Stop to smell the virtual roses: a mixed-methods pilot study on the impact of multisensory virtual reality nature experiences on feelings of relaxation
Bibliographic record
Abstract
Introduction This study aimed to investigate the psychological and physiological impacts of audio-visual (AV) and audio-visual-olfactory (AVO) stimuli within an immersive virtual nature environment. Methods Twenty-two nurses from the mental health in-patient ward of a Canadian hospital participated in the study. Each participant chose one of the three available immersive scenarios (beach, lake, waterfall) to experiment with under the AV and AVO conditions. Psychological assessments were conducted via questionnaires to investigate relaxation levels and other measures of user experience. Although the AVO condition demonstrated the greatest improvement in relaxation relative to baseline, no significant differences were observed either between the conditions or across the scenarios. Physiological metrics were collected using an in-house instrumented Oculus Quest 2 virtual reality (VR) headset that allowed us to investigate brain activity via electroencephalography (EEG). Results Results show a significant difference between the two conditions in certain brain regions. Significant differences in neural patterns were also seen for the participants who reported improvements in relaxation, relative to those who did not report any improvements. For these latter-referenced participants, decreased relaxation resulted from the non-congruence of the presented smells with participant expectations. Furthermore, neuromarkers measured from the EEG, such as frontal alpha asymmetry (a measure of approach/withdrawal), engagement score, as well as valence and arousal indices suggested increased relaxation levels in the AVO condition. Conclusion Our results suggest that multisensory immersive experiences can impact both physiological and psychological outcomes, resulting in increased relaxation levels and enhanced sense engagement for certain scenes. The instrumented VR headset enabled the monitoring of user neural and behavioural patterns, thus allowing for new insights to be gained beyond those achievable with only questionnaires.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".