Neighbourhood greenness moderates the association between physical activity and geriatric-relevant health outcomes: an analysis of the CLSA
Bibliographic record
Abstract
BACKGROUND: The purpose of this analysis was to evaluate the relationship between baseline physical activity levels of older adults and geriatric-relevant health outcomes at 3-year follow-up, and to determine whether baseline neighbourhood characteristics alter this association. METHODS: Data from the Canadian Longitudinal Study on Aging (CLSA) were used to assess geriatric-relevant outcomes of physical impairment, medication use, severity of daily pain, and depressive symptoms. Data from the Canadian Active Living Environments (Can-ALE) and the Normalized Difference Vegetative Index (NDVI) were used to determine neighbourhood walkability and greenness, respectively. The analytic sample included adults who were 65 years or older at baseline [Formula: see text]. Adjusted odds ratios and 95% confidence intervals for the base relationships were calculated using proportional odds logistic regression (physical impairment, pain, medication use), and linear regression (depressive symptoms). Moderation effects of environmental factors were assessed using greenness and walkability. RESULTS: The base relationships showed protective associations between each additional hour per week of total physical activity and physical impairment [Formula: see text] daily pain severity [Formula: see text] medication use [Formula: see text], and depressive symptoms [Formula: see text]. Additive moderation effects were seen when greenness was added to physical impairment [Formula: see text], daily pain severity [Formula: see text], and depressive symptoms [Formula: see text] but no moderation was seen with walkability. Sex differences were observed. For example, greenness moderation was found in severity of daily pain in males but not in females. CONCLUSION: Future research investigating geriatric-relevant health outcomes and physical activity should consider neighbourhood greenness as a potential moderator.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".