The training-injury prevention paradox: should athletes be training smarter and harder?

Movement Demands of Elite Under-20s and Senior International Rugby Union Players

This study compared the movement demands of elite international Under-20 age grade (U20s) and senior international rugby union players during competitive tournament match play. Forty elite professional players from an U20 and 27 elite professional senior players from international performance squads were monitored using 10Hz global positioning systems (GPS) during 15 (U20s) and 8 (senior) international tournament matches during the 2014 and 2015 seasons. Data on distances, velocities, accelerations, decelerations, high metabolic load (HML) distance and efforts, and number of sprints were derived. Data files from players who played over 60 min (n = 258) were separated firstly into Forwards and Backs, and more specifically into six positional groups; FR-Front Row (prop & hooker), SR-Second Row, BR-Back Row (Flankers & No.8), HB-Half Backs (scrum half & outside half), MF-Midfield (centres), B3 -Back Three (wings & full back) for match analysis. Linear mixed models revealed significant differences between U20 and senior teams in both the forwards and backs. In the forwards the seniors covered greater HML distance (736.4 ± 280.3 vs 701.3 ± 198.7m, p = 0.01) and severe decelerations (2.38 ± 2.2 vs 2.28 ± 1.65, p = 0.05) compared to the U20s, but performed less relative HSR (3.1 ± 1.6 vs 3.2 ± 1.5, p < 0.01), moderate (19.4 ± 10.5 vs 23.6 ± 10.5, p = 0.01) and high accelerations (2.2 ± 1.9 vs 4.3 ± 2.7, p < 0.01) and sprint•min-1 (0.11 ± 0.06 vs 0.11 ± 0.05, p < 0.01). Senior backs covered a greater relative distance (73.3 ± 8.1 vs 69.1 ± 7.6 m•min-1, p < 0.01), greater High Metabolic Load (HML) distance (1138.0 ± 233.5 vs 1060.4 ± 218.1m, p < 0.01), HML efforts (112.7 ± 22.2 vs 98.8 ± 21.7, p < 0.01) and heavy decelerations (9.9 ± 4.3 vs 9.5 ± 4.4, p = 0.04) than the U20s backs. However, the U20s backs performed more relative HSR (7.3 ± 2.1 vs 7.2 ± 2.1, p <0.01) and sprint•min-1 (0.26 ± 0.07 vs 0.25 ± 0.07, p < 0.01). Further investigation highlighted differences between the 6 positional groups of the teams. The positional groups that differed the most on the variables measured were the FR and MF groups, with the U20s FR having higher outputs on HSR, moderate & high accelerations, moderate, high & severe decelerations, HML distance, HML efforts, and sprints•min-1. For the MF group the senior players produced greater values for relative distance covered, HSR, moderate decelerations, HML distance and sprint•min-1. The BR position group was most similar with the only differences seen on heavy accelerations (U20s higher) and moderate decelerations (seniors higher). Findings demonstrate that U20s internationals appear to be an adequate 'stepping stone' for preparing players for movement characteristics found senior International rugby, however, the current study highlight for the first time that certain positional groups may require more time to be able to match the movement demands required at a higher playing level than others. Conditioning staff must also bear in mind that the U20s players whilst maintaining or improving match movement capabilities may require to gain substantial mass in some positions to match their senior counterparts.

Training loads and injury risk in Australian football-differing acute: chronic workload ratios influence match injury risk

Aims

(1) To investigate whether a daily acute:chronic workload ratio informs injury risk in Australian football players; (2) to identify which combination of workload variable, acute and chronic time window best explains injury likelihood.

Methods

Workload and injury data were collected from 53 athletes over 2 seasons in a professional Australian football club. Acute:chronic workload ratios were calculated daily for each athlete, and modelled against non-contact injury likelihood using a quadratic relationship. 6 workload variables, 8 acute time windows (2–9 days) and 7 chronic time windows (14–35 days) were considered (336 combinations). Each parameter combination was compared for injury likelihood fit (using R²).

