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Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis.

Shoulder-to-helmet (STH) impacts have been shown to cause approximately twenty percent of concussions in football yet little research has investigated shoulder pad design and STH impacts. This study aimed to characterize STH impacts and identify the effect of shoulder pad design on the struck head kinematics. Additional padding was added to a shoulder pad, and was then compared to an unmodified control shoulder pad. Participants performed a series of tests where they struck a helmeted Hybrid III dummy with both shoulder pad variations to compare struck head linear and rotational kinematics. The study found the modified shoulder pad reduced peak linear acceleration by 31% (Δµ = - 9.13 g's (- ∞, - 7.25), (p = 4.10e-08)), rotational acceleration by 28% (Δµ = - 565 rad s

Background: The prevalence of unreported concussions is high, and undiagnosed concussions can lead to worse postconcussion outcomes. It is not clear how those with a history of undiagnosed concussion perform on subsequent standard concussion baseline assessments. Purpose: To determine if previous concussion diagnosis status was associated with outcomes on the standard baseline concussion assessment battery. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Concussion Assessment, Research, and Education (CARE) Consortium participants (N = 29,934) self-reported concussion history with diagnosis status and completed standard baseline concussion assessments, including assessments for symptoms, mental status, balance, and neurocognition. Multiple linear regression models were used to estimate mean differences and 95% CIs among concussion history groups (no concussion history [n = 23,037; 77.0%], all previous concussions diagnosed [n = 5315; 17.8%], ≥1 previous concussions undiagnosed [n = 1582; 5.3%]) at baseline for all outcomes except symptom severity and Brief Symptom Inventory–18 (BSI-18) score, in which negative binomial models were used to calculate incidence rate ratios (IRRs). All models were adjusted for sex, race, ethnicity, sport contact level, and concussion count. Mean differences with 95% CIs excluding 0.00 and at least a small effect size (≥0.20), and those IRRs with 95% CIs excluding 1.00 and at least a small association (IRR, ≥1.10) were considered significant. Results: The ≥1 previous concussions undiagnosed group reported significantly greater symptom severity scores (IRR, ≥1.38) and BSI-18 (IRR, ≥1.31) scores relative to the no concussion history and all previous concussions diagnosed groups. The ≥1 previous concussions undiagnosed group performed significantly worse on 6 neurocognitive assessments while performing better on only 2 compared with the no concussion history and all previous concussions diagnosed groups. There were no between-group differences on mental status or balance assessments. Conclusion: An undiagnosed concussion history was associated with worse clinical indicators at future baseline assessments. Individuals reporting ≥1 previous undiagnosed concussions exhibited worse baseline clinical indicators. This may suggest that concussion-related harm may be exacerbated when injuries are not diagnosed.

To develop a predictive model for sport-related concussion in collegiate athletes and military service academy cadets using baseline data collecting during the pre-participation examination. Baseline assessments were performed in 15,682 participants from 21 US academic institutions and military service academies participating in the CARE Consortium Study during the 2015–2016 academic year. Participants were monitored for sport-related concussion during the subsequent season. 176 baseline covariates mapped to 957 binary features were used as input into a support vector machine model with the goal of learning to stratify participants according to their risk for sport-related concussion. Performance was evaluated in terms of area under the receiver operating characteristic curve (AUROC) on a held-out test set. Model inputs significantly associated with either increased or decreased risk were identified. 595 participants (3.79%) sustained a concussion during the study period. The predictive model achieved an AUROC of 0.73 (95% confidence interval 0.70–0.76), with variable performance across sports. Features with significant positive and negative associations with subsequent sport-related concussion were identified. This predictive model using only baseline data identified athletes and cadets who would go on to sustain sport-related concussion with comparable accuracy to many existing concussion assessment tools for identifying concussion. Furthermore, this study provides insight into potential concussion risk and protective factors.

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.

