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Tannath J. Scott, Heidi R. Thornton, Macfarlane T.U. Scott, Ben J. Dascombe and Grant M. Duthie

when compared with HIR (>4 m/s), the distance covered over a predefined high-power metabolic threshold (HP metTh ) was strongly affected by position, with greater acceleration demands exhibited from middle forwards (covering 76% more distance at HP metTh than HIR) than outside backs (37%). 3 However

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Gregory T. Levin, Paul B. Laursen and Chris R. Abbiss

Purpose:

To assess the reliability of a 5-min-stage graded exercise test (GXT) and determine the association between physiological attributes and performance over stochastic cycling trials of varying distance.

Methods:

Twenty-eight well-trained male cyclists performed 2 GXTs and either a 30-km (n = 17) or a 100-km stochastic cycling time trial (n = 9). Stochastic cycling trials included periods of high-intensity efforts for durations of 250 m, 1 km, or 4 km depending on the test being performing.

Results:

Maximal physiological attributes were found to be extremely reliable (maximal oxygen uptake [VO2max]: coefficient of variation [CV] 3.0%, intraclass correlation coefficient [ICC] .911; peak power output [PPO]: CV 3.0%, ICC .913), but a greater variability was found in ventilatory thresholds and economy. All physiological variables measured during the GXT, except economy at 200 W, were correlated with 30-km cycling performance. Power output during the 250-m and 1-km efforts of the 30-km trial were correlated with VO2max, PPO, and the power output at the second ventilatory threshold (r = .58–.82). PPO was the only physiological attributed measured during the GXT to be correlated with performance during the 100-km cycling trial (r = .64).

Conclusions:

Many physiological variables from a reliable GXT were associated with performance over shorter (30-km) but not longer (100-km) stochastic cycling trials.

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Carl Foster, Jose A. Rodriguez-Marroyo and Jos J. de Koning

Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.

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-Ramos * Alejandro Torrejón * Alejandro Pérez-Castilla * Antonio J. Morales-Artacho * Slobodan Jaric * 1 03 2018 13 3 290 297 10.1123/ijspp.2017-0239 ijspp.2017-0239 Differences Between Relative and Absolute Speed and Metabolic Thresholds in Rugby League Tannath J. Scott * Heidi R. Thornton * Macfarlane

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Jerónimo Aragón-Vela, Yaira Barranco-Ruiz, Cristina Casals-Vázquez, Julio Plaza-Díaz, Rafael A. Casuso, Luis Fontana and Jesús F. Rodríguez Huertas

). However, our method does not allow to confirm that the EMG threshold of such a rapid sport movement is because of the metabolic threshold described by other authors for classical effort tests ( Galen & Malek, 2014 ). In classical tests, it is easy to confirm that the EMG break point coincides with that of

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Dan Weaving, Nicholas E. Dalton, Christopher Black, Joshua Darrall-Jones, Padraic J. Phibbs, Michael Gray, Ben Jones and Gregory A.B. Roe

absolute speed and metabolic thresholds in rugby league . Int J Sports Physiol Perform . 2018 ; 13 : 298 – 304 . 28657854 10.1123/ijspp.2016-0645 32. Roe G , Halkier M , Beggs C , Till K , Jones B . The use of accelerometers to quantify collisions and running demands of rugby union match

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José R. Alvero-Cruz, Robert A. Standley, Manuel Avelino Giráldez-García and Elvis A. Carnero

. Regarding the Roecker et al 19 equation, the results were obtained from faster runners than those in our study, and no validation studies were performed to confirm bias. In addition, the fact is that these 2 equations use metabolic thresholds that are dependent on operator decisions for their determination

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Mark Kramer, Mark Watson, Rosa Du Randt and Robert W. Pettitt

utility of both a straight-line and shuttle-based AOT as appropriate aerobic fitness tests for male rugby union players. More importantly, CS reflects a critical metabolic threshold that is representative of the sustainable relative aerobic intensity that separates sustainable from nonsustainable exercise

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Fergus O’Connor, Heidi R. Thornton, Dean Ritchie, Jay Anderson, Lindsay Bull, Alex Rigby, Zane Leonard, Steven Stern and Jonathan D. Bartlett

; 32 ( 20 ): 1858 – 1866 . PubMed ID: 24016304 doi:10.1080/02640414.2013.823227 24016304 10.1080/02640414.2013.823227 7. Scott T , Thornton H , Scott M , Dascombe B , Duthie G . Differences between relative and absolute speed and metabolic thresholds in rugby league . Int J Sports

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Dajo Sanders and Teun van Erp

3, >VT 2 /LT 2 ). While the determination of such zones around a metabolic threshold/inflection point is advocated, 28 , 29 when data on such thresholds are not available or data are analyzed retrospectively, studies have also used a 5-zone model based on a percentage of maximal HR to quantify