The optimal course and frequency of Tai Chi for knee osteoarthritis: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Objectives: Knee osteoarthritis (KOA) is a highly prevalent degenerative joint disease worldwide and an important cause of disability. Currently, medication and surgical interventions are commonly used in clinical practice, but there are limitations such as significant side effects and high medical costs. Tai Chi, as a non-pharmacologic intervention, is recommended for its safety and few adverse effects. However, there is still a lack of consensus on the optimal course and frequency of Tai Chi intervention, and there is an urgent need to optimize clinical intervention protocols. In order to scientifically assess the optimal course and frequency of Tai Chi for the treatment of KOA, this study integrates the existing evidence through a systematic review and meta-analysis, and aims to provide standardized protocols for Tai Chi training in clinical practice. Methods: PubMed, Embase, Cochrane Library, Web of Science, Scopus, EBSCO, CNKI, Wanfang Database, and VIP database were searched from establishment to August 30, 2025. Two reviewers independently extracted data and assessed the quality of the literature and the certainty of the evidence for each outcome according to the Cochrane Risk of Bias Tool and the Grading of Recommendations, Assessment, Development & Evaluation (GRADE) approach. Outcome measures included Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, WOMAC stiffness, WOMAC physical function, Visual Analogue Scale (VAS) pain, 36-item Short Form Health Survey (SF-36) Physical Component Summary (PCS), and SF-36 Mental Component Summary (MCS). For combined outcomes, standardized mean difference (SMD) and 95% confidence interval (CI) were calculated. Review Manager 5.4.1, Stata 15.0 and GRADE profiler software were used to statistically analyze and plot the included information. Results: < 0.01). Conclusion: This meta-analysis suggests that Tai Chi is effective in improving pain, stiffness, physical function, and physical health in patients with KOA. Patients with KOA should consider their specific conditions and choose a Tai Chi training protocol that suits their needs. The preliminary results of this meta-analysis indicate that for patients with pain and physical functional limitations, a long-term (>16 weeks)/three times weekly Tai Chi training regimen may be selected; for patients experiencing knee stiffness, a short-term (≤16 weeks)/three times weekly Tai Chi training regimen may be considered; and for KOA patients seeking to improve physical health through Tai Chi training, a short-term (≤16 weeks)/twice weekly Tai Chi training regimen may be selected. However, the number of large-sample studies in this review is limited, and more studies are urgently needed to confirm these results. Systematic review registration: Identifier-CRD42024599921, https://www.crd.york.ac.uk/PROSPERO/myprospero.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.031 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".