Effectiveness of Tai Chi as a non-invasive intervention for mild cognitive impairment in the elderly: A comprehensive review and meta-analysis
Bibliographic record
Abstract
The aging population warrants the increase of mild cognitive impairment (MCI) prevalence, a condition that could progress to dementia. Efforts have been made to improve the MCI and prevent its progression, including the introduction of Tai Chi, a Chinese traditional exercise. The aim of this systematic review and meta-analysis was to evaluate the efficacy of Tai Chi in attenuating MCI among the elderly population. Records investigating the effect of Tai Chi exercise intervention on cognitive function among elderly patients were searched systematically from PubMed, ScienceDirect, Google Scholar, and Europe PMC as of April 13, 2023. The risk of bias (RoB 2.0) quality assessment was employed in the quality appraisal of the studies included. Review Manager 5.4.1 was used for data extraction and meta-analysis, where the standard mean difference (SMD) and 95% confidence interval (95%CI) were computed. Eight randomized control trials with a total of 1379 participants were included in this meta-analysis. Six trials assessed Montreal Cognitive Assessment scores, where its pooled analysis suggested that Tai Chi was as effective as conventional exercise (SMD=0.15, 95%CI: -0.11 to 0.40, p=0.26). However, pooled analysis of the Mini-Mental Status Examination suggested that Tai Chi intervention more effectively improved cognitive function and reduced the rate of cognitive impairment in elderly patients (SMD=0.36, 95%CI: 0.18 to 0.54, p<0.01) as compared to the control group. This systematic review and meta-analysis suggest that, in some extent, Tai Chi is efficacious in improving cognitive function and slowing down the rate of cognitive impairment among elderly patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".