The official magazine of the Football Medicine & Performance Association
football medicine & performance
Issue 31 Winter 2019/20
Feature
Karen Carney A Pioneer for the Womens’ Game In this issue Injuries in Football: It’s Time to Stop Chasing the Training Load Unicorn Cautious Return to Play Could Prevent Muscle Injuries FMPA Conference 2020 Neurodegenerative Disease Among Former Footballers
Legal Ţ Education Ţ Recruitment Ţ Wellbeing
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CONTENTS FEATURES
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Injury Mitigation in Team Sports. Part-2: The risk management approach Colin W. Fuller
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What to do and When to do it? The Tricky Question of Specialisation in Youth Football Laura Finnegan
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Injuries in Football: It’s Time to Stop Chasing the Training Load Unicorn Franco M. Impellizzeri, Aaron J. Coutts, Maurizio Fanchini, Alan McCall
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Training the Semi-Professional Footballer Daniel Bernardin, Dylan Mernagh
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Karen Carney A Pioneer for the Women’s Game Sean Carmody
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Wrist Injuries in Goalkeepers Raj Bhatia, Adam Esa, Sam Haines
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Neurodegenerative Disease Mortality Among Former Professional Soccer Players – Summary Emma Russell
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Job Insecurity: Reducing Its Negative Effect on Your Wellbeing Caroline Marlowe
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ABOUT
Cautious Return to Play Could Prevent Muscle Injuries in Professional Football Håkan Bengtsson, Jan Ekstrand, Markus Waldén, Martin Hägglund
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Generalised Joint Hypermobility – Why should it be screened for within a football setting? Adam Johnson
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FMPA Conference 2020
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Understanding and Developing Relationships in the Modern Football Hierarchy Dr Daniel Parnell, Professor Barry Drust
Football Medicine & Performance Association 6A Cromwell Terrace, Gisburn Road, Barrowford, Lancashire, BB9 8PT T: 0333 456 7897 E: info@fmpa.co.uk W: www.fmpa.co.uk FMPA_Official Officialfmpa fmpa_official LinkedIn: Football Medicine & Performance Association FMPA_Register FMPARegister fmpa_register Chief Executive Officer Eamonn Salmon eamonn.salmon@fmpa.co.uk
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Contributors Colin W. Fuller, Franco M. Impellizzeri, Aaron J. Coutts, Maurizio Fanchini, Alan McCall, Håkan Bengtsson, Jan Ekstrand, Markus Waldén, Martin Hägglund, Adam Johnson, Dr Daniel Parnell, Professor Barry Drust, Laura Finnegan, Daniel Bernardin, Dylan Mernagh, Raj Bhatia, Adam Esa, Sam Haines, Emma Russell, Caroline Marlowe
Marketing/Advertising Charles Whitney 0845 004 1040
Print Media Village www.media-village.co.uk
Administration Assistant Amie Hodgson amie.hodgson@fmpa.co.uk
COVER IMAGE England’s Karen Carney during the FIFA Women’s World Cup Third Place Play-Off at the Stade de Nice, Nice. Richard Sellers/PA Wire/PA Images
Football Medicine & Performance Association. All rights reserved. The views and opinions of contributors expressed in Football Medicine & Performance are their own and not necessarily of the FMPA Members, FMPA employees or of the association. No part of this publication may be reproduced or transmitted in any form or by any means, or stored in a retrieval system without prior permission except as permitted under the Copyright Designs Patents Act 1988. Application for permission for use of copyright material shall be made to FMPA. For permissions contact admin@fmpa.co.uk
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INJURIES IN FOOTBALL: IT’S TIME TO STOP CHASING THE TRAINING LOAD UNICORN FEATURE / FRANCO M. IMPELLIZZERI, AARON J. COUTTS, MAURIZIO FANCHINI, ALAN MCCALL Training on the edge To reach or maintain top levels of human performance sometimes we need to “train at or near the limits of human capacity”. Athletes and coaches might deliberately accept an increased injury risk as a trade-off for the performance benefits. In contrast, this is often seen very differently by upper management, supporters and media. On one side they want the best performance, on the other side they do not want to accept the risks inherent to the process leading to such a performance. Indeed, they can be very quick to point fingers when an injury occurs. This has raised the pressure and stress on support staff within football teams. We are not saying that this absconds staff of any responsibility, but these responsibilities have been exaggerated by the idea that there are effective ways (including tools) to control negative effects of pushing performance limits i.e. injury. In the past few years this has been fuelled by part of the scientific community (in good or in bad faith). What we contend is the belief that injuries can be well controlled for in
