Associations of Sleep with Food Cravings, Diet, and Obesity in Adolescence
Bibliographic record
Abstract
Background: Sleep and dietary intake/quality can contribute to excess weight gain, but food cravings may influence these relationships. This cross-sectional study examined the relationship of adolescents’ sleep characteristics with dietary intake/quality and obesity and whether food cravings mediated these relationships. Methods: Sleep measures were calculated based on 24-h accelerometry, and height and weight were directly measured to calculate body mass index (BMI) z-scores. Food cravings were assessed by the Food Craving Inventory (FCI). Dietary intake and quality were calculated based on dietary recalls. Multivariable linear regression was used to examine the associations among sleep, food cravings, dietary intake/quality, and obesity, adjusting for confounders. Results: In total, 256 adolescents (ages 10–16 years) had complete data; 42% were non-White and 45% were boys. Sleep efficiency was inversely associated with sweet cravings and FCI-28. Sleep duration, meeting the sleep duration guidelines, and fruit/vegetable cravings were each positively associated with dietary quality. Sleep duration was negatively associated with BMI z-score. Mediation models were not performed as no sleep parameter was associated with both cravings and dietary intake/quality or BMI z-score. Conclusions: Associations existed among poor sleep, quantity and quality, with more frequent food cravings and worse dietary quality. Sleep may underlie adolescent obesogenic behaviors.
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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 | 0.000 |
| 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".