Nature-based interventions for physical health conditions: A systematic review and meta-analysis
Bibliographic record
Abstract
Nature-based interventions (NBIs) are activities, strategies, or programs taking place in natural settings, such as exercising in greenspaces, to improve the health and well-being of people by integrating the benefits of nature exposure with healthy behaviours. Current reviews on NBIs do not report the effects on different groups of physical health conditions. The purpose of this systematic review and meta-analysis was to identify and synthesize the evidence of the effect of NBIs on physical health outcomes and biomarkers of physical health conditions. Overall, 20,201 studies were identified through searching MEDLINE, Embase, CINAHL, SPORTDiscus, and CENTRAL databases up to June 7, 2024. Inclusion criteria were: 1) randomized controlled intervention studies; 2) population with a physical health condition; 3) NBIs vs. different intervention or no intervention; and 4) measuring physical health outcomes and/or biomarkers. Twenty-six studies were included in the review, 15 of which contributed to the meta-analysis. Compared to control groups, NBIs groups showed significant improvements in: diastolic blood pressure (MD -3.73 mmHg [-7.46 to −0.00], I2 = 62%) and heart rate (MD -7.44 bpm [-14.81 to −0.06], I2 = 0%) for cardiovascular conditions, fatigue (SMD -0.50 [-0.82 to −0.18], I2 = 16%) for central nervous system conditions, and body fat percentage (MD -3.61% [-5.05 to −2.17], I2 = 0%) for endocrine conditions. High effect heterogeneity was found in several analyses and the included studies had moderate-to-high risk of bias (RoB). The non-significant outcomes showed a direction of effect in favour of NBI groups for cardiovascular, central nervous system, endocrine, musculoskeletal, and respiratory conditions. This review found some beneficial effects in favour of NBIs for health outcomes in at least three condition groups though RoB and inconsistent effects limited some interpretations. NBIs are promising therapies that healthcare professionals can consider integrating into clinical practice.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".