Estimating quality of life with biomarkers among older Korean adults: A machine-learning approach. Archives of gerontology and geriatrics, 87, 103966.
Sung-Ha Leea, Incheol Choia,b, Woo-Young Ahnb, Enyoung Shinc, Sung-Il Chod, Sunyoung Kime,
Bumjo Ohc,
*
a Center for Happiness Studies, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
b Department of Psychology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
c Department of Family Medicine, SMG- SNU Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Republic of Korea
d School of Public Health and Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
e Department of Family Medicine, Kyung Hee University Medical Center, 23, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
ABSTRACT
Background: While health-related quality of life (HRQoL) has clinical value, its determinants, particularly objective health-related measurements, have not been fully explored. This study seeks to identify the biological indicators that relate to HRQoL among a group of older Korean adults using a machine-learning approach.
Methods: We used physical and mental scores from the 36-item Short Form Health Survey (SF-36) to measure HRQoL among older Korean adults who participated in the Korean Longitudinal Study of Aging (KLoSA) biomarker pilot study (N = 385). The variables for the multivariate penalized regression analysis included demographic factors, medical measurements, physical performance, and health-related behaviors.
Results: The multivariate profiles identified several significant biomarkers that relate to quality of life. Among the 20 variables, handgrip strength was the most powerful indicator in both men and women for the SF-36 physical scores, followed by walking speed. Age and total sleep duration exclusively were significantly associated with the SF-36 physical scores only in women, whereas body mass index, blood pressure, and sit-to-stand times were unique elements in men.
Conclusions: The present study suggests significant physical indicators that explain quality of life in elderly populations, using a data-driven approach. Based on these findings, maintaining a good level of physical performance is considered a key element of successful aging
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Estimating quality of life with biomarkers among older Korean adults: A machine-learning approach. Archives of gerontology and geriatrics, 87, 103966.
Sung-Ha Leea, Incheol Choia,b, Woo-Young Ahnb, Enyoung Shinc, Sung-Il Chod, Sunyoung Kime,
Bumjo Ohc,
*
a Center for Happiness Studies, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
b Department of Psychology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
c Department of Family Medicine, SMG- SNU Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Republic of Korea
d School of Public Health and Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
e Department of Family Medicine, Kyung Hee University Medical Center, 23, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
ABSTRACT
Background: While health-related quality of life (HRQoL) has clinical value, its determinants, particularly objective health-related measurements, have not been fully explored. This study seeks to identify the biological indicators that relate to HRQoL among a group of older Korean adults using a machine-learning approach.
Methods: We used physical and mental scores from the 36-item Short Form Health Survey (SF-36) to measure HRQoL among older Korean adults who participated in the Korean Longitudinal Study of Aging (KLoSA) biomarker pilot study (N = 385). The variables for the multivariate penalized regression analysis included demographic factors, medical measurements, physical performance, and health-related behaviors.
Results: The multivariate profiles identified several significant biomarkers that relate to quality of life. Among the 20 variables, handgrip strength was the most powerful indicator in both men and women for the SF-36 physical scores, followed by walking speed. Age and total sleep duration exclusively were significantly associated with the SF-36 physical scores only in women, whereas body mass index, blood pressure, and sit-to-stand times were unique elements in men.
Conclusions: The present study suggests significant physical indicators that explain quality of life in elderly populations, using a data-driven approach. Based on these findings, maintaining a good level of physical performance is considered a key element of successful aging
*저작권 안내
DBpia에서 제공되는 모든 저작물의 저작권은 원저작자에게 있으며, 누리미디어는 각 저작물의 내용을 보증하거나 책임을 지지 않습니다. 그리고 DBpia에서 제공되는 저작물은 DBpia와 구독계약을 체결한 기관소속 이용자 혹은 해당 저작물의 개별 구매자가 비영리적으로만 이용할 수 있습니다. 그러므로 이에 위반하여 DBpia에서 제공되는 저작물을 복제, 전송 등의 방법으로 무단 이용하는 경우 관련 법령에 따라 민, 형사상의 책임을 질 수 있습니다.
*Copyright Information
Copyright of all literary works provided by DBpia belongs to the copyright holder(s)and Nurimedia does not guarantee contents of the literary work or assume responsibility for the same. In addition, the literary works provided by DBpia may only be used by the users affiliated to the institutions which executed a subscription agreement with DBpia or the individual purchasers of the literary work(s)for non-commercial purposes. Therefore, any person who illegally uses the literary works provided by DBpia by means of reproduction or transmission shall assume civil and criminal responsibility according to applicable laws and regulations.