Results

The ratio of moderate speed running workload (18–24 km/h) in the previous 3 days (acute time window) compared with the previous 21 days (chronic time window) best explained the injury likelihood in matches (R²=0.79) and in the immediate 2 or 5 days following matches (R²=0.76–0.82). The 3:21 acute:chronic workload ratio discriminated between high-risk and low-risk athletes (relative risk=1.98–2.43). Using the previous 6 days to calculate the acute workload time window yielded similar results. The choice of acute time window significantly influenced model performance and appeared to reflect the competition and training schedule.

Conclusions

Daily workload ratios can inform injury risk in Australian football. Clinicians and conditioning coaches should consider the sport-specific schedule of competition and training when choosing acute and chronic time windows. For Australian football, the ratio of moderate speed running in a 3-day or 6-day acute time window and a 21-day chronic time window best explained injury risk.

Training volume and soft tissue injury in professional and non-professional rugby union players: a systematic review

AIM

To investigate the relationship between training volume and soft tissue injury incidence, and characterise soft tissue injury in rugby union players.

DESIGN

A systematic search of electronic databases was performed. The search strategy combined terms covering: training volume and injury, and rugby union, and players of all levels.

DATA SOURCES

Medline, SPORTDiscus, Web of Science, Embase, PubMed.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were included if they reported: male rugby union players, a clear definition of a rugby union injury, the amount of training volume undertaken by participants, and epidemiological data for soft-tissue injuries including the number or incidence.

RESULTS

15 studies were eligible for inclusion. Overall match and training injury incidence ranged from 3.3 to 218.0 injuries/1000 player match hours and 0.1-6.1 injuries/1000 player training hours, respectively. Muscle and tendon as well as joint (non-bone) and ligament injuries were the most frequently occurring injuries. The lower limb was the most prevalent injury location. Injury incidence was higher in professional rugby union players than non-professional players. Contact events were responsible for the greatest injury incidence. For non-contact mechanisms, running was responsible for the highest injury incidence. Inconsistent injury definitions hindered reliable comparison of injury data. The lack of reporting training volumes in hours per player per week limited the ability to investigate associations between training volume and injury incidence.

CONCLUSIONS

A higher level of play may result in higher match injury incidence. Muscle and tendon injuries were the most common type of soft tissue injury, while the lower limb was the most common location of injury in rugby union players, and running was responsible for the highest injury incidence during non-contact events.

The Relationship of Practice Exposure and Injury Rate on Game Performance and Season Success in Professional Male Basketball

The objectives of this study were to determine the relationship among game performance, injury rate, and practice exposure in a professional male basketball team. A retroospective analysis of prospective collected data was conducted over seven consecutive seasons (2007/2008 to 2013/2014). Data collection included sports performance during competition (statistical evaluation), injury rate, and total exposure (games and practices). Over the surveillance period, 162 injuries (91 practice; 71 matches) occurred over 32,668 hours of exposure (556 games and 2005 practices). There was a strong positive correlation between: 1) exposure (total number of practices and hours of exposure) and the total number of injuries (r = 0.77; p = 0.04); 2) exposure (total hours of exposure and total hours of practice exposure) and performance (total team ranking) (r = 0.77 and p = 0.04, and r = 0.8 and p = 0.03, respectively); and 3) total number of injuries and performance (total team ranking) (r = 0.84; p = 0.02). While increasing practice and competition time is related to greater team performance, it also increases the number of injuries. However, higher injury rates were not associated with worse overall team performance. Efforts to reduce high-risk activity during practice, optimally replaced with injury prevention training, might help to reduce injury risk.

Game injuries in relation to game schedules in the National Basketball Association

OBJECTIVES

Injury management is critical in the National Basketball Association (NBA), as players experience a wide variety of injuries. Recently, it has been suggested that game schedules, such as back-to-back games and four games in five days, increase the risk of injuries in the NBA. The aim of this study was to examine the association between game schedules and player injuries in the NBA.

