Effect of Tai Chi on Cognitive Function among Older Adults with Cognitive Impairment: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cognitive decline occurs in all persons during the aging process and drugs can only alleviate symptoms and are expensive. Some researches demonstrated that Tai Chi had potential in preventing cognitive decline while others' results showed Tai Chi had no influence on cognitive impairment. Therefore, we conduct a systematic review and meta-analysis to assess the efficacy and safety of cognitive impairment patients practicing Tai Chi. METHODS: A comprehensive literature search was carried out in multiple databases, including PubMed, Cochrane, MEDLINE (Ovid), Web of Science, Embase, Scopus, PsycInfo (Ovid), CKNI, Wan Fang, VIP, SinoMed, and ClinicalTrails, from their inception to 1 July 2020 to collect randomized controlled trials about practicing Tai Chi for patients with cognitive impairment. Primary outcomes included changes of cognitive function and secondary outcomes included changes of memory functions. Data were extracted by two independent individuals and Cochrane Risk of Bias tool version 2.0 was applied for the included studies. Systematic review and meta-analysis were performed by RevMan 5.3 software. RESULTS: The results included 827 cases in 9 studies, of which 375 were in the experimental group and 452 were in the control group. Meta-analysis showed that Mini-Mental State Examination WMD = 1.52, 95% CI [0.90, 2.14]; Montreal Cognitive Assessment WMD = 3.5, 95% CI [0.76, 6.24]; Clinical Dementia Rating WMD = -0.55, 95% CI [-0.80, -0.29]; logical memory delayed recall WMD = 1.1, 95% CI [0.04, 2.16]; digit span forward WMD = 0.53, 95% CI [-0.65, 1.71]; and digit span backward WMD = -0.1, 95% CI [-0.38, 0.19]. No adverse events were reported in the included articles. CONCLUSION: There is limited evidence to support that practicing Tai Chi is effective for older adults with cognitive impairment. Tai Chi seems to be a safe exercise, which can bring better changes in cognitive function score.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| 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".