Evaluating the Impact of Immersiveness in Virtual Reality Simulations on Anxiety Reduction for MRI Procedures: A Preliminary Study
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) examinations are frequently associated with significant anxiety and phobias in patients, negatively impacting imaging quality and patient compliance. In this study, we explore the use of Virtual Reality Exposure Therapy (VRET) to reduce MRI-related anxiety by examining physiological and subjective responses across three virtual scenarios: a 2D video, a 360° video, and a fully immersive VR environment. The study aimed to determine how different levels of immersion and the order in which scenarios are experienced impact anxiety. Thirteen participants engaged in all three scenarios, with heart rate (HR), skin temperature (SKT), and electrodermal activity (EDA) monitored, and self-reported anxiety assessments collected before, during, and after the study. Results showed no significant differences in average or maximum heart rates between the three scenarios. However, the fully immersive VR environment generally elicited higher HR peaks, higher EDA, and lower SKT, suggesting stronger physiological responses in some participants. Self-reported anxiety decreased after the VR experience, particularly for participants with moderate to high anxiety levels prior to the sessions, independent of the scenario order. These findings suggest that individual responses to VRET vary, emphasizing the need for personalized approaches rather than a one-size-fits-all solution. While larger studies are necessary to validate these outcomes, the results suggest that incorporating real-time biofeedback monitoring in VRET could allow for dynamic adjustments to exposure levels based on participants’ physiological responses, creating a more adaptive and therapeutic environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".