Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months.

The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.

Feasibility of Cell-Free DNA Measurement from the Earlobe during Physiological Exercise Testing.

Circulating, cell-free DNA (cfDNA) has been discussed as an upcoming blood-based biomarker in exercise physiology, reflecting important aspects of exercise load. cfDNA blood sampling has evolved from elaborate venous to efficient capillary sampling from the fingertips. In this study, we aimed to evaluate the principal feasibility of cfDNA blood sampling from the earlobe. Therefore, we obtained cfDNA concentrations from the fingertips, earlobe, and the antecubital vein during physiological exercise testing. Significantly higher concentrations were obtained from the earlobe compared to fingertip samples. All of the measurement methods showed good to excellent repeatability (ICCs of 0.85 to 0.93). In addition, the control experiments revealed that repeated sampling from the earlobe but not from the fingertips increased cfDNA at rest. In summary, cfDNA sampling is feasible for all sampling sources. However, at rest, cfDNA collected from the earlobe tend to increase over time in the absence of physical load, potentially limiting this sampling method.