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  • Author: Jonathan Esteve-Lanao x
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Fernando Naclerio, Eneko Larumbe-Zabala, Mar Larrosa, Aitor Centeno, Jonathan Esteve-Lanao and Diego Moreno-Pérez

The impact of animal protein blend supplements in endurance athletes is scarcely researched. The authors investigated the effect of ingesting an admixture providing orange juice and protein (PRO) from beef and whey versus carbohydrate alone on body composition and performance over a 10-week training period in male endurance athletes. Participants were randomly assigned to a protein (CHO + PRO, n = 15) or a nonprotein isoenergetic carbohydrate (CHO, n = 15) group. Twenty grams of supplement mixed with orange juice was ingested postworkout or before breakfast on nontraining days. Measurements were performed pre- and postintervention on body composition (by dual-energy X-ray absorptiometry), peak oxygen consumption (V˙O2peak), and maximal aerobic speed. Twenty-five participants (CHO + PRO, n = 12; CHO, n = 13) completed the study. Only the CHO + PRO group significantly (p < .05) reduced whole-body fat (mean ± SD) (−1.02 ± 0.6 kg), total trunk fat (−0.81 ± 0.9 kg), and increased total lower body lean mass (+0.52 ± 0.7 kg), showing close to statistically significant increases of whole-body lean mass (+0.57 ± 0.8 kg, p = .055). Both groups reduced (p < .05) visceral fat (CHO + PRO, −0.03 ± 0.1 kg; CHO, −0.03 ± 0.5 kg) and improved the speed at maximal aerobic speed (CHO + PRO, +0.56 ± 0.5 km/hr; CHO, +0.35 ± 0.5 km/hr). Although consuming animal protein blend mixed with orange juice over 10 weeks helped to reduce fat mass and to increase lean mass, no additional performance benefits in endurance runners were observed.

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Iker Muñoz, Stephen Seiler, Javier Bautista, Javier España, Eneko Larumbe and Jonathan Esteve-Lanao

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, <VT), zone 2 (moderate intensity, between VT and RCT), and zone 3 (high 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.

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Iker Muñoz, Roberto Cejuela, Stephen Seiler, Eneko Larumbe and Jonathan Esteve-Lanao

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; <AeT), zone 2 (moderate intensity; between AeT and AnT), and zone 3 (high 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.

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Jonathan Esteve-Lanao, Eneko Larumbe-Zabala, Anouar Dabab, Alberto Alcocer-Gamboa and Facundo Ahumada

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 (F 5,266 = 3078.69, P < .001, ω2 = 0.31), as well as the effect of blocks of runners by final position (F 4,266 = 957.62, P < .001, ω2 = 0.08). A significant general decrease in speed by lap was also found (F 5,1330 = 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.

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Daniel A. Boullosa, Fábio Y. Nakamura, Jonatan R. Ruiz, Stephen Seiler, Jonathan Esteve-Lanao, Alejandro Lucia, John A. Hawley and David T. Martin