The Effect of a Virtual Reality-Mediated Gamified Rehabilitation Program on Pain, Disability, Function, and Balance in Knee Osteoarthritis: A Prospective Randomized Controlled Study
Bibliographic record
Abstract
Background: This prospective randomized controlled study is the first study that evaluates the disease-specific gamification through virtual reality (VR) glasses on pain, disability, functionality, and balance in knee osteoarthritis (OA). Materials and Methods: The demographic data of the patients were recorded. A total of 73 patients were divided into two groups (35 in experimental group and 38 in control group). All patients were evaluated with pain (visual analog scale [VAS]), functionality (Lysholm functional knee score [LFKS], 6 minutes walking test [6MWT]), disability (Western Ontario and McMaster Universities Arthritis Index [WOMAC]), and balance ( Berg Balance Scale [BBS] ) before treatment, after treatment (3th weeks), and 4 weeks after treatment (7th weeks). In the experimental group, plus the conservative treatment, a total of 15 sessions of a disease-specific gamification through VR glasses were applied. Results: VAS and WOMAC scores of the experimental group were lower at the 3th and 7th weeks than those of the control group ( P = 0.005, P = 0.000), ( P = 0.000). LFKS of the experimental group was higher at the 3th and 7th weeks than that of the control group ( P = 0.005, P = 0.013). No difference was found between the groups in terms of 6MWTs ( P > 0.05). BBS score of the experimental group was higher in the 7th week than that of the control group ( P = 0.021). Conclusion: In knee OA, the disease-specific gamification through VR glasses added to the conservative treatment has a positive effect on pain, functionality, and balance. Side effects such as mild nausea, headache that did not last long, require additional treatment. In light of this, disease-specific gamification through VR glasses is effective and safe in knee OA, more studies that reveal the long-term effect on structural healing must be planned.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".