Healthy aging in the neighborhood: Examining the relationship between the micro-scale built environment and walking in older adults
Bibliographic record
Abstract
Evidence suggests that neighborhood-built environments influence walking behavior in older adults. Most studies to date have examined how macro-scale features (connectivity, land-use mix, and population density) encourage walking in this population. Findings about neighborhood macro-scale features and walking, however, are not often practical to apply in existing neighborhood settings, as changing these features can require substantial reconfiguration of the neighborhood layout. Altering micro-scale features of neighborhoods (e.g., presence and quality of sidewalks, benches) may be a relatively cost-effective and efficient method of creating environments that are conducive to walking. This dissertation adopted an explanatory mixed methods approach to better understand the relationship between the micro-scale environment and walking. The main findings of this dissertation are: 1. Reporting a research design and an integration strategy in mixed methods studies in the built environment and health field could help to strengthen our ability to gain new insights into the multidimensional nature of the relationship between the built environment and health.2. Virtual-STEPS is a reliable tool for virtually assessing the micro-scale environment of neighborhoods. Percentage agreement between virtual and field audits, and for inter-rater agreement was 80% or more for most items. There was high reliability between virtual and field audits with Kappa and ICC statistics indicating that 50.0% of items had almost perfect agreement and 32.5% of items had substantial agreement. Inter-rater reliability was also high with 42.5% of items with almost perfect agreement and 27.5% of items with substantial agreement.3. The micro-scale environment collectively promoted leisure walking in adults. The grand micro-scale score was associated with elevated odds of walking for leisure for at least 150 minutes per week in adults from Montreal and Toronto, even after accounting for self-selection. Conversely, the association between micro-scale walkability and walking for utilitarian purposes was inconclusive. 4. The micro-scale environment promoted leisure walking in older adults. The grand micro-scale score was associated with greater odds of walking for leisure for at least 150 minutes per week. After stratifying for health conditions, the grand micro-scale score and the traffic calming total section score were only associated with walking for leisure in the sample with health conditions, further the aesthetics section score became significantly associated with walking for leisure in the sample of older adults with health conditions. 5. Semi-structured interviews conducted with older adults living in the suburbs of Montreal during the COVID-19 pandemic revealed that aesthetics, pedestrian infrastructure, proximity to shops/facilities, and building characteristics were perceived as walk-friendly, whereas traffic as well as unsafe intersections were perceived as barriers to walking. Older adults also reported avoiding crowded parks and crowded or narrow boardwalks, sidewalks, and walking paths due to difficulties with physical distancing. Interventions to improve the micro-scale environment of neighborhoods could increase walking for leisure in older adults, a vulnerable population group, that may be particularly sensitive to the micro-scale features of their neighborhood environment
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".