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

Injury Surveillance and Training Load Methods Used by Health Professionals in Tennis: An Online Multinational Survey

CONTEXT

Injury surveillance and training load monitoring are both essential for the development of preventative strategies for gradual-onset musculoskeletal injuries in elite sport. Our aim was to survey health professionals working with elite tennis players on whether and how they monitor injuries and training load.

DESIGN

A cross-sectional multinational online open-survey.

METHODS

The survey was developed and advertised in English, Spanish, and Portuguese languages on social media channels, a tennis academic journal, professional tennis associations, and clinical networks of the research team, from December 2020 to April 2021.

RESULTS

72 health professionals from 27 different countries working with elite tennis players responded to the survey. Injury surveillance is performed by 94% (68/72) of the survey respondents, with only 10% (7/68) using the consensus-recommended Oslo Sports Trauma Research Center questionnaire. Most common barriers identified by health professionals to injury surveillance are time constraints (66%), lack of knowledge (43%), and technology (31%). Training load monitoring is performed by 50% (36/72) of the health professionals working with elite tennis players. Main metrics monitored are weekly differences in training load (72%) and acute:chronic workload ratio (58%). Most common reasons for training load monitoring are injury prevention (94%), training planning (81%), and player feedback (53%).

CONCLUSIONS

Despite a high percentage of health professionals implementing injury surveillance metrics, most of them do not use any validated method. Only half of health professionals working with elite tennis players monitor training load. Lack of knowledge and technology were the main barriers reported for adequately monitoring injuries.

Association of Training and Game Loads to Injury Risk in Junior Male Elite Ice Hockey Players: A Prospective Cohort Study

BACKGROUND

Training and game loads are potential risk factors of injury in junior elite ice hockey, but the association of training and game loads to injuries is unknown.

PURPOSE

To investigate the association of chronic training and game loads to injury risk in junior male elite ice hockey players.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHODS

In this prospective cohort study, we monitored all health problems among 159 male junior ice hockey players (mean age, 16 years; range, 15-19 years) at sports-specific high schools during the 2018-2019 school year. Players reported their health problems every week using the Oslo Sports Trauma Research Center Overuse Questionnaire on Health Problems (OSTRC-H2). The number of training sessions and games was reported for 33 weeks. We calculated the previous 2-week difference in training/game loads as well as the cumulative training/game loads of the previous 2, 3, 4, and 6 weeks and explored potential associations between training/game loads and injury risk using mixed-effects logistic regression.

RESULTS

The players reported 133 acute injuries, 75 overuse injuries, and 162 illnesses in total, and an average of 8.8 (SD ±3.9) training sessions and 0.9 (SD ± 1.1) games per week. We found no association between the difference of the two previous weeks or the previous 2- 3- and 4-week cumulative, training or game load and acute injuries, nor the difference of the two previous weeks, or the previous 4- and 6-week cumulative, training or game load and overuse injuries (OR, ∼1.0; P > .05 in all models).

CONCLUSION

In the current study of junior elite ice hockey players, there was no evidence of an association between cumulative exposure to training/game loads and injury risk.

Training, Wellbeing and Recovery Load Monitoring in Female Youth Athletes

Participation in youth sports is ever-increasing, along with training and competition demands placed upon youth athletes. Young athletes may experience high training loads due to playing several sports, as well as participating in school physical education. Therefore, monitoring youth athlete load is an emerging area of research that may help limit non-functional overreaching, injury, or illness and assist with long-term athlete development. This narrative review highlights that multiple measures have been explored to monitor both internal and external load. However, the validity, reliability and practicality of these measures are often not fully understood in female youth populations. The most commonly used external monitoring methods are GPS tracking and TRIMP whereas common internal monitoring tools are questionnaires, perceived exertion rating and heart rate measures. The reporting of injuries and menstrual cycles is also crucial for providing completeness when monitoring an athlete. It has been suggested that the combination of training load, recovery and wellbeing monitoring variables is the optimal way to monitor an athlete's fatigue levels. Whichever monitoring method is applied, in a youth population it is important that the protocol can be individualised, is inexpensive and can be easily implemented and reported so that the monitoring is sustainable.

