Smartkuber: A Serious Game for Cognitive Health Screening of Elderly Players
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to design and develop a serious game for cognitive health screening of the elderly, namely Smartkuber, and evaluate its construct, criteria (concurrent and predictive), and content validity, assessing its relationship with the Montreal Cognitive Assessment (MoCA) test. Furthermore, the study aims to evaluate the elderly players' game experience with Smartkuber. SUBJECTS AND METHODS: Thirteen older adults were enrolled in the study. The game was designed and developed by a multidisciplinary team. The study follows a mixed methodological approach, utilizing the In-Game Experience Questionnaire to assess the players' game experience and a correlational study, to examine the relationship between the Smartkuber and MoCA scores. The learning effect is also examined by comparing the mean game scores of the first and last game sessions of each player (Delta scores). RESULTS: All 13 participants (mean age: 68.69, SD: 7.24) successfully completed the study. Smartkuber demonstrated high concurrent validity with the MoCA test (r = 0.81, P = 0.001) and satisfying levels of predictive and content validity. The Delta scores showed no statistically significant differences in scoring, thus indicating no learning effects during the Smartkuber game sessions. CONCLUSIONS: The study shows that Smartkuber is a promising tool for cognitive health screening, providing an entertaining and motivating gaming experience to elderly players. Limitations of the study and future directions are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".