Analysis of multiple chronic disease characteristics in middle-aged and elderly South Koreans by exercise habits based on association rules mining algorithm

Abstract

Abstract Background The term, “multiple chronic diseases” (MCD), describes a patient with two or more chronic conditions simultaneously at the same time. Compared with general chronic diseases, it is linked to poorer health outcomes, more difficult clinical management, and higher medical expenses. Several existing MCD guidelines support a healthy lifestyle including regular physical activities but do not include specific exercise therapy recommendations. This study aimed to understand the prevalence and model of MCD in middle-aged and elderly South Koreans by comparing MCD characteristics with exercise habits, to provide a theoretical basis for the implementation of exercise therapy in these patients. Methods The data of 8477 participants aged > 45 years from the “2020 Korean Health Panel Survey” were used to analyze the current status of MCD in the middle-aged and elderly. The Chi-square test for categorical variables and the t-test for continuous variables. the used software was IBM SPSS Statistics 26.0 and IBM SPSS Modeler 18.0. Results In this study, the morbidity rate of MCD was 39.1%. Those with MCD were more likely to be female (p < 0.001), seniors over 65 years of age (p < 0.001), with low education level, no regular exercise behavior (p < 0.01). Chronic renal failure (93.9%), depression (90.4%), and cerebrovascular disease (89.6%) were the top three diseases identified in patients with MCD. A total of 37 association rules were identified for the group of individuals who did not engage in regular exercise. This equated to 61% more than that of the regular exercise group, who showed only 23 association rules. In the extra association rules, cardiovascular diseases (150%), spondylosis (143%), and diabetes (125%) are the three chronic diseases with the highest frequency increase. Conclusions Association rule analysis is effective in studying the relationship between various chronic diseases in patients with MCD. It also effectively helps with the identification of chronic diseases that are more sensitive to regular exercise behaviors. The findings from this study may be used to formulate more appropriate and scientific exercise therapy for patients with MCD.

Keywords

• Multiple chronic disease
• Association rules mining algorithm
• Exercise