Can Traditional Board Games Prevent or Slow Down Cognitive Impairment? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Traditional board games can entail significant skills encompassing several cognitive functions across different domains. Therefore, they may potentially represent effective cognitive interventions in the aging population with or without Alzheimer's disease or other types of dementia. OBJECTIVE: We aimed at verifying the hypothesis that traditional board games can prevent or slow down cognitive decline, through a systematic review on traditional board games and dementia. METHODS: We searched five databases with tailored search strings. We included studies assessing the impact of board games on elderly subjects at risk of or suffering from cognitive impairment, or subjects with cognitive impairment irrespective of age. Studies where the effect of board games was not separated by cards or other games were excluded. A meta-analysis was performed for specific cognitive and non-cognitive outcomes. RESULTS: Board games improved mental function, as measured by Montreal Cognitive Assessment (p = 0.003) and Mini-Mental State Examination (p = 0.02). Ska and Go improved Trail Making Test -A, while Mahjong improved executive functions. There was no consistent effect across different games on Digit Span or Categorical Fluency. Chess improved quality of life measured with the WHO-QoL-OLD scale (p < 0.00001). Mahjong temporarily improved depressive symptoms. Go increased BDNF levels and left middle temporal gyrus and bilateral putamen metabolism. CONCLUSIONS: Traditional board games may slow global cognitive decline and improve the quality of life in elderly subjects. Different games have varying impacts on specific cognitive domains, possibly mediated by functional and biological factors.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".