Individual and Neighborhood Characteristics of Walking Activity in People Living With Dementia: A Proof of Concept for Quantitative Spatial Analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Creating dementia-friendly neighborhoods is crucial for enhancing outdoor walking activity and maintaining social participation among people living with dementia (PLWD). This study utilizes GPS and geographic information system technologies to conduct a quantitative spatial analysis, revealing how individual and neighborhood characteristics are associated with PLWD's walking activity characteristics. METHODS: Twenty-five participants from Metro Vancouver had their regular walking routes (RWR) from home to neighborhood destinations recorded using GPS technology. Spatial analysis tools and existing survey data were utilized to construct the research geodatabase. Nonparametric tests (Spearman's rank correlation, Kendall's tau, and Mann-Whitney) and parametric tests (Pearson correlation and point biserial) assessed associations between independent variables, including sociodemographic characteristics of participants (e.g., age and health) and their neighborhoods (e.g., age structure and language barriers), and built environment features (e.g., land use diversity), with two dependent variables: length of RWR and walking time to regular destination within the 20-min walkshed. RESULTS: Longer RWR and increased walking time to regular destination within the 20-min walkshed are associated with fewer physical health limitations, gentler terrain, proximity to green spaces, and bus stops. In addition, higher land use diversity, proximity to secondary streets, integrated sidewalks, higher number of benches and intersections, and a slight street network curvature showed positive associations with RWR length and walking time to regular destination within the 20-min walkshed. CONCLUSION: GPS and geographic information system technologies provide a unique quantitative method for understanding mobility patterns among PLWD. While limited by participant numbers, this exploratory study provides directions for future investigations. Significance/Implications: This study offers insights into designing dementia-friendly neighborhoods that support social engagement and physical activity among PLWD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".