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Prediction model of health-related quality of life in older adults according to gender using a decision tree model: a study based on the Korea National Health and Nutrition Examination Survey
Article de Ko | WPRIM | ID: wpr-1043420
Bibliothèque responsable: WPRO
ABSTRACT
Purpose@#The aim of this study was to predict the subgroups vulnerable to poorer health-related quality of life (HRQoL) according to gender in older adults. @*Methods@#Data from 5,553 Koreans aged 65 or older were extracted from the Korea National Health and Nutrition Examination Survey. HRQoL was assessed using the EQ-5D tool. Complex sample analysis and decision-tree analysis were conducted using SPSS for Windows version 27.0. @*Results@#The mean scores of the EQ-5D index were 0.93 ± 0.00 in men and 0.88 ± 0.00 in women. In men, poorer HRQoL groups were identified with seven different pathways, which were categorized based on participants’ characteristics, such as restriction of activity, perceived health status, muscle exercise, age, relative hand grip strength, suicidal ideation, the number of chronic diseases, body mass index, and income status. Restriction of activity was the most significant predictor of poorer HRQoL in elderly men. In women, the poorer HRQoL groups were identified with nine different pathways, which were categorized based on participants’ characteristics, such as perceived health status, restriction of activity, age, education, unmet medical service needs, anemia, body mass index, relative hand grip, and aerobic exercise. Perceived health status was the most significant predictor of poorer HRQoL in elderly women. @*Conclusion@#This study presents a predictive model of HRQoL in older adults according to gender and can be used to detect individuals at risk of poorer HRQoL.
Texte intégral: 1 Indice: WPRIM langue: Ko Texte intégral: Journal of Korean Biological Nursing Science Année: 2024 Type: Article
Texte intégral: 1 Indice: WPRIM langue: Ko Texte intégral: Journal of Korean Biological Nursing Science Année: 2024 Type: Article