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Updated: Mar 18, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Montreal Cognitive Assessment and Mini-Mental State Examination reliable change indices in healthy older adults
Miloslav Kopecek1, Ondrej Bezdicek1,2, Zdenek Sulc1
1National Institute of Mental Health, Klecany, Czech Republic.
Objective:
Cognitive tests are used repeatedly to assess the treatment response or progression of cognitive disorders. The Montreal Cognitive Assessment (MoCA) is a valid screening test for mild cognitive impairment. The aim of our study was to establish 90% reliable change indices (RCI) for the MoCA together with the Mini-Mental State Examination (MMSE) in cognitively healthy older adults.
Method:
We analyzed 197 cognitively healthy and functional independent volunteers aged 60-94 years, who met strict inclusion criteria for four consecutive years. The RCI methods by Chelune and Hsu were used.
Results:
For 1, 2, and 3 years, the 90% RCI for MoCA using Chelune's formula were -4 ≤, ≥4; -4 ≤, ≥4 and -5 ≤, ≥4 points, respectively, and -3 ≤, ≥3 for the MMSE each year. Ninety percent RCI for MoCA using Hsu's formula ranged from -6 to 0, respectively, and +3 to +8 dependent on the baseline MoCA.
Conclusion:
Our study demonstrated RCI for the MoCA and MMSE in a 3-year time period that can be used for the estimation of cognitive decline or improvement in clinical settings. Copyright © 2016 John Wiley & Sons, Ltd.
