Phytoncides and immunity from forest to facility: A systematic review and meta-analysis
Bibliographic record
Abstract
Forest bathing is a traditional Japanese custom that involves immersing oneself in forest settings for extended periods. It is recognized for its positive impacts on psychological and physiological well-being. Phytoncides play a key role in the benefits of forest bathing and have begun to be investigated for their immunotherapeutic potential. It is important to investigate their immunomodulating effects within both forest and clinical settings. We conducted a systematic review and meta-analysis to investigate the effects of phytoncides on immune functioning. A PICO-SD framework was used to screen studies from databases, including PubMed, Web of Science, Scopus, Cochrane, and Trent University’s Omni portal. Selection criteria involved studies of adults aged 18+ exposed to phytoncides, comparing those exposed with control groups. The outcomes of eligible studies focused on immunological measures, excluding survey and qualitative research. Risk of bias was assessed using the ROBINS-I tool for non-randomized controlled trials and the ROB-2 for randomized controlled trials. Six studies (79 participants) were included in this meta-analysis. The meta-analysis included standardized mean difference effect sizes (Cohen’s d) with a random effects model using the Hartung-Knapp adjustment and 95 % confidence intervals for continuous data. This review found favourable immunological outcomes of phytoncide treatment, including increases in NK cells, T-cells, and cytotoxic effector molecules. Meta-analysis indicated a significant increase in NK cell activation (Effect Size: 2.50; 95 % CI [1.94–3.05]; p < 0.05; I2 = 50.47 %). The evolving landscape of phytoncide research calls for randomized controlled trials using specific phytoncides to establish the efficacy and safety of phytoncides in diverse healthcare settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".