Previous experience, aerobic capacity and body composition are the best predictors for Olympic distance triathlon performance: Predictors in amateur triathlon

Paulo J Puccinelli; Giscard H O Lima; João B Pesquero; Claudio A B de Lira; Rodrigo L Vancini; Pantelis T Nikolaids; Beat Knechtle; Marilia S Andrade

doi:10.1016/j.physbeh.2020.113110

Previous experience, aerobic capacity and body composition are the best predictors for Olympic distance triathlon performance: Predictors in amateur triathlon

Physiol Behav. 2020 Oct 15:225:113110. doi: 10.1016/j.physbeh.2020.113110. Epub 2020 Jul 29.

Authors

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

Affiliations

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.

PMID: 32738318
DOI: 10.1016/j.physbeh.2020.113110

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˙O_2max), 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˙O_2max (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.

Keywords: Maximal aerobic velocity; Sports medicine; Sports performance; Sports physiology.

MeSH terms

Bicycling*
Body Composition
Female
Humans
Male
Oxygen Consumption
Physical Endurance
Running*
Swimming
Time Factors