Torbjørn Soligard, Martin Schwellnus, Juan-Manuel Alonso, Roald Bahr, Ben Clarsen, H Paul Dijkstra, Tim Gabbett, Michael Gleeson, Martin Hägglund, Mark R Hutchinson, Christa Janse van Rensburg, Karim M Khan, Romain Meeusen, John W Orchard, Babette M Pluim, Martin Raftery, Richard Budgett, Lars Engebretsen
Fulltekst link: http://bjsm.bmj.com/content/50/17/1030.long
Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport.
We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.
Martin Schwellnus, Torbjørn Soligard, Juan-Manuel Alonso, Roald Bahr, Ben Clarsen, H Paul Dijkstra, Tim J Gabbett, Michael Gleeson, Martin Hägglund, Mark R Hutchinson, Christa Janse Van Rensburg, Romain Meeusen, John W Orchard, Babette M Pluim, Martin Raftery, Richard Budgett, Lars Engebretsen
Fulltext link: http://bjsm.bmj.com/content/50/17/1043.long
The modern-day athlete participating in elite sports is exposed to high training loads and increasingly saturated competition calendar. Emerging evidence indicates that inappropriate load management is a significant risk factor for acute illness and the overtraining syndrome. The IOC convened an expert group to review the scientific evidence for the relationship of load—including rapid changes in training and competition load, competition calendar congestion, psychological load and travel—and health outcomes in sport.
This paper summarises the results linking load to risk of illness and overtraining in athletes, and provides athletes, coaches and support staff with practical guidelines for appropriate load management to reduce the risk of illness ansd overtraining in sport.
These include guidelines for prescription of training and competition load, as well as for monitoring of training, competition and psychological load, athlete well-being and illness. In the process, urgent research priorities were identified.
Anna E Saw, Luana C Main, Paul B Gastin
Fulltext link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4789708/
BACKGROUND: Monitoring athlete well-being is essential to guide training and to detect any progression towards negative health outcomes and associated poor performance. Objective (performance, physiological, biochemical) and subjective measures are all options for athlete monitoring.
OBJECTIVE: We systematically reviewed objective and subjective measures of athlete well-being. Objective measures, including those taken at rest (eg, blood markers, heart rate) and during exercise (eg, oxygen consumption, heart rate response), were compared against subjective measures (eg, mood, perceived stress). All measures were also evaluated for their response to acute and chronic training load.
METHODS: The databases Academic search complete, MEDLINE, PsycINFO, SPORTDiscus and PubMed were searched in May 2014. Fifty-six original studies reported concurrent subjective and objective measures of athlete well-being. The quality and strength of findings of each study were evaluated to determine overall levels of evidence.
RESULTS: Subjective and objective measures of athlete well-being generally did not correlate. Subjective measures reflected acute and chronic training loads with superior sensitivity and consistency than objective measures. Subjective well-being was typically impaired with an acute increase in training load, and also with chronic training, while an acute decrease in training load improved subjective well-being.
SUMMARY: This review provides further support for practitioners to use subjective measures to monitor changes in athlete well-being in response to training. Subjective measures may stand alone, or be incorporated into a mixed methods approach to athlete monitoring, as is current practice in many sport settings.
Windt J, Gabbett TJ.
Fulltext link: http://bjsm.bmj.com/content/51/5/428.long
Injury aetiology models that have evolved over the previous two decades highlight a number of factors which contribute to the causal mechanisms for athletic injuries. These models highlight the pathway to injury, including (1) internal risk factors (eg, age, neuromuscular control) which predispose athletes to injury, (2) exposure to external risk factors (eg, playing surface, equipment), and finally (3) an inciting event, wherein biomechanical breakdown and injury occurs.
The most recent aetiological model proposed in 2007 was the first to detail the dynamic nature of injury risk, whereby participation may or may not result in injury, and participation itself alters injury risk through adaptation. However, although training and competition workloads are strongly associated with injury, existing aetiology models neither include them nor provide an explanation for how workloads alter injury risk.
Therefore, we propose an updated injury aetiology model which includes the effects of workloads. Within this model, internal risk factors are differentiated into modifiable and non-modifiable factors, and workloads contribute to injury in three ways: (1) exposure to external risk factors and potential inciting events, (2) fatigue, or negative physiological effects, and (3) fitness, or positive physiological adaptations. Exposure is determined solely by total load, while positive and negative adaptations are controlled both by total workloads, as well as changes in load (eg, the acute:chronic workload ratio).
Finally, we describe how this model explains the load-injury relationships for total workloads, acute:chronic workload ratios and the training load-injury paradox.
