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Frailty is a geriatric syndrome that may result in poor health outcomes such as hospitalization, disability, psychological distress, and reduced life satisfaction, and it is also associated with higher healthcare costs. The aim of this study is to classify frailty in elders at an early stage (pre-frail) to lower the risk of frailty and, hence, improve the quality of life. The other two classes in the classification task are frail and robust (non-frail). To achieve this, a dataset based on gait was utilized, which was recorded by an Inertial Measurement Unit (IMU) sensor, including gyroscope and accelerometer data. In this study, two approaches were assessed: the first used advanced Deep Learning (DL) algorithms to analyze raw IMU signals, and the second used conventional Machine Learning (ML) methods with hand-crafted features. The DL model, i.e., InceptionTime, beat the other algorithms in the DL approach with a remarkable test accuracy of 98%. On the ML side, Random Forest reported the most successful ML method, which achieved a test accuracy of 63.3%. For a careful assessment of the models, other evaluation metrics like Precision, Recall, and F1-score were also evaluated. The evaluation of both approaches produces research benefits for the classification of frailty in older people and allows for the investigation of new areas, promoting deeper comprehension and well-informed decision-making, particularly in healthcare systems. [ABSTRACT FROM AUTHOR]

Purpose: A relationship between decreased plantar cutaneous sensation and impaired balance function has been reported in patients with peripheral neuropathy and diabetes. This cross-sectional study aimed to investigate the relationship between plantar sensation and postural balance, as well as the association between plantar sensation and sarcopenia-related motor function in community-dwelling older adults., Methods: The participants included 1659 community-dwelling older adults with a mean age of 74 ± 5 years, of which 43% were male patients. Plantar cutaneous sensation thresholds were assessed using an automatic plantar sensation testing device. Postural balance was measured using one-leg standing (OLS) time. Grip strength, five-time sit-to-stand (STS) time, and normal gait speed were measured as components of muscle strength and physical function related to sarcopenia. The skeletal muscle mass index (SMI) and leg phase angle were obtained using bioelectrical impedance analysis., Results: Age, sex, body mass index, and leg phase angle, but not SMI and grip strength, were independently associated with the plantar sensation threshold. Plantar sensation threshold was independently associated with the OLS time (P = 0.001) and STS time (P = 0.001) after adjusting for potential confounders. No significant association was found between plantar sensation threshold and normal gait speed (P = 0.741)., Conclusion: Plantar sensation was independently associated with postural balance and lower limb function in community-dwelling older adults. The assessment of plantar sensation could be useful for identifying factors contributing to poor postural balance and lower limb motor function., Competing Interests: Declarations. Conflict of interest: The authors have no conflict of interest to disclose. Ethical approval: All study procedures were approved by the Institutional Review Board of Kyoto University Graduate School of Medicine (G1327-2) and conducted in accordance with the principles of the Declaration of Helsinki. Informed consent: Written informed consent was obtained from all participants., (© 2025. The Author(s), under exclusive licence to European Geriatric Medicine Society.)