Feasibility and Effects of Cognitive Training with the COGNIPLAT Game Platform in Elderly with Mild Cognitive Impairment: Pilot Randomized Controlled Trial
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the effectiveness of a new multi-domain multimodal cognitive training game platform, COGNIPLAT, in improving cognitive performance in elderly with mild cognitive impairment (MCI). The platform combines standard serious games and cognitive stimulation leveraging virtual and augmented reality technologies. A double-arm, evaluator-blinded randomized controlled trial was conducted with 21 elderly participants in the MCI spectrum, with 11 in the intervention group (INT) and 10 in the control group (CTL). Feasibility was assessed in terms of adherence, effective learning, and perceived usefulness. The INT attended 24 training sessions, 60 minutes long, twice a week, whereas the CTL engaged in normal daily activities and usual care. Results showed that the INT had a statistically significant change in the Montreal Cognitive Assessment score, stages List B Recall, Short-term delayed Recall, and Long-term delayed Recall of the Rey Auditory Verbal Learning Test (RAVLT), Trail Making Test-A and B test scores, Digit Span Test (DST) Forward Span, and Functional Activities Questionnaire score. A trend level difference was also found for the RAVLT Recognition and the DST Backward Span. No significant differences were found for the CTL in any of the metrics. The completion rate of the INT was 91%, and the attendance rate was 100% for participants who completed the follow-up segment of the study. The engagement level was high, and effective learning was observed between the participants. The perceived usability and usefulness of the game platform was assessed as high. This study provides evidence of a positive effect of a multi-domain multimodal-based cognitive training program in elderly with MCI, with broader benefits on cognition by inducing more cooperative transfer effects over different domains.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it