The Influence of In-Season Training Loads on Injury Risk in Professional Rugby Union

Cross, Matthew; Williams, Seán; Trewartha, Grant; Kemp, Simon; Stokes, Keith

doi:10.1123/ijspp.2015-0187

Cited by 158 publications

(223 citation statements)

References 20 publications

(34 reference statements)

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“…Each training-load measure was independently modeled as a fixed effects predictor variable, both by itself (linear model) and with a squared term included to investigate possible nonlinear effects (nonlinear model). 11,24 10 Colby et al 9 Week-to-week change Absolute difference between current and previous week's training-load totals Rogalski et al, 10 Cross et al 24 Training monotony A measure of the day-to-day consistency of a player's training load within a given week: daily mean/SD Foster 2…”

Section: Resultsmentioning

confidence: 99%

“…Measures of cumulative load have been strongly associated with injury risk in elite Australian footballers 9,10 and rugby union. 24 It may be that these cumulative load measures describe the accumulation of fatigue within players, which may result in a reduction in the stress-bearing capacity of tissue 26 and thus an increased likelihood of injury. In addition, accumulated fatigue may alter neuromuscular control responses, such that potentially hazardous movement strategies are employed that increase the likelihood of injury.…”

Section: Discussionmentioning

confidence: 99%

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Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

Williams

Trewartha²,

Cross³

et al. 2017

International Journal of Sports Physiology and Performance

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Purpose: Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner's perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures. Methods: Training loads were collected from 173 professional rugby union players during the 2013-14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models. Results: Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component. Conclusions: The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of "load" in the data.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

Williams

Trewartha²,

Cross³

et al. 2017

International Journal of Sports Physiology and Performance

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“…Fatigue experiences play an important role in performance decrements under highly demanding circumstances [15,16] and could indicate higher injury risks [27,28]. The current study aimed to provide insight in general experiences of match fatigue, to identify potential differences in fatigue between players' positions, and -based on effort-recovery theory [9] -to identify the contribution of general performance capacity to these fatigue experiences.…”

Section: Discussionmentioning

confidence: 99%

Fatigue experiences in competitive soccer: development during matches and the impact of general performance capacity

Barte

Nieuwenhuys

Geurts

et al. 2017

Fatigue: Biomedicine, Health & Behavior

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Background: Fatigue as a result of prolonged activity may increase injury risk and decrease performance. Purpose: To provide insight in the development of fatigue experiences during soccer matches and the extent to which general performance capacity (i.e. overall physical fitness, psychological fitness, and recovery) contributes to these fatigue experiences. Methods: 450 Soccer players from the highest divisions in the Netherlands completed a questionnaire to assess (a) fatigue experiences (feeling tired, physically exhausted, not fit, weak, and mentally exhausted: not at all [1] -very much [7]) during a typical match; and (b) subjective indices of general performance capacity (i.e. general capacity to handle workload). Results: On average, fatigue levels were reported to be moderate. Fatigue increased primarily during the second half of the match and only a small decrease in fatigue was observed during half time. Generally, higher intensity fatigue experiences were reported by attackers than defenders. Regression analyses showed that players' general performance capacity explained a substantial part of fatigue at the start of a match and also predicted the development of fatigue experiences during a match, with low performance capacity being associated with higher baseline levels of fatigue and a steeper increase in fatigue over time. Conclusions: The observed association between players' general performance capacity and their fatigue levels during matches suggests that periodic screening of general performance capacity can be informative with regard to the employability of players across different stages of match-play and as such, aid in reducing injury risk and increasing performance.

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“…Moreover, with the irruption of new more accessible technology and software, the use of methods to control and quantify the TL have been spread in different sports [3][4][5]. Furthermore, beyond the improvement of performance perspective, the quantification of the TL can prevent from suffering non-functional overreaching, injuries or illnesses avoiding workload spikes [6][7][8].…”

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confidence: 99%

Answers to be Solved in Training Load Quantification

Muñoz¹

2017

RISM

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The necessity of measuring all the variables related to training process to understand the improvement of the athlete´s performance, has made the quantification of training load (TL) and the study of its relationship with performance raise interest among the scientific community and coaches [1,2]. Moreover, with the irruption of new more accessible technology and software, the use of methods to control and quantify the TL have been spread in different sports [3][4][5]. Furthermore, beyond the improvement of performance perspective, the quantification of the TL can prevent from suffering non-functional overreaching, injuries or illnesses avoiding workload spikes [6][7][8].For quantifying the TL there are different variables which can be categorized as either external (watts, speed, etc.) or internal (physiological and psychological). Traditionally, in endurance sports some internal variables have been used to quantify the TL such as heart rate [9-11], blood lactate and Ratings of Perceived Exertion [12,13]. One of the main reasons for that might be based on the necessity of distinguishing and measuring in an individual way the biological stress imposed by a training session in each athlete. The lack of capacity for measuring the internal work with external training quantification methods makes it impossible to compare the different responses of the athletes in a given training session [12]. In last decades several quantifying training proposals based on training zones have emerged as useful methods for monitoring the TL in endurance sports [2,10,11,14,15]. By means of these different methods, it is possible to compare intra-inter athletes' TL and the relationship between TL and performance. More recently, it has been confirmed the capacity of comparing different endurance sports by an objective method such as the Cejuela-Anta & EsteveLanao´s [10] ECO system [16].

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The Influence of In-Season Training Loads on Injury Risk in Professional Rugby Union

Cited by 158 publications

References 20 publications

Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

Fatigue experiences in competitive soccer: development during matches and the impact of general performance capacity

Answers to be Solved in Training Load Quantification

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