NEIGHBORHOOD WALKABILITY INFLUENCES ASSOCIATIONS BETWEEN PHYSICAL ACTIVITY AND GAIT MEASURES IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Neighborhood walkability can influence physical activity and of older adults. Residential neighborhoods of participants (N=186, 77±6 years, 70% females) were audited for walkability using Google Street View. Factor analysis categorized neighborhood walkability as high, medium, and low. Gait quality was derived from a 4-m instrumented walkway (pace, variability, walk-ratio) and accelerometry signals at the lower back during a 6-minute walk test (adaptability, similarity, and smoothness). Activity was step-count from seven-day actigraphy. We studied associations between gait variables and step-count across high, medium, and low walkability neighborhoods using linear regression (age and sex as covariates). Pace(m/s) [High(β=0.46, p<.05), Medium(β=0.43, p<.05), Low(β=0.25, p>.05)], adaptability(m/s2) [High(β=0.47, p<.05), Medium(β=0.24, p<.05), Low(β=0.37, p<.05)], and similarity [High(β=0.39, p<.05), Medium(β=0.28, p<.05), and Low(β=0.18, p>.05)] were associated with step-count, stronger associations for high walkability neighborhoods (p for interactions <0.01). No associations with variability, walk-ratio, and smoothness were found. Associations between gait and activity differed by neighborhood walkability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".