Contribution to Characterizing Time-Dependent Handgrip Strength Profiles

The maximum value of HandGrip Strength (usually named by HGS) is an important biomarker that indicates several physical conditions such as diabetes, malnutrition, sarcopenia, frailty and general functional physical capacity. It is also relevant for evaluation purposes in the context of rehabilitation, in patients suffering from hand musculoskeletal or other pathologies. The HGS dynamometry is non-invasive, portable, easy to perform, fast and reliable, making it suitable for routine assessment in different health and sports-related areas. Besides the HGS value (maximum value), other parameters such as rate of force development and sustainability of force, obtained by measuring time-dependent HGS (HGS(t)), have also been used in different assessment contexts utilizing modern dynamometers such as the BodyGrip (now on its commercialized version, Gripwise). A universal definition of the parameters characterizing HGS(t) curves has been proposed, and a study based on an artificial neural network model used those parameters to predict the frailty of elderly women. In this work, a new methodology was used to investigate the characteristics of HGS(t). The main idea is to identify and explore a first or second-order continuous-time transfer function, which can be used to estimate the force profile, using the Mean Squared Error and the overall percentual fit as measures of the approximation quality. The authors consider the results to be promising for future exploitation of the model to investigate and extract the characteristics of the time-dependent HGS (HGS(t)).