0444 Examining Sleep Duration and Social Jetlag From a Popular Wearable Sleep Tracker in France and Canada
Bibliographic record
Abstract
Abstract Introduction Many population estimates of sleep duration and quality rely primarily on self-reported data. Passive and ubiquitous digital tracking and wearable devices may provide more accurate estimates of sleep duration and quality. Our objective was to identify trends in sleep duration and social jetlag using data from a popular mobile sleep application (app) in France and Canada ‘iSommeil.’ Methods We examined sleep using 8,207 nights from iSommeil, a popular sleep-tracking app in France and Canada. In this analysis, we explored sleep data collected from this app from 2,126 users. We examine sleep parameters by sex and between week and weekend. Specifically, we explore social jetlag, as calculated by the midpoint of sleep during the weekend, subtracted from the midpoint of sleep during the week. Results Women represented 1,254 (59.7%) of the sample and men represented 857 (40.3%) of the sample. Among women, 16.4% of the sample averaged <6 hours of sleep; 51.9% averaged 6-7.99 hours of sleep, and 31.7% averaged >=8 hours of sleep. Among men, 17.4% averaged <6 hours of sleep; 58.4% averaged 6-7.99 hours of sleep, and 24.2% averaged >=8 hours of sleep. Social jetlag scores among all users averaged 31.4, yet the average for men was 27.1 while that for women was 36.4. Conclusion Our study of data from a popular sleep tracker in France and Canada showed that sleep duration of 6-7.99 hours was most observed among the majority of participants. Our results also showed that women had higher social jetlag scores than men. Future research may compare sleep measures obtained via wearable sleep trackers with validated research-grade measures of sleep. Support
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".