The effects of walking in nature on negative and positive affect in adult psychiatric outpatients with major depressive disorder: A randomized-controlled study
Bibliographic record
Abstract
Abstract Background While walking in nature has been shown to improve affect in adults from the community to a greater extent than walking in urban settings, it is unknown whether such findings can be generalized to individuals suffering from depression. Using a parallel group design, this randomized controlled trial examined the effects of a single walk in nature versus urban settings on negative and positive affect in adult psychiatric outpatients diagnosed with major depressive disorder (MDD). Method Participants recruited from a psychiatric outpatient clinic for adults with MDD were randomly assigned to a nature or urban walk condition. Thirty-seven adults (mean age=49 years) completed a single 60-minute walk. Negative and positive affect were assessed using The Positive and Negative Affect Schedule or PANAS at 6 time points: before the walk, halfway during the walk, immediately post-walk, at home before bedtime, 24 hours post-walk, and 48 hours post-walk. Results Controlling for baseline levels of affect before the walk, individuals who walked in nature experienced overall lower levels of negative affect, F (1, 35.039)=4.239, p =.047, compared to those who walked in urban settings. Positive affect did not differ across walk conditions. Limitations The generalizability of results are limited by the small sample size and the presence of more female than male participants. Conclusions Walking in nature might be a useful strategy to improve the affect of adults with MDD. Future research should investigate different ways to integrate the beneficial effects of nature exposure into existing treatment plans for psychiatric outpatients with MDD.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".