Environmental Factors Affecting Physical Activity Levels of Older Adults
Bibliographic record
Abstract
Many individuals worldwide, especially older individuals, do not achieve enough regular physical activity. Since falls increase with age, it is crucial to understand how environmental factors might contribute to physical activity. This thesis aimed to investigate the literature and empirical data to inform our understanding of how environmental factors might affect the physical activity and mobility of older adults. A systematic review evaluated 64 articles that investigated the relationship between greenspace, weather and season on physical activity levels of older adults aged 60 and older. Environmental factors studied included season, daylight, air quality, greenspace, and weather. Weather conditions include temperature, precipitation, wind, humidity, barometric pressure, and cloud cover. Greenspace, moderate temperatures, and longer daylight were associated with more physical activity. A sample of older adults extracted from the Canadian Longitudinal Study on Aging (CLSA) who had incurred a wrist fracture were evaluated to determine the relationship between precipitation, active living environment index, barometric pressure, relative humidity, temperature, sulfur dioxide, ozone, PM2.5 and NO2 on PASE levels. Regression analysis demonstrated that sulfur dioxide and the active living environment (ALE) index were correlated with a higher PASE score. Overall, this information should be considered by urban planners and landscape architects to design cities/towns that would encourage physical activity. This also has implications for health professionals in planning adaptive physical activity strategies in collaboration with older adults; and for policymakers to consider the potential impact of climate change on the physical activity levels of older adults in different communities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".