Geographic Correlates of Fitness and Cardiovascular Health in a Training Intervention Among Older Adults
Bibliographic record
Abstract
0511 Where a person lives is not usually thought of as an important predictor of their health. Lifestyle and genetic explanations predominateFinevertheless, where someone lives may differ in many aspects potentially related to health. Purpose: To determine differences between access to physical activity facilities, and changes in VO2max, systolic blood pressure (SBP) and BMI following a 12-month community-based physical activity intervention (STEP). Methods: Geographic Information Systems (GIS) was used to map subject dwelling and community physical activity facilities for 41 older (x age = 73+/−3 y; 19 male; 22 female) adults who completed a 12-month physical activity intervention as part of a larger RCT (Step Test Exercise Prescription) of 360 older adults across Canada. Physical activity facilities (parks, walking paths, health clubs etc) were identified on digitized maps for the city of London, Ontario and corroborated by telephone directory and GPS. All distances were compared to primary clinical outcomes including VO2max, SBP and BMI. Subjects were grouped according to whether they maintained/improved (responders) or declined (non-responders) in fitness compared to the group mean change after 12-months. Results: At baseline, subjects who were closer than the group mean to aggregate facilities tended to have higher VO2max, lower SBP and BMI while no pattern for individual facilities was observed. After the STEP intervention, Responders tended to show a smaller increase in SBP and BMI and were closer to aggregate facilities, walking paths and open playing fields. Conclusion: Subjects with closer proximity to physical activity facilities showed higher VO2max and favorable SBP and BMI profile while those who improved VO2max, had less SBP and BMI increase following a 12- month intervention tended to live closer to physical activity opportunities.Table
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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) | 0.001 | 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".