Physical Activity in Older Adults: an Ecological Approach
Bibliographic record
Abstract
BACKGROUND: Studies identifying correlates of physical activity (PA) at all levels of the ecological model can provide an empirical basis for designing interventions to increase older adults' PA. PURPOSE: Applying ecological model principles, this study concurrently examined individual, psychosocial, and environmental correlates of older adults' PA to determine whether built environment factors contribute to PA over and above individual/demographic and psychosocial variables. METHODS: Using a cross-sectional observational design, 726 adults, aged ≥66 years, were recruited from two US regions. Explanatory variables included demographics, self-efficacy, social support, barriers, and environmental variables measured by using geographic information systems (GIS) and self-report. Outcomes included reported walking for errands and leisure/exercise and accelerometer-measured daily moderate to vigorous PA (MVPA). Analyses employed mixed-model regressions with backward elimination. RESULTS: For daily MVPA, the only significant environmental variable was GIS-based proximity to a park (p < 0.001) after controlling for individual/demographic and psychosocial factors. Walking for errands was positively related to four environmental variables: reported walking/cycling facilities (p < 0.05), GIS-based intersection density (p < 0.01), mixed land use (p < 0.01), and private recreation facilities (p < 0.01). Walking for leisure/exercise was negatively related to GIS-based mixed land use (p < 0.05). Non-Hispanic white race/ethnicity, self-efficacy, and social support positively related to all three PA outcomes (p < 0.05). CONCLUSIONS: Correlates of older adults' PA were found at all ecological levels, supporting multiple levels of influence and need for multilevel interventions. Environmental correlates varied by PA outcome. Walking for errands exhibited the most environmental associations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".