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Updated: Aug 26, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Preliminary Validation Study of the French-Quebec Version of the Mild Behavioral Impairment Checklist
Émilie Pépin1,2, Noémie Tanguay2,3, Marie-Pier Roy2,3
1Universite du Quebec à Trois-Rivières, Trois-Rivières, Québec, Canada.
Background:
Alzheimer disease dementia may be preceded by cognitive stages during which behavioral and psychological changes can occur. More precisely, behavioral symptoms may be observed during the subjective cognitive decline (SCD) or the mild cognitive impairment (MCI) stages; these symptoms can be measured using the Mild Behavioral Impairment Checklist (MBI-C).
Objective:
To validate the French-Quebec version of the MBI-C in individuals ages 60-85 years.
Method:
The sample included 60 participants (20 MCI, 20 SCD, 20 cognitively healthy) and their informants. To assess the discriminant validity of the MBI-C, a Kruskal-Wallis analysis with a multiple comparisons test was performed on the MBI-C Total score. To determine convergent validity, Spearman correlations were calculated between the MBI-C subscales and a set of validation tools. Finally, test-retest reliability was assessed with Spearman correlations of MBI-C scores between two test sessions.
Results:
All of the analyses indicated satisfactory psychometric properties for the French-Quebec version of the MBI-C.
Conclusion:
This validation study reveals that the MBI-C can be used successfully in dementia risk assessments. From now on, the use of a validated MBI-C will be possible in the French-Quebec population.
