Longitudinal Measurement Properties of the Montreal Cognitive Assessment
Bibliographic record
Abstract
Abstract Background: The Montreal Cognitive Assessment (MoCA) has started to be widely used in longitudinal investigations to measure changes in cognition. However, the longitudinal measurement properties of MoCA have not been investigated. We aimed to examine the measurement invariance of individual MoCA items across four time points. Methods: We used longitudinal data collected between 2014 and 2017 from a cohort study on health and well-being of older adults in Hong Kong. The Cantonese version of the MoCA was used. We applied multiple group confirmatory factor analysis of ordinal variables to examine measurement invariance by educational level and across time points. Invariant items were identified by sequential model comparisons. Results: We included 1029 participants that answered MoCA items across all time points. We found that items Cube, Clock Hand and Clock Number had significantly different item parameters between participants with and without formal education at all time points. The selected model (RMSEA=0.031; SRMR=0.064) indicated that eight items (Trail, Cube, Clock Shape, Clock Number, Clock Hand, Abstraction, Short-term Memory, and Orientation) did not exhibit measurement invariance over time. However, the differences in item parameter estimate over time were marginal. Accounting for the lack of measurement invariance did not substantially affect classification properties based on cutoff values at the 2nd ( major neurocognitive disorder) and 7th (mild cognitive impairment) percentile. Conclusion: Our findings support using MoCA to assess changes in cognition over time in the study population. Future research should examine the longitudinal measurement properties of the test in other populations with different characteristics.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".