Device-measured weekend catch-up sleep, mortality, and cardiovascular disease incidence in adults
Bibliographic record
Abstract
STUDY OBJECTIVE: Attempting to recover a sleep debt by extending sleep over the weekend is a common compensatory behavior in the population and is recommended by sleep-focused organizations. However, the purported benefits of catch-up sleep are based on a limited number of cross-sectional studies that relied on self-reported sleep. The objective of this study was to examine the association between accelerometer-derived weekend catch-up sleep and mortality and incident cardiovascular disease (CVD) in adults. METHODS: A prospective cohort study of UK adults who wore wrist-attached accelerometers was conducted. Weekend catch-up sleep was defined as a longer average sleep duration on weekends compared to weekdays. Participants were categorized into four groups: no weekend catch-up sleep (reference); > 0 to < 1 hour; ≥ 1 to < 2 hours; and ≥ 2 hours difference. Associations between weekend catch-up sleep and mortality and incident CVD were assessed using Cox proportional hazards regression, adjusted for potential confounders. RESULTS: A total of 73 513 participants (sample for mortality) and 70 518 participants (sample for CVD incidence) were included, with an average (SD) follow-up period of 8.0 (0.9) years. In multivariable-adjusted models, weekend catch-up sleep was not associated with mortality (≥ 2 hours group: hazard ratio [HR], 1.17 [95% CI: 0.97 to 1.41]) or incident CVD (HR, 1.05 [95% CI, 0.94 to 1.18]). Dose-response analyses treating catch-up sleep as a continuous measure or analyses restricted to adults sleeping less than 6 hours on weekdays at baseline were in agreement with these findings. CONCLUSIONS: Weekend catch-up sleep was not associated with mortality or CVD incidence. These findings do not align with previous evidence and recommendations by sleep authorities suggesting that extending sleep over the weekend may offer protective health benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".