DESIGN

Descriptive epidemiology study.

METHODS

The present study analyzed game injuries and game schedules in the 2012-13 through 2014-15 regular seasons. Game injuries by game schedules and players' profiles were examined using an exact binomial test, the Fisher's exact test and the Mann-Whitney-Wilcoxon test. A Poisson regression analysis was performed to predict the number of game injuries sustained by each player from game schedules and injured players' profiles.

RESULTS

There were a total of 681 cases of game injuries sustained by 280 different players during the three years (total N=1443 players). Playing back-to-back games or playing four games in five days alone was not associated with an increased rate of game injuries, whereas a significant positive association was found between game injuries and playing away from home (p<0.05). Playing back-to-back games and away games were significant predictors of frequent game injuries (p<0.05).

CONCLUSIONS

Game schedules could be one factor that impacts the risk of game injuries in the NBA. The findings could be useful for designing optimal game schedules in the NBA as well as helping NBA teams make adjustments to minimize game injuries.

The Sports-Related Injuries and Illnesses in Paralympic Sport Study (SRIIPSS): a study protocol for a prospective longitudinal study

BACKGROUND

Paralympic sport provides sporting opportunities for athletes with a disability, with the Paralympic Games as the main event. Participation in sport is, however, associated with a significant risk for sustaining injuries and illnesses. Our knowledge of sports-related injuries and illnesses in Paralympic sport is very limited and there are no large-scale epidemiological cohort studies. The purpose here is to present a protocol for a prospective longitudinal study: The Sports-Related Injuries and Illnesses in Paralympic Sport Study (SRIIPSS).

METHODS/DESIGN

An argument-based method for investigation of design problems was used to structure the study protocol. The primary requirement of the protocol is to allow prospective studies over time and include exposure to both training and competition. To reflect the complexity of Paralympic sport with athletes' pre-existing impairments, use of assistive equipment, pain and other and medical issues, it is required that the data collection system is specifically adapted to Paralympic sport. To allow the collection of data, at the same time as there is limited access to coaches and medical personnel, it is advantageous that data can be collected online directly from the athletes. Based on this a self-report athlete monitoring system will be developed, where the athletes can enter data weekly via their mobile phones or lap-tops. Data will be collected from around 100 Swedish Paralympic athletes for approximately 1 year, which will allow us to i) prospectively estimate the annual incidence of sports-related injuries and illnesses and ii) explore risk factors and mechanisms for sustaining sports-related injuries and illnesses based on athlete exposure and training loads.

DISCUSSION

For effective implementation of injury and illness prevention measures, comprehensive epidemiological knowledge is required. This study will be the first prospective longitudinal self-report study of sports-related injuries and illnesses in Paralympic sport over a longer period of time. The results will eventually contribute to the development of evidence-based preventive measures specifically adapted to Paralympic sport in order to provide safe and healthy sport participation. Thereby, the project will be of relevance for Paralympic athletes at all levels and to the Paralympic Movement.

TRIAL REGISTRATION

The study is registered at ClinicalTrials.gov (Identifier: NCT02788500; Registration date: 22 May 2016).

How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury

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.

A simple method for quantifying jump loads in volleyball athletes

OBJECTIVES

Evaluate the validity of a commercially available wearable device, the Vert, for measuring vertical displacement and jump count in volleyball athletes. Propose a potential method of quantifying external load during training and match play within this population.

DESIGN

Validation study.

METHODS

The ability of the Vert device to measure vertical displacement in male, junior elite volleyball athletes was assessed against reference standard laboratory motion analysis. The ability of the Vert device to count jumps during training and match-play was assessed via comparison with retrospective video analysis to determine precision and recall. A method of quantifying external load, known as the load index (LdIx) algorithm was proposed using the product of the jump count and average kinetic energy.

