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Associate Editor Stefan M Duma oversaw the review of this article.

Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of 14 existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degrees-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions substantially over predict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.

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Understanding the in-game demands placed on athletes may allow practitioners to design improved training protocols to prepare athletes for competitive demands. This study aimed to quantify the competitive movement demands of professional American football athletes and to determine any inter-positional differences that may exist. Player tracking data were collected from 2018 to 2020 regular season games of the National Football League. Distance, maximum velocity, high-velocity efforts and distance, and acceleration and deceleration efforts and distance were used to evaluate competitive movement demands. To determine position-specific demands, each player was classified by their designated position, and velocity data from competitive games were used to develop position-specific velocity thresholds. One-way ANOVA and post hoc Bonferroni statistical analysis were used to determine inter-positional difference. Significant (p < 0.05) positional differences were found for all load metrics with respect to competitive game demands. Generally, wide receivers and defensive backs had faster maximum velocities, higher distances, and more acceleration and deceleration efforts and distance than other positions. Linebackers accumulated the most high-velocity efforts and distance. Lineman had the lowest values for all assessed metrics. These findings may assist the performance staff in developing improved training and return-to-play protocols with the aim of improving player performance and mitigating injury.