Frailty level prediction in older age using hand grip strength functions over time

Abstract

Frailty syndrome can be defined as a clinical state in which there is a rise in individual vulnerability, developing an increase in both the dependence of the person and mortality. Frailty is completely related to age. A fundamental factor to apply rehabilitative interventions successfully resides in having a simple and reliable method capable of identifying frailty syndrome. Frailty indexes (FI) have several sources of uncertainty trough the opinion of the patients, white coat effect and external factors. Moreover, in the clinical practice, the experience of the geriatricians led them to determine an approximation of the frailty level only with a simple handshake. Hand grip strength (HGS) has been widely used in tests by investigators and therapists to be able to diagnose sarcopenia and frailty, as it is a reliable indicator of the overall muscle strength, which decreases with age. Most researches focused mainly on peak HGS, which will not give insight on how the patient’s strength was distributed over time. In the present work it is proposed to evaluate HGS behavior over a period of time, and to develop a system based on Machine Learning for the identification of frailty levels using physiological features, FI and the classical signal processing based on statistics of the HGS signals. The starting hypothesis is that it can be identified the “way” of performing HGS correlated with the level of frailty. To achieve this goal a clinical study was designed and carried out with a cohort of 70 elderly persons, in two Hospitals.