Segmental bioimpedance analysis as a predictor of injury and performance status in professional basketball players: a new application potential? - Bertuccioli, A.; Cardinali, M.; Benelli, P. (Life, 2022)
Bioelectrical impedance vector analysis (BIVA) is a technique used for the assessment of body composition based on the electrical properties of biological tissues and for evaluating variations related to hydration and nutrition status changes. The present study aimed to investigate the possibility of predicting performance status and injuries using segmental BIVA analysis. Data were collected from 14 professional male athletes aged between 20 and 39 years of Caucasian and Afro-American ethnicity belonging to the US Victoria Libertas Pallacanestro Pesaro team in the Italian Serie A basketball championship. From an analysis of training injuries, the data highlight a possible positive link between the number of training injuries and upper hemisoma reactance (XCEmsSup) (t = 2.881, p = 0.007), an inverse relationship between training injury duration and higher right lower limb reactance (XCLegDx) (t = -4.213, p < 0.001), and an inverse relationship between injury duration and higher body mass index (t = -4.213, p < 0.001), highlighting how higher cellularity seems less prone to severe training injuries. Analyzing match-day injuries, right upper-limb higher reactance (XCArmdx) negatively correlates with match-day number of injuries (t = -4.469, p < 0.001), right upper limb resistance (RZArmDx) negatively correlates with lower match-day injury duration (t = -4.202, p < 0.001), and trunk resistance (RZTrunk) positive correlates with lower match-day injury duration (t = 2.803, p = 0.008), in contrast with the training data analysis. Analyzing the relationship between the BIVA parameters and performance indicators, right upper limb resistance (RzArmDx) has a positive link with plus-minus (t = 2.889, p = 0.007); however, RzArmDx negatively correlates with assist number (t = -3.362, p = 0.002), and BMI is directly proportional to assist number (t = 2.254, p = 0.032). These first data suggest a good correlation between the cellularity of different body districts and the risk of injuries in training but still leave several doubts surrounding the concrete predictive potential regarding performance and injuries during competitions while considering the numerous factors involved. Further studies on BIVA and similar applications could provide tools for managing athlete health and physical integrity preservation and potentially help us better understand the factors involved in improving performance.