Abstract
Triathlon is a sport of endurance that includes three events: swimming, cycling and running. With the fastest overall finisher being the winner. PURPOSE: To establish prediction equations that would predict swimming, cycling, running and total time in Sprint distance triathlon from shorter distance tests. METHODS: Twenty-eight elite triathletes (12 females and 16 males, age 20.1 ± 3.7 years, height 1.71 ± 0.11 m, weight 61.9 ± 20.2 kg, BMI 21.05 ± 1.76) participated in this study. Competition performance was measured during the 2018 Santo Domingo CAMTRI Sprint Triathlon American Cup and Iberoamerican Championships. The shorter distance (ShD) tests were performed at a following training camp two, three and four days after competition (400 meters swim (400MS), 10 Km bike (10 KB), 3 Km run (3KR), respectively). The subjects were instructed to perform tests at their maximum capacity. VO2 max was estimated (VO2Est) with the Boukhar formula: VO2 peak = (V400m (m·sec-1) + 0.16) / 0.024 (r = 0.84) (Boukhar, 2015). Correlations between test times and total times were calculated using a 2-tailed Pearson correlation analysis and were considered statistically significant at p < 0.05. All statistical analyses were conducted using IBM SPSS Statistics for Windows version 27. RESULTS: Time trials for the ShD tests were: 400MS 301.21 ± 27.05 sec, 10 KB 927.98 ± 74.16 sec, 3KR 619.40 ± 72.50 sec. Competition Total Time (CTT) in seconds (sec) was significantly correlated (p < 0.01) with 400MS (r = 0.811), 10 KB (r = 0.862), 3KR (r = 0.841), VO2 Est (r = -0.804). The multiple regression analyses yielded the following equation for CTT (sec) = 4.038 (400MS) + 1.694 (10 KB) +1.061(3KR) + 135.963 (r = 0.904). Furthermore, regression equations were determined for each discipline: CST (Competition swim time in sec) = 1.692 (400MS) + 86.76 (r = 0.920); CBT (Competition bike time in sec) = 2.081(10 KB) + 150.07 (r = 0.863); and CRT (Competition run time in sec) = 1.55 (3KR) + 196.63 (r = 0.762). CONCLUSIONS: The equations presented can be used by triathlon athletes and coaches to predict their performance in an upcoming sprint triathlon competition. This information can be used to establish a realistic and a comprehensive pacing strategy for each discipline. Also, the ShD could be included during mesocycles without causing too much fatigue.
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