Development and Validation of an Interactive Game-Based Digital Cognitive Assessment Tool
Bibliographic record
Abstract
Abstract Early screening and intervention are essential for mitigating cognitive decline, particularly for the prevention of Alzheimer’s disease (AD) through timely detection of mild cognitive impairment (MCI). Despite advances in digital assessments, barriers still limit their adoption among older Chinese adults. This study developed a digital cognitive assessment tool integrating gaming scenarios and Chinese cultural elements for screening MCI in China (IGD-CAT) and evaluated its concurrent and discriminative validity. A theoretical framework was established through literature review, expert panel, and Delphi survey, and IGD-CAT was created via interdisciplinary collaboration. A total of 218 participants from communities and hospitals completed traditional assessments followed by IGD-CAT and were classified as normal cognition (NC) or MCI. Concurrent validity was examined through correlations with Montreal Cognitive Assessment (MoCA) scores, and discriminatory validity was assessed using a random forest model. IGD-CAT comprises 14 tasks across seven domains: time orientation, attention, memory, language, calculation, visuospatial ability, and executive function. Among 196 participants (mean age 63.6 ± 5.81 years), 93 were classified as MCI and 103 as NC. Task scores correlated positively with MoCA (r = 0.140–0.387), while completion times correlated negatively (r=-0.476 to -0.168). The total IGD-CAT score correlated with MoCA (r = 0.529, P<.001), whereas total completion time correlated negatively (r=-0.549, P<.001). In the test set, the random forest model achieved 89.65% sensitivity, 96.55% specificity, and an AUC of 0.96. IGD-CAT covers multiple cognitive domains, is time-efficient, and age-friendly. With high sensitivity and specificity, it is suitable for application in primary care, community, hospital, and home settings.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".