Purpose: The aim of the present study was to develop a novel index using fuzzy logic procedures conflating cardiorespiratory and pulmonary kinetics during dynamic exercise as a representative indicator for exercise tolerance. Methods: Overall 69 data sets were re-analyzed: (age: 29 ± 1.2 y [mean ± SEM], height: 179 ± 1.0 cm; body mass: 78 ± 1.4 kg; peak oxygen uptake (V.O2peak): 48 ± 1.1 ml·min-1·kg-1), that comprised pseudo random binary sequence work rate (WR) changes between 30 and 80 W on a cycle ergometer, with additional voluntary exhaustion to estimate V.O2peak. Heart rate (HR), stroke volume (SV) and gas exchange (pulmonary oxygen uptake (V.O2pulm)) were measured beat-to-beat and breath-by-breath, respectively. For estimation of muscle oxygen uptake (V.O2musc) kinetics and for the analysis of kinetic responses of the parameters of interest (perfusion (Q. = HR·SV), V.O2pulm, V.O2musc) the approach of Hoffmann et al. (2013) was applied. For calculation of the Fuzzy Kinetics Index Q., V.O2pulm, and V.O2musc were used as input variables for the subsequent fuzzy- and defuzzyfication procedures. Results: For both absolute and relative V.O2peak a significant correlation has been observed with FKI, whereas the correlation coefficient is higher for relative (r = 0.430; p < 0.001; n = 69) compared to absolute V.O2peak (r = 0.358; p < 0.01; n = 69). No significant correlations have been found between FKI and age, height or body mass (p > 0.05 each). Conclusions: The significant correlations between FKI and V.O2peak represent a physiological connection between the regulatory and the capacitive system and its exercise performance. In turn, the application of FKI can serve as an indicator for healthy participants to assess exercise tolerance and sport performance.
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