Sleep and binge eating in early adolescents: a prospective cohort study
Bibliographic record
Abstract
PURPOSE: To determine the prospective associations between sleep disturbance and binge-eating disorder and behaviors in a national sample of early adolescents in the United States (US). METHODS: We analyzed prospective cohort data from the Adolescent Brain Cognitive Development Study (N = 9428). Logistic regression analyses were used to determine the associations between several sleep variables (e.g., overall sleep disturbance, disorders of initiating and maintaining sleep [insomnia], duration; Year 2) and binge-eating disorder and behaviors (Year 3), adjusting for sociodemographic Year 2 binge-eating covariates. RESULTS: Overall sleep disturbance was prospectively associated with higher odds of binge-eating disorder (OR = 3.62, 95% CI 1.87-6.98) and binge-eating behaviors (OR = 1.59, 95% CI 1.17-2.16) 1 year later. Disorders of initiating and maintaining sleep were prospectively associated with higher odds of binge-eating disorder (OR = 1.12, 95% CI 1.05-1.19) and binge-eating behaviors (OR = 1.06, 95% CI 1.03-1.10). Sleep duration under 9 h was prospectively associated with greater binge-eating behaviors. CONCLUSIONS: Sleep disturbance, insomnia symptoms, and shorter sleep duration were prospectively associated with binge eating in early adolescence. Healthcare providers should consider screening for binge-eating symptoms among early adolescents with sleep disturbance. LEVEL OF EVIDENCE: Level III: Evidence obtained from well-designed cohort or case-control analytic studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".