of recovery can only be garnered from controlled testing in recovered and fatigued states (ie, sensitivity to load), regardless of laboratory or field environments. More important, tests require practicality in combination with the athlete’s belief of the task’s relevance for competitive
Michael Kellmann, Maurizio Bertollo, Laurent Bosquet, Michel Brink, Aaron J. Coutts, Rob Duffield, Daniel Erlacher, Shona L. Halson, Anne Hecksteden, Jahan Heidari, K. Wolfgang Kallus, Romain Meeusen, Iñigo Mujika, Claudio Robazza, Sabrina Skorski, Ranel Venter and Jürgen Beckmann
David L. Carey, Justin Crow, Kok-Leong Ong, Peter Blanch, Meg E. Morris, Ben J. Dascombe and Kay M. Crossley
Training-load prescription in team-sport athletes is a balance between performance improvement 1 , 2 and injury-risk reduction. 3 – 6 The manipulation of training intensity, duration, and frequency to induce improvements in athletic performance is a fundamental objective of training
Catherine Mason and Matt Greig
experiencing pain, and for 76% of riders this pain was in the lower back. 3 Kraft et al 4 postulated that the cause of low back pain in riders might be an overuse syndrome of the lumbar spine as a result of the repetitive compressive, torsional, and bending loads absorbed by the rider. 5 The authors used
Harry G. Banyard, Kazunori Nosaka, Alex D. Vernon and G. Gregory Haff
Resistance-training intensity is typically derived from a percentage of an actual or estimated 1-repetition maximum (1-RM) assessment. 1 Once a 1-RM load is determined, a strength coach can periodize the relative intensity of the training sessions to maximize adaptation and allow for recovery. 2
Franco M. Impellizzeri, Samuele M. Marcora and Aaron J. Coutts
The concepts of internal and external training load were first presented at the Eighth Annual Congress of the European College of Sport Science in Salzburg, Austria (2003) 1 at an invited session and symposium organized by Tom Reilly. The content of this presentation was included in 2 follow
Håvard Wiig, Thor Einar Andersen, Live S. Luteberget and Matt Spencer
Monitoring and managing training load may assist to achieve the desired training outcome 1 and reduce injury risk. 2 , 3 However, quantifying training load accurately and reliably is challenging in team sports due to the complexity of movements and actions, and the constant shifting intensities
Andrea Monte, Francesca Nardello and Paola Zamparo
The effects of different loads on kinematic and kinetic variables during sled towing were investigated with the aim to identify the optimal overload for this specific sprint training.
Thirteen male sprinters (100-m personal best: 10.91 ± 0.14 s) performed 5 maximal trials over a 20-m distance in the following conditions: unloaded and with loads from 15% to 40% of the athlete’s body mass (BM). In these calculations the sled mass and friction were taken into account. Contact and flight times, stride length, horizontal hip velocity (vh), and relative angles of hip, knee, and ankle (at touchdown and takeoff) were measured step by step. In addition, the horizontal force (Fh) and power (Ph) and maximal force (Fh0) and power (Ph0) were calculated.
vh, flight time, and step length decreased while contact time increased with increasing load (P < .001). These variables changed significantly also as a function of the step number (P < .01), except between the 2 last steps. No differences were observed in Fh among loads, but Fh was larger in sled towing than in unloaded. Ph was unaffected by load up to +20%BM but decreased with larger loads. Fh0 and Ph0 were achieved at 20%BM. Up to 20%BM, no significant effects on joint angles were observed at touchdown and takeoff, while at loads >30%BM joint angles tended to decrease.
The 20%BM condition represents the optimal overload for peak power production—at this load sprinters reach their highest power without significant changes in their running technique (eg, joint angles).
Craig Twist, Jamie Highton, Matthew Daniels, Nathan Mill and Graeme Close
Player loads and fatigue responses are reported in 15 professional rugby league players (24.3 ± 3.8 y) during a period of intensified fixtures. Repeated measures of internal and external loads, perceived well-being, and jump flight time were recorded across 22 d, comprising 9 training sessions and matches on days 5, 12, 15, and 21 (player exposure: 3.6 ± 0.6 matches). Mean training loads (session rating of perceived exertion × duration) between matches were 1177, 1083, 103, and 650 AU. Relative distance in match 1 (82 m/min) and match 4 (79 m/min) was very likely lower in match 2 (76 m/min) and likely higher in match 3 (86 m/min). High-intensity running (≥5.5 m/s) was likely to very likely lower than match 1 (5 m/min) in matches 2–4 (2, 4, and 3 m/min, respectively). Low-intensity activity was likely to very likely lower than match 1 (78 m/min) in match 2 (74 m/min) and match 4 (73 m/min) but likely higher in match 3 (81 m/min). Accumulated accelerometer loads for matches 1–4 were 384, 473, 373, and 391 AU, respectively. Perceived well-being returned to baseline values (~21 AU) before all matches but was very likely to most likely lower the day after each match (~17 AU). Prematch jump flight times were likely to most likely lower across the period, with mean values of 0.66, 0.65, 0.62, and 0.64 s before matches 1–4, respectively. Across a 22-d cycle with fixture congestion, professional rugby league players experience cumulative neuromuscular fatigue and impaired match running performance.
Alice D. LaGoy, Caleb Johnson, Katelyn F. Allison, Shawn D. Flanagan, Mita T. Lovalekar, Takashi Nagai and Chris Connaboy
Heavy external loads carried by warfighters throughout training and during recent military operations such as Operation Enduring Freedom and Operation Iraqi Freedom compromise the warfighter’s ability to execute operational tasks, altering joint mechanics and muscle activation patterns. 1 – 4
Irineu Loturco, Lucas A. Pereira, Ciro Winckler, Weverton L. Santos, Ronaldo Kobal and Michael McGuigan
The load–velocity relationship is widely recognized for its ability to accurately predict the 1-repetition maximum (1RM) in both lower-body and upper-body exercises. 1 – 3 With the data generated by linear-regression models, practitioners can frequently monitor and adjust the resistance