Sleep efficiency and the metabolic risk score in very active older women and men
Bibliographic record
Abstract
Abstract Study Objectives Sleep disruption is a risk factor for obesity, diabetes, and cardiovascular disease in older adults. How physical activity (PA) interacts with the negative cardiometabolic effects of poor sleep is not known. We objectively measured sleep efficiency (SE) in very active older adults and examined the association between SE and a continuous Metabolic Syndrome Risk Score (cMSy). Methods Very active older adults (age ≥65 years) from a Master’s Ski Team (Whistler, Canada) were recruited. Each participants wore an activity monitor (SenseWear Pro) continuously for 7 days to provide measures of both daily energy expenditure (metabolic equivalents, METs) and SE. All components of the metabolic syndrome were measured and a principal component analysis was used to compute a continuous metabolic risk score (cMSy, sum of eigenvalues ≥1.0). Results A total of 54 participants (mean age 71.4 years, SD 4.4 years, and 24 men and 30 women) were recruited and had very high PA levels (>2.5 h per day of exercise). Initially, there was no significant association between SE and cMSy (p = 0.222). When stratified by biological sex, only men showed a significant negative association between SE and cMSy (Standardized β = −0.364 ± 0.159, p = 0.032). Conclusions Only older men show a significant negative association between poor SE and increased cardiometabolic risk, despite high levels of PA.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".