Smart Aging Platform for Evaluating Cognitive Functions in Aging: A Comparison with the MoCA in a Normal Population
Bibliographic record
Abstract
Background: Smart Aging is a Serious games (SGs) platform in a 3D virtual environment in which users perform a set of screening tests that address various cognitive skills. The tests are structured as 5 tasks of activities of daily life in a familiar environment. The main goal of the present study is to compare a cognitive evaluation made with Smart Aging with those of a classic standardized screening test, the Montreal Cognitive Assessment (MoCA). Methods: One thousand one-hundred thirty-one healthy adults aged between 50 and 80 (M=64.3±8.3) were enrolled in the study. They received a cognitive evaluation with the MoCA and the Smart Aging platform. Participants were grouped according to their MoCA global and specific cognitive domain (i.e., memory, executive functions, working memory, visual spatial elaboration, language, and orientation) scores and we explored differences among these groups in the Smart Aging indices. Results: One thousand eighty-six older adults (M=64.0±8.0) successfully completed the study and were stratified according to their MoCA score: Group 1 with MoCA < 27 (n=360); Group 2 with 27≥MoCA<29 (n=453); and Group 3 with MoCA≥29 (n=273). MoCA groups significantly differed in most of the Smart Aging indices considered, in particular as concerns accuracy (ps<.001) and time (ps<.001) for completing most of the platform tasks. Group 1 was outperformed by the other two Groups and was slower than them in these tasks, which were those supposed to assess memory and executive functions. In addition, significant differences across groups also emerged when considering the single cognitive domains of the MoCA and the corresponding performances in each Smart Aging task. In particular, this platform seems to be a good proxy for assessing memory, executive functions, working memory and visual spatial processes. Conclusion: These findings demonstrate the validity of Smart Aging for assessing cognitive functions in normal aging. Future studies will validate this platform also in the clinical aging populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".