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Eva Piatrikova, Nicholas J. Willsmer, Ana C. Sousa, Javier T. Gonzalez, and Sean Williams

also been recently reduced by the validation of the swimming 3-minute all-out test (3MT), 7 allowing easier application of the CS concept in swimming, where several constraints (eg, multidisciplinary nature, lack of resources, time, expertise) limit regular testing and prescription of individualized

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Anne Hecksteden, Werner Pitsch, Ross Julian, Mark Pfeiffer, Michael Kellmann, Alexander Ferrauti, and Tim Meyer

Purpose:

Assessment of muscle recovery is essential for the daily fine-tuning of training load in competitive sports, but individual differences may limit the diagnostic accuracy of group-based reference ranges. This article reports an attempt to develop individualized reference ranges using a Bayesian approach comparable to that developed for the Athlete Biological Passport.

Methods:

Urea and creatine kinase (CK) were selected as indicators of muscle recovery. For each parameter, prior distributions and repeated-measures SDs were characterized based on data of 883 squad athletes (1758 data points, 1–8 per athlete, years 2013–2015). Equations for the individualization procedure were adapted from previous material to allow for discrimination of 2 physiological states (recovered vs nonrecovered). Evaluation of classificatory performance was carried out using data from 5 consecutive weekly microcycles in 14 elite junior swimmers and triathletes. Blood samples were collected every Monday (recovered) and Friday according to the repetitive weekly training schedule over 5 wk. On the group level, changes in muscle recovery could be confirmed by significant differences in urea and CK and validated questionnaires. Group-based reference ranges were derived from that same data set to avoid overestimating the potential benefit of individualization.

Results:

For CK, error rates were significantly lower with individualized classification (P vs group-based: test-pass error rate P = .008; test-fail error rate P < .001). For urea, numerical improvements in error rates failed to reach significance.

Conclusions:

Individualized reference ranges seem to be a promising tool to improve accuracy of monitoring muscle recovery. Investigating application to a larger panel of indicators is warranted.

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Sarpreet Kahlon, Kiah Brubacher-Cressman, Erica Caron, Keren Ramonov, Ruth Taubman, Katherine Berg, F. Virginia Wright, and Alicia J. Hilderley

current physical activity level and abilities. Assess environmental and social factors influencing physical activity. Identify preferred activities. Set goals as collaborative process. Program design Individualize program type to goals (e.g., strength, skills). Select interventionist to fit program type

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Harry G. Banyard, Kazunori Nosaka, Alex D. Vernon, and G. Gregory Haff

was necessary to improve the correlations (accuracy) of the LVPs (Figure  4 ). Correlation ranges for the individualized linear regression LVPs (PV: r  = .89–.99; MPV: r  = .90–.99; MV: r  = .90–.99) and individualized polynomial regression LVPs (PV: r  =.89–.99; MPV: r  = .90–.99; MV: r  = .91

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Steve W. Thompson, David Rogerson, Alan Ruddock, Harry G. Banyard, and Andrew Barnes

submaximal loads (CVs > 10%) is evident 7 , 11 ; and that poor reliability of velocity at 1RM (ICC = .19–.66; CV = 15.7%–22.5%) can also be observed across a range of exercises. 2 , 7 – 10 Moreover, individualized LVPs seemingly provide stronger relationships between load and velocity. 7 , 10 , 11 With

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Gustavo Tomazoli, Joao B. Marques, Abdulaziz Farooq, and Joao R. Silva

load individualization method has been commonly applied to “reassess” the competitive demands in soccer. 16 , 17 , 19 , 20 This individualization approach is performed through the use of different physiological attributes, such as MAS or maximal sprinting speed (MSS) 19 , 20 or through a combination

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Tue A.H. Lassen, Lars Lindstrøm, Simon Lønbro, and Klavs Madsen

increase variation and probably reduce statistical power in studies where the possible effect on performance is quite small. Specifically, there is a large individual variability in time-to-peak pH after SBS, as shown by Sparks et al. ( 2017 ). Consequently, it is crucial to individualize the timing and to

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Juan A. Escobar Álvarez, Juan P. Fuentes García, Filipe A. Da Conceição, and Pedro Jiménez-Reyes

observe the effect of training programs aimed at reducing F-V IMB, to reach optimal balance and enhance performance, are limited. 17 , 18 Previous studies with futsal, soccer, and rugby players, analyzed the effectiveness of an optimized and individualized training program specifically designed to

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Claire E. Badenhorst, Katherine E. Black, and Wendy J. O’Brien

athletes who have been identified as at risk of LEA. The strength of an individualized and biological marker would aid in resolving issues that currently plague the effective and early identification of LEA and restoration of EA in athletes ( Burke et al., 2018c ). Evidence would support the use of

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Christopher R.J. Fennell and James G. Hopker

contrary, the optimal duration is most likely highly individual, dependent on training status and desired session outcome. 6 Researchers have attempted to use self-selected recovery durations as a method of individualization demonstrating the method to be effective when participants are well familiarized