Shona L. Halson
Fulltext link https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213373/
Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury.
In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature.
Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity.
The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized.
Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.
Tim J Gabbett
Fulltext link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4789704/
BACKGROUND: There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury.
MAIN THESIS: This paper describes the ‘Training-Injury Prevention Paradox’ model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se, but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load (‘internal’ and ‘external’ loads) and suggests capturing both recent (‘acute’) training loads and more medium-term (‘chronic’) training loads to best capture the player’s training burden. I describe the critical variable-acute:chronic workload ratio-as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries.
SUMMARY: The appropriately graded prescription of high training loads should improve players’ fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.
Michael K. Drew, Caroline F. Finch
BACKGROUND: Clinically it is understood that rapid increases in training loads expose an athlete to an increased risk of injury; however, there are no systematic reviews to qualify this statement.
OBJECTIVE: The aim of this systematic review was to determine training and competition loads, and the relationship between injury, illness and soreness.
METHODS: The MEDLINE, SPORTDiscus, CINAHL and EMBASE databases were searched using a predefined search strategy. Studies were included if they analysed the relationship between training or competition loads and injury or illness, and were published prior to October 2015. Participants were athletes of any age or level of competition. The quality of the studies included in the review was evaluated using the Newcastle-Ottawa Scale (NOS). The level of evidence was defined as strong, ‘consistent findings among multiple high-quality randomised controlled trials (RCTs)’; moderate, ‘consistent findings among multiple low-quality RCTs and/or non-randomised controlled trials (CCTs) and/or one high-quality RCT’; limited, ‘one low-quality RCT and/or CCTs, conflicting evidence’; conflicting, ‘inconsistent findings among multiple trials (RCTs and/or CCTs)’; or no evidence, ‘no RCTs or CCTs’.
RESULTS: A total of 799 studies were identified; 23 studies met the inclusion criteria, and a further 12 studies that were not identified in the search but met the inclusion criteria were subsequently added to the review. The largest number of studies evaluated the relationship between injuries and training load in rugby league players (n = 9) followed by cricket (n = 5), football (n = 3), Australian Football (n = 3), rugby union (n = 2),volleyball (n = 2), baseball (n = 2), water polo (n = 1), rowing (n = 1), basketball (n = 1), swimming (n = 1), middle-distance runners (n = 1) and various sports combined (n = 1). Moderate evidence for a significant relationship was observed between training loads and injury incidence in the majority of studies (n = 27, 93 %). In addition, moderate evidence exists for a significant relationship between training loads and illness incidence (n = 6, 75 %). Training loads were reported to have a protective effect against injury (n = 9, 31 %) and illness (n = 1, 13 %). The median (range) NOS score for injury and illness was 8 (5-9) and 6 (5-9), respectively.
LIMITATIONS: A limitation of this systematic review was the a priori search strategy. Twelve further studies were included that were not identified in the search strategy, thus potentially introducing bias. The quality assessment was completed by only one author.
CONCLUSIONS: The results of this systematic review highlight that there is emerging moderate evidence for the relationship between the training load applied to an athlete and the occurrence of injury and illness.
IMPLICATIONS: The training load applied to an athlete appears to be related to their risk of injury and/or illness. Sports science and medicine professionals working with athletes should monitor this load and avoid acute spikes in loads. It is recommended that internal load as the product of the rate of perceived exertion (10-point modified Borg) and duration be used when determining injury risk in team-based sports. External loads measured as throw counts should also be monitored and collected across a season to determine injury risk in throwing populations. Global positioning system-derived distances should be utilised in team sports, and injury monitoring should occur for at least 4 weeks after spikes in loads.
Paula J. Robson-Ansley, Michael Gleeson & Les Ansley
Fatigue is often a consequence of physical training and the effective management of fatigue by the coach and athlete is essential in optimizing adaptation and performance. In this paper, we explore a range of practical and contemporary methods of fatigue management for Olympic athletes.
We assesses the scientific merit of methods for monitoring fatigue, including self-assessment of training load, self-scored questionnaires, and the usefulness of saliva and blood diagnostic markers for indicating fatigued and under-recovered athletes, effective nutrition and hydration strategies for optimizing recovery and short-term recovery methods.
We conclude that well-accepted methods such as sufficient nutrition, hydration, and rest appear to be the most effective strategies for optimizing recovery in Olympic athletes.
Michael K Drew, Jill Cook, Caroline F Finch
Fulltext link: http://bjsm.bmj.com/content/50/21/1306.long
Training loads contribute to sports injury risk but their mitigation has rarely been considered in a sports injury prevention framework. A key concept behind monitoring training loads for injury prevention is to screen for those at increased risk of injury so that workloads can be adjusted to minimise these risks.
