Impact of green and blue spaces on physical activity: An umbrella review
Bibliographic record
Abstract
Current evidence is unclear on the association between aspects of green (e.g., trails, parks) and blue (e.g., lakes) spaces and physical activity (PA). This association is nuanced as types and characteristics of green and blue spaces may induce varying impacts on PA domains. Therefore, an umbrella review was conducted to summarize and appraise relevant systematic reviews to provide a comprehensive synthesis of high-quality evidence on the association between green and blue spaces and PA. The review was planned, conducted and reported in accordance with the guidelines by the Joanna Briggs Institute. A systematic review was considered eligible for inclusion if it included at least two primary quantitative studies evaluating green and/or blue space features and PA. Findings were synthesized narratively. Sixteen systematic reviews involving 6,907,131 individuals published between 2017 and 2025 were included in the review. Thirteen systematic reviews examined green spaces, one review examined blue spaces, and two reviews examined both green and blue space-related interventions. Positive associations were observed for green space availability, aesthetics and types, and blue space availability, with active recreation and total PA. Despite the heterogeneity in the assessment of green and blue spaces and PA domains, this review reveals important associations between green space availability, aesthetics and PA. Identification of specific features and characteristics of green and blue spaces linked with PA may help leverage the maximum associated health benefits and inform urban design. • This review links green space availability, aesthetics, and physical activity. • Green and blue space access increases active recreation and total physical activity. • Measurement of green and blue spaces and physical activity varies across studies. • Identifying green space features that promote activity can guide healthy city design.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".