Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults.

ABSTRACT: Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body

SUBMITTER: Hirata K 

PROVIDER: S-EPMC7877571 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature