A Virtual Reality–Supported Intervention for Pulmonary Rehabilitation of Patients With Chronic Obstructive Pulmonary Disease: Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The uptake of traditional pulmonary rehabilitation classes by patients with chronic obstructive pulmonary disease (COPD) is poor because of personal factors that prevent accessibility to the venue. Therefore, there is a need for innovative methods of pulmonary rehabilitation, and virtual reality (VR) could be a promising technology for patients with COPD to access services remotely. OBJECTIVE This study aimed to investigate whether VR improves compliance with pulmonary rehabilitation among patients with COPD, a particularly vulnerable patient group (Medical Research Council [MRC] 4 or 5), and whether VR provides a credible alternative to traditional pulmonary rehabilitation programs. METHODS This was an 8-week patient trial using an innovative VR pulmonary rehabilitation program. A purposive sample of 10 patients with COPD graded MRC 4 or 5 and registered at a selected health care center and a hospital in Cumbria, United Kingdom, were included. Qualitative (focus groups and interviews) data were collected, and to further support the qualitative findings, quantitative data (self-report patient surveys) were gathered before and after the 8-week trial. The 5 self-reported surveys included the Patient Activation Measure, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Short Physical Performance Battery, and the Edmonton Frail Scale. RESULTS In a thematic analysis of the qualitative data, 11 themes emerged specific to delivering pulmonary rehabilitation using VR. The quantitative data further support the qualitative findings by revealing significant improvements in all physical measures. CONCLUSIONS Overall, this study demonstrates how remotely supervised VR-based pulmonary rehabilitation could help to overcome current issues and limitations associated with providing this service to patients with COPD at scale.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".