Dual-Task Gait Performance Following Head Impact Exposure in Male and Female Collegiate Rugby Players.

Background: Gait impairments have been well-studied in concussed athletes. However, the sex-specific effect of cumulative head impacts on gait is not well understood. When a cognitive task is added to a walking task, dual-task gait assessments can help amplify deficits in gait and are representative of tasks in everyday life. Dual-task cost is the difference in performance from walking (single-task) to walking with a cognitive load (dual-task). Purpose: The objectives of this study were to explore the differences between sexes in 1) dual-task gait metrics, 2) gait metric changes from pre-season to post-concussion and post-season, and 3) the dual-task costs associated with gait metrics. Study Design: Cross-sectional study. Methods: Over two seasons, 77 female athlete-seasons and 64 male athlete-seasons from collegiate club rugby teams participated in this study. Subjects wore inertial sensors and completed walking trials with and without a cognitive test at pre-season, post-season, and post-concussion (if applicable). Results: Females athletes showed improvement in cadence (mean = 2.7 step/min increase), double support time (mean = -0.8% gait cycle time decrease), gait speed (mean = 0.1 m/s increase), and stride length (mean = 0.2 m increase) in both task conditions over the course of the season (p < 0.030). Male athletes showed no differences in gait metrics over the course of the season, except for faster gait speeds and longer stride lengths in the dual-task condition (p < 0.034). In all four gait characteristics, at baseline and post-season, females had higher dual-task costs (mean difference = 4.4, p < 0.003) than the males. Conclusions: This results of this study showed little evidence suggesting a relationship between repetitive head impact exposure and gait deficits. However, there are sex-specific differences that should be considered during the diagnosis and management of sports-related concussion. Level of Evidence: Level 2b.

In-Season Concussion Symptom Reporting in Male and Female Collegiate Rugby Athletes.

Symptom inventories are generally only collected after a suspected concussion, but regular in-season monitoring may allude to clinical symptoms associated with repetitive subconcussive impacts and potential undiagnosed concussions. Despite sex-specific differences in symptom presentation and outcome of concussion, no return-to-play protocol takes sex into account. The objective of this study was to monitor a cohort of contact-sport athletes and compare the frequency and severity of in-season concussion-like symptom reporting between sexes. Graded symptom checklists from 144 female and 104 male athlete-seasons were administered weekly to quantify the effect of subconcussive impacts on frequency and severity of in-season symptom reporting. In-season, mean symptom severity score (SSS) (p = 0.026, mean difference of 1.8), mean number of symptoms (p = 0.044, mean difference of 0.9), max SSS (p < 0.001, mean difference of 19.2), and max number of symptoms (p < 0.001, mean difference of 6.8) were higher in the females. The females' survey results showed differences between elevated and concussed SSS (p < 0.005, mean difference of 28.1) and number of symptoms reported (p = 0.001, mean difference of 6.6). The males did not have a difference in SSS (p = 0.97, mean difference of 1.12) nor in number of symptoms (p = 0.35, mean difference of 1.96) from elevated to concussed athletes. Rugby players report concussion-like symptoms in the absence of a diagnosed concussion in-season. Female athletes reported elevated symptom frequencies with greater severities than the males, but both sexes reported considerable levels throughout the season.

A Two-Phased Approach to Quantifying Head Impact Sensor Accuracy: In-Laboratory and On-Field Assessments.

Measuring head impacts in sports can further our understanding of brain injury biomechanics and, hopefully, advance concussion diagnostics and prevention. Although there are many head impact sensors available, skepticism on their utility exists over concerns related to measurement error. Previous studies report mixed reliability in head impact sensor measurements, but there is no uniform approach to assessing accuracy, making comparisons between sensors and studies difficult. The objective of this paper is to introduce a two-phased approach to evaluating head impact sensor accuracy. The first phase consists of in-lab impact testing on a dummy headform at varying impact severities under loading conditions representative of each sensor's intended use. We quantify in-lab accuracy by calculating the concordance correlation coefficient (CCC) between a sensor's kinematic measurements and headform reference measurements. For sensors that performed reasonably well in the lab (CCC ≥ 0.80), we completed a second phase of evaluation on-field. Through video validation of impacts measured by sensors on athletes, we classified each sensor measurement as either true-positive and false-positive impact events and computed positive predictive value (PPV) to summarize real-world accuracy. Eight sensors were tested in phase one, but only four sensors were assessed in phase two. Sensor accuracy varied greatly. CCC from phase one ranged from 0.13 to 0.97, with an average value of 0.72. Overall, the four devices that were implemented on-field had PPV that ranged from 16.3 to 91.2%, with an average value of 60.8%. Performance in-lab was not always indicative of the device's performance on-field. The methods proposed in this paper aim to establish a comprehensive approach to the evaluation of sensors so that users can better interpret data collected from athletes.