the practical setting and there are scientific ways in which we can reduce the risk of them occurring. Of course, there are evidence-based approaches and principles, but the effects of these methods should be superior to the effect of the uncontrollable and unpredictable factors to be appreciated and visible in the real setting. In the modern view of injury, the role of contextual factors and multifactorial nature of their occurrence is becoming increasingly acknowledged.1, 9 In this article we focus on the suggestion that training load metrics can provide ‘magical’ figures to stop injuries from occurring. What we want to show is that the training load can just provide information to allow the coach to do his/her job better (i.e. informed decision making). However, metrics should not be used to determine if the training is too much, too low, or if the progression is just right or even wrong. Data itself does not mean anything and it is only with the expert knowledge and experience that can give meaning to these numbers. Scientists should stop giving these magical numbers
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powers that they just don’t have. We should acknowledge and highlight that injury occurrence is a complex phenomenon and, given its multifactorial nature, it is difficult to identify a single cause. We can work on the factors potentially having some links with injury mechanisms. However, even considering these factors, there are still too many influencing variables outside of our control and difficult to quantify. And rarely, even if advisable, we can work on all relevant contextual levels (socio-cultural, environmental, individual)(Figure 1).1 So when we implement some interventions we should hope for the best, but prepare for the worst. This article is intended to be contentious and thought-provoking, but at this point we believe it is needed. It is necessary to face and accept reality and stop chasing this training load unicorn. We have written this article from a sport science and coach perspective. From the sport science perspective we briefly explain why the studies trying to find an association between
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Care structure Popularity of sport Socio economic position
Country
Association Governance Policy Budget Sports
Athlete Socio-cultural level
Beliefs Perceptions Attitude
Individual level
INJURY
Environmental / policy level
Level Culture Social Structures
Figure 1. A socioecological model of sports injuries incorporating context at multiple levels. Reproduced with permission [Bolling et al. Context Matters: Revisiting the First Step of the ‘Sequence of Prevention’ of Sports Injuries. Sports Medicine, 48(10):2227-2234, 2018]
training load and injuries are weak and/or biased. From a coach perspective we believe that we should accept the uncertainty of the job, taking our responsibilities without jumping on dogmas that are used to protect positions (“I was working within the sweet spot so it is not my fault”) or to create the illusion of control to face our cognitive dissonance. No one responsible, all responsible Sacking specific staff members because of higher injury occurrence assumes managers have identified the causes of injury occurrence. Whilst this is theoretically possible, it is extremely unlikely or quite difficult to do. The first big assumption is assuming that the increase in injuries is not due to random variation. The second idea is that the higher injury rate is due to a specific factor, which is most of the time assumed to be the physical training, while it in fact may be due to other contextual factors. We all know that sometimes the change in staff is just a way to show supporters that the management is doing something to address a situation to take away pressure from coaches and/or players for poor performance. In that case, even if questionable, communication should be handled appropriately because being fired and blamed for an increase in injuries can ruin careers and reputations. In reality, the increase in injury occurrence over one or few seasons cannot be attributed to a specific cause.
