THE EFFECTS OF VIRTUAL REALITY-BASED EXERCISE ON PAIN AND FUNCTION IN OLDER ADULTS WITH KNEE OSTEOARTHRITIS: A RANDOMIZED CONTROLLED STUDY
Bibliographic record
Abstract
Introduction: This study aimed to investigate the effects of virtual reality therapy on pain, joint stiffness, physical function, balance, and fall risk in patients with knee osteoarthritis. Materials and Method: A total of fifty-four patients with primary knee osteoarthritis were randomly assigned into three equal groups. The first group received conventional physiotherapy, the second group received conventional physiotherapy combined with virtual reality-based training, and the third group received virtual reality-based training alone. All participants underwent fifteen sessions over a three-week period. Pain was evaluated using the visual analog scale; functional status, stiffness, and physical function were assessed with the Western Ontario and McMaster Universities Osteoarthritis Index. Balance was assessed with the Berg Balance Scale, and fall risk was evaluated using the Tetrax posturography system. Results: Statistically significant improvements were found in all groups in terms of pain, stiffness, physical function, and balance after treatment (p < 0.001). However, pain, physical function, and total osteoarthritis index scores were significantly better in the first and second groups compared to the third group (p < 0.05). In the stiffness subscale, the first group showed greater improvement than the second group (p = 0.026). No significant differences were detected among the groups in balance or fall risk scores (p > 0.05). Conclusion: Although virtual reality therapy alone has positive effects on patients with knee osteoarthritis, it appears to be less effective than conventional physiotherapy or its combination with virtual reality. Virtual reality may serve as a supportive method within conventional rehabilitation programs. Keywords: Osteoarthritis; Pain; Virtual Reality; Accidental Falls.
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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 | 0.005 | 0.003 |
| 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 | 0.000 |
| 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".