Sleep characteristics and health-related quality of life in 9- to 11-year-old children from 12 countries
Bibliographic record
Abstract
Introduction: Previous studies have linked short sleep duration, poor sleep quality, and late sleep timing with lower health-related quality of life (HRQoL) in children. However, almost all studies relied solely on self-reported sleep information, and most studies were conducted in high-income countries. To address these gaps, we studied both device-measured and self-reported sleep characteristics in relation to HRQoL in a sample of children from 12 countries that vary widely in terms of economic and human development. Methods: The study sample included 6,626 children aged 9-11 years from Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, the United Kingdom, and the United States. Waist-worn actigraphy was used to measure total sleep time, bedtime, wake-up time, and sleep efficiency on both weekdays and weekends. Children also reported ratings of sleep quantity and quality. HRQoL was measured by the KIDSCREEN-10 survey. Multilevel regression models were used to determine the relationships between sleep characteristics and HRQoL. Results: Results showed considerable variation in sleep characteristics, particularly duration and timing, across study sites. Overall, we found no association between device-measured total sleep time, sleep timing or sleep efficiency, and HRQoL. In contrast, self-reported ratings of poor sleep quantity and quality were associated with HRQoL. Conclusions: Self-reported, rather than device-based, measures of sleep are related to HRQoL in children. The discrepancy related to sleep assessment methods highlights the importance of considering both device-measured and self-reported measures of sleep in understanding its health effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.007 | 0.001 |
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 teacher head, 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".