Seasonal Variation In Physical Activity By Province In Canada
Bibliographic record
Abstract
Self-reported leisure-time physical activity levels (LTPA) vary geographically across Canada and this variability may be partially explained by seasonal patterns. Despite having climatic seasonal variations that differ significantly across the country, relatively little is known about the seasonal variation of LTPA in provinces across Canada. Purpose To examine the seasonal variation pattern of LTPA, by province and territory, in Canada. Methods Pooled data from cycles 1.1 (2000/01) and 2.1 (2003) of the cross-sectional Canadian Community Health Survey (ages 20+yrs; N=230,679) were used for this study. Weighted prevalences of LTPA (≥1.5 kcal/kg/day (kkd), moderately active; ≥3 kkd, active; ≥6 kkd, very active) were calculated by month and by season (Spring, Summer, Fall, Winter) for each province (10) and the territories combined (1). Bootstrap variance estimation was done where necessary. Adjusted and unadjusted multiple logistic regressions were also run to quantify the effects of season, province and province-season interactions on being physically active (≥1.5 kkd, ≥ 3 kkd, ≥6 kkd) while controlling for age, sex, marital status, education, employment status, children in the home, smoking status and BMI. To confirm that the seasonality trend of each province was statistically similar (parallelism hypothesis) a profile analysis was completed using the F-test. Results The proportion of Canadians who report being physically active (≥3 kkd) varies significantly across seasons with summer the highest (28.1%, p < 0.05) and winter the lowest (15.7%, p < 0.05). Significant provincial variation was also observed with BC the highest (29.6%) and NF the lowest (16.6%), and a general trend of declining activity prevalences from west to east. Despite substantial provincial variation, the seasonal variation within each province follows a similar pattern, with only minor province-season interactions, even when different levels of activity were assessed (≥1.5 kkd, ≥ 3 kkd, ≥6 kkd). Generally, younger age groups (aged 20–34), men, non-smokers, singles/never-married, individuals of acceptable weight (BMI ≥ 25), those with some post-secondary education or higher, those who did not work and individuals without children have increased odds of being physically active. Conclusion These results confirm that there is significant seasonal and provincial variation in Canada and add evidence that within each province the seasonal variation pattern is very similar.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".