Tai Chi exercise versus rehabilitation for the elderly with cerebral vascular disorder: a single-blinded randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Cerebral vascular disorder (CVD) might result in a quantifiable decrease in quality of life, which is determined not only by the neurological deficits but also by impairment of cognitive functions. There are few studies that report on the cognitive effect of Tai Chi exercise (Tai Chi) on the elderly with CVD. The purpose of the present study was to examine the cognitive effect of Tai Chi on the elderly with CVD using P300 measurement, in addition to the General Health Questionnaire (GHQ) and Pittsburgh Sleep Quality Index (PSQI). METHODS: A total of 34 patients with CVD were recruited from outpatient Akistu-Kounoike Hospital and randomly assigned to receive Tai Chi (n= 17) or rehabilitation (n= 17) in group sessions once a week for 12 weeks. To examine the time courses of each score (P300 amplitude, P300 latency, GHQ score and PSQI score), repeated-measures analysis of variance was carried out with groups and time as factors. RESULTS: For the time courses of P300 amplitudes and latencies, there were no significant effects of interaction between group and time. However, significant time-by-group interactions were found for Sleep Quality (P= 0.006), GHQ total score (P= 0.005), anxiety/insomnia score (P= 0.034), and severe depression score (P= 0.020). CONCLUSIONS: Tai Chi might therefore be considered a useful non-pharmacological approach, along with rehabilitation, for the maintenance of cognitive function in the elderly with CVD and might be a more useful non-pharmacological approach for the improvement of sleep quality and depressive symptoms in the elderly with CVD than rehabilitation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".