RESULTS

Correlation between two separate Vert devices and three-dimensional trajectory data were good to excellent for all jump types performed (r=0.83-0.97), with a mean bias of between 3.57-4.28cm. When matched against jumps identified through video analysis, the Vert demonstrated excellent precision (0.995-1.000) evidenced by a low number of false positives. The number of false negatives identified with the Vert was higher resulting in lower recall values (0.814-0.930).

CONCLUSIONS

The Vert is a commercially available tool that has potential for measuring vertical displacement and jump count in elite junior volleyball athletes without the need for time-consuming analysis and bespoke software. Subsequently, allowing the collected data to better quantify load using the proposed algorithm (LdIx).

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

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.

How do training and competition workloads relate to injury? The workload-injury aetiology model

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.

A detailed quantification of differential ratings of perceived exertion during team-sport training

OBJECTIVES

To investigate the application of differential ratings of perceived exertion (dRPE) to team-sport training.

DESIGN

Single cohort, observational study.

METHODS

Twenty-nine professional rugby union players were monitored over a six-week intensified training period. Training sessions were classified as: high-intensity intervals, repeated high-intensity efforts, speed, skill-based conditioning, skills, whole-body resistance, or upper-body resistance. After each session, players recorded a session rating of perceived exertion (sRPE; CR100^®), along with differential session ratings for breathlessness (sRPE-B), leg muscle exertion (sRPE-L), upper-body muscle exertion (sRPE-U), and cognitive/technical demands (sRPE-T). Each score was multiplied by the session duration to calculate session training loads. Data were analysed using mixed linear modelling and multiple linear regression, with magnitude-based inferences subsequently applied.

RESULTS

Between-session differences in dRPE scores ranged from very likely trivial to most likely extremely large and within-session differences amongst dRPE scores ranged from unclear to most likely very large. Differential RPE training loads combined to explain 66-91% of the variance in sRPE training loads, and the strongest associations with sRPE training load were with sRPE-L for high-intensity intervals (r=0.67; 90% confidence limits ±0.22), sRPE-B for repeated high-intensity efforts (0.89; ±0.08) and skill-based conditioning (0.67; ±0.19), sRPE-T for Speed (0.63; ±0.17) and Skills (0.51; ±0.28), and sRPE-U for resistance training (whole-body: 0.61; ±0.21, upper-body: 0.92; ±0.07).

CONCLUSIONS

Differential RPE can provide a detailed quantification of internal load during training activities commonplace in team sports. Knowledge of the relationships between dRPE and sRPE can isolate the specific perceptual demands of different training modes.

Sports-related workload and injury risk: simply knowing the risks will not prevent injuries: Narrative review

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.

Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players?

AIM

To determine whether players who completed a greater number of planned preseason training sessions were more or less likely to be injured during the competitive season.

METHODS

A cohort of 30 elite rugby league players was prospectively studied during their 17-week preseason and 26-round competitive season. Injuries were recorded using a match time loss definition. Preseason participation was quantified as the number of 'full' training sessions that players completed, excluding modified, rehabilitation or missed sessions. In-season training load variables, collected using global positioning system (GPS) data, included distance covered (m), high-speed distance covered (m) and the percentage of distance covered at high speeds (%). Multilevel logistic regression models were used to determine injury likelihood in the current and subsequent week, with random intercepts for each player. Odds ratios (OR) were used as effect size measures to determine the changes in injury likelihood with (1) a 10-session increase in preseason training participation or (2) standardised changes in training load variables.

RESULTS

Controlling for training load in a given week, completing 10 additional preseason sessions was associated with a 17% reduction in the odds of injury in the subsequent week (OR=0.83, 95% CI=0.70 to 0.99). Increased preseason participation was associated with a lower percentage of games missed due to injury (r=-0.40, p<0.05), with 10 preseason sessions predicting a 5% reduction in the percentage of games missed.

CONCLUSIONS

Maximising participation in preseason training may protect elite rugby league players against in-season injury.