Immersion in a relaxing virtual reality environment is associated with similar effects on stress and anxiety as heart rate variability biofeedback
Bibliographic record
Abstract
Practicing guided breathing at 0.1 Hz in virtual reality yields psychological and physiological benefits. Nonetheless, it remains uncertain whether these effects surpass those induced in a real-world setting. Indeed, the potential influence of the virtual environment on perceived stress and anxiety is not yet fully understood. In this experiment, we aimed to compare the effects of heart rate variability biofeedback combining both haptic and visual cues in real and virtual reality settings among the same group of participants. Additionally, to discern whether the psychological benefits arise from viewing an environment in virtual reality or from the act of performing guided breathing in this specific setting, a “control” immersion condition was introduced. 36 healthy sport students (9 females) participated in this study, performing both the real and virtual reality protocols in a randomized order. Anxiety and stress levels were assessed using the STAI-Y questionnaire and a visual analog scale, respectively. Physiological effects were assessed through measures of heart rate variability, and the performance of cardiac coherence was compared between the real and virtual implementations of guided breathing. As expected, both real and virtual reality heart rate variability biofeedback led to similar physiological modulations and cardiac coherence performances. A decrease in stress and anxiety was observed in both protocols, particularly among participants who initially reported higher stress or anxiety levels. However, no additional changes in psychological states were observed when performing guided breathing while immersed in the virtual environment.
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 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".