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A resilient and robust player is likely the result of a process starting early. Pretending that a practitioner within a few weeks or even a few seasons can compensate what has not been done for years is quite unrealistic. For example, it is becoming a widespread idea that players who have poor running technique have an increased injury risk. Although we don’t have strong evidence on that, it is reasonable since poor technique (as well as any factors altering it like an injury, complaint etc.) can have an effect on load distribution on tissues. Addressing this would mean to start working and investing in this direction earlier in the development of a career, that is work done within the youth academies. Inviting an expert in sprinting and sprint mechanics may be a useful start, but the definitive step is a systematic investment in this direction rather than brief or one-off interventions. Similarly, recruiting a player with previous injuries means recruiting someone supposed to be at higher risk of reinjury. As science and medical staff we are there to inform upper management and coaches of potential injury risks when signing new players. Once the decision is taken, key stakeholders should be aware of the implications and risks. Congested match periods can be another factor increasing the injury risk, as a minimum because matches have a higher risk of injuries.6 Indeed, it is logical that an increase in exposure to matches will increase the risk of injury. But these matches and the
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revenue generated are what fuel all of the stakeholders working in the football industry. Hence reducing the number of matches is not easy when people realise the financial consequences of doing so. Currently, we have to acknowledge this risk and accept it without blaming or attributing the fault to others. Not sleeping enough, resting adequately, eating well etc. are also factors that can influence injury risk and hence these are also responsibilities of the players that, however, cannot be forced to change (maybe they should be educated earlier in their career, e.g. academies). It is clear that the causes of injuries are really multifactorial and not only related to the training completed or planned. This is for example what is shown in more modern and realistic multifactorial (at least conceptually) injury models.1, 9 By appreciating this multifactorial nature, it becomes clear that training load is just one component and treating it as this magical unicorn is clearly delusional. Even more so given these magical metrics, supposedly related to injury risk, have been developed using suboptimal methodological criteria and over-interpreted. Role of training load monitoring in injury prevention More than 15 years ago we introduced in the scientific literature the concepts of internal and external load to identify measurable components of the training process (Figure 2).10, 11 The assessment of
football medicine & performance
Structure specific strength
PERFORMANCE (INJURY)
PLAN
Training program
DETERMINANTS
Structure specific stress and strain TRAINING TARGETS
Application of TRAINING PRINCIPLES
EXTERNAL LOAD
INTERNAL LOAD
IMPLEMENTATION
Training load monitoring
PROCESS CONTROL
TRAINING OUTCOME
Figure 2. Simplified model of the training process,10, 11 adapted to injury prevention
these components allows to understand whether the external load has achieved the planned psychophysiological response (internal load) and whether that load has induced the expected adaptations (indirectly assessed by measuring the training outcomes). A variation of this model accounting for biomechanical factors has also been proposed.20 This can help evaluating the efficacy of the training. However, before considering if a training program is effective or not effective, the coach and the staff need to know whether the athlete has done what was planned. This is the main goal of training monitoring. The development of a training program should be based on understanding the determinants (e.g. limiting factors) of the performance, which are the systems targeted by training. In the context of injury prevention, these determinants are the factors related to injury risk. When programming it is the practitioner defining the load and its progression. This is done combining experience and available knowledge, but it is a subjective process and indeed it can differ from one practitioner to another. Therefore, the central part of the training process is the training plan and the practitioner or staff brains. Monitoring is just a tool to determine whether it has been implemented and executed as planned (we all know that the coaching sessions can differ from what is initially recommended). What we consider a dogma is the idea that, from an injury
prevention perspective, training load metrics coming from the monitoring system can inform us about whether the training load and its progression is right or wrong and is giving the staff indications on whether the risk of injuries is increasing or decreasing. This idea is based on two elements: one is related to study findings that practitioners believe, since published in scientific journals, should be “trusted�; and the second is that apparently some of these findings fit common and well-known training principles. We contend that the scientific publications in the field on monitoring and injury prevention are poor or with fundamental limitations, and as a consequence do not provide any scientific evidence (at best some relations that is not possible to distinguish from random and inconsistent associations). The fact that findings apparently fit the training principles is just because among all the several and diverse results available, some researchers have fished few results and popularised those fitting our beliefs and make more sense, ignoring the others (confirmation bias). Our alternative approach is more pragmatically to come back to what was done for decades that is the progression is decided by the coach who uses training principles such as overload progression and that this progression is evaluated by the coach based on athlete responses. Training load measures give the coach information about the extent to which the athlete is
