Sedentary behavior and sleep efficiency in active community-dwelling older adults
Bibliographic record
Abstract
OBJECTIVES: Previous studies have demonstrated that aerobic exercise interventions have a positive impact on sleep efficiency in older adults. However, little work has been done on the impact of sedentary behavior (sitting, watching television, etc.) on sleep efficiency. METHODS: 54 Community-dwelling men and women >65 years of age living in Whistler, British Columbia (mean 71.5 years) were enrolled in this cross-sectional observational study. Measures of sleep efficiency as well as average waking sedentary (ST), light (LT), and moderate (MT) activity were recorded with Sensewear accelerometers worn continuously for 7 days. RESULTS: From the univariate regression analysis, there was no association between sleep efficiency and the predictors LT and MT. There was a small negative association between ST and sleep efficiency that remained significant in our multivariate regression model containing alcohol consumption, age and gender as covariates. (standardized β correlation coefficient -0.322, p=0.019). Although significant, this effect was small (an increase in sedentary time of 3 hours per day was associated with an approximately 5% reduction in sleep efficiency). CONCLUSIONS: This study found a small significant association between the time spent sedentary and sleep efficiency, despite high levels of activity in this older adult group.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".