Personalized machine learning approach to injury monitoring in elite volleyball players

Abstract

We implemented a machine learning approach to investigate individual indicators of training load and wellness that may predict the emergence or development of overuse injuries in professional volleyball. In this retrospective study, we collected data of 14 elite volleyball players (mean ± SD age: 27 ± 3 years, weight: 90.5 ± 6.3 kg, height: 1.97 ± 0.07 m) during 24 weeks of the 2018 international season. Physical load was tracked by manually logging the performed physical activities and by capturing the jump load using wearable devices. On a daily basis, the athletes answered questions about their wellness, and overuse complaints were monitored via the Oslo Sports Trauma Research Center (OSTRC) questionnaire. Based on training load and wellness indicators, we identified subgroups of days with increased injury risk for each volleyball player using the machine learning technique Subgroup Discovery. For most players and facets of overuse injuries (such as reduced sports participation), we have identified personalized training load and wellness variables that are significantly related to overuse issues. We demonstrate that the emergence and development of overuse injuries can be better understood using daily monitoring, taking into account interactions between training load and wellness indicators, and by applying a personalized approach. Highlights. With detailed, athlete-specific monitoring of overuse complaints and training load, practical insights in the development of overuse injuries can be obtained in a player-specific fashion contributing to injury prevention in sports.. A multi-dimensional and personalized approach that includes interactions between training load variables significantly increases the understanding of overuse issues on a personal basis.. Jump load is an important predictor for overuse injuries in volleyball.

Lower Back Injuries in NCAA Female Volleyball Athletes: A 5-Year Epidemiologic Characterization

“…Individualized daily monitoring also allows athlete-specific strength and weakness profiles to help minimize future injuries. 22 …”

“…To minimize this, clinicians should help emphasize the need for proper preseason conditioning and continuity with team ATs. 22 , 30 It is also important that clinicians keep in mind that female volleyball student-athletes have a higher likelihood of sustaining injuries in the preseason and during practice. Most of these LBIs require less than 24 hours to recover.…”

Weekly Wellness Variations to Identify Non-Functional Overreaching Syndrome in Turkish National Youth Wrestlers: A Pilot Study

“…Considering the relationship between wellness variables and the level of performance of athletes and risk of their injury, we hypothesized lower wellness patterns in the early preparation season and higher wellness patterns in the end of preparation season. According to the findings of previous studies, these variables may be appropriate for identifying NFOR syndrome in wrestlers in the preparation season [2,3,15] Thus, the purpose of the current study was to conduct investigations into (1) the weekly patterns of wellness through measuring wDOMS, fatigue (wFatigue), stress (wStress), sleep (wSleep), and wHI across the preparation season (PS) and (2) to analyze the variations of wellness variables between early, mid-, and end-PS.…”

“…This is particularly true in terms of controlling and monitoring the intensity, volume, duration, and frequency of training. Training load monitoring provides a very beneficial perspective for controlling the training load in athletes, which results in adaptation and improves the level of performance, while to a large extent, preventing the risk of injury, overtraining, and non-functional overreaching syndrome (NFOR) [1,2]. Players may enter Sustainability 2021, 13, 4667 2 of 12 into NFOR if the balance between training load and recovery is disturbed [1].…”

“…Overtraining itself is a major cause of injury in athletes. Coaches and athletes need to be fully aware of this risk and know how to identify this condition via its symptoms [2]. Such symptoms include sleep disturbance, decreased appetite, weight loss, headache, allergic responses, increased resting heart rate, and fatigue [3].…”