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really doing what was planned, and how he/she is coping with the load so that the training can be adjusted (not only in terms of load but also contents). This is because the available evidence is not strong enough to use new metrics to understand whether the load is too much or too low. The evaluation of the adequacy of training load is based on a more holistic evaluation that consider various training components including the nature of the training stimulus completely ignored in the scientific studies. Training load-injury studies: the biggest fishing expedition ever Sport science should provide an objective appreciation of problems and an unbiased attempt to address them. But sport scientists are also humans and bias cannot be avoided. However, this bias can be limited and most of the research methods have been developed exactly for this aim. The idea that you are applying findings coming from studies and therefore they should be evidence based is not correct. Studies can be good and bad. Being published itself is not a guarantee of study quality and strength of the findings. The review process leading to publication, despite it represents the best approach available at the moment, is not perfect. For this reason, the methodology of the studies (risk of bias) should be evaluated and if insufficient then findings should not be considered or interpreted within the limit of the study. This should be, for example, the role of a good sport scientist working within
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feature (not arbitrary estimated). The one and four weeks, used for the acute and chronic load, are approximation of these time decays, that correspond to the time needed to dissipate the training effects. In other words, the time decays have nothing to do with the time windows for the calculation of an average. The parameters of the model are calculated for each individual and do not use the same values for all of the athletes. Finally, it is an additive model and not a ratio like the ACWR. Therefore, in no way can it provide a framework or a relevant reference model. In addition, the use of the ratio between acute and chronic is another problem well-known by statisticians for a long time5. Ratios add unnecessary noise and the ACWR in particular does not properly normalise the acute by the chronic neither mathematically nor conceptually.15 However, the domino effect is that other researchers have tried (and are still trying) to develop new metrics without any conceptual framework and rationale underpinning them17 they are just numbers without any practical meaning. Embracing these metrics just generates additional confusion.
a team or organisation. Not only searching for literature but evaluating its strength and quality. This means that other than specific knowledge in the sport area, good knowledge on research methods and statistics is paramount. The first critical aspect we warn practitioners is related to the nature and goal of the studies. Even if the studies would be methodologically sound (but they are not), they are all descriptive in nature with just a few ‘failed’ attempts to create predictive models. Indeed, no studies have tried to establish a cause-effect relation between training load and injuries in team sports. Predictive models, even if appropriately developed and their predictive validity demonstrated, can be useful but are not deemed to provide causal associations. This means that manipulating the features included in the model cannot change the likelihood of a future event (unless there is cause-effect relation that should be purposely examined). To check this is the case, just read the aims section of any paper to see whether the researchers have tried to establish a causal relation and how. Although experimental studies are the gold standard for cause-effect, there are methods developed to use observational data for causal inferences and these should be explicitly reported in the papers.18 Whilst these methods do not guarantee a cause-effect, they do provide higher level of evidence than studies just running correlational/regression analysis until they do not find something. Just for this fundamental reason, no recommendations on how to manipulate the training load to change injury occurrence can be provided.
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There are other methodological problems suggesting some of the observed associations can be just artefacts and spurious, questioning their relevance even from a pure descriptive perspective for creating theories or principles. The first problem is conceptual. Associations that have been reported in the literature with new metrics that lack any biological, physiological or mechanical explanation connecting them with injury mechanisms. Injury occurs because the stress and strain experienced by a tissue structure exceeded the tissue structure strength. Training load metrics and indicators (in relation to injuries) should reflect these two components: tissues structure strength or structure stress and strain. A conceptual framework on the mechanical causes of injuries has been recently presented14 (https://osf.io/ preprints/sportrxiv/vzxga/). No studies in football have presented plausible reference for conceptual frameworks associating the training load measures to these mechanical causes of injuries. Most studies just used what provided by companies and devices. The only attempt to reconcile metrics such as acute (last week), chronic load (last 4 weeks) and their ratio (acute:chronic workload ratio, ACWR)7, 8 is the Banister model. Liberally adapting this famous model ‘acute load’ was used as indicator of fatigue and ‘chronic load’ of fitness. However, this model was created for performance (mainly endurance sports) and not injuries. Additionally, the model has no physiological explanation (it is a mathematical model). This model is additive and combines two equations that use training load adjusted using time decays calculated from the data
