Forest soundscapes improve mood, restoration and cognition, but not physiological stress or immunity, relative to industrial soundscapes
Bibliographic record
Abstract
Exposure to natural environments has consistently been shown to boost human health. However, population-level benefit is constrained by both inequitable access to high-quality natural spaces and the lack of medical prescriptions for nature-based therapy. Addressing these challenges requires an improved understanding of the mechanisms linking environmental attributes to positive health outcomes. A systematic, standardised experimental approach is needed to support this effort. This manuscript presents two complementary experiments-a randomised controlled trial (n = 100) and a counterbalanced crossover trial (n = 30)-designed to assess the effect of a 30-min exposure to forest and industrial acoustic environments on selected biomarkers. This is the first in a series of laboratory experiments which isolate and expose individual senses to natural and industrial stimuli, while measuring biological parameters previously shown to respond positively to whole-body, real-world, nature immersion. Forest acoustics (recorded in a UK temperate rainforest, featuring bird song, running water, wind and rainfall) significantly improved biomarkers of mood, restoration and cognition, relative to industrial acoustics (recorded in Liverpool and London city centre), but not heart rate, blood pressure, heart rate variability, salivary cortisol or secretory Immunoglobulin A. These findings suggest that acoustic elements of forest environments play a role in mediating enhanced psychological state and cognition but do not appear to influence physiological stress or immunological parameters. This work advances understanding of how nature influences human biology and takes steps towards addressing existing challenges to nature-based therapy. In the short-term, these findings highlight the potential of acoustic interventions for individuals with limited access to nature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".