Associations between objectively measured nighttime sleep duration, sleep timing, and sleep quality and body composition in toddlers in the Guelph Family Health Study
Bibliographic record
Abstract
The prevalence of child obesity is a worldwide public health concern. Good sleep quality is associated with reduced adiposity in older children and adults. More research is needed in younger children to help mitigate risk of obesity. In addition, we aimed to address limitations found in previous studies such as relying on subjective measures, or only including one parameter of sleep, using only one body composition parameter, and/or not adjusting for relevant covariates. This cross-sectional study examined baseline data from 48 toddlers aged 1 to <3 years enrolled in the Guelph Family Health Study. Nighttime sleep duration (total sleep time; TST), sleep timing (sleep onset and offset time), and sleep quality (wake after sleep onset; WASO) were measured using 24 h accelerometry for 7 consecutive days. Height, body weight, and waist circumference were measured, and BMI z-scores and waist-to-height ratios were calculated. Percent fat mass and fat mass index were calculated using bioelectrical impedance analysis. Linear regression models were used to estimate associations between sleep parameters and body composition outcomes, with adjustments for relevant covariates (age, sex, household income, screen time, energy intake, physical activity, household stress). Nighttime sleep onset time was positively associated with waist-to-height ratio ([Formula: see text] = 0.004, p = 0.04). Sleep offset time was negatively associated with BMI z-score ([Formula: see text] = −0.48, p = 0.02). TST and WASO were not associated with any body composition outcome. Building healthy sleep habits may prevent childhood obesity; longitudinal research in a larger sample is warranted. This study was registered on ClinicalTrials.gov (NCT02939261).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".