Identifying Risk Factors For Head Impact Exposure In High School Football Using A Validated Instrumented Mouthguard

Abstract

Head impact sensor technology is evolving, and recent advances include the design and validation of instrumented mouthguards that accurately measure head impact kinematics. The use of such devices, in conjunction with video-based impact detection algorithms, can be useful in elucidating the true nature of head impact exposure in contact sports such as American football. PURPOSE: To characterize patterns of head impact exposure in high school football using a validated instrumented mouthguard sensor. METHODS: High school football athletes (n = 116) wore Stanford instrumented mouthguards (the “MiG2.0”) during practices and games in their 2018 and 2019 Fall seasons. A validated impact detection algorithm made specifically for the MiG2.0 in American football was used to filter out false-positive accelerative events recorded by the MiG2.0 devices. Peak linear acceleration (PLA) and peak rotational acceleration (PRA) of verified head impacts were saved for analyses. Helmet model, level of competition (JV or varsity), body mass index (BMI), age, and prior concussion history were also recorded at the beginning of each season for each athlete. RESULTS: 66969 events were recorded by the MiG2.0 sensors across 888 athlete exposures. The impact detection algorithm determined that 713 events were true head impacts. Athletes wearing Riddell Speedflex helmets sustained head impacts of lower PRA than those wearing VICIS Zero1 helmets (p < 0.01). Athletes with a previous concussion history sustained head impacts of lower PLA than those without (p < 0.04). No statistically significant differences were found between peak kinematics of head impacts between athletes of varying age, BMI, or level of competition. Higher number of previous concussions, varsity competition, and higher BMI were associated with more frequent head impacts (p < 0.036). CONCLUSION: The MiG2.0 instrumented mouthguard sensor, when used with a validated impact detection algorithm, is an effective tool for characterizing head impact exposure in high school football. In our cohort, concussion history and helmet model affected the severity of head impacts sustained by athletes, and the in vivo performance of helmets may differ from previous reports of laboratory helmet testing. Athletes with more football experience may sustain head impacts more frequently.