Estimating Health-Related Quality of Life Based on Demographic Characteristics, Questionnaires, Gait Ability, and Physical Fitness in Korean Elderly Adults

Myeounggon Lee, Yoonjae Noh, Changhong Youm, Sangjin Kim, Hwayoung Park, Byungjoo Noh, Bohyun Kim, Hyejin Choi and Hyemin Yoon
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Myeounggon Lee: Center for Neuromotor and Biomechanics Research, Department of Health and Human Performance, University of Houston, Houston, TX 77004, USA
Yoonjae Noh: Department of Management Information Systems, Dong-A University, Busan 49236, Korea
Changhong Youm: Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
Sangjin Kim: Department of Management Information Systems, Dong-A University, Busan 49236, Korea
Hwayoung Park: Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
Byungjoo Noh: Department of Kinesiology, Jeju National University, Jeju 63243, Korea
Bohyun Kim: Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
Hyejin Choi: Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
Hyemin Yoon: Department of Management Information Systems, Dong-A University, Busan 49236, Korea

IJERPH, 2021, vol. 18, issue 22, 1-18

Abstract: The elderly population in South Korea accounted for 15.5% of the total population in 2019. Thus, it is important to study the various elements governing the process of healthy aging. Therefore, this study investigated multiple prediction models to determine the health-related quality of life (HRQoL) in elderly adults based on the demographics, questionnaires, gait ability, and physical fitness. We performed eight physical fitness tests on 775 participants wearing shoe-type inertial measurement units and completing walking tasks at slower, preferred, and faster speeds. The HRQoL for physical and mental components was evaluated using a 36-item, short-form health survey. The prediction models based on multiple linear regression with feature importance were analyzed considering the best physical and mental components. We used 11 variables and 5 variables to form the best subset of features underlying the physical and mental components, respectively. We laid particular emphasis on evaluating the functional endurance, muscle strength, stress level, and falling risk. Furthermore, stress, insomnia severity, number of diseases, lower body strength, and fear of falling were taken into consideration in addition to mental-health-related variables. Thus, the study findings provide reliable and objective results to improve the understanding of HRQoL in elderly adults.

Keywords: health-related quality of life; physical and mental components; elderly adults; machine learning; prediction model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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