This review describes how advances in management of workload can be applied as a preventive measure. Primary prevention involves screening for preparticipation load risk factors, such as low training loads, prior to a training period or competition. Secondary prevention involves screening for workloads that are known to precede an injury developing so that modification can be undertaken to mitigate this risk. Tertiary prevention involves rehabilitation practices that include a graded return to training programme to reduce the risk of sustaining a subsequent injury.
The association of training loads with injury incidence is now established. Prevention measures such as rule changes that affect the workload of an athlete are universal whereas those that address risk factors of an asymptomatic subgroup are more selective. Prevention measures, when implemented for asymptomatic individuals exhibiting possible injury risk factors, are indicated for an athlete at risk of developing a sports injury. Seven key indicated risks and associated prevention measures are proposed.
Laura Bowen, Aleksander Stefan Gross, Mo Gimpel, François-Xavier Li1
Full-tekst link: https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/27450360/
AIM: The purpose of this study was to investigate the relationship between physical workload and injury risk in elite youth football players.
METHODS: The workload data and injury incidence of 32 players were monitored throughout 2 seasons. Multiple regression was used to compare cumulative (1, 2, 3 and 4-weekly) loads and acute:chronic (A:C) workload ratios (acute workload divided by chronic workload) between injured and non-injured players for specific GPS and accelerometer-derived variables:total distance (TD), high-speed distance (HSD), accelerations (ACC) and total load. Workloads were classified into discrete ranges by z-scores and the relative risk was determined.
RESULTS: A very high number of ACC (≥9254) over 3 weeks was associated with the highest significant overall (relative risk (RR)=3.84) and non-contact injury risk (RR=5.11). Non-contact injury risk was significantly increased when a high acute HSD was combined with low chronic HSD (RR=2.55), but not with high chronic HSD (RR=0.47). Contact injury risk was greatest when A:C TD and ACC ratios were very high (1.76 and 1.77, respectively) (RR=4.98).
CONCLUSIONS: In general, higher accumulated and acute workloads were associated with a greater injury risk. However, progressive increases in chronic workload may develop the players’ physical tolerance to higher acute loads and resilience to injury risk.
Tim J. Gabbett, Douglas G. Whyte, Timothy B. Hartwig, Holly Wescombe, Geraldine A. Naughton
BACKGROUND: The expectation that training enhances performance is well explored in professional sport. However, the additional challenges of physical and cognitive maturation may require careful consideration when determining workloads to enhance performance in adolescents.
OBJECTIVE: The objective of this study was to determine the state of knowledge on the relationship between workloads, physicalperformance, injury and/or illness in adolescent male football players.
METHODS:A systematic review of workloads, physical performance, injury and illness in male adolescent football players was conducted. Studies for this review were identified through a systematic search of six electronic databases (Academic Search Complete, CINAHL, PsycINFO, PubMed, SPORTDiscus, and Web of Science). For the purpose of this review, load was defined as the cumulative amount of stress placed on an individual from multiple training sessions and games over a period of time, expressed in terms of either the external workloads performed (e.g., resistance lifted, kilometres run) or the internal response (e.g., heart rate, rating of perceived exertion) to that workload.
RESULTS: A total of 2,081 studies were initially retrieved from the six databases, of which 892 were duplicates. After screening the titles, abstracts and full texts, we identified 23 articles meeting our criteria around adolescent football players, workloads, physical performance, injury and/or illness. Seventeen articles addressed the relationship between load and physical performance, four articles addressed the relationship between load and injury and two articles addressed both. A wide range of training modalities were employed to improve the physical performance of adolescent football players, with strength training, high-intensity interval training, dribbling and small-sided games training, and a combination of these modalities in addition to normal football training, resulting in improved performances on a wide range of physiological and skill assessments. Furthermore, there was some (limited) evidence that higher workloads may be associated with the development of better physical qualities, with one study demonstrating enhanced submaximal interval shuttle run performance with each additional hour of training or game play. Of the few studies examining negative consequences associated with workloads, increases in training load led to increases in injury rates, while longer training duration was associated with a greater incidence of illness.
CONCLUSION: The combined capacity for adolescent males to grow, train and improve physical performance highlights and underscores an exciting responsiveness to training in the football environment. However, the capacity to train has some established barriers for adolescents experiencing high workloads, which could also result in negative consequences. Additional research on stage-appropriate training for adolescent male footballers is required in order to address the knowledge gaps and enhance safe and efficient training practices.
