|(1)||Neurology Department, Hospitais da Universidade de Coimbra, Praceta Mota Pinto, 3000-075 Coimbra, Portugal|
|(2)||Faculty of Medicine, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, Celas, 3000-354 Coimbra, Portugal|
|(3)||Psychological Evaluation Department, Faculty of Psychology and Educational Sciences, University of Coimbra, Rua do Colégio Novo, Apartado 6153, 3001-802 Coimbra, Portugal|
Received: 20 August 2009 Revised:9 November 2009 Accepted:12 November 2009 Published online: 25 November 2009
The Montreal Cognitive Assessment (MoCA) is a cognitive screening instrument created with the purpose of overcoming some of the insufficiencies of the Mini-Mental State Examination (MMSE). The MoCA evaluates more cognitive areas and is comprised of more complex tasks as compared with the MMSE, which makes it a more sensitive instrument in the detection of Mild Cognitive Impairment (MCI), a state that often progresses to dementia. In this study we performed an analysis of the psychometric and diagnostic properties of the Portuguese experimental version of the MoCA in a clinical sample of 212 subjects with MCI and several dementia subtypes in a memory clinic setting. Additionally, we performed a Confirmatory Factor Analysis (CFA) to assess the MoCA's latent factorial structure. In a clinical population, the MoCA is a valid and reliable instrument with good psychometric properties, revealing high sensitivity in identifying MCI and dementia patients who generally score within the normal range on the MMSE. By using the parcels method, CFA results showed very good/excellent adjustment indexes. The practical implications of this CFA study allow us to propose a two factor model factorial structure for the MoCA: a first factor designated MEMORY, which includes memory, language and orientation subtests (the latter being closely correlated with the former), and a second factor designated ATTENTION/EXECUTIVE FUNCTIONS, comprised of attention, executive functions and visuospacial abilities tasks.
Kevin McGrew PhD
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