Beyond sleep duration: distinct sleep dimensions are associated with obesity in children and adolescents
Bibliographic record
Abstract
OBJECTIVE: Short sleep duration is recognized as a significant risk factor in childhood obesity; however, the question as to how sleep contributes to the development of obesity remains largely unknown. The majority of pediatric studies have relied on sleep duration as the exclusive measure of sleep; this insular approach may be misleading given that sleep is a dynamic multidimensional construct beyond sleep duration, including sleep disturbances and patterns. Although these sleep dimensions partly overlap, it is necessary to determine their independent relation with obesity, which in turn, may inform a more comprehensive understanding of putative pathophysiological mechanisms linking sleep and obesity. The aim of the present study was to investigate whether sleep dimensions including sleep duration, disturbances, and patterns were individually associated with obesity, independent of multiple covariates. The second objective was to examine whether sleep disturbances and patterns were independently associated with obesity, after adjusting for sleep duration. METHODS: Participants included 240 healthy children and adolescents (Mage=12.60, s.d.=1.98; 45.8% females). Anthropometric measures included measured waist and hip circumference, body mass index Z-score, and percent body fat. Subjective sleep measures included sleep duration, sleep disturbances, sleep quality, and sleep patterns from youth- and parental report. RESULTS: Youth with larger adiposity and body composition measures reported poorer sleep quality (β avg=-0.14, P<0.01), more sleep disturbances (β avg=0.13, P<0.05), and showed a delayed sleep phase pattern (β avg=0.15, P<0.05), independent of age, sex, pubertal status, physical activity, screen time, socioeconomic status, and sleep duration. Shorter sleep duration was significantly associated with obesity; however, this link was attenuated after adjustment of covariates. CONCLUSIONS: The results suggest that sleep measures beyond duration may more precisely capture influences that drive the negative association between sleep and obesity, and thus, yield more robust associations. As such, future studies are needed to better understand how distinct sleep dimensions confer risk for childhood obesity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".