Good nutrition is essential for the physical development of adolescent athletes, however data on dietary intakes of adolescent rugby players are lacking. This study quantified and evaluated dietary intake in 87 elite male English academy rugby league (RL) and rugby union (RU) players by age (under 16 (U16) and under 19 (U19) years old) and code (RL and RU). Relationships of intakes with body mass and composition (sum of 8 skinfolds) were also investigated. Using 4-day diet and physical activity diaries, dietary intake was compared with adolescent sports nutrition recommendations and the UK national food guide. Dietary intake did not differ by code, whereas U19s consumed greater energy (3366 ± 658 vs. 2995 ± 774 kcal·day-1), protein (207 ± 49 vs. 150 ± 53 g·day-1) and fluid (4221 ± 1323 vs. 3137 ± 1015 ml·day-1) than U16s. U19s consumed a better quality diet than U16s (greater intakes of fruit and vegetables; 4.4 ± 1.9 vs. 2.8 ± 1.5 servings·day-1; nondairy proteins; 3.9 ± 1.1 vs. 2.9 ± 1.1 servings·day-1) and less fats and sugars (2.0 ± 1. vs. 3.6 ± 2.1 servings·day-1). Protein intake vs. body mass was moderate (r = .46, p < .001), and other relationships were weak. The findings of this study suggest adolescent rugby players consume adequate dietary intakes in relation to current guidelines for energy, macronutrient and fluid intake. Players should improve the quality of their diet by replacing intakes from the fats and sugars food group with healthier choices, while maintaining current energy, and macronutrient intakes.
Deborah R. Smith, Ben Jones, Louise Sutton, Roderick F.G.J. King and Lauren C. Duckworth
Robert J. Naughton, Barry Drust, Andy O’Boyle, Ryland Morgans, Julie Abayomi, Ian G. Davies, James P. Morton and Elizabeth Mahon
While traditional approaches to dietary analysis in athletes have focused on total daily energy and macronutrient intake, it is now thought that daily distribution of these parameters can also influence training adaptations. Using 7-day food diaries, we quantified the total daily macronutrient intake and distribution in elite youth soccer players from the English Premier League in U18 (n = 13), U15/16 (n = 25) and U13/14 squads (n = 21). Total energy (43.1 ± 10.3, 32.6 ± 7.9, 28.1 ± 6.8 kcal·kg-1·day-1), CHO (6 ± 1.2, 4.7 ± 1.4, 3.2 ± 1.3 g·kg- 1·day-1) and fat (1.3 ± 0.5, 0.9 ± 0.3, 0.9 ± 0.3 g·kg-1·day-1) intake exhibited hierarchical differences (p < .05) such that U13/14 > U15/16 > U18. In addition, CHO intake in U18s was lower (p < .05) at breakfast, dinner and snacks when compared with both squads but no differences were apparent at lunch. Furthermore, the U15/16s reported lower relative daily protein intake than the U13/14s and U18s (1.6 ± 0.3 vs. 2.2 ± 0.5, 2.0 ± 0.3 g·kg-1). A skewed distribution (p < .05) of daily protein intake was observed in all squads, with a hierarchical order of dinner (~0.6 g·kg-1) > lunch (~0.5 g·kg-1) > breakfast (~0.3 g·kg-1). We conclude elite youth soccer players do not meet current CHO guidelines. Although daily protein targets are achieved, we report a skewed daily distribution in all ages such that dinner > lunch > breakfast. Our data suggest that dietary advice for elite youth players should focus on both total daily macronutrient intake and optimal daily distribution patterns.
Cloe Cummins, Blake McLean, Mark Halaki and Rhonda Orr
To quantify the external training loads of positional groups in preseason training drills.
Thirty-three elite rugby league players were categorized into 1 of 4 positional groups: outside backs (n = 9), adjustables (n = 9), wide-running forwards (n = 9), and hit-up forwards (n = 6). Data for 8 preseason weeks were collected using microtechnology devices. Training drills were classified based on drill focus: speed and agility, conditioning, and generic and positional skills.
Total, high-speed, and very-high-speed distance decreased across the preseason in speed and agility (moderate, small, and small, respectively), conditioning (large, large, and small) and generic skills (large, large, and large). The duration of speed and generic skills also decreased (77% and 48%, respectively). This was matched by a concomitant increase in total distance (small), high-speed running (small), very-high-speed running (moderate), and 2-dimensional (2D) BodyLoad (small) demands in positional skills. In positional skills, hit-up forwards (1240 ± 386 m) completed less very-high-speed running than outside backs (2570 ± 1331 m) and adjustables (2121 ± 1163 m). Hit-up forwards (674 ± 253 AU) experienced greater 2D BodyLoad demands than outside backs (432 ± 230 AU, P = .034). In positional drills, hit-up forwards experienced greater relative 2D BodyLoad demands than outside backs (P = .015). Conversely, outside backs experienced greater relative high- (P = .007) and very-high-speed-running (P < .001) demands than hit-up forwards.
Significant differences were observed in training loads between positional groups during positional skills but not in speed and agility, conditioning, and generic skills. This work also highlights the importance of different external-load parameters to adequately quantify workload across different positional groups.
Iker Muñoz, Roberto Cejuela, Stephen Seiler, Eneko Larumbe and Jonathan Esteve-Lanao
To describe training loads during an Ironman training program based on intensity zones and observe training–performance relationships.
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).
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).
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.
Iker Muñoz, Stephen Seiler, Javier Bautista, Javier España, Eneko Larumbe and Jonathan Esteve-Lanao
To quantify the impact of training-intensity distribution on 10K performance in recreational athletes.
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).
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).
Polarized training can stimulate greater training effects than between-thresholds training in recreational runners.
Trent Stellingwerff, James P. Morton and Louise M. Burke
athlete should be quantified as best as possible against these performance determinants. From this construct, the coach will strategically develop the various macro- (months to years), meso- (weeks to months), and microcycles (days to within days) aspects of training periodization and its specific
Caoimhe Tiernan, Mark Lyons, Tom Comyns, Alan M. Nevill and Giles Warrington
the weekly mean (SE) of training load and salivary cortisol on a Monday and Friday morning across the 10-week training period. Figure 1 —(A) Monday salivary cortisol. (B) Friday salivary cortisol. (C) Training load. Data are mean (SE). Week 1 was baseline, and week 3, download week. * P < .05, ** P
Ian N. Bezodis, David G. Kerwin, Stephen-Mark Cooper and Aki I.T. Salo
performance and underpinning variables alongside the training program being followed will provide a unique scientific insight into the effects of a periodized training program on sprint performance. Therefore, the purpose of this study was to understand how training periodization influences sprint performance
Thorben Hülsdünker, Clara Rentz, Diemo Ruhnow, Hannes Käsbauer, Heiko K. Strüder and Andreas Mierau
. Finally, because all the players regularly participated in national and international tournaments, we accounted for the athletes’ availability during the training period to ensure similar training times between groups. Experimental Protocol Questionnaires confirmed that athletes did not suffer from
Michelle Smith, Hayley E. McEwan, David Tod and Amanda Martindale
the helpful practices that TSEPs can engage with throughout the professional training period. Nevertheless, to advance on these insights, there is scope to examine how these practices may contribute to the cognitive development of TSEPs. For example, if peer mentoring offers guidance to a TSEP, it may