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.

Association Between Preseason Fitness Level and Risk of Injury or Illness in Male Elite Ice Hockey Players: A Prospective Cohort Study

Background:

Little is known about the association between physical fitness and the risk of injury or illness in ice hockey. The least-fit players may be more prone to injury and illness.

Purpose:

To examine the association between preseason fitness level and injury or illness risk among elite ice hockey players during the regular season.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

A total of 133 male ice hockey players in the GET League (the premier professional league in Norway) completed 8 different exercises (40-m sprint, countermovement jump, 3000-m run, squat, bench press, chin-ups, brutal bench, and box jump) at the annual 1-day preseason testing combine. During the 2017-2018 competitive season, the players reported all health problems (acute injuries, overuse injuries, and illnesses) weekly (31 weeks) using the Oslo Sports Trauma Research Center Questionnaire on Health Problems.

Results:

Overall, the players reported 191 acute injuries, 82 overuse injuries, and 132 illnesses. The least-fit tercile of players did not report more health problems (mean, 3.0; 95% CI, 2.2-3.8) compared with the most-fit (mean, 3.4; 95% CI, 2.6-4.2) or the medium-fit (mean, 2.7; 95% CI, 1.9-3.5) players. The most-fit players reported more substantial health problems (mean, 2.0; 95% CI, 1.6-2.5) compared with the medium-fit (mean, 1.3; 95% CI, 0.8 -1.8) and least-fit (mean, 1.8; 95% CI, 1.3-2.3) (P = .02) players. There was no association between low physical fitness and number of health problems when comparing the least-fit tercile of the players with the rest of the cohort (P > .05); however, there was an association between low physical fitness and greater severity of all health problems when comparing the least-fit tercile of players to the rest of the cohort after adjusting for time on ice per game, playing position, and age (P = .02).

Conclusion:

Low physical fitness was not associated with increased rate of injury or illness but was associated with greater severity of all health problems after adjusting for time on ice per game, playing position, and age.

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.

Magnitude, Frequency, and Accumulation: Workload Among Injured and Uninjured Youth Basketball Players

Overuse injuries are common in basketball. Wearable technology enables the workload to be monitored in sport settings. However, workload-injury models lack a biological basis both in the metrics recorded and how workload is accumulated. We introduce a new metric for monitoring workload: weighted jump height, where each jump height is weighted to represent the expected effect of the jump magnitude on damage to the tendon. The objectives of this study were to use principal components analysis to identify distinct modes of variation in all workload metrics accumulated over 1, 2, 3, and 4 weeks and to examine differences among the modes of variation in workload metrics between participants before the injury and uninjured participants. Forty-nine youth basketball players participated in their typical basketball practices and games, and lower extremity injuries were classified as patellar or Achilles tendinopathy, other overuse, or acute. An inertial measurement unit recorded the number and height of all jumps, and session rating of perceived exertion was recorded. The previous 1-, 2-, 3-, and 4-week workloads of jump count, jump height, weighted jump height, and session rating of perceived exertion were summed for each participant-week. Principal components analysis explained the variance in the accumulated workload variables. Using the retained principal components, the difference between the workload of injured participants in the week before the injury and the mean workload of uninjured participants was described for patellar or Achilles tendinopathy, overuse lower extremity injury, and any lower extremity injury. Participants with patellar or Achilles tendinopathy and overuse lower extremity injuries had a low workload magnitude for all variables in the 1, 2, 3, and 4 weeks before injury compared with the weeks before no injury. Participants with overuse lower extremity injuries and any lower extremity injury had a high previous 1-week workload for all variables along with a low previous 3- and 4-week jump count, jump height, and weighted jump height before injury compared with the weeks before no injury. Weighted jump height represents the cumulative damage experienced by tissues due to repetitive loads. Injured youth basketball athletes had a low previous 3- and 4-week workloads coupled with a high previous 1-week workload.