An Umbrella Review of the Best and Most Up-to-Date Evidence on the Built Environment and Physical Activity in Older Adults ≥60 Years
Bibliographic record
Abstract
Objectives: To present the best and most up-to-date evidence on associations between built environment (BE) attributes and overall and specific domains of physical activity (PA) (i.e., leisure, transport, walking, and cycling) in older adults (≥60 years). Methods: An umbrella review was undertaken to compile evidence from systematic reviews using the Joanna Briggs Institute methodology. A comprehensive search (updated 16 August 2022), inclusion/exclusion of articles via title/abstract and full-text reviews, data extraction, and critical appraisal were completed. Only reviews with a good critical appraisal score were included. Results: Across three included systematic reviews, each BE attribute category was positively associated with ≥1 PA outcome. A larger number of significant associations with BE attributes were reported for transport walking (13/26), total walking (10/25), and total PA (9/26), compared to leisure walking (4/34) and transport cycling (3/12). Fewer associations have been examined for leisure cycling (1/2). Conclusion: Although the causality of findings cannot be concluded due to most primary studies being cross-sectional, these best and most up-to-date findings can guide necessary future longitudinal and experimental studies for the (re)design of age-friendly 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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".