A new statistical approach to training load and injury risk: separating the acute from the chronic load.

The relationship between recent (acute) training load relative to long-term (chronic) training load may be associated with sports injury risk. We explored the potential for modelling acute and chronic loads separately to address current statistical methodology limitations. We also determined whether there was any evidence of an interaction in the association between acute and chronic training loads and injury risk in football. A men's Qatar Stars League football cohort (1 465 players, 1 977 injuries), where training load was defined as the number of minutes of activity, and a Norwegian elite U-19 football cohort (81 players, 60 injuries), where training load was defined as the session rating of perceived exertion (sRPE). Mixed logistic regression was run with training load on the current day (acute load) and cumulative past training load estimated by distributed lag non-linear models (chronic load) as independent variables. Injury was the outcome. An interaction between acute and chronic training load was modelled. In both football populations, we observed that the risk of injury on the current day for different values of acute training load was highest for players with low chronic load, followed by high and then medium chronic load. The slopes varied substantially between different levels of chronic training load, indicating an interaction. Modelling acute and chronic loads separately in regression models is a suitable statistical approach for analysing the association between relative training load and injury risk in injury prevention research. Sports scientists should also consider the potential for interactions between acute and chronic load.

Does load management using the acute:chronic workload ratio prevent health problems? A cluster randomised trial of 482 elite youth footballers of both sexes.

BACKGROUND: The acute:chronic workload ratio (ACWR) is commonly used to manage training load in sports, particularly to reduce injury risk. However, despite its extensive application as a prevention intervention, the effectiveness of load management using ACWR has never been evaluated in an experimental study. AIM: To evaluate the effectiveness of a load management intervention designed to reduce the prevalence of health problems among elite youth football players of both sexes. METHODS: We cluster-randomised 34 elite youth football teams (16 females, 18 males) to an intervention group (18 teams) and a control group (16 teams). Intervention group coaches planned all training based on published ACWR load management principles using a commercially available athlete management system for a complete 10-month season. Control group coaches continued to plan training as normal. The prevalence of health problems was measured monthly in both groups using the Oslo Sports Trauma Research Centre Questionnaire on Health Problems. RESULTS: The between-group difference in health problem prevalence (primary outcome) was 1.8%-points (-4.1 to 7.7 %-points; p=0.55) with no reduction in the likelihood of reporting a health problem in the intervention group (relative risk 1.01 (95% CI 0.91 to 1.12); p=0.84) compared with the control group. CONCLUSIONS: We observed no between-group difference, suggesting that this specific load management intervention was not successful in preventing health problems in elite youth footballers. TRIAL REGISTRATION NUMBER: ISRCTN18177140.

More than a Metric: How Training Load is Used in Elite Sport for Athlete Management.

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.

Prepare to fail or failing to prepare? Acute performance after the 11+ with and without strength exercises.

Objectives: The 11+ is an effective injury prevention warm-up programme but is often poorly adopted in practice. One reason for low compliance is the claim that the strength training part of the programme acutely impairs muscle performance before the football activity. This study aims to compare the acute effects of the 11+ with (WU+S) or without (WU-S) the strength training part on performance. Methods: Fifteen female junior football players completed WU+S and WU-S on two separate days in randomised order. Maximal voluntary torque in knee extension and flexion (60°/s and 180°/s) and countermovement jump (CMJ) were tested before and after performing the warm-up protocol. Sprint performance and rating of perceived exertion (RPE) were assessed post-warm-up. Results: Warm-up with strength training reduced peak torque in knee flexion at 180°/s more than WU-S, while no differences were found at 60°/s. Knee extension work was reduced more with WU+S than WU-S at 180°/s, but no differences at 60°/s. Peak torque angle and CMJ were unaffected. Players were slower on 20 and 30 m sprints after WU+S than WU-S. The RPE was higher after WU+S than WU-S, but there were no differences in readiness to train between the two protocols. Conclusion: Performing the 11+ programme as a warm-up routine with the strength training part can impair subsequent knee flexion torque at high velocity and sprint performance in female junior football players compared with performing the 11+ warm-up without the strength part.

Prevalence and burden of health problems in top-level football referees.

