Effectiveness of balance training on pain and functional outcomes in knee osteoarthritis: A systematic review and meta-analysis
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Knee osteoarthritis (OA) is a musculoskeletal disorder that causes pain and increasing loss of function, resulting in reduced proprioceptive accuracy and balance. Therefore, the goal of this systematic review and meta-analysis is to evaluate the effectiveness of balance training on pain and functional outcomes in knee OA. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> “PubMed”, “Scopus”, “Web of Science”, “Cochrane”, and “Physiotherapy Evidence Database” were searched for studies conducted between January 2000 and December 2021. Randomized controlled trials (RCTs) that investigated the effectiveness of balance training in knee OA, as well as its effects on pain and functional outcome measures, were included. Conference abstracts, case reports, observational studies, and clinical commentaries were not included. Meta-analysis was conducted for the common outcomes, i.e., Visual Analog Scale (VAS), The Timed Up and Go (TUG), Western Ontario and McMaster Universities Arthritis Index (WOMAC). The PEDro scale was used to determine the quality of the included studies. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> This review includes 22 RCTs of which 17 articles were included for meta-analysis. The included articles had 1456 participants. The meta-analysis showed improvement in the VAS scores in the experimental group compared to the control group [ <ns4:italic>I</ns4:italic> <ns4:sup>2</ns4:sup> = 92%; mean difference= -0.79; 95% CI= -1.59 to 0.01; p<0.05] and for the WOMAC scores the heterogeneity ( <ns4:italic>I</ns4:italic> <ns4:sup>2</ns4:sup> ) was 81% with a mean difference of -0.02 [95% CI= -0.44 to 0.40; p<0.0001]. The TUG score was analyzed, the <ns4:italic>I</ns4:italic> <ns4:sup>2</ns4:sup> was 95% with a mean difference of -1.71 [95% CI= -3.09 to -0.33; p<0.0001] for the intervention against the control group. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Balance training significantly reduced knee pain and improved functional outcomes measured with TUG. However, there was no difference observed in WOMAC. Although due to the heterogeneity of the included articles the treatment impact may be overestimated. </ns4:p> <ns4:p> <ns4:bold>Registration:</ns4:bold> The current systematic review was registered in PROSPERO on 7th October 2021 (registration number <ns4:ext-link xmlns:ns5="http://www.w3.org/1999/xlink" ext-link-type="uri" ns5:href="https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=276674">CRD42021276674</ns4:ext-link> ). </ns4:p>
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".