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The aim of this study was to describe the pacing distribution during 6 editions of the world cross-country championships. METHODS: Data from the 768 male runners participating from 2007 to 2013 were considered for this study. Blocks of 10 participants according to final position (eg, 1st to 10th, 11 to 20th, etc) were considered. RESULTS: Taking data from all editions together, the effect of years was found to be significant (F5,266 = 3078.69, P < .001, ω2 = 0.31), as well as the effect of blocks of runners by final position (F4,266 = 957.62, P < .001, ω2 = 0.08). A significant general decrease in speed by lap was also found (F5,1330 = 2344.02, P < .001, ω2 = 0.29). Post hoc analyses were conducted for every edition where several pacing patterns were found. All correlations between the lap times and the total time were significant. However, each lap might show different predicting capacity over the individual outcome. DISCUSSION: Top athletes seem to display different strategies, which allow them to sustain an optimal speed and/or kick as needed during the critical moments and succeed. After the first group (block) of runners, subsequent blocks always displayed a positive pacing pattern (fast to slow speed). Consequently, a much more stable pacing pattern should be considered to maximize final position. CONCLUSIONS: Top-10 finishers in the world cross-country championships tend to display a more even pace than the rest of the finishers, whose general behavior shows a positive (fast-to-slow) pattern. [ABSTRACT FROM AUTHOR]

Purpose: To describe training loads during an Ironman training program based on intensity zones and observe training–performance relationships. Methods: Nine triathletes completed a program with the same periodization model aiming at participation in the same Ironman event. Before and during the study, subjects performed ramp-protocol tests, running, and cycling to determine aerobic (AeT) and anaerobic thresholds (AnT) through gas-exchange analysis. For swimming, subjects performed a graded lactate test to determine AeT and AnT. Training was subsequently controlled by heart rate (HR) during each training session over 18 wk. Training and the competition were both quantified based on the cumulative time spent in 3 intensity zones: zone 1 (low intensity; AnT). Results: Most of training time was spent in zone 1 (68% ± 14%), whereas the Ironman competition was primarily performed in zone 2 (59% ± 22%). Significant inverse correlations were found between both total training time and training time in zone 1 vs performance time in competition (r = –.69 and –.92, respectively). In contrast, there was a moderate positive correlation between total training time in zone 2 and performance time in competition (r = .53) and a strong positive correlation between percentage of total training time in zone 2 and performance time in competition (r = .94). Conclusions: While athletes perform with HR mainly in zone 2, better performances are associated with more training time spent in zone 1. A high amount of cycling training in zone 2 may contribute to poorer overall performance. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the impact of training-intensity distribution on 10K performance in recreational athletes. Methods: 30 endurance runners were randomly assigned to a training program emphasizing low-intensity, sub-ventilatory-threshold (VT), polarized endurance-training distribution (PET) or a moderately high-intensity (between-thresholds) endurance-training program (BThET). Before the study, the subjects performed a maximal exercise test to determine VT and respiratory-compensation threshold (RCT), which allowed training to be controlled based on heart rate during each training session over the 10-wk intervention period. Subjects performed a 10-km race on the same course before and after the intervention period. Training was quantified based on the cumulative time spent in 3 intensity zones: zone 1 (low intensity, RCT). The contribution of total training time in each zone was controlled to have more low-intensity training in PET (±77/3/20), whereas for BThET the distribution was higher in zone 2 and lower in zone 1 (±46/35/19). Results: Both groups significantly improved their 10K time (39min18s ± 4min54s vs 37min19s ± 4min42s, P < .0001 for PET; 39min24s ± 3min54s vs 38min0s ± 4min24s, P < .001 for BThET). Improvements were 5.0% vs 3.6%, ~41 s difference at post-training-intervention. This difference was not significant. However, a subset analysis comparing the 12 runners who actually performed the most PET (n = 6) and BThET (n = 16) distributions showed greater improvement in PET by 1.29 standardized Cohen effect-size units (90% CI 0.31–2.27, P = .038). Conclusions: Polarized training can stimulate greater training effects than between-thresholds training in recreational runners. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented by Daniel A. Boullosa, Fábio Y. Nakamura, and Jonatan R. Ruiz, confirming the effectiveness of “polarized training” for rowing performance, which in turn has elicited several responses.