Effects of Active Video Games Combined with Conventional Physical Therapy on Perceived Functionality in Older Adults with Knee or Hip Osteoarthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Osteoarthritis (OA) leads to functional decline in older adults. This study aimed to evaluate the effectiveness of active video games (AVGs) as a complement to conventional physical therapy (CPT) in improving functional disability. Methods: Sixty participants were randomly assigned to an experimental group (EG, n = 30, 68.7 ± 5.4 years), which received CPT combined with AVGs, or to a control group (CG, n = 30, 69.0 ± 5.5 years), which received CPT alone. Sessions were performed three times a week for ten weeks. Functional disability was assessed using the WOMAC index before, during, and after the intervention. Secondary outcomes included the Global Rating of Change (GRoC), the Minimal Clinically Important Difference, and patient trajectories through functional disability strata. Results: The EG showed progressive improvements in all WOMAC scores, with moderate to large increases by the end of the intervention, while the CG only showed significant changes in the later stages. The EG demonstrated greater improvements in WOMAC pain and the GroC scale (p < 0.05), maintaining most of the gains at follow-up, whereas the CG showed regression. Additionally, the EG had a higher proportion of responders, particularly for pain, while the CG had a predominance of non-responders and adverse responders. In the EG, 70% improved their functional disability stratification compared to 50% in the CG. Conclusion: Integration of AVGs with CPT further improves perceived functional disability in older adults with OA. Future research should explore these findings further.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".