가천대학교 운동재활융합연구소

Abstract 

Background Korea’s aging population and the lack of older adult participation in sports are increasing medical expenses. 

Aims This study aimed to segment older adult sports participants based on their demographic characteristics and exercise practice behavior and applied artifcial neural network and logistic regression models to these segments to best predict the efect of medical cost reduction. It presents strategies for older adult sports participation. 

Methods A sample comprising data on 1,770 older adults aged 50 years and above was drawn from the 2019 National Sports Survey. The data were analyzed through frequency analysis, hierarchical and K-means clustering, artifcial neural network, logistic regression, cross-tabulation analyses, and one-way ANOVA using SPSS 23 and Modeler 14.2. 

Results The participants were divided into fve clusters. The artifcial neural network and logistic analysis models showed that the cluster comprising married women in their 60s who participated in active exercise had the highest possibility of reducing medical expenses.

Discussion Targeting women in their 60s who actively participate in sports, the government should expand the supply of local gymnasiums, community centers, and sports programs. If local gymnasiums and community centers run sports programs and appoint appropriate sports instructors, the most efective medical cost reduction efect can be obtained. 

Conclusions This study contributes to the feld by providing insights into the specifc demographic segments to focus on for measures to reduce medical costs through sports participation. 

Keywords Neural networks model, Logistic regression model, Older adult participants, Medical costs