Been Seonjae, Byeon Haewon
Department of Digital Anti-Aging Healthcare, Graduate School (BK21), Inje University, Gimhae 50834, Republic of Korea.
Healthcare (Basel). 2023 Apr 20;11(8):1181. doi: 10.3390/healthcare11081181.
This study aimed to test a predictive model for depression in older adults in the community after the COVID-19 pandemic and identify influencing factors using the International Classification of Functioning, Disability, and Health (ICF). The subjects of this study were 9920 older adults in South Korean local communities. The analysis results of path analysis and bootstrapping analysis revealed that subjective health status, instrumental activities of daily living (IADL), number of chronic diseases, social support satisfaction, household economic level, informal support, and participation in social groups were factors directly influencing depression, while formal support, age, gender, education level, employment status, and participation in social groups were factors indirectly affecting it. It will be needed to prepare measures to prevent depression in older adults during an infectious disease pandemic, such as the COVID-19 pandemic, based on the results of this study.
本研究旨在测试新冠疫情后社区老年人抑郁症的预测模型，并使用国际功能、残疾和健康分类（ICF）确定影响因素。本研究的对象是韩国当地社区的9920名老年人。路径分析和自抽样分析的结果显示，主观健康状况、日常生活活动能力（IADL）、慢性病数量、社会支持满意度、家庭经济水平、非正式支持以及参与社会群体是直接影响抑郁症的因素，而正式支持、年龄、性别、教育水平、就业状况以及参与社会群体是间接影响抑郁症的因素。需要根据本研究结果，制定措施以预防传染病大流行（如新冠疫情）期间老年人的抑郁症。
