Differential Correlations Between Anthropometry, Training Volume, and Performance in Male and Female Ironman Triathletes

Abstract

We investigated in 27 male Ironman triathletes aged 30.3 (9.1) years, with 77.7- (9.8) kg body mass, 1.78- (0.06) m body height, 24.3- (2.2) kg·m⁻² body mass index (BMI), and 14.4 (4.8) % body fat and in 16 female Ironman triathletes aged 36.6 (7.0) years, with 59.7- (6.1) kg body mass, 1.66- (0.06) m body height, 21.5 (1.0) kg·m⁻² BMI, and 22.8 (4.8) % body fat to ascertain whether anthropometric or training variables were related to total race time. The male athletes were training 14.8 (3.2) h·wk⁻¹ with a speed of 2.7 (0.6) km·h⁻¹ in swimming, 27.3 (3.0) in cycling, and 10.6 (1.4) in running. The female athletes trained for 13.9 (3.4) h·wk⁻¹ at 2.1 (0.8) km·h⁻¹h in swimming, 23.7 (7.6) km·h⁻¹ in cycling, and 9.0 (3.7) km·h⁻¹ in running, respectively. For male athletes, percent body fat was highly significantly (r² = 0.583; p < 0.001) associated with total race time. In female triathletes, training volume showed a relationship to total race time (r² = 0.466; p < 0.01). Percent body fat was unrelated to training volume for both men (r² = 0.001; p > 0.05) and women (r² = 0.007; p > 0.05). We conclude that percent body fat showed a relationship to total race time in male triathletes, and training volume showed an association with total race time in female triathletes. Presumably, the relationship between percent body fat, training volume, and race performance is genetically determined.

Is Body Fat a Predictor Variable for Race Performance in Recreational Female Ironman Triathletes?

“…In contrast to these studies, we found no association between anthropometric characteristics and race performance in female Ironman triathletes, as has been found in a study with smaller samples of recreational female Ironman triathletes [17,18]. This might be because of gender, or the longer distance compared with the Olympic distance.…”

Trends in Triathlon Performance: Effects of Sex and Age

“…[45] Low body fat is an important predictor variable for total time performance in triathlon. For example, Knechtle et al [46] showed that low body fat was associated with faster race times in male Ironman triathletes but not in females.…”

Regression Equation from Dual Energy X Ray Absorptiometry (DEXA) for Estimating Muscle Mass Segment

“…The estimated muscle mass for the designed regression formulas promote the specific knowledge of the muscle component and allow comparing the differences between rugby and soccer players, for example, where significant differences in the muscle mass of the upper limb may be found, assuming the rugby player needs better muscle development in his arms and forearm than an average soccer player (Barraza et al, 2009) or compared to other collective sports, such as basketball, volleyball or handball (Bayios et al, 2006;Carvajal et al, 2009;Gholami & Rad, 2010, Papadopoulou & Papadopoulou, 2010, individual sports (Sanchez-Muñoz et al, 2006;Kong & Heer, 2008;Knechtle et al, 2010a;Knechtle et al, 2010b;Bejan, 2010;Borgard, 2010) or between collective and individual sports. These formulas also allow establishing differences in the athlete himself, in different temporal moments, trained or untrained, injured or under normal performance conditions, etc.…”