A Usability Study of MoCA Solo, a Digital AI‐Assisted Version of the MoCA Test
Bibliographic record
Abstract
BACKGROUND: MoCA Solo from Montreal Cognitive Assessment (MoCA) is a digital version of the MoCA test. It is self-administered, with AI Assistance, by the patient via an application on a tablet device with minimal supervision. The MoCA test has become the gold standard for detecting cognitive impairment in both clinical and research settings worldwide. However, the traditional paper-and-pencil test presents challenges in administration and scoring due to its sensitivity to human variation and error. The MoCA Solo, with automated administration, data collection and AI assisted scoring, would address these limitations and could enhance accessibility, efficiency and reliability. This study aims to assess whether older adults with a range of cognitive impairment levels can complete the MoCA Solo with minimal supervision. METHOD: 40 older adults will be recruited at the MoCA Clinic as well as a senior residence in Montreal, Quebec. Participants' cognitive level will range between mild dementia to normal cognition (MoCA Score 11-30). Eligibility criteria will include: fluency in English or French, adults aged 50 years or over, at least 6 years of formal education, MoCA score ≥11. Participants will complete the MoCA Solo independently followed by a user experience questionnaire. The test completion rate will be evaluated at each level of cognitive impairment. User experience feedback will also be reviewed for future improvements in the application. RESULT: Results from the 40 participants will be available by July 2024. CONCLUSION: The MoCA Solo is emerging as a reliable and efficient tool for assessing cognitive impairment in older adults. Preliminary findings will inform its accessibility and usability, offering insights into its feasibility for widespread use in clinical and research settings across varying levels of cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".