Paulo J Puccinelli1, Giscard H O Lima2, João B Pesquero2, Claudio A B de Lira3, Rodrigo L Vancini4, Pantelis T Nikolaids5, Beat Knechtle6, Marilia S Andrade7. 1. Department of Physiology, Federal University of São Paulo, Brazil. Electronic address: paulopuccinelli@hotmail.com. 2. Departament of Biophysics, Federal University of São Paulo, Brazil. 3. Human and Exercise Physiology Division, Faculty of Physical Education and Dance, Federal University of Goiás, Brazil. 4. Center of Physical Education and Sports, Federal University of Espírito Santo, Brazil. 5. Exercise Physiology Laboratory, Nikaia, Greece. 6. Institute of Primary Care, University of Zurich, Switzerland; Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland. 7. Department of Physiology, Federal University of São Paulo, Brazil.
Abstract
OBJECTIVE: Present study examines predictors of the overall race time and disciplines in the Olympic distance triathlon. METHODS: Thirty-nine male and six female triathletes were evaluated for anthropometric, physiological, genetic, training, clinical and circadian characteristics. Body composition, maximum capacity for oxygen uptake (V˙O2max), maximum aerobic velocity (MAV), anaerobic threshold (AT), triathlon experience (TE) and XX genotype for α-actinin 3 affected total race time (p<0.05). RESULTS: Total race time can be predicted by MAV (ß = -0.430, t = -3.225, p = 0.003), TE (ß = -0.378, t = -3.605, p = 0.001), and percentage of lean mass (%LM) (ß = -0.332, t = -2.503, p = 0.017). Swimming can be predicted by MAV (ß = -0.403, t = -3.239, p = 0.002), TE (ß = -0.339, t = -2.876, p = 0.007), and AT%V˙O2max (ß = 0.281, t = 2.278, p = 0.028). Cycling can be predicted by MAV (ß = -0.341, t = -2.333, p = 0.025), TE (ß = -0.363, t = -3.172, p = 0.003), and %LM (ß = -0.326, t = -2.265, p = 0.029). In running split, MAV (ß = -0.768, t = -6.222, p < 0.001) was the only parameter present in the best multiple linear regression model. CONCLUSION: The most important variables in multiple regression models for estimating performance were MAV, TE, AT and %LM.
OBJECTIVE: Present study examines predictors of the overall race time and disciplines in the Olympic distance triathlon. METHODS: Thirty-nine male and six female triathletes were evaluated for anthropometric, physiological, genetic, training, clinical and circadian characteristics. Body composition, maximum capacity for oxygen uptake (V˙O2max), maximum aerobic velocity (MAV), anaerobic threshold (AT), triathlon experience (TE) and XX genotype for α-actinin 3 affected total race time (p<0.05). RESULTS: Total race time can be predicted by MAV (ß = -0.430, t = -3.225, p = 0.003), TE (ß = -0.378, t = -3.605, p = 0.001), and percentage of lean mass (%LM) (ß = -0.332, t = -2.503, p = 0.017). Swimming can be predicted by MAV (ß = -0.403, t = -3.239, p = 0.002), TE (ß = -0.339, t = -2.876, p = 0.007), and AT%V˙O2max (ß = 0.281, t = 2.278, p = 0.028). Cycling can be predicted by MAV (ß = -0.341, t = -2.333, p = 0.025), TE (ß = -0.363, t = -3.172, p = 0.003), and %LM (ß = -0.326, t = -2.265, p = 0.029). In running split, MAV (ß = -0.768, t = -6.222, p < 0.001) was the only parameter present in the best multiple linear regression model. CONCLUSION: The most important variables in multiple regression models for estimating performance were MAV, TE, AT and %LM.
Authors: Paulo J Puccinelli; Claudio A B de Lira; Rodrigo L Vancini; Pantelis T Nikolaidis; Beat Knechtle; Thomas Rosemann; Marilia S Andrade Journal: Healthcare (Basel) Date: 2022-04-25