Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players

Bowen, Laura; Gross, Aleksander Stefan; Gimpel, Mo; Li, François-Xavier

doi:10.1136/bjsports-2015-095820

Cited by 249 publications

(424 citation statements)

References 24 publications

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“…23 Although the risk of team sport athletes sustaining a contact injury has been associated with poor high-intensity running ability, 24 changes in locomotor distance and acceleration workloads (i.e. acute:chronic workload ratios >1.7), 5 and session-RPE workloads, 25 to date no study has investigated the influence of acute and chronic contact workloads on contact injury risk in professional team sport. Given that the acute:chronic workload ratio derived from distance covered is associated with injury in rugby league players.…”

Section: Discussionmentioning

confidence: 99%

Wearable microtechnology can accurately identify collision events during professional rugby league match-play

Hulin

Gabbett²,

Johnston

et al. 2017

Journal of Science and Medicine in Sport

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Objectives: Collision frequency during rugby league matches is associated with team success, greater and longer lasting fatigue and increased injury risk. This study researched the sensitivity and specificity of microtechnology to count collision events during rugby league matches. Design: Diagnostic accuracy studyMethods: While wearing a microtechnology device (Catapult, Optimeye S5), eight professional rugby league players were subjected to a total of 380 collision events during matches. Video footage of each match was synchronised with microtechnology data. The occurrence of each collision event was coded in comparison with whether that event was or was not detected by microtechnology.Results: Microtechnology detected 371 true-positive collision events (sensitivity = 97.6 ± 1.5%). When low-intensity (<1 PlayerLoad AU), short duration (<1 second) events were excluded from the analysis, specificity was 91.7 ± 2.5%, accuracy was 92.7 ± 1.3%, positive likelihood ratio was 11.4 ×/÷ 1.4 and the typical error of estimate was 7.8% ×/÷ 1.9 (d = 0.29 ×/÷ 1.9, small). Microtechnology collisions 2 were strongly and positively correlated with video coded collision events (r = 0.96). The ability of microtechnology to detect collision events improved as the intensity and duration of the collision increased. Conclusions:Microtechnology can identify 97.6% of collision events during rugby league match-play.The typical error associated with measuring contact events can be reduced to 7.8%, with accuracy (92.7%) and specificity (91.7%) improving, when low-intensity (<1 PlayerLoad AU) and short duration (<1 second) collision reports are excluded. This provides practitioners with a measurement of contact workload during professional rugby league matches.

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Section: Discussionmentioning

confidence: 99%

Wearable microtechnology can accurately identify collision events during professional rugby league match-play

Hulin

Gabbett²,

Johnston

et al. 2017

Journal of Science and Medicine in Sport

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“…27,40 This analytical approach has recently been reported to identify injury risk in a variety of athletes. 27,54,55 However, while interesting for monitoring injury risk, the validity of the ACWR has recently been questioned 56,57 as the rolling average fails to account for the decaying nature of fitness and fatigue effects over time, and therefore it may not accurately represent variations in the manner in which loads are accumulated. An alternative method is to use an exponentially weighted moving average 58 for the calculation of acute and chronic loads, which assigns a decreasing weighting to compensate for the latency effects of loads.…”

Section: Analyzing Training-load Datamentioning

confidence: 99%

Monitoring Athlete Training Loads: Consensus Statement

Bourdon¹,

Cardinale²,

Murray³

et al. 2017

International Journal of Sports Physiology and Performance

718

735

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Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

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“…High-speed-running distance (also called stride work, which involves high activation of hamstring muscles) and acceleration/deceleration patterns (also called mechanical work, involving tight muscles) are believed to be the most important variables to be tracked since they refer to a more neuromuscular-oriented type of load, which is likely more linked with injury risk. 3,10,11 Metabolic power is a hybrid measure based on both level 1 and level 2 types of variables and is meant to provide a good estimate of the overall cost of high-intensity actions while combining the actual cost of highspeed (level 1) and accelerated (level 2) running. 12 Unfortunately, however, practitioners are left with a difficult dilemma when selecting their variables, since their validity and reliability is likely inversely related to their importance in terms of load monitoring, that is, high-speed running, acceleration/deceleration work, and metabolic power being the least valid and reliable variables.…”

Section: S2-36mentioning

confidence: 99%

“…• Injuries are most generally related to inappropriate volumes of accelerations 10 or high-speed running 11 ; there is in contrast little evidence to suggest that spikes in overall energy consumption per se may play a role in injury etiology.…”

Section: Adding Value To Load-monitoring Systems?mentioning

confidence: 99%

Player-Tracking Technology: Half-Full or Half-Empty Glass?

Buchheit¹,

Simpson²

2017

International Journal of Sports Physiology and Performance

158

154

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With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players' training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).

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Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players

Cited by 249 publications

References 24 publications

Wearable microtechnology can accurately identify collision events during professional rugby league match-play

Wearable microtechnology can accurately identify collision events during professional rugby league match-play

Monitoring Athlete Training Loads: Consensus Statement

Player-Tracking Technology: Half-Full or Half-Empty Glass?

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