Outdoor walking and physical activity and their relationship with neighbourhood walkability in older adults with self-reported difficulty in walking outdoors
Bibliographic record
Abstract
Introduction: Neighbourhood walkability has been suggested to influence walking behaviors. However, few studies focused on their relationships in Canadian older adults. The aims of this study were (1) to compare outdoor walking and moderate-to-vigorous physical activity time (MVPA) time in older adults with self-reported difficulty in different cities in Canada; (2) to estimate the associations between outdoor walking and subscale scores and the total scores of neighbourhood walkability; and (3) to estimate the associations between MVPA time and subscale scores and the total scores of neighbourhood walkability. Methods: This was a secondary data analysis of the Getting Older adults OUTdoors (GO-OUT) study. We used data from the baseline evaluation from 190 participants who had self-reported difficulty in outdoor walking in Edmonton (n=51), Winnipeg (n=53), Toronto (n=50), and Montreal (n=36). We compared the between-city differences in outdoor walking and MVPA time. We also attempted to use parametric tests to investigate the relationships between neighbourhood walkability, assessed by Neighbourhood Environmental Walkability Scale (NEWS), and outdoor walking and MVPA. Since the assumptions of normality, homogeneity of variances and homoscedasticity were all violated, Kruskal-Wallis test and Spearman’s rho were conducted. Results: We found (1) significant differences in MVPA time but not outdoor walking time between participants who resided in Edmonton, Winnipeg, Toronto, and Montreal, and (2) significant but weak associations between land-use mix diversity and land-use mix access, and outdoor walking (Spearman’s rho = 0.172 to 0.233) and (3) between residential density, land-use mix access and street connectivity and MVPA time (Spearman’s rho = -0.235 to 0.208). Conclusion: Several aspects of neighbourhood walkability play a significant role in outdoor walking and MVPA time among community-dwelling older adults. Understanding the relationships between neighbourhood walkability and outdoor walking and MVPA can help identify facilitators and barriers to walking, which could in turn influence their walking habits in the neighbourhood.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".