Built environment factors and their impacts on outdoor walking activity among people living with dementia: a spatial analysis approach
Bibliographic record
Abstract
Introduction As the population of people living with dementia in Canada continues to grow, understanding the built environment’s role in facilitating outdoor activity is increasingly critical. While prior qualitative and quantitative research has established the benefits of outdoor walking for the physical, mental, and social well-being of people living with dementia, empirical spatial analysis of built environment factors influencing their walking behavior remains limited. Methods This study serves as a proof of concept, demonstrating the feasibility of applying spatial analysis to assess the impact of built environment variables on outdoor walking among people living with dementia. Using data from 25 participants in Metro Vancouver, this study integrates Geographic Positioning System (GPS) and Geographic Information System (GIS) tracking with exploratory factor analysis (EFA) and multiple linear regression (MLR) to examine the relationship between built-environment characteristics and walking distances. Results Despite the small sample size, statistical analyses met standard validity criteria, identifying three key factors influencing walking distance: (1) Macro environment—accessibility to public transportation and street network characteristics (p = 0.007, 439.6 m increase), (2) Micro environment—pedestrian-oriented design (p = 0.065, 286.5 m increase), and (3) General characteristics—mixed land use and sidewalk suitability (p = 0.015, 388.5 m increase). Discussion These findings provide preliminary evidence of the built environment’s role in shaping mobility for people living with dementia, offering valuable insights for public health policy makers, urban planners and designers, and transportation professionals in designing dementia-friendly neighborhoods. By integrating spatial analysis with environmental design principles, this study contributes to the development of inclusive and accessible urban environments for people living with dementia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".