A compositional analysis of time spent in physical activity, sedentary behaviour, and sleep with quality of life in Canadian older adults aged 65 years and above: findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Associations between daily time spent in physical activity (PA), sedentary behaviour (SB), and sleep (collectively referred to as "movement behaviour") and quality of life (QOL) are typically studied without considering they are compositional, co-dependant variables. Study objectives were to use compositional data analysis to: (1) examine the relationship between movement behaviour composition and QOL, (2) estimate the degree to which changing time spent in any movement behaviour within the movement behaviour composition is associated with QOL. 7918 older Canadian adults ≥65 years of age from the Canadian Longitudinal Study on Aging were studied using a quasi-longitudinal study design. Daily time spent in PA and SB were derived from self-reported Physical Activity Scale for the Elderly responses. Nighttime sleep was self-reported separately. QOL was assessed through the Satisfaction with Life Scale. Movement behaviour composition was significantly associated with QOL. Relative time spent in SB was negatively associated with QOL (HR = 0.89 (95% CI: 0.86-0.93)). Relative time spent in sleep was positively associated with QOL (HR = 1.10 (95% CI: 1.05-1.16)). Time displacement estimates revealed that greatest change in QOL occurred when time spent in PA was decreased and replaced with SB (HR = 0.96 (95% CI: 0.92-0.99) for 45 min/day displacement). Using compositional data analysis is advantageous because it shows how reduction in SB and increase in PA and sleep can lead to improvements in QOL for older Canadian adults.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".