BACKGROUND: Top-level football referees take decisions during strenuous physical activity, and often under great mental pressure. Despite their central role in a football match, little is known about referees' health problems, particularly in female referees. AIM: To investigate the prevalence and burden of health problems in female and male top-level referees. STUDY DESIGN: Prospective cohort study. METHOD: Fifty-five Norwegian male and female top-level referees reported health problems (injuries and illnesses) in pre-season and during the 2020 competitive season, using the Oslo Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H2). RESULTS: We recorded data for 49 weeks with a compliance of 98%. On average, 34% (95% CI 31-36%) of referees reported at least one health problem each week, and 20% (95% CI 19-22%) reported substantial health problems. Female referees reported more health problems than male referees, and on-field referees reported more health problems than assistant referees. Gradual-onset injuries were most prevalent and caused the greatest absence from training and matches, whereas illnesses represented only a small portion to the overall burden of health problems. The injury incidence was three injuries per athlete-year (95% CI 2.5-3.5) and 11 injuries per 1000 match hours (95% CI 7-18). The illness incidence was 1.4 illnesses per athlete-year (95% CI 1.1-1.8). Injuries to the lower legs and feet represented the highest burden of health problems. CONCLUSION: Top-level referees, especially females, reported a high prevalence of health problems. Gradual-onset injuries to the lower leg and foot represented the highest injury burden.

Physical Performance Profiles in Norwegian Premier League Female Football: A Descriptive Study.

PURPOSE: To map anthropometric and physical performance profiles in Norwegian premier league female football players. METHODS: During preseason, the physical qualities of 107 players were tested on Keiser leg press, countermovement jump, 40-meter sprint, and agility. Descriptive statistics were presented as mean (SD) and median [interquartile range]. Pearson correlations analyses were made for all performance tests, and results presented as R value with 95% CIs. RESULTS: The female players were 22 (4) years of age, stature 169.0 (6.2) cm, body weight 65.3 (6.7) kg; force 2122 (312) N, power 1090 (140) W, sprint 40 m 5.75 (0.21) seconds, agility dominant 10.18 (0.32) seconds, nondominant 10.27 (0.31) seconds, and countermovement jump 32.6 (4.1) cm. Outfield players were faster and more agile than goalkeepers, a difference of 40 m, agility of dominant, and nondominant leg, respectively: 0.20 [0.09-0.32], 0.37 [0.21-0.54], and 0.28 [0.12-45]; P < .001. Goalkeepers and central defenders were taller and heavier compared with fullbacks, central midfielders, and wide midfielders (P ranging from <.02). A difference was found between dominant and nondominant legs for the agility test, showing that players are faster when changing direction with the dominant leg. CONCLUSIONS: Our study presents anthropometric and physical performance profiles of Norwegian premier league female football players. We found no difference for the physical qualities strength, power, sprint, agility, and countermovement jump between any outfield playing positions in female premier league players. There was a difference between outfield players and goalkeepers for sprint and agility.

Assessing the cumulative effect of long-term training load on the risk of injury in team sports.

Objectives: Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports. Methods: First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice. Results: DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure. Conclusion: DLNM can be used to assess the cumulative effect of training load on injury risk.

Injury characteristics in Norwegian male professional football: A comparison between a regular season and a season in the pandemic.

The Coronavirus Disease-19 (COVID-19) pandemic forced the Norwegian male premier league football season to reschedule, reducing the fixture calendar substantially. Previous research has shown that a congested match schedule can affect injury rates in professional football. Therefore, we aimed to investigate whether the Norwegian premier league teams suffered more injuries in the more match congested 2020 season than in the regular 2019-season. We invited all teams having participated in both seasons to export their injury data. Only teams that used the same medical staff to register injuries in both seasons were included, and to maximize data comparability between seasons, we applied a time-loss injury definition only. Seven of 13 teams agreed to participate and exported their injury data. Both seasons had 30 game weeks, but the 2020 season was 57 days shorter than the 2019 season. The match injury incidence did not differ significantly [incidence rate ratio 0.76 (0.48-1.20; p = 0.24) in the 2020 season compared to the 2019 season. Furthermore, we found no differences in the number of injuries, days lost to injury, matches missed to injury, or injury severity. We could not detect any differences between the two seasons, suggesting the congested match calendar combined with the safety measures in the 2020 season can be a safe alternative in future seasons.

