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Matthew I. Black, Joseph C. Handsaker, Sam J. Allen, Stephanie E. Forrester and Jonathan P. Folland

better running economy than recreational endurance runners. Due to the influence of speed on energy cost, we recommend that future investigations primarily compare energy-cost measurements at the same absolute running speed. Identification of the most economical running speed may be important to

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Pedro L. Valenzuela, Guillermo Sánchez-Martínez, Elaia Torrontegi, Javier Vázquez-Carrión, Manuela González, Zigor Montalvo and Grégoire P. Millet

have conditioned these findings, and it is possible to hypothesize that different results might have been observed in other populations (eg, untrained individuals). Moreover, it must be noted that the HRV analysis performed allows the identification of a “global state of fatigue,” but it does not allow

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Arne Jaspers, Tim Op De Beéck, Michel S. Brink, Wouter G.P. Frencken, Filip Staes, Jesse J. Davis and Werner F. Helsen

given external training load. 32 , 33 The identification of key ELIs may aid in the evaluation of players’ training dose and response over time using efficiency ratios (ie, the proportion between RPE and ELIs). 34 , 35 For example, some ELIs may be perceived as less exerting at the end of preseason or

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Disa J. Smee, Anthony Walker, Ben Rattray, Julie A. Cooke, Ben G. Serpell and Kate L. Pumpa

assessment of body composition which would result in late identification of obesity. This could be associated with consequences such as increased risk of cardiovascular events ( Fahs et al., 2011 ) or a reduction in the ability to safely undertake firefighting tasks ( Michaelides et al., 2008 ). Therefore

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Michele Lastella, Gregory D. Roach, Grace E. Vincent, Aaron T. Scanlan, Shona L. Halson and Charli Sargent

-based (27%) and running (4%) exercises. Data extracted from the training diaries allowed identification of training and rest days. Daily data were used to quantify internal training load using the sRPE method developed by Foster et al. 21 Specifically, players recorded the start and end time for each

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Katie E. McGibbon, Megan E. Shephard, Mark A. Osborne, Kevin G. Thompson and David B. Pyne

identification testing parameters and performance in elite junior swimmers . J Sci Med Sport . 2018 ; 21 ( 12 ): 1281 – 1285 . PubMed ID: 29804652 doi: 10.1016/j.jsams.2018.05.006 29804652 12. Bates D , Maechler M , Bolker B , Walker S . Fitting linear mixed-effects models using lme4 . J Stat

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Sean H. Kerr, Tiffanye M. Vargas, Mimi Nakajima and Jim Becker

Warner coaches signed an informed consent document to participate in the study, they were given a demographic questionnaire and the RoCKAS questionnaire. Responses were kept completely confidential and no personal identification was utilized in the research. The questionnaire took 30 min to complete. The

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Nicolas Berryman, Iñigo Mujika, Denis Arvisais, Marie Roubeix, Carl Binet and Laurent Bosquet

with comprehensive meta-analysis ( www.meta-analysis.com ). Results The literature search allowed identification of 554 potentially relevant publications, of which 28 studies met all inclusion criteria. Exclusion criteria are detailed in Figure  1 . Sport disciplines included in this meta-analysis are

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John F. Fitzpatrick, Kirsty M. Hicks and Philip R. Hayes

comparisons between players possible, from a performance and talent identification standpoint. Therefore, it could be suggested that a joint approach, utilizing both arbitrary and individualized thresholds, is most advantageous. Another novel aspect of the present study was the inclusion of time spent above

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Olaf Prieske, Helmi Chaabene, Christian Puta, David G. Behm, Dirk Büsch and Urs Granacher

drop-height has a significant impact on the final DJ performance (eg, DJ height, reactive strength index [RSI]). 5 , 6 In particular, the RSI (calculated as the ratio between jump time/height and ground contact time) has been considered an important marker for the identification of the “optimal drop