Evaluation of the efficacy of Tai Chi on the cognitive function of patients with mild cognitive dysfunction and research on its mechanism
Bibliographic record
Abstract
Introduction: Studies have revealed that Tai Chi can enhance cognitive functions among patients with mild cognitive impairment (MCI). However, the precise mechanisms underlying this improvement remain elusive. Methods: Consequently, we conducted a study involving 54 elderly inpatients with MCI residing in a combined medical and elderly care facility in Chengdu, who were randomly divided into three groups: a control group engaging in daily living activities, a Tai Chi group that performed Tai Chi exercises in addition to control group activities, and a walking group that undertook walking activities as a supplement to the control group regimen. The intervention period lasted for 24 weeks, comprising 12 weeks of exercise and an additional 12 weeks of follow-up. The Montreal Cognitive Assessment (MoCA), Trail Making Test-A (TMT-A), Auditory Verbal Learning Test (AVLT), and biochemical assessments (measuring brain-derived neurotrophic factor, BDNF, and platelet factor 4, PF4) were administered to investigate overall cognitive function, executive function, memory capacity, and changes in serum concentrations of BDNF and PF4 before, after, and during the follow-up period. Data were analyzed using IBM SPSS 26.0, with statistical methods encompassing descriptive analysis, ANOVA, rank-sum test, repeated measures ANOVA, and generalized estimating equations. Results: Our findings indicated that after 24 weeks of intervention, the Tai Chi group exhibited improvements in cognitive function, executive function, and memory compared to the control group. This enhancement may be attributed to an increased expression of serum BDNF. Discussion: In conclusion, our study underscores the potential of Tai Chi in ameliorating cognitive function among elderly patients with mild cognitive impairment, thereby offering significant implications for clinical prevention and treatment strategies targeting this condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".