Measuring cognitive performance in older adults through completion time and accuracy in brain games
Bibliographic record
Abstract
Objectives This study aimed to compare the cognitive performance of brain games in older adults with different sociodemographic, cognitive function, and mental health and determine their associated factors. Methods This observational study recruited 101 participants (mean age = 72.3 ± 6.83 years) from institutions for older adults. All participants provided data on sociodemographic, cognitive function (Montreal Cognitive Assessment [MoCA]), anxiety (Beck Anxiety Inventory), and depression levels (Beck Depression Inventory). The Jenga, as a brain game, was used to indicate cognitive performance by measuring the completion time and accuracy in building a 10-level Jenga tower. Data were analyzed using the Chi-square test and binary logistic regression. Results Completion time and accuracy of Jenga games were significantly different among age groups, education levels, and cognitive function (All P < 0.05) but not in gender, marital status, anxiety, and depression levels (All P > 0.05). Age is significantly associated with both time and accuracy, with older individuals (≥75) showing a greater likelihood of taking longer time and lower accuracy to complete the task, while a higher education level is associated with significantly higher odds of shorter completion time and higher accuracy; normal cognitive function (MoCA ≥26) is associated with both shorter completion time and better accuracy in the task (All P < 0.05). Conclusion The findings show that age, education, and cognitive function may affect completion time and accuracy, suggesting the potential for Jenga to be used as an assessment tool or cognitive training. However, the generalization of this study may be limited to the institutionalized population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".