Wearable technologies for assessing the effects of nature on physiological states
Bibliographic record
Abstract
Forest bathing (FB) has been shown to have quantifiable positive effects on human physical and mental health, but few studies have employed non-invasive wearable technolo- gies to monitor autonomic nervous system signals. This study investigated the impacts of a 90-minute Nature Break activity on the physiological response of 10 individuals and the psychological response of 38 (age=43.55± 11.61 years) individuals. Autonomic nervous system response was assessed through continuous measurement of electrodermal activity (EDA), fingertip temperature, and blood volume pulse (BVP) using a wearable fingertip sensor. Psychological distress was assessed using the Profile of Mood States (POMS). Our results showed a decrease in the negative dimensions of POMS and an increase in the positive (vigor) dimension following Nature Break. Moderate evidence for a difference pre-forest and post-forest was found for the mean of the standard deviation of EDA slopes (BF10 = 4.462). Significant differences across stops was found for the mean of the standard deviation of EDA slopes(p<0.05), mean of the me- dian skin temperatures(p<0.05), and average HR (p<0.001), but not for the average HRV features or the slopes of the HR. Mean HR was found to decrease throughout Nature Break. Future research should further investigate the use of EDA and skin temperature as measures of ANS activity in order to develop a better understanding of the changes in these signals in the FB context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".