Implementation of COGNIVITRA, an Information- and Communications-Technology-Based Solution for Dual-Task Training, in Patients at Risk of Cognitive Impairment
Bibliographic record
Abstract
Mild cognitive impairment (MCI) is characterized by a modest decline in cognitive function that, while noticeable, does not severely impact daily life, allowing individuals to maintain their independence—a key factor distinguishing it from dementia. Currently, there are no treatments available that can modify the course of the disease, although cognitive and physical activities have shown potential in slowing its progression. In response to the need for more accessible cognitive care, COGNIVITRA, an information- and communications-technology-based solution, was developed to extend cognitive training into the home environment. This platform not only facilitates communication between patients and care providers but also holds promise for enhancing cognitive care accessibility and potentially influencing the economic aspects of healthcare institutions. To evaluate the usability, impact, and effectiveness of COGNIVITRA, a 12-week (6 mandatory + 6 voluntary) multicenter study was conducted, with an expected total sample size of 20 professionals, 90 patients and 20 caregivers and involving two settings (clinical and home settings) and the collection of various data types at baseline and after 6 or 12 weeks of training, including sociodemographic information, cognitive assessments, and usability metrics. These metrics included the System Usability Scale (SUS), the International Classification of Functioning-Based Usability Scales (ICF-US I and II), the Unified Theory of Acceptance and Use of Technology (UTAUT), health-related quality of life measures such as the EQ-5D-5L, cognitive domain assessments via the Montreal Cognitive Assessment (MoCA), and physical assessments such as the Timed 25-Foot Walk (T25-FW) test. The study included 22 patients, 2 caregivers, and 24 professionals. The usability evaluation revealed that patients, particularly those participating in the home study, showed improved SUS scores, suggesting an enhanced user experience with the platform. The ICF-US I results further supported this finding by indicating that COGNIVITRA was particularly effective as a supportive tool in terms of satisfaction and ease of learning. Despite a higher incidence of errors during the home study, the observational grid questionnaire demonstrated high success rates for task completion. Professionals involved in the study also reported high SUS scores and provided positive feedback regarding device usability. Overall, the participants expressed increased satisfaction with the platform, as reflected in their responses. The UTAUT analysis confirmed a generally positive attitude toward the use of COGNIVITRA. However, when assessing effectiveness, the analysis revealed a noninferiority positive trend in the EQ-5D-5L, T25-FW, and MoCA scores, indicating that while there were positive changes, they were not statistically significant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".