Comparison Of Device-measured Sleep Behaviors In Home Vs. Office Work Locations.
Bibliographic record
Abstract
Purpose: Healthy sleep behaviors can positively influence 24-hour movement behaviors and health outcomes. Working from home (WFH) has become common and may impact sleep by adding flexibility to the workday; however, little is known about the sleep behaviors of those who WFH vs. in-office workers. This study examined device-measured sleep duration across work locations and odds of meeting the sleep recommendation from the Canadian 24-hour movement guidelines. Methods: This secondary analysis utilized baseline data from the RESET BP clinical trial. RESET BP recruited inactive, desk-based workers with untreated elevated blood pressure. Worksite location (home vs. in-office) was determined by self-report. The RESET SLEEP ancillary study was conducted in a subset of RESET BP participants (n = 174, in-office n = 96, n = 78 mean age: 44.2 ± 10.7 years, 50% women) and added sleep measurement by a wrist worn Actiwatch for 7 days. For this analysis, we used device-measured mean total sleep time (TST) to align with the sleep guideline that recommends “7-9 hours of good quality sleep”. We also assessed the odds of meeting this guideline. Linear regression compared TST, and logistic regression calculated the odds of meeting the sleep guideline across work locations, adjusting for age and job sector where appropriate. Results: Demographic characteristics did not differ across worksite location (p > 0.05). Overall, participants spent an average of 7.06 ± 0.77 (SD) hours/day sleeping. TST significantly differed across groups (p = 0.03), with office-based workers obtaining 6.94 ± 0.76 hours of sleep each night, while home-based workers obtained 7.20 ± 0.09 hours of sleep each night. Odds of meeting the 24-hour movement sleep guideline was 23% lower in those who worked in-office (OR = 0.77, 95% CI: 0.34, 1.73; p = 0.53). Conclusion: Home workers obtained more sleep than those who worked in an office and were non significantly more likely to meet the sleep duration guideline. Further research is required to understand the determinants of this increase in TST and to determine the physical and mental outcomes associated with this difference in sleep behaviors. Supported by the National Institutes of Health R01 HL134809, R01 HL147610, and UL1TR001857.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".