A machine learning approach to identify risk factors for running-related injuries: study protocol for a prospective longitudinal cohort trial

Abstract

Abstract Background Running is a very popular sport among both recreational and competitive athletes. However, participating in running is associated with a comparably high risk of sustaining an exercise-related injury. Due to the often multifactorial and individual reasons for running injuries, a shift in thinking is required to account for the dynamic process of the various risk factors. Therefore, a machine learning approach will be used to comprehensively analyze biomechanical, biological, and loading parameters in order to identify risk factors and to detect risk patterns in runners. Methods The prospective longitudinal cohort study will include competitive adult athletes, running at least 20 km per week and being free of injuries three months before the start of the study. At baseline and the end of the study period, subjective questionnaires (demographics, injury history, sports participation, menstruation, medication, psychology), biomechanical measures (e.g., stride length, cadence, kinematics, kinetics, tibial shock, and tibial acceleration) and a medical examination (BMI, laboratory: blood count, creatinine, calcium, phosphate, parathyroid hormone, vitamin D, osteocalcin, bone-specific alkaline phosphatase, DPD cross-links) will be performed. During the study period (one season), continuous data collection will be performed for biomechanical parameters, injuries, internal and external load. Statistical analysis of the data is performed using machine learning (ML) methods. For this purpose, the correlation of the collected data to possible injuries is automatically learned by an ML model and from this, a ranking of the risk factors can be determined with the help of sensitivity analysis methods. Discussion To achieve a comprehensive risk reduction of injuries in runners, a multifactorial and individual approach and analysis is necessary. Recently, the use of ML processes for the analysis of risk factors in sports was discussed and positive results have been published. This study will be the first prospective longitudinal cohort study in runners to investigate the association of biomechanical, bone health, and loading parameters as well as injuries via ML models. The results may help to predict the risk of sustaining an injury and give way for new analysis methods that may also be transferred to other sports. Trial registration: DRKS00026904 (German Clinical Trial Register DKRS), date of registration 18.10.2021.