Anna E. Saw, Luana C. Main & Paul B. Gastin
Athlete self-report measures (ASRM) are a common and cost-effective method of athlete monitoring. It is purported that ASRM be used to detect athletes at risk of overtraining, injury, or illness, allowing intervention through training modification. However, it is not known whether ASRM are actually being used for or are achieving these objectives in the applied sport setting.
Therefore, the aim of this study was to better understand how ASRM are being used in elite sports and their role in athletic preparation. Semistructured interviews were conducted one-on-one with athletes, coaches, and sports science and medicine staff (n = 30) at a national sporting institute. Interview recordings were transcribed and analyzed for emergent themes.
Twelve day-to-day and 7 longer-term practices were identified which contributed to a 4-step process of ASRM use (record data, review data, contextualize, and act). In addition to the purported uses, ASRM facilitated information disclosure and communication among athletes and staff and between staff, and improved the understanding and management of athletepreparation. These roles of ASRM are best achieved through engagement of athletes, coaches, and support staff in the systematic cyclic process.
Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA
Fulltext link: http://bjsm.bmj.com/content/50/4/231.long
AIM: Investigate whether acute workload (1 week total distance) and chronic workload (4-week average acute workload) predict injury in elite rugby league players.
METHODS: Data were collected from 53 elite players over two rugby league seasons. The ‘acute:chronic workload ratio’ was calculated by dividing acute workload by chronic workload. A value of greater than 1 represented an acute workload greater than chronic workload. All workload data were classified into discrete ranges by z-scores.
RESULTS: Compared with all other ratios, a very-high acute:chronic workload ratio (≥2.11) demonstrated the greatest risk of injury in the current week (16.7% injury risk) and subsequent week (11.8% injury risk). High chronic workload (>16 095 m) combined with a very-high 2-week average acute:chronic workload ratio (≥1.54) was associated with the greatest risk of injury (28.6% injury risk). High chronic workload combined with a moderate workload ratio (1.02-1.18) had a smaller risk of injury than low chronic workload combined with several workload ratios (relative risk range from 0.3 to 0.7×/÷1.4 to 4.4; likelihood range=88-94%, likely). Considering acute and chronic workloads in isolation (ie, not as ratios) did not consistently predict injury risk.
CONCLUSIONS: Higher workloads can have either positive or negative influences on injury risk in elite rugby league players. Specifically, compared with players who have a low chronic workload, players with a high chronic workload are more resistant to injury with moderate-low through moderate-high (0.85-1.35) acute:chronic workload ratios and less resistant to injury when subjected to ‘spikes’ in acute workload, that is, very-high acute:chronic workload ratios ∼1.5.
Hulin BT, Gabbett TJ, Caputi P, Lawson DW, Sampson JA.
Fulltext link: http://bjsm.bmj.com/content/50/16/1008.long
BACKGROUND: Between-match recovery time, and acute and chronic workloads likely affect subsequent match-injury risk in elite rugby league players.
METHODS: Workloads of 28 players throughout two seasons were calculated during short (<7 days), and long (≥7 days) between-match recovery times. ‘Acute’ workloads (1 week) greater than ‘chronic’ workloads (4-week rolling average acute workload) resulted in acute:chronic workload ratios above 1.
RESULTS: No difference was found between the match-injury risk of short and long between-match recovery periods (7.5±2.5% vs 6.8±2.5%). When players had a short recovery between matches, high chronic workloads (18.9-22.0 km) were associated with a smaller risk of match injury than chronic workloads <18.9 km (relative risk (RR) range 0.27-0.32 (CI 0.08 to 0.92); likelihood range 90-95%, likely). Players who had shorter recovery and acute:chronic workload ratios ≥1.6, were 3.4-5.8 times likely to sustain a match injury than players with lower acute:chronic workload ratios (RR range 3.41-5.80 (CI 1.17 to 19.2); likelihood range 96-99%, very likely). Acute:chronic workload ratios between 1.2 and 1.6 during short between-match recovery times demonstrated a greater risk of match injury than ratios between 1.0 and 1.2 (RR=2.88 (CI 0.97 to 8.55); likelihood=92%, likely).
CONCLUSIONS: Contrary to the philosophy that high workloads and shorter recovery equate to increased injury risk, our data suggest that high and very-high chronic workloads may protect against match injury following shorter between-match recovery periods. Acute:chronic workload ratios ∼1.5 are associated with a greater risk of match injury than lower acute:chonic workload ratios. Importantly, workloads can be manipulated to decrease the match-injury risk associated with shorter recovery time between matches.
Blanch P, Gabbett TJ.
