A Single Session of Virtual Reality Improved Tiredness, Shortness of Breath, Anxiety, Depression and Well-Being in Hospitalized Individuals with COVID-19: A Randomized Clinical Trial
Bibliographic record
Abstract
Background: In 2020, the world was surprised by the spread and mass contamination of the new Coronavirus (COVID-19). COVID-19 produces symptoms ranging from a common cold to severe symptoms that can lead to death. Several strategies have been implemented to improve the well-being of patients during their hospitalization, and virtual reality (VR) has been used. However, whether patients hospitalized for COVID-19 can benefit from this intervention remains unclear. Therefore, this study aimed to investigate whether VR contributes to the control of pain symptoms, the sensation of dyspnea, perception of well-being, anxiety, and depression in patients hospitalized with COVID-19. Methods: A randomized, double-blind clinical trial was designed. Patients underwent a single session of VR and usual care. The experimental group (n = 22) received VR content to promote relaxation, distraction, and stress relief, whereas the control group (n = 22) received non-specific VR content. Results: The experimental group reported a significant decrease in tiredness, shortness of breath, anxiety, and an increase in the feeling of well-being, whereas the control group showed improvement only in the tiredness and anxiety. Conclusions: VR is a resource that may improve the symptoms of tiredness, shortness of breath, anxiety, and depression in patients hospitalized with COVID-19. Future studies should investigate the effect of multiple VR sessions on individuals with COVID-19.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".