Using multisensory virtual reality nature immersion as a therapeutic modality for improving HRV and cognitive functions in post-traumatic stress disorder: a pilot-study
Bibliographic record
Abstract
Introduction: Immersive virtual reality (VR) applications are burgeoning within healthcare as they promote high levels of engagement. Notwithstanding, existing solutions only stimulate two of our five senses (audio and visual), thus may not be optimal in the sense of promoting immersion and of “being present”. In this paper, we explore the benefits of an immersive multisensory experience as a therapeutic modality for participants suffering from post-traumatic stress disorder (PTSD). Methods: In addition to 360-degree videos and corresponding natural sounds, nature smells are also presented by means of a portable ION 2 scent diffusion device attached to an Oculus Quest 2 VR head-mounted display. A 3-week 12-sessions protocol was applied to a sample of 20 participants diagnosed with PTSD. Results and discussion: We report the outcomes seen from a battery of qualitative metrics, including cognitive functioning tests, psychological symptoms, severity of PTSD, and several self-reported questionnaires and heart rate variability (HRV) metrics. Results are compared not only between pre-and post intervention, but also after a 3-month follow-up period. Results suggest a decrease in the severity of PTSD, as well as improvements in processing speed and sustained attention post-intervention, but also sustained decrease in the severity of PTSD and in dissociative tendencies at the 3-month follow-up. Overall, participants rated the experience as highly immersive and produced very mild to no symptoms of cybersickness, thus corroborating the feasibility and usefulness of the proposed multisensory immersive VR tool for reducing PTSD symptoms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".