Virtual reality intervention alleviates dyspnoea in patients recovering from COVID-19 pneumonia
Bibliographic record
Abstract
Background Immersive virtual reality (iVR)-based digital therapeutics are gaining clinical attention in the field of pain management. Based on known analogies between pain and dyspnoea, we investigated the effects of visual respiratory feedback on persistent dyspnoea in patients recovering from coronavirus disease 2019 (COVID-19) pneumonia. Methods We performed a controlled, randomised, single-blind, crossover proof-of-concept study (feasibility and initial clinical efficacy) to evaluate an iVR-based intervention to alleviate dyspnoea in patients recovering from COVID-19 pneumonia. Included patients reported persistent dyspnoea (≥5 on a 10-point scale) and preserved cognitive function (Montreal Cognitive Assessment score >24). Assignment was random and concealed. Patients received synchronous (intervention) or asynchronous (control) feedback of their breathing, embodied via a gender-matched virtual body. The virtual body flashed in a waxing and waning visual effect that could be synchronous or asynchronous to the patient's respiratory movements. Outcomes were assessed using questionnaires and breathing recordings. Results Study enrolment was open between November 2020 and April 2021. 26 patients were enrolled (27% women; median age 55 years, interquartile range (IQR) 18 years). Data were available for 24 of 26 patients. The median rating on a 7-point Likert scale of breathing comfort improved from 1 (IQR 2) at baseline to 2 (IQR 1) for synchronous feedback, but remained unchanged at 1 (IQR 1.5) for asynchronous feedback (p<0.05 between iVR conditions). Moreover, 91.2% of all patients were satisfied with the intervention (p<0.0001) and 66.7% perceived it as beneficial for their breathing (p<0.05). Conclusion Our iVR-based digital therapy presents a feasible and safe respiratory rehabilitation tool that improves breathing comfort in patients recovering from COVID-19 infection presenting with persistent dyspnoea. Future research should investigate the intervention's generalisability to persistent dyspnoea with other aetiologies and its potential for preventing chronification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".