Effect of Nature Walks on Depression and Anxiety: A Systematic Review
Bibliographic record
Abstract
The benefits of nature for our health have been an increasing research focus in recent years. In the context of a global increase in mental health diagnoses, the potential health benefits of nature have attracted attention. One practical nature treatment is to walk in nature. However, evidence for this practice on mental health has not been comprehensively appraised to date. This systematic review synthesized the effects of nature walks for depression and anxiety, and evaluated the methodological rigor of studies. Academic databases including ProQuest, PsycINFO, Science Direct, and Google Scholar were utilized to identify eligible articles, which were examined using the Newcastle–Ottawa Scale. Of 385 articles initially retrieved, 12 studies met all the eligibility criteria (nine pre-post within-subject studies, two quasi-experimental studies, and one experimental between-subjects study). These studies demonstrated that nature walks were effective for state anxiety but not generalized anxiety and the effects for depression were inconsistent. Findings indicate that nature walks may be effective for mental health, especially for reducing state anxiety. However, the quality of the included studies varied, and sample sizes were small, suggesting a need for more rigorous and large-scale research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".