Can Yoga Therapy Help to Decrease Osteoarthritis-related Pain in Adults with Knee Osteoarthritis?
Bibliographic record
Abstract
Objective: The objective of this selective EBM review is to determine whether or not yoga therapy can help to decrease osteoarthritis-related pain in adults with knee osteoarthritis. Study Design: A review of two randomized controlled trials and one randomized control trial pilot study that were published in English in peer-reviewed journals after 2007. Data Sources: All articles were selected from peer-reviewed journals and found via PubMed. Outcome(s) Measured: Patient perception of pain using either a Walking Numerical Rating Scale (WNRS) and/or a Western Ontario and McMaster Universities OA Index scale (WOMAC). Results: All three studies found a significant decrease in pain levels among patients participating in a hatha yoga intervention as compared with those participating in alternate therapeutic programs and/or controls. In Ebnezar et al. the mean walking pain score after 90 days of yoga therapy reduced 64.88% from baseline (P<0.001), while the control group reduced 41.98% (P<0.001). The Cheung et al. 2017 study reported a decrease in WOMAC pain scores by 2.8 points as compared with a reduction of 1.2 amongst an Aerobics and Strengthening Exercise program group and an increase of 0.2 amongst an education control group (P<0.05). They also reported a significant difference in both WOMAC and visual analog pain score means adjusted for baseline at 8 weeks between the yoga and ASE interventions and between the yoga and control groups. The Cheung et al. 2014 RCT also found found a significantly lower (pvalue=0.01) mean self-reported WOMAC pain score in the yoga group as compared to a wait-list control group at 8 weeks. Conclusions: The results of all three RCTs reviewed provides evidence that regular hatha yoga practice can reduce patient’s perception of knee osteoarthritis-related pain while actively engaged in a yoga program.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".