Speaker
Description
Sport-related concussions (SRCs) represent a major public health concern, accounting for more than 200,000 annual Emergency Department visits in the United States. Biomechanically, SRCs arise from head impacts that generate high-magnitude linear and rotational accelerations. Increasing evidence from human studies indicates that repetitive head impact exposure (HIE) reduces concussion tolerance among contact-sport athletes. Despite this, prior efforts to quantify the relationship between HIE and incident concussion have often relied on overly simplistic statistical approaches.
In this study, we analyze data collected from helmet-mounted accelerometers that record instantaneous head accelerations and detect head acceleration events (HAEs). The longitudinal dataset includes HAEs from collegiate football players across multiple playing positions and institutions. We model HAE counts using modern count-data methods and apply functional data analysis techniques to characterize temporal patterns of HAEs across a competitive season. Our approach evaluates how these patterns vary by player position and across schools.
Specifically, we employ a Tucker tensor decomposition of a matrix of functional observations, yielding interpretable and stable estimates of school- and position-specific mean HAE trajectories. To fit the proposed model, we develop an efficient estimation procedure that integrates an expectation–maximization (EM) algorithm with nonparametric function estimation via penalized splines. Simulation studies demonstrate strong performance and stable recovery of underlying patterns under diverse data-generating scenarios.
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