Wuqinxi Qigong as an Alternative Exercise for Improving Risk Factors Associated with Metabolic Syndrome: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: The improvement of living standards has led to increases in the prevalence of hypokinetic diseases. In particular, multifactorial complex diseases, such as metabolic syndrome, are becoming more prevalent. Currently, developing effective methods to combat or prevent metabolic syndrome is of critical public health importance. Thus, we conducted a systematic review to evaluate the existing literature regarding the effects of Wuqinxi exercise on reducing risk factors related to metabolic syndrome. Methods: Both English- and Chinese-language databases were searched for randomized controlled trials investigating the effects of Wuqinxi on these outcomes. Meanwhile, we extracted usable data for computing pooled effect size estimates, along with the random-effects model. Results: The synthesized results showed positive effects of Wuqinxi exercise on systolic blood pressure (SBP, SMD = 0.62, 95% CI 0.38 to 0.85, p < 0.001, I2 = 24.06%), diastolic blood pressure (DBP, SMD = 0.62, 95% CI 0.22 to 1.00, p < 0.001, I2 = 61.28%), total plasma cholesterol (TC, SMD = 0.88, 95% CI 0.41 to 1.36, p < 0.001, I2 = 78.71%), triglyceride (TG, SMD = 0.87, 95% CI 0.49 to 1.24, p < 0.001, I2 = 67.22%), low-density lipoprotein cholesterol (LDL-C, SMD = 1.24, 95% CI 0.76 to 1.72, p < 0.001, I2 = 78.27%), and high-density lipoprotein cholesterol (HDL, SMD = 0.95, 95% CI 0.43 to 1.46, p < 0.001, I2 = 82.27%). In addition, regression results showed that longer-duration Wuqinxi intervention significantly improved DBP (β = 0.00016, Q = 5.72, df = 1, p = 0.02), TC (β = −0.00010, Q = 9.03, df = 1, p = 0.01), TG (β = 0.00012, Q = 6.23, df = 1, p = 0.01), and LDL (β = 0.00011, Q = 5.52, df = 1, p = 0.02). Conclusions: Wuqinxi may be an effective intervention to alleviate the cardiovascular disease risk factors of metabolic syndrome.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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, 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".