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
Eva Piatrikova, Nicholas J. Willsmer, Ana C. Sousa, Javier T. Gonzalez and Sean Williams
Anne Hecksteden, Werner Pitsch, Ross Julian, Mark Pfeiffer, Michael Kellmann, Alexander Ferrauti and Tim Meyer
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.
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.
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.
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.
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
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
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
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
Ric Lovell and Grant Abt
To report the intensity distribution of Premier League soccer players’ external loads during match play, according to recognized physiological thresholds. The authors also present a case in which individualized speed thresholds changed the interpretation of time–motion data.
Eight outfield players performed an incremental treadmill test to exhaustion to determine the running speeds associated with their ventilatory thresholds. The running speeds were then used to individualize time–motion data collected in 5 competitive fixtures and compared with commonly applied arbitrary speed zones.
Of the total distance covered, 26%, 57%, and 17% were performed at low, moderate, and high intensity, respectively. Individualized time– motion data identified a 41% difference in the high-intensity distance covered between 2 players of the same positional role, whereas the player-independent approach yielded negligible (5–7%) differences in total and high-speed distances covered.
The authors recommend that individualized speed thresholds be applied to time–motion-analysis data in synergy with the traditional arbitrary approach.
Montse C. Ruiz, Yuri Hanin and Claudio Robazza
In this investigation we describe an individualized approach in the assessment of athletes’ experiences associated with successful and poor performances. Two studies were conducted to develop a profiling procedure to assess eight modalities of performance-related states. In Study 1, six high-level athletes assessed their states before most successful and unsuccessful performances using a preliminary 71-item stimulus list developed by a panel of four emotion researchers. They also rated the intensity of their states on a modified Borg’s CR-10 scale. In Study 2, five top-level divers assessed their states before multiple dives (three successful and three unsuccessful) using a revised 74-item list. The perceived impact on performance was also examined using an open-ended question. Individual profiles reflected two typical curves discriminating successful and unsuccessful performances. High individual variability in item content and intensity was found. Athletes reported a wide range of interrelated experiences associated with their performances. Our findings support the practical utility of individualized profiling to assess athletes’ performance-related states.
Catarina Sousa, Ronald E. Smith and Jaume Cruz
Coach Effectiveness Training (CET) has been shown to have positive effects on a range of outcome variables, especially in young athletes (Smith & Smoll, 2005). Based on CET principles, and coupled with behavioral feedback, an individualized goal-setting intervention was developed and assessed using a replicated case study approach. Outcome variables included observed, athlete-perceived, and coach-perceived behaviors measured before the intervention and late in the season, as well as coaches’ evaluations of the intervention. Four soccer coaches selected three target behaviors that they wished to improve after viewing videotaped behavioral feedback. Behavioral assessment revealed that two of the coaches achieved positive changes on all three of their targeted behaviors. A third coach improved on two of the three targeted behaviors. The fourth coach did not achieve any of the established goals. We conclude that this approach is sufficiently promising to warrant additional research, and we discuss strengths and limitations of the study.
Jean-Benoît Morin and Pierre Samozino
Recent studies have brought new insights into the evaluation of power-force-velocity profiles in both ballistic push-offs (eg, jumps) and sprint movements. These are major physical components of performance in many sports, and the methods the authors developed and validated are based on data that are now rather simple to obtain in field conditions (eg, body mass, jump height, sprint times, or velocity). The promising aspect of these approaches is that they allow for more individualized and accurate evaluation, monitoring, and training practices, the success of which is highly dependent on the correct collection, generation, and interpretation of athletes’ mechanical outputs. The authors therefore wanted to provide a practical vade mecum to sports practitioners interested in implementing these power-force-velocity–profiling approaches. After providing a summary of theoretical and practical definitions for the main variables, the authors first detail how vertical profiling can be used to manage ballistic push-off performance, with emphasis on the concept of optimal force–velocity profile and the associated force–velocity imbalance. Furthermore, they discuss these same concepts with regard to horizontal profiling in the management of sprinting performance. These sections are illustrated by typical examples from the authors’ practice. Finally, they provide a practical and operational synthesis and outline future challenges that will help further develop these approaches.