Not straightforward: modelling non-linearity in training load and injury research.

OBJECTIVES: To determine whether the relationship between training load and injury risk is non-linear and investigate ways of handling non-linearity. METHODS: We analysed daily training load and injury data from three cohorts: Norwegian elite U-19 football (n=81, 55% male, mean age 17 years (SD 1)), Norwegian Premier League football (n=36, 100% male, mean age 26 years (SD 4)) and elite youth handball (n=205, 36% male, mean age 17 years (SD 1)). The relationship between session rating of perceived exertion (sRPE) and probability of injury was estimated with restricted cubic splines in mixed-effects logistic regression models. Simulations were carried out to compare the ability of seven methods to model non-linear relationships, using visualisations, root-mean-squared error and coverage of prediction intervals as performance metrics. RESULTS: No relationships were identified in the football cohorts; however, a J-shaped relationship was found between sRPE and the probability of injury on the same day for elite youth handball players (p<0.001). In the simulations, the only methods capable of non-linear modelling relationships were the quadratic model, fractional polynomials and restricted cubic splines. CONCLUSION: The relationship between training load and injury risk should be assumed to be non-linear. Future research should apply appropriate methods to account for non-linearity, such as fractional polynomials or restricted cubic splines. We propose a guide for which method(s) to use in a range of different situations.

Facilitators and barriers for implementation of a load management intervention in football.

BACKGROUND: In a recent randomised controlled trial, we found that a commonly used training load management approach was not effective in preventing injuries and illnesses in Norwegian elite youth footballers. AIM: To investigate players' and coaches' barriers and facilitators to a load management approach to prevent injuries and illnesses and their attitudes and beliefs of load management and injuries and illnesses in general. METHODS: We asked players and coaches about their views on injury risk in football, the benefits and limitations of load management in general and implementation of load management in football. The questionnaires used were based on similar studies using the Reach, Effectiveness, Adoption, Implementation and Maintenance framework. RESULTS: We recorded answers from 250 players and 17 coaches. Most players (88%) reported that scientific evidence showing improved performance from the intervention measures is a key facilitator to completing the intervention. Similarly, coaches reported that the most important facilitator was scientific evidence that the preventive measures were effective (100%). Players reported that the coach's attitude to preventive measures was important (86%), and similarly, 88% of coaches reported that the player's attitude was important. CONCLUSIONS: By having a mutual positive attitude towards the intervention, players and coaches can positively contribute to each other's motivation and compliance. Both players and coaches reported scientific evidence for load management having injury-preventive and performance-enhancing effect and being time efficient as important facilitators. TRIAL REGISTRATION NUMBER: Trial registration number.

A Cherry, Ripe for Picking: The Relationship Between the Acute-Chronic Workload Ratio and Health Problems.

OBJECTIVE: To investigate whether the relationship between the acute-chronic workload ratio (ACWR) and health problems varies when different methodological approaches are used to quantify it. DESIGN: Prospective cohort study. METHODS: An online questionnaire was used to collect daily health and training information from 86 elite youth footballers for 105 days. The relationship between players' training load and health was analyzed using a range of different definitions of ACWR and health problems. We used 21-day and 28-day chronic periods, coupled and uncoupled calculations, and the exponentially weighted moving average and rolling average. Acute-chronic workload ratio data were categorized as low, medium, or high, using predefined categories and z scores. We compared medium to high, medium to low, and low to high categories. The outcome was defined in 3 ways: "all health problems," "all injuries," and "new noncontact injuries." We performed random-effects logistic regression analyses of all combinations, for a total of 108 analyses. RESULTS: We recorded 6250 athlete-days and 196 health problems. Of the 108 analyses performed, 23 (21%) identified a statistically significant (P<.05) association between the ACWR and health problems. A greater proportion of significant associations were identified when using an exponentially weighted moving average (44% of analyses), when comparing low to high categories (33%), and when using the "all health problems" definition (33%). CONCLUSION: The relationship between the ACWR and health problems was dependent on methodological approach. J Orthop Sports Phys Ther 2021;51(4):162-173. Epub 20 Jan 2021. doi:10.2519/jospt.2021.9893.