assessed by interviews, questionnaires and hospital records. A group-based semiparametric mixture model was used to estimate developmental patterns of sleep duration. The relationship between sleep duration patterns and BMI was tested using multivariate logistic regression models to control for potentially confounding factors on 1138 children. RESULTS: Four sleep duration patterns were identified: short persistent (5.2%), short increasing (4.7%), 10-hour persistent (50.7%), and 11-hour persistent (39.4%). After controlling for potentially confounding factors, the risk for overweight or obesity was almost 4.2 times higher for short persistent sleepers (odds ratio [OR], 4.2; 95% confidence interval [CI], 1.6 to 11.1; P = 0.003) than for 11-hour persistent sleepers. CONCLUSIONS: Persistently short sleep duration (<10 h) during early childhood significantly increases the risk of excess weight or obesity in childhood, and appears to be independent of other obesogenic factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".