Feasibility of Virtual Reality Exercises at Home for Post–COVID-19 Condition: Cohort Study
Bibliographic record
Abstract
BACKGROUND: Between 30% to 76% of COVID-19 patients have persistent physical and mental symptoms, sometimes up to 9 months after acute COVID-19. Current rehabilitation is mostly focused on the physical symptoms, whereas experts have agreed on the need for a biopsychosocial approach. A novel approach such as virtual reality (VR) rehabilitation at home might benefit patients and therapists, especially considering the expected rush of patients with post-COVID-19 condition needing rehabilitation. OBJECTIVE: The aim of this study was to investigate the feasibility of self-administered VR exercises at home for post-COVID-19 condition. METHODS: This was a single-arm feasibility study in an outpatient care setting. Patients who needed physiotherapy because of post-COVID-19 condition were included as determined by the treating physiotherapist. Participants performed VR physical exercises at home for a period of 6 weeks and were allowed to perform VR mental exercise through applications available on the VR platform to reduce stress and anxiety and promote cognitive functioning. The main outcomes were related to feasibility (ie, duration and frequency of VR use), safety (ie, adverse events), patient satisfaction, and reasons to withdraw. Physical performance, daily activities, cognitive functioning, anxiety and depression, and the quality of life were measured before and after. RESULTS: In total, 48 patients were included; 1 (2%) patient did not start VR, and 7 (15%) patients withdrew, mostly due to dizziness. Almost 70% (33/47) of participants reported experiencing any adverse event during VR exercising. However, only 25% (9/36) recalled these events at the end of the intervention period. The majority (27/36, 75%) of the patients described VR as having a positive influence on their recovery, and the global satisfaction score was 67%. The average VR use was 30 minutes per session, 3-4 times a week for 3-6 weeks. The overall use of VR applications was almost equally distributed over the 3 sets of VR exercises (physical, relaxing, and cognitive). However, the use frequency of physical exercises seemed to decrease over time, whereas the use of cognitive and relaxation exercises remained stable. Physical performance and quality of life outcomes were significantly improved after 6 weeks. CONCLUSIONS: VR physical exercises at home is feasible and safe with good acceptance in a significant percentage of patient with post-COVID-19 condition. TRIAL REGISTRATION: ClinicalTrials.gov NCT04505761; https://clinicaltrials.gov/ct2/show/NCT04505761.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".