Comparative Analysis of MoCA and DigiMoCA Test Results: A Pilot Study
Bibliographic record
Abstract
This study examined the cognitive performance of older adults aged 60 and above using the Montreal Cognitive Assessment (MoCA) test and DigiMoCA, a digital tool for cognitive screening administered by means of a smart speaker, to investigate whether the additional variables utilised by DigiMoCA allow for the identification of significant differences between individuals with depressive symptoms and those with mild cognitive impairment, which are not detected using the original MoCA test. A total of 73 senior adults located in Northwestern Spain, 22 male and 51 female, participated in this study. Subjects were divided into four groups based on the presence of depressive symptoms and mild cognitive impairment, with the aim of analysing the results of each dimension of the MoCA and DigiMoCA tests and assessing the additional insights provided by the digital administration tool. The results indicate significant differences among groups. Individuals with depressive symptoms exhibited poorer performance in forward number span, attention, and clock drawing compared to healthy controls. Furthermore, individuals with depressive symptoms and mild cognitive impairment exhibited significantly worse memory and orientation compared to those with cognitive impairment alone. Correlations revealed that a greater severity of depressive symptoms was associated with poorer performance across cognitive domains, including visuospatial skills, attention, language, memory, and phonemic verbal fluency. This study also illustrated how the exploitation of additional variables systematically captured by digital instruments, such as completion times or response delays to individual interactions, may facilitate the early identification of cognitive and depressive conditions, providing initial evidence about the importance of integrating advanced digital tools in cognitive assessment to inspire the development of more effective, personalised interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".