Effects of exergames on rehabilitation outcomes in patients with osteoarthritis. A systematic review
Bibliographic record
Abstract
PURPOSE: To analyze the effects of exergames on rehabilitation outcomes in osteoarthritis (OA) patients. MATERIALS AND METHODS: A systematic review was reported according to the PRISMA statement. Randomized controlled trials (RCTs) were searched in Pubmed, Scopus, WoS, CINAHL, and PEDro (inception to November 2023). Studies that applied non-immersive exergames and assessed physical, functional, cognitive, pain, and psychosocial outcomes were included. Comparisons were other exercise modalities and non-intervention. Methodological quality was assessed with PEDro scale, and risk of bias (RoB) was assessed with Cochrane RoB-2 tool. RESULTS: Eight studies were included (total of participants = 401). The mean PEDro score was 6.1, and seven studies had high RoB. Seven studies involved knee OA and one cervical OA. The most frequent duration for interventions was four weeks. Exergames were more effective than controls in at least one outcome in all studies. The outcomes for which exergames were most effective were functional disability, postural balance, muscle strength, proprioception, gait, range of motion, pain, quality of life, depression, and kinesiophobia. CONCLUSION: Non-immersive exergames constitute an effective strategy for optimizing several relevant outcomes in rehabilitation. However, more RCTs with high methodological quality are required to deepen the knowledge about the multidimensional effects of exergames in OA patients.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".