Comparison of Balance Sensor-Based Sarcopenia Screening and the Short Physical Performance Battery (SPPB)

Abstract

Sarcopenia is a progressive condition characterized by muscle mass and strength loss in older adults. Traditional assessment methods, such as the Short Physical Performance Battery (SPPB), are time-consuming. This study explores using a balance sensor to assess sarcopenia risk by collecting center of gravity (CoG) data from 144 participants. Machine learning methods, including random forests, were applied to classify sarcopenia status. The proposed model achieved an AUC of 0.88, outperforming conventional methods. Our findings suggest that balance sensors provide a rapid, accurate, and non-invasive alternative for sarcopenia screening, enabling timely interventions in clinical and community settings.