Park Hyojin, Ha Juyoung
Nursing Department of Neurosurgery, Dong-Eui Medical Center, Busan, Korea.
College of Nursing, Pusan National University, Yangsan, Korea.
J Korean Acad Nurs. 2020 Apr;50(2):191-199. doi: 10.4040/jkan.2020.50.2.191.
The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI).
This study was a secondary data analysis research using data from "the 4th Korea Longitudinal Study of Ageing" of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a χ²-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2.
In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors.
The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.
本研究旨在比较正常认知组和轻度认知障碍组的社会人口学特征,并建立轻度认知障碍(MCI)的预测模型。
本研究是一项二次数据分析研究,使用了韩国就业信息服务中心“第四次韩国老龄化纵向研究”的数据。共有6405人参与研究,其中包括1329名MCI患者和5076名认知能力正常的人。基于问卷调查项目,本研究使用了28个变量。分析方法包括χ²检验、逻辑回归分析、决策树分析、预测错误率,以及使用SPSS 23.0和SAS 13.2计算的ROC曲线。
MCI组的平均年龄为71.4岁,65.8%的参与者为女性。两组在性别、年龄和教育程度上存在统计学显著差异。通过逻辑回归分析确定的MCI预测因素包括性别、年龄、教育程度、日常生活工具性活动(IADL)、自我感知健康状况、参与群体、文化活动和生活满意度。对MCI预测因素的决策树分析确定教育程度、年龄、生活满意度和IADL为预测因素。
MCI逻辑回归模型的准确性略高于决策树模型。本研究建立的MCI预测模型的应用可能有助于识别有MCI风险的中老年人。因此,本研究可能有助于预防和减少痴呆症。