The Effect of Different Traditional Chinese Exercises on Blood Lipid in Middle-Aged and Elderly Individuals: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Although the impact of physical exercise on blood lipids is well documented, less information is available regarding the effect of traditional Chinese exercises (TCEs), and it is unclear what the best TCE treatment for dyslipidemia in middle-aged and elderly individuals is. The aim of this study was to systematically assess the effects of TCEs (Taijiquan, TJQ; Wuqinxi, WQX; Baduanjin, BDJ; Liuzijue, LZJ; Yijinjing, YJJ; Dawu, DW) on blood lipids in middle-aged and elderly individuals. Chinese and English databases were searched, including PubMed, China National Knowledge Infrastructure, Wanfang Database, Chongqing VIP, and Web of Science. A total of 42 randomized controlled trials (RCTs) including 2977 subjects were analyzed. Outcome indicators include total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triacylglyceride (TAG), and high-density lipoprotein cholesterol (HDL-C). Summary mean differences (MD) were calculated using pairwise and network meta-analysis with a random-effects model. The results of this study showed that compared to non-exercise intervention (NEI), all six kinds of TCE treatment had some kind of influence on blood lipid indicators, among which WQX and TJQ could improve all four blood lipid indicators, whereas BDJ was effective on three indicators but not on TC. The results of cumulative probability ranking showed that WQX (84.9%, 73.8%, 63.4%, 63.1% to TC, TAG, HDL-C, LDL-C, respectively) was at the top spot being the best intervention, followed by BDJ (55.6%, 83.7%, 68.4%, 56.1%) and TJQ (73.7%, 47.6%, 63.1%, 54.1%). The network meta-analysis of RCTs demonstrates that WQX may be the best TCE treatment for dyslipidemia in middle-aged and elderly individuals.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".