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Without delving too deep into technical details (these will be addressed soon in purposely prepared scientific publications), among the methodological problems of the literature there are suboptimal statistical analyses (e.g. not accounting for the recurrence of injuries), low sample size, overfitting, selective reporting, p-harking, poor handling of missing data, etc. In addition, there are the poor practices favoured by the lack of a reference conceptual framework, which gives the researchers too many degrees of freedom, i.e. they can choose and select whatever they want increasing the risk of false discoveries. Just by manipulating one feature such as the selection of the number and the reference categories typical in some statistical analysis, it has been shown that it can generate at least 42% of false discoveries.4 Indeed, it is typical to classify metrics such as ACWR in categories. In soccer, some studies have used 3,13 others 416 and others 63 categories. The very high false discoveries were obtained just varying one feature of the analysis! Well, there are unfortunately, several other features that have been modified without any justification: data trimming methods, selection of metrics, training load measures, time windows for calculating the averages (from one day to weeks), time lag between training load calculation and injury occurrence, missing data handling, etc. Last but not least, even injury definitions are not consistent among studies: contact, combination of contact and non-contact, match-loss, both training and match-loss injuries, complaints requiring medical attention, complaints or injuries requiring a training session modification, combination of upper and lower body injuries and often unspecified severities/days lost. With these premises it is clear that the area is very prone
football medicine & performance to false discoveries and confirmation bias: if you torture the data long enough, they will confess whatever you want them to! Even the famous U-shape ACWR model has been created using inappropriate methods. Indeed, a retraction request was unsuccessfully submitted, citing that the model was purely “illustrative”. The errors were not questioned but just the “illustrative” nature (the letter and the review process documents can be found here: https://osf.io/preprints/sportrxiv/ gs8yu/).12 Unfortunately, although illustrative and developed with flawed methods, this model was published more than seven times in scientific journals including two consensus statements, one of which was from the International Olympic Committee (IOC).2, 19 The latter IOC presenting the model as validated and a request of clarification is still pending (https://bjsm.bmj.com/ content/50/17/1030.responses).19 And we are sure most of the readers know this famous figure but not the errors it contains. And that’s why a retraction was deemed necessary. So once again we advise practitioners not to have faith in this model also because the practical implications are worrying. Indeed, if the model would be right (luckily it is not), it would indicate that tapering and recovery weeks increase the risk of injuries (i.e. ACWR <0.8). Would you bet your job on this? If you do, it is at your own risk. To those saying that these studies raised the attention on training load, we remind that this is well-known among coaches, fitness trainers and sport scientists for at least the past half-century (overload progression and various periodization strategies); but we concede this may not be the case for other practitioners who are perhaps not familiar with basic training methodology. Conclusion: take the red pill By acknowledging that training load metrics cannot be used to identify whether the load is appropriate, or whether the progression is increasing the risk of injuries, our only solution is to come back to what was done for decades. That is to say, working on how to develop a training plan that defines the progression based on expert knowledge and ”best practice”, eventually adjusting the progression in relation to the athlete tolerance and response. Practitioners should ideally work on the components causally related to injuries: tissue structure, strength and strain. In other words, the good practitioner will use his/her knowledge, experience and skills to solve training program design problems to guide the athlete. This approach should be used to assist the players not only to cope but to thrive with ‘training on the edge’ and achieve those top levels of human performance needed for competitive advantage.
According to the training process framework,10 it is possible to have information about training efficacy. Training load monitoring can help to quantify overload progression to see if what was done was coherent with what was planned, including the progression that it is not defined by any magical metric but is planned and decided by the practitioner (expert knowledge). In rehabilitation or return to train and play, having historical records of training load, we can know the training load that players should be able to tolerate when re-integrating. The role of the support staff is to provide the coaches with the information necessary so that they can make an informed decision. Furthermore, we should always remember that, in the practical setting it is difficult to act on all contextual factors even if in an ideal world it would be the desired approach. This means we should downsize our expectations because we are trying to optimise only few factors out of all those that can potentially influence injury risk. Whatever we do is just an attempt driven by subjective decisions, and as such we should accept the uncertainty and the risks inherent in this attempt, which are largely unavoidable. Researchers and
1. Bolling C, van Mechelen W, Pasman HR, Verhagen E. Context Matters: Revisiting the First Step of the ‘Sequence of Prevention’ of Sports Injuries. Sports medicine (Auckland, N.Z.). 2018;48:2227-2234.
