Effectiveness and Safety of Tuina Therapy Combined With Yijinjing Exercise for Neck Pain: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Neck pain with high incidence and recurrence rates significantly impairs patients’ quality of life and imposes a considerable economic burden. Traditional Chinese medicine therapies such as Yijinjing exercise and Tuina have shown promising efficacy in alleviating the local symptoms of neck pain. However, there is currently insufficient high-level evidence to robustly support these findings. Objective This study aims to evaluate the efficacy and safety of combining Yijinjing exercise with Tuina for the treatment of neck pain. Methods PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, Chinese Biomedical Database, VIP Chinese Science and Technology Periodicals Full-Text database, and Wanfang database will be systematically searched for all relevant randomized controlled trials (RCTs) from their inception to September 2025, without language or publication status restrictions. The Cochrane Risk of Bias 2 assessment tool will be used to evaluate the risk of bias in the included studies, and the GRADE (Grades of Recommendation, Assessment, Development, and Evaluation) system will be employed to grade the quality of evidence. Heterogeneity will be evaluated through I2 statistics and Cochran’s Q test: a fixed-effect model will be used when I2<50% and P≥.01. If I2≥50% or P<.01, subgroup analysis will be conducted. When heterogeneity still exists, sensitivity analysis or exploratory subgroup analysis will be performed. If it cannot be explained ultimately, the random-effects model will be adopted and the GRADE evidence level will be reduced. Results As of June 2025, we have completed the preliminary screening of titles and abstracts for 573 studies. The full-text screening is expected to be completed by September 2025, and data analysis is planned to be completed by December 2025. About two-thirds of the studies were published after 2015. Geographically, the samples in the studies were highly concentrated in Asia. The results were comprehensively developed around the core outcomes. The primary outcome was presented by changes in the visual analog scale. The secondary outcomes were evaluated by the neck disability index, self-rating anxiety scale score, mean vertebral artery blood flow velocity, and Cobb angle. Conclusions If the results of this study confirm the effectiveness of massage combined with Yijinjing, it can provide a direction for the nonpharmaceutical treatment of neck pain. However, some studies have risks of bias such as insufficient standardization of massage operations and difficulty in implementing blinding methods. The expected heterogeneity is significant due to differences in intervention plans and patients’ cultural backgrounds, and the original RCTs are few and regionally concentrated, with limited extrapolation of conclusions. In the future, it is necessary to optimize the plan and supplement data through high-quality multicenter research to enhance reliability. Trial Registration PROSPERO CRD420251026508; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251026508 International Registered Report Identifier (IRRID) DERR1-10.2196/77864
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.052 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".