Analysis and prediction of older adult sports participation in South Korea using artificial neural networks and logistic regression models

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 artificial neural network and logistic regression models to these segments to best predict the effect 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, artificial neural network, logistic regression, cross-tabulation analyses, and one-way ANOVA using SPSS 23 and Modeler 14.2. RESULTS: The participants were divided into five clusters. The artificial 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 effective medical cost reduction effect can be obtained. CONCLUSIONS: This study contributes to the field by providing insights into the specific demographic segments to focus on for measures to reduce medical costs through sports participation.