scientific journals should be careful in the messages they popularise because they can create or fuel dogmas. And once a dogma enters into daily practice, challenging it is an immense work and not always successful. The exaggerated role of training load and associated metrics in injury reduction is an example. Likewise, practitioners should be careful not to jump on the unicorn-chasing bandwagon just because a metric has been published and popularised. As practitioners we probably need to educate senior management and coaches that preventing injuries is not as simple as what it has been made out to be or what many salespeople are claiming, even those coming in as so-called saviours to performance teams with their misleading sales pitches. If someone in your team or wanting to come into your team proclaims that they have the magical system to prevent all injuries the well-known saying, ‘if it sounds too good to be true, then it probably is’ should be heeded! This article is an attempt in this direction, and we hope it can sensitise stakeholders on this important issue. Is this too much to ask? Welcome back to the real world of training for performance!
11. Impellizzeri FM, Rampinini E, Marcora SM. Physiological assessment of aerobic training in soccer. J Sports Sci. 2005;23:583-592.
2. Bourdon PC, Cardinale M, Murray A, et al. Monitoring Athlete Training Loads: Consensus Statement. Int J Sports Physiol Perform. 2017;12:S2161-S2170.
12. Impellizzeri FM, Woodcock S, McCall A, Ward P, Coutts AJ. The acute-chronic workload ratio-injury figure and its ‘sweet spot’ are flawed. SportRxiv,. 2019;
3. Bowen L, Gross AS, Gimpel M, Bruce-Low S, Li FX. Spikes in acute:chronic workload ratio (ACWR) associated with a 5-7 times greater injury rate in English Premier League football players: a comprehensive 3-year study. Br J Sports Med. 2019;
13. Jaspers A, Kuyvenhoven JP, Staes F, Frencken WGP, Helsen WF, Brink MS. Examination of the external and internal load indicators’ association with overuse injuries in professional soccer players. Journal of science and medicine in sport. 2018;21:579-585.
4. Carey DL, Crossley KM, Whiteley R, et al. Modeling Training Loads and Injuries: The Dangers of Discretization. Medicine and science in sports and exercise. 2018;50:2267-2276.
14. Kalkhoven J, Watsford M, Impellizzeri FM. A conceptual model and detailed framework for stressrelated, strain-related, and overuse athletic injury. 2019;
5. Curran-Everett D. Explorations in statistics: the analysis of ratios and normalized data. Adv Physiol Educ. 2013;37:213-219.
15. Lolli L, Batterham AM, Hawkins R, et al. The acute-to-chronic workload ratio: an inaccurate scaling index for an unnecessary normalisation process? Br J Sports Med. 2018;
6. Ekstrand J, Hägglund M, Waldén M. Injury incidence and injury patterns in professional football: the UEFA injury study. British Journal of Sports Medicine. 2011;45:553-558.
16. Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ. The acute:chonic workload ratio in relation to injury risk in professional soccer. Journal of science and medicine in sport. 2017;20:561-565.
7. Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50:273-280.
17. Moussa I, Leroy A, Sauliere G, Schipman J, Toussaint J-F, Sedeaud A. Robust Exponential Decreasing Index (REDI): adaptive and robust method for computing cumulated workload. BMJ Open Sport &amp; Exercise Medicine. 2019;5:e000573.
8. Gabbett TJ, Hulin BT, Blanch P, Whiteley R. High training workloads alone do not cause sports injuries: how you get there is the real issue. Br J Sports Med. 2016;50:444-445. 9. Hulme A, Thompson J, Nielsen RO, Read GJM, Salmon PM. Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling. Br J Sports Med. 2018; 10. Impellizzeri FM, Marcora SM, Coutts AJ. Internal and External Training Load: 15 Years On. Int J Sports Physiol Perform. 2019;14:270-273.
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18. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd. Philadelphia: Lippincott Williams & Wilkins; 2012. 19. Soligard T, Schwellnus M, Alonso JM, et al. How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. Br J Sports Med. 2016;50:1030-1041. 20. Vanrenterghem J, Nedergaard NJ, Robinson MA, Drust B. Training Load Monitoring in Team Sports: A Novel Framework Separating Physiological and Biomechanical Load-Adaptation Pathways. Sports Medicine. 2017;47:2135-2142.
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