Comparing the Relaxing Effects of Different Virtual Reality Environments in the Intensive Care Unit: Observational Study
Bibliographic record
Abstract
BACKGROUND: After a prolonged intensive care unit (ICU) stay, approximately 50%-75% of all critically ill patients suffer from neurocognitive late effects and a reduction of health-related quality of life. It is assumed that the noisy and stressful ICU environment leads to sensory overload and deprivation and potentially to long-term cognitive impairment. OBJECTIVE: In this study, we investigated three different virtual reality environments and their potentially restorative and relaxing effects for reducing sensory overload and deprivation in the ICU. METHODS: A total of 45 healthy subjects were exposed to three different environments, each 10 minutes in length (dynamic, virtual, natural, and urban environments presented inside the head-mounted display, and a neutral video on an ICU TV screen). During the study, data was collected by validated questionnaires (ie, restoration and sickness) and sensors to record physiological parameters (240 hertz). RESULTS: The results showed that the natural environment had the highest positive and restorative effect on the physiological and psychological state of healthy subjects, followed by the urban environment and the ICU TV screen. CONCLUSIONS: Overall, virtual reality stimulation with head-mounted display using a dynamic, virtual and natural environment has the potential, if directly used in the ICU, to reduce sensory overload and deprivation in critically ill patients and thus to prevent neurocognitive late effects.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".