Fulltekst link: http://bjsm.bmj.com/content/50/8/471.long
The return to sport from injury is a difficult multifactorial decision, and risk of reinjury is an important component. Most protocols for ascertaining the return to play status involve assessment of the healing status of the original injury and functional tests which have little proven predictive ability. Little attention has been paid to ascertaining whether an athlete has completed sufficient training to be prepared for competition.
Recently, we have completed a series of studies in cricket, rugby league and Australian rules football that have shown that when an athlete’s training and playing load for a given week (acute load) spikes above what they have been doing on average over the past 4 weeks (chronic load), they are more likely to be injured. This spike in the acute:chronic workload ratio may be from an unusual week or an ebbing of the athlete’s training load over a period of time as in recuperation from injury.
Our findings demonstrate a strong predictive (R(2)=0.53) polynomial relationship between acute:chronic workload ratio and injury likelihood. In the elite team setting, it is possible to quantify the loads we are expecting athletes to endure when returning to sport, so assessment of the acute:chronic workload ratio should be included in the return to play decision-making process.
Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ.
OBJECTIVES: To examine the association between combined sRPE measures and injury risk in elite professional soccer.
DESIGN: Observational cohort study.
METHODS: Forty-eight professional soccer players (mean±SD age of 25.3±3.1 yr) from two elite European teams were involved within a one season study. Players completed a test of intermittent-aerobic capacity (Yo-YoIR1) to assess player’s injury risk in relation to intermittent aerobic capacity. Weekly workload measures and time loss injuries were recorded during the entire period. Rolling weekly sums and week-to-week changes in workload were measured, allowing for the calculation of the acute:chronic workload ratio, which was calculated by dividing the acute (1-weekly) and chronic (4-weekly) workloads. All derived workload measures were modelled against injury data using logistic regression. Odds ratios (OR) were reported against a reference group.
RESULTS: Players who exerted pre-season 1-weekly loads of ≥1500 to ≤2120AU were at significantly higher risk of injury compared to the reference group of ≤1500AU (OR=1.95, p=0.006). Players with increased intermittent-aerobic capacity were better able to tolerate increased 1-weekly absolute changes in training load than players with lower fitness levels (OR=4.52, p=0.011). Players who exerted in-season acute:chronic workload ratios of >1.00 to <1.25 (OR=0.68, p=0.006) were at significantly lower risk of injury compared to the reference group (≤0.85).
CONCLUSIONS: These findings demonstrate that an acute:chronic workload of between 1.00 and 1.25 is protective for professional soccer players. A higher intermittent-aerobic capacity appears to offer greater injury protection when players are exposed to rapid changes in workload in elite soccer players. Moderate workloads, coupled with moderate-low to moderate-high acute:chronic workload ratios, appear to be protective for professional soccer players.
Malone S, Roe M, Doran DA, Gabbett TJ, Collins KD.
PURPOSE: To examine the association between combined session rating of perceived exertion (RPE) workload measures and injury risk in elite Gaelic footballers.
METHODS: Thirty-seven elite Gaelic footballers (mean ± SD age 24.2 ± 2.9 y) from 1 elite squad were involved in a single-season study. Weekly workload (session RPE multiplied by duration) and all time-loss injuries (including subsequent-wk injuries) were recorded during the period. Rolling weekly sums and wk-to-wk changes in workload were measured, enabling the calculation of the acute:chronic workload ratio by dividing acute workload (ie, 1-weekly workload) by chronic workload (ie, rolling-average 4-weekly workload). Workload measures were then modeled against data for all injuries sustained using a logistic-regression model. Odds ratios (ORs) were reported against a reference group.
RESULTS: High 1-weekly workloads (≥2770 arbitrary units [AU], OR = 1.63-6.75) were associated with significantly higher risk of injury than in a low-training-load reference group (<1250 AU). When exposed to spikes in workload (acute:chronic workload ratio >1.5), players with 1 y experience had a higher risk of injury (OR = 2.22) and players with 2-3 (OR = 0.20) and 4-6 y (OR = 0.24) of experience had a lower risk of injury. Players with poorer aerobic fitness (estimated from a 1-km time trial) had a higher injury risk than those with higher aerobic fitness (OR = 1.50-2.50). An acute:chronic workload ratio of (≥2.0) demonstrated the greatest risk of injury.
CONCLUSIONS: These findings highlight an increased risk of injury for elite Gaelic football players with high (>2.0) acute:chronic workload ratios and high weekly workloads. A high aerobic capacity and playing experience appears to offer injury protection against rapid changes in workload and high acute:chronic workload ratios. Moderate workloads, coupled with moderate to high changes in the acute:chronic workload ratio, appear to be protective for Gaelic football players.