Perceptions of physiotherapists about virtual reality using exergames in cardiovascular rehabilitation: a qualitative study
Bibliographic record
Abstract
Background/Aims Cardiovascular rehabilitation is a secondary preventative care model for outpatients. Despite its benefits, cardiac rehabilitation is greatly underused. Virtual reality‑based therapy could be a complementary treatment for traditional cardiac rehabilitation which promotes physical activity, enhancing motivation and increasing adherence to rehabilitation programmes. This qualitative study examined physiotherapists' perceptions toward using virtual reality‑based therapy based on exergames as part of cardiac rehabilitation programmes. Methods This qualitative study used focus groups to evaluate the perceptions of nine physiotherapists who have used virtual reality‑based therapy with exergames in cardiac rehabilitation. Results Participants' attitudes towards virtual reality‑based therapy were overall positive, and found it could improve patients' adherence to a cardiac rehabilitation programme. Barriers to VRBT were also identified, including technical difficulties, patients being insecure about using a new therapeutic modality and managing the level of effort during the session. Suggestions for improvements included training for clinical staff, and having a introductory session for patients to help get used to the therapy and to find out what games they would prefer to play. Conclusions Virtual reality‑based therapy can provide a change from the traditional treatment routine; however, there are some barriers, including potential safety concerns and technical issues. Feedback on haemodynamic parameters and identification of signs and symptoms of a cardiac event could be incorporated into games made specifically for patients undergoing cardiac rehabilitation. Implications for practice Finding different ways to engage patients is needed. Based on the perceptions of physiotherapists from this study, VRBT could be one of the solutions to reach these goals for patients receiving cardiac rehabilitation.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".