Machine learning-based muscle mass estimation using gait parameters in community-dwelling older adults: A cross-sectional study

• Published in: Archives of Gerontology and Geriatrics Vol. 103; p. 104793
• Main Authors: Fujita, Kosuke, Hiyama, Takahiro, Wada, Kengo, Aihara, Takahiro, Matsumura, Yoshihiro, Hamatsuka, Taichi, Yoshinaka, Yasuko, Kimura, Misaka, Kuzuya, Masafumi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022; Elsevier BV
• Subjects: eXtreme gradient boosting; Gait; Machine learning; Older adults; Sarcopenia; Skeletal muscle mass; Sarcopenia; eXtreme gradient boosting; Gait; Skeletal muscle mass; Older adults; Machine learning
• ISSN: 0167-4943; 1872-6976; 1872-6976
• DOI: 10.1016/j.archger.2022.104793

•Machine learning-based gait analyses aid in muscle mass classification in older adults.•high and low skeletal muscle mass index could be classified reasonably accurate.•The machine learning model had 59.5% sensitivity and 81.4% specificity.•Dominant gait parameters: stride length, hip dynamic ROM, trunk rotation variability.•These aforementioned parameters are in the order of the strongest effect. Loss of skeletal muscle mass is associated with numerous factors such as metabolic diseases, lack of independence, and mortality in older adults. Therefore, developing simple, safe, and reliable tools for assessing skeletal muscle mass is needed. Some studies recently reported that the risks of the incidence of geriatric conditions could be estimated by analyzing older adults’ gait; however, no studies have assessed the association between gait parameters and skeletal muscle loss in older adults. In this study, we applied machine learning approach to the gait parameters derived from three-dimensional skeletal models to distinguish older adults’ low skeletal muscle mass. We also identified the most important gait parameters for detecting low muscle mass. Sixty-six community-dwelling older adults were recruited. Thirty-two gait parameters were created using a three-dimensional skeletal model involving 10-meter comfortable walking. After skeletal muscle mass measurement using a bioimpedance analyzer, low muscle mass was judged in accordance with the guideline of the Asia Working Group for Sarcopenia. The eXtreme gradient boosting (XGBoost) model was applied to discriminate between low and high skeletal muscle mass. Eleven subjects had a low muscle mass. The c-statistics, sensitivity, specificity, precision of the final model were 0.7, 59.5%, 81.4%, and 70.5%, respectively. The top three dominant gait parameters were, in order of strongest effect, stride length, hip dynamic range of motion, and trunk rotation variability. Machine learning-based gait analysis is a useful approach to determine the low skeletal muscle mass of community-dwelling older adults.