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Updated: May 1, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Montreal Cognitive Assessment (MoCA): Normative Data for the State of Kerala, South India
Thomas Iype1, Sreelakshmi P Raghunath2, Stella M Paddick3
1Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, Executive Member of Health Action by People.
Background:
Montreal cognitive assessment (MoCA) is a tool that is widely accepted across the world to measure mild cognitive impairment (MCI). The original cut-off score of MoCA falsely screens a large population of Indians as having MCI.
Objective:
The aim of this study was to develop the normative data for MoCA for the older population of Kerala, South India.
Material And Methods:
We conducted the study among 959 cognitively normal older individuals of Kalliyoor village of Thiruvananthapuram district, Kerala. The validated Malayalam version of MoCA [MoCA-M] was administered by trained volunteers. The mean, median, and 10th percentile of the scores [domain-specific and total] were calculated in various age and educational groups.
Results:
The mean (SD) MoCA score was 19.4 (7.3). The 10th percentile for the total MoCA score was 9. The 10th percentile for all domains was zero, except for orientation. As age advanced, MoCA scores significantly reduced. The mean total MoCA scores dropped from 20.1 (7) [for ages between 65 and 75 years] to 7.4 (1.6) [for ages above 85 years]. We also obtained a significant improvement in scores among subjects with higher educational standards.
Conclusion:
The study throws light into the performance of MoCA among the Indian population. This study defines the norms for the Indian population and suggests redefining the threshold for positively screening for MCI using MoCA-M.
