Bioimpedance patterns and bioelectrical impedance vector analysis (BIVA) of road cyclists. Issue 22 (17th November 2018)

• Record Type: Journal Article
• Authors: Giorgi, Andrea; Vicini, Maurizio; Pollastri, Luca; Lombardi, Erica; Magni, Emilio; Andreazzoli, Andrea; Orsini, Marco; Bonifazi, Marco; Lukaski, Henry; Gatterer, Hannes
• Is Part Of: Journal of sports sciences. Volume 36:Issue 22(2018)
• Page Start: 2608
• Page End: 2613
• Publication Date: 2018-11-17
• Subjects: Road cyclists -- anthropometry -- body composition -- phase angle -- reference values -- performance level; Sports -- Periodicals; Sports -- Physiological aspects -- Periodicals; Sports -- Psychological aspects -- Periodicals; 612.044
• DOI: 10.1080/02640414.2018.1470597
• Languages: English
• ISSNs: 0264-0414

Abstract

ABSTRACT: Bioelectrical impedance vector-analysis (BIVA) describes cell-mass, cell function and hydration status of an individual or a group. The goal of the present investigation was to provide bioelectrical impedance data for 525 male road cyclists (155 professionals, 79 elite, 59 elite-youth, and 232 amateurs) at the time of their optimal performance level. Data were plotted on the resistance-reactance (R-Xc) graph to characterize cyclists group vectors using BIVA. Compared to the general male population, the mean vector position of the road cyclists indicates a higher body cell mass (BCM) and phase angle (p<0.001). The vector position of the high-performance, compared to the amateur cyclists showed similar patterns with higher BCM and phase angles and higher reactance values for the high-performance athletes (p<0.001). The bio-impedance data were used to calculate the 50%, 75%, and 95% tolerance ellipses of each group of cyclists. The characteristic vector positions of the road cyclists indicate normal hydration and greater muscle mass and function of the high-performance cyclists compared to amateur cyclists and the normal population. The cyclists specific tolerance ellipses, particularly the high-performance cyclists might be used for classifying a cyclist according to the individual vector position and to define target vector regions for lower level cyclists.