Late and Instable Sleep Phasing is Associated With Irregular Eating Patterns in Eating Disorders
Bibliographic record
Abstract
BACKGROUND: Sleep problems are common in eating disorders (EDs). PURPOSE: We evaluated whether sleep-phasing regularity associates with the regularity of daily eating events. METHODS: ED patients (n = 29) completed hourly charts of mood and eating occasions for 2 weeks. Locomotor activity was recorded continuously by wrist actigraphy for a minimum of 10 days, and sleep was calculated based on periods of inactivity. We computed the center of daily inactivity (CenDI) as a measure of sleep phasing and consolidation of the daily inactivity (ConDI) as a measure of daily sleep rhythm strength. We assessed interday irregularities in the temporal structure of food intake using the standard deviation (SD) of frequency (IFRQ), timing (ITIM), and interval (IINT) of food intake. A self-evaluation of other characteristics included mood, anxiety, and early trauma. RESULTS: A later phasing of sleep associated with a lower frequency of eating (eating frequency with the CenDI rho = -0.49, p = .007). The phasing and rhythmic strength of sleep correlated with the degree of eating irregularity (CenDI with ITIM rho = 0.48, p = .008 and with IINT rho = 0.56, p = .002; SD of CenDI with ITIM rho = 0.47, p = .010, and SD of ConDI with IINT rho = 0.37, p = .048). Childhood Trauma Questionnaire showed associations with variation of sleep onset (rho = -0.51, p = .005) and with IFRQ (rho = 0.43, p = .023). CONCLUSIONS: Late and variable phasing of sleep associated robustly with irregular pattern of eating. Larger data sets are warranted to enable the analysis of diagnostic subgroups, current medication, and current symptomatology and to confirm the likely bidirectional association between eating pattern stability and the timing of sleep.
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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 | 0.002 |
| 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".