Normative values for the physical activity scale for the elderly in community-dwelling men and women 45 to 85 years old: an analysis from the CLSA
Bibliographic record
Abstract
BACKGROUND: Monitoring and improving physical activity levels is essential for promoting healthy aging. The objective of this study was to create age-specific normative values for the Physical Activity Scale for the Elderly (PASE) among community-dwelling women and men aged 45-85 years old. METHODS: 36,701 participants (47% female) aged 45-85 years old, free of any mobility limitation or activities of daily living disability from the Canadian Longitudinal Study on Aging (CLSA) were included. Best fitting models were identified using Generalized Akaike Information Criteria values and cross-validation. Seasonal differences for males and females were also explored. RESULTS: Separate models for males and females are presented, providing a range of percentile values (5-95%) in charts and tables. Total PASE scores were highest in 45-year-olds and decreased with age. Seasonal differences were not substantial or consistent at the population level. CONCLUSIONS: The age- and sex- specific normative values provided can improve the interpretability of PASE scores among middle-aged and older adults. In addition to PA guideline cut-offs, normative values provide further information for monitoring physical activity by allowing for more personalized observations that account for healthy variation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".