The Effectiveness and Safety of Tai Chi on Knee Pain: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Although Tai Chi has shown potential benefits for managing chronic pain, its clinical effectiveness specifically for knee pain remains inconclusive. Methods: We systematically searched ten electronic databases for randomized controlled trials (RCTs) investigating the effects of Tai Chi on knee pain. Results: This systematic review and meta-analysis included 11 RCTs involving 706 participants; among them, three studies (n = 169) were eligible for meta-analysis. A comprehensive search of ten electronic databases was conducted up to March 2025. The included RCTs were conducted in the United States (n = 5), China (n = 3), South Korea (n = 2), and Turkey (n = 1). Compared to health education, Tai Chi significantly improved knee pain, as measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score (mean difference (MD) = −0.60; 95% CI: −6.52 to −3.28; p < 0.00001) and the Visual Analogue Scale (VAS) (MD = −1.44; 95% CI: −1.95 to −0.93; p < 0.00001). Tai Chi also significantly improved knee function compared to health education (WOMAC function score—MD = −13.49; 95% CI: −17.11 to −9.87; p < 0.00001). Four RCTs comparing Tai Chi with no intervention reported favorable effects on knee pain and function; however, a meta-analysis was not possible due to limited data. In contrast, two studies comparing Tai Chi with active controls, such as physical therapy and resistance training, found no significant differences in pain or functional outcomes. Two studies reported increased knee pain during initial Tai Chi sessions, but no adverse events occurred after postural corrections. Conclusions: While Tai Chi appears promising for knee pain management, further large-scale, high-quality RCTs with rigorous methodology are needed to establish definitive evidence.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".