Nonimmersive Virtual Reality-Based Exercises Improve Muscle Excitability and Balance in Patients with Knee Osteoarthritis: A Sham-Controlled Study
Bibliographic record
Abstract
Objective: Pain, decreased muscle strength, regression in activities of daily living (ADL), narrowing of joint range of motion (ROM), impairment of proprioceptive sense, and deterioration in static-dynamic balance are frequently observed in knee osteoarthritis (KOA). The aim of this study is to examine the effect of nonimmersive virtual reality (NIVR) application on muscle excitability and motor neuron pool activation level around the knee, balance, proprioception, physical function level, independence levels in ADL, muscle endurance, and patient satisfaction in patients with KOA. Materials and Methods: Forty patients with KOA were randomized to an experimental group (EG; n = 20) and a sham-controlled group (SG; n = 20). The EG received 45 minutes of traditional physiotherapy and 30 minutes of NIVR-based exercises for 3 weeks, 5 days a week, while the SG received traditional physiotherapy and a sham virtual reality (VR) application for 30 minutes for the same period. Primary outcomes were muscle excitability (maximal voluntary contraction [MVC]), motor neuron pool activation level, and balance. Secondary outcomes included proprioception, endurance, independence in ADL, pain level, physical functional condition (Western Ontario and McMaster Universities Arthritis Index [WOMAC]), and treatment satisfaction. Results: Findings showed a significant difference in endurance, pain level, and independence in ADL scores in favor of the EG (all values, P < 0.05). Moreover, WOMAC, static and dynamic balance (differences timed up and go [TUG]: EG: −4.75, SG: −2.10, P = 0.02, d = 0.907), MVC, and proprioception scores also showed the highest differences (most values, P < 0.001). Conclusion: Nonimmersive VR applications are a feasible approach for KOA and are effective approaches for increasing muscle excitability, static and dynamic balance, muscle endurance, proprioception, independence in ADL, treatment satisfaction, and reducing pain intensity in KOA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".