Is Self-Reported Physical Activity Participation Associated with Lower Health Services Utilization among Older Adults? Cross-Sectional Evidence from the Canadian Community Health Survey
Bibliographic record
Abstract
Purpose. To examine relationships between leisure time physical activity (LTPA) and health services utilization (H) in a nationally representative sample of community-dwelling older adults. Methods. Cross-sectional data from 56,652 Canadian Community Health Survey respondents aged ≥ 50 years (48% M; 52% F; mean age 63.5 ± 10.2 years) were stratified into three age groups and analysed using multivariate generalized linear modeling techniques. Participants were classified according to PA level based on self-reported daily energy expenditure. Nonleisure PA (NLPA) was categorized into four levels ranging from mostly sitting to mostly lifting objects. Results. Active 50-65-year-old individuals were 27% less likely to report any GP consultations (ORadj = 0.73; P < 0.001) and had 8% fewer GP consultations annually (IRRadj = 0.92; P < 0.01) than their inactive peers. Active persons aged 65-79 years were 18% less likely than inactive respondents to have been hospitalized overnight in the previous year (ORadj = 0.82, P < 0.05). Higher levels of NLPA were significantly associated with lower levels of HSU, across all age groups. Conclusion. Nonleisure PA appeared to be a stronger predictor of all types of HSU, particularly in the two oldest age groups. Considering strategies that focus on reducing time spent in sedentary activities may have a positive impact on reducing the demand for health services.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".