Characteristics of the cognitive domains of the montreal cognitive assessment scale in the elderly from the perspective of structural equation modeling
10.3760/cma.j.issn.0254-9026.2022.11.006
- VernacularTitle:结构方程模型视角下的老年人蒙特利尔认知评估量表内部各认知域联系特点分析
- Author:
Xiaodong PAN
1
;
Yiran HE
;
Yaling LIU
;
Tiantian CHANG
;
Pei ZHANG
Author Information
1. 210009 江苏省省级机关医院，南京 210009
- Keywords:
Structural equation modeling;
Aging;
Cognitive impairment;
Montreal Cognitive Assessment
- From:
Chinese Journal of Geriatrics
2022;41(11):1297-1302
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyzed the characteristics of the cognitive domains of the montreal cognitive assessment(MOCA)scale in cognitively impaired or normal elderly people from the perspective of structural equation modeling(SEM).Methods:There were 335 old individuals in this study, including 166 cognitively normal individuals, 130 patients with mild cognitive impairment(MCI)and 39 individuals with dementia.The average age of the individuals was 81.5±9.0.Data on the Montreal cognitive assessment(MoCA, Beijing version)were gathered.Following exploratory factor analysis and selection of latent and manifest variables, a structural equation model was established.After assessment of data from the domains of the scale in the elderly, participants were divided into a normal group and a patient group, which formed the basis of the model.Results:The KMO value of the data calculated by exploratory factor analysis was 0.762.The dimensions measured by the scale were divided into four main latent variables: memory, visuospatial execution, language ability and attention.The CMIN value of the overall model was 44.039 and the P value was 0.168.The parameters of the overall model and individual dimensions all indicated a good fit.The model showed that visuospatial execution had the largest impact on cognitive function, with a path coefficient of 0.742, and language ability had the least impact, with a path coefficient of only 0.091.As a latent variable, attention had path coefficients of 0.372 and 0.236 for memory, 1.663 and 1.102 for visuospatial execution, and 1.090 and 0.798 for language ability, respectively, for the two groups, with clear statistically significant differences between the groups(all P<0.05). Conclusions:SEM can improve researchers' overall understanding of the impacts of the individual components of the scale and their use and interpretation of the scale.
