Leisure Sedentary Time is Associated with Self-Reported Falls in Middle-aged and Older Females and Males: an Analysis of the CLSA
Bibliographic record
Abstract
Aim: The purpose of this analysis was to report the prevalence of falls and falls-related injuries among those reporting different volumes of weekly sedentary time, and to understand the association of sedentary time and falls, accounting for functional fitness. Methods: Baseline and first follow-up data from the Canadian Longitudinal Study on Aging (CSLA) were analyzed (n=22,942). Participants self-reported whether they had a fall in the past 12 months (at baseline) and whether they had an injury that was a result of a fall (follow-up). In-home interviews collected self-reported leisure sedentary time using the Physical Activity Scale for Elderly. Functional fitness was assessed using grip strength, timed-up-and-go, and chair rise tests during clinic visits. Results: The prevalence of falls was higher among those who reported higher sedentary time. For example, among males aged 65 and older who reported lower sedentary time (<1,080 min/week), the prevalence of falls in the past 12 months (at baseline) was 7.8% compared to 9.8% in those reporting higher sedentary time. The odds of reporting a fall (at baseline) was 21% higher in those who reported higher sedentary time (OR: 1.21; 95%CI: 1.11-1.33) in adjusted models. No associations were found between sedentary time and injuries due to a fall. Conclusions: Reporting high volumes of sedentary time may increase the risk of falls. Future research using device-based estimates of total sedentary time and breaks in sedentary time is needed to further elucidate this association.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".