Playing Mahjong for 12 Weeks Improved Executive Function in Elderly People With Mild Cognitive Impairment: A Study of Implications for TBI-Induced Cognitive Deficits
Bibliographic record
Abstract
Background : Mild cognitive impairment (MCI) is common among old people. So far effective treatment is lacking that can stabilize or reverse cognitive decline associated with MCI. Recent studies suggest that playing mahjong may improve the attention and memory in the elderly. But its effect on the executive function remains unknown. Methods : Fifty-six old people (74.3±4.3 years of age) with MCI from the first Nursing Home of Nanchong city were randomized into mahjong and control groups (N=28, each group). Subjects in the mahjong group played mahjong 3 times a week for 12 weeks while people in the control group only assumed normal daily activity. The executive function was evaluated using the Montreal Cognitive Assessment–Beijing edition (MoCA-B), the Shape Trail Test (STT) and The Functional Activities Questionnaire (FAQ) before, and at 6 and 12 weeks after the mahjong administration. Results : There were no baseline differences in MoCA-B, STT and FAQ between the two groups. The scores of MoCA-B, STT, and FAQ, however, improved significantly in the mahjong group but not in the control group after the 12-week mahjong administration. Significant correlations were also found between STT and FAQ scores. Conclusions: Playing Mahjong for 12 weeks improved the executive function of the elderly with MCI. Because Mahjong is a simple low-cost entertainment activity, it could be applied to slow down or reverse the progression of cognitive decline in people with MCI including those with traumatic brain injury.
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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 | 0.001 |
| 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".