Virtual Reality Videos for Symptom Management in Hospice and Palliative Care
Bibliographic record
Abstract
Objective To learn more about the effect of virtual reality videos on patients' symptoms near the end of life, including which are most effective, how long the effect lasts, and which patients benefit the most. Patients and Methods We conducted a prospective study of 30 patients in a regional hospice and palliative care program from March 11, 2022, through July 14, 2023. Using a head-mounted display virtual reality, all participants viewed a 15-minute video of serene nature scenes with ambient sounds. Fifteen patients also participated in a second session of viewing bucket-list video clips they selected. Symptoms were measured with the revised Edmonton Symptom Assessment Scale before, immediately after, and 2 days after each experience. Participants rated their bucket-list selections by level of previous experience, strength of connection, and overall video quality. Functional status was also recorded. Results Nature scenes significantly improved total symptom scores (30% decrease, P <.001), as well as scores for drowsiness, tiredness, depression, anxiety, well-being, and dyspnea. The improved scores were not sustained 2 days later. Overall, bucket-list videos did not significantly improve symptoms. Neither previous experience with an activity nor a strong connection correlated with significant improvement; however, when patients rated video quality as outstanding, scores improved (31% decrease, P =.03). Patients with lower functional status tended to have more symptoms beforehand and improve the most. Conclusion Serene nature head-mounted display virtual reality scenes safely reduce symptoms at the end of life. Bucket-list experiences may be effective if they are high-quality. More infirm patients may benefit the most.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".