ObjectiveTo examine the association between estimated age at first exposure (eAFE) to American football and clinical measures throughout recovery following concussion.MethodsParticipants were recruited across 30 colleges and universities as part of the National Collegiate Athletic Association (NCAA)–Department of Defense Concussion Assessment, Research and Education Consortium. There were 294 NCAA American football players (age 19 ± 1 years) evaluated 24–48 hours following concussion with valid baseline data and 327 (age 19 ± 1 years) evaluated at the time they were asymptomatic with valid baseline data. Participants sustained a medically diagnosed concussion between baseline testing and postconcussion assessments. Outcome measures included the number of days until asymptomatic, Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) composite scores, Balance Error Scoring System (BESS) total score, and Brief Symptom Inventory 18 (BSI-18) subscores. The eAFE was defined as participant's age at the time of assessment minus self-reported number of years playing football.ResultsIn unadjusted regression models, younger eAFE was associated with lower (worse) ImPACT Visual Motor Speed (R2 = 0.031, p = 0.012) at 24–48 hours following injury and lower (better) BSI-18 Somatization subscores (R2 = 0.014, p = 0.038) when the athletes were asymptomatic. The effect sizes were very small. The eAFE was not associated with the number of days until asymptomatic, other ImPACT composite scores, BESS total score, or other BSI-18 subscores.ConclusionEarlier eAFE to American football was not associated with longer symptom recovery, worse balance, worse cognitive performance, or greater psychological distress following concussion. In these NCAA football players, longer duration of exposure to football during childhood and adolescence appears to be unrelated to clinical recovery following concussion.

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.

Background: Timely removal from activity after concussion symptoms remains problematic despite heightened awareness. Previous studies indicated potential adverse effects of continuing to participate in physical activity immediately after sustaining a concussion. Hypothesis/Purpose: The purpose was to determine the effect of timing of removal from play after concussion on clinical outcomes. It was hypothesized that immediate removal from activity after sport-related concussion (SRC) would be associated with less time missed from sport, a shorter symptomatic period, and better outcomes on acute clinical measures. Study Design: Cohort study; Level of evidence, 3. Methods: Data were reported from the National Collegiate Athletic Association and Department of Defense Grand Alliance: Concussion Awareness, Research, and Education (CARE) Consortium. Participants with 506 diagnosed SRCs from 18 sports and 25 institutions and military service academies were analyzed and classified as either immediate removal from activity (I-RFA) or delayed removal from activity (D-RFA). Outcomes of interest included time missed from sport attributed to their SRC, symptom duration, and clinical assessment scores. Results: There were 322 participants (63.6%) characterized as D-RFA. I-RFA status was associated with significantly less time missed from sport ( R2 change = .022-.024, P < .001 to P = .001) and shorter symptom duration ( R2 change = .044-.046, P < .001 [all imputations]) while controlling for other SRC recovery modifiers. These athletes missed approximately 3 fewer days from sport participation. I-RFA athletes had significantly less severe acute SRC symptoms and were at lower risk of recovery taking ≥14 days (relative risk = .614, P < .001, small-medium effect size) and ≥21 days (relative risk = .534, P = .010, small effect size). Conclusion: I-RFA is a protective factor associated with less severe acute symptoms and shorter recovery after SRC. Conveying this message to athletes, coaches, and others involved in the care of athletes may promote timely injury reporting.

ObjectiveThe objective of this study was to quantify elevated in-season presentation of concussion-like symptoms in the absence of diagnosed concussion in a cohort of collegiate rugby players.BackgroundIt is well known that many sports-related concussions are unreported. Athletes that do not immediately report concussion symptoms and continue to participate in activities are at higher risk for longer recoveries and sustain post-concussion symptoms longer. How regularly athletes experience elevated concussion symptoms in-season is unknown.Design/MethodsAthletes from men's and women's rugby teams were recruited and consented in accordance with the Virginia Tech Institutional Review Board. 63 males and 78 females participated over three seasons. Subjects completed a symptom and exposure query (SEQ) weekly throughout their season. The SEQ asked subjects if, in the past week, they experienced the 27 symptoms from the Graded Symptom Checklist for concussion. Subjects graded each symptom on a scale of 0–6, with 0 being no presentation and 6 being the most severe presentation. The graded severities of each symptom were summed to compute the overall Symptom Severity Score (SSS). Surveys that indicated confounding circumstances leading to symptoms were removed from analysis. The 99th percentile of SSS from baseline data was used as a metric of “elevated SSS,” which corresponded to an SSS of 11.Results1,214 SEQs were collected. There were 77 surveys from 43 athletes, 10 (15.8%) men and 33 (42.3%) women, who reported elevated SSS. In a given season, 16.3% of males and 41.7% of females reported elevated symptoms at least once. The surveys identified 11 additional suspected concussions based on symptom scores beyond the 8 that were clinically diagnosed.ConclusionsThis provides some evidence that constellations of concussion symptoms are commonly experienced by collegiate rugby athletes in-season. Some are at severities typically associated with concussion, but most are below current clinical concussion diagnostic thresholds.

ObjectiveThe primary aim is to document the cumulative neuropathologic burden of sport-related concussion via brain biomarkers (e.g., S100B and GFAP) in collegiate athletes at baseline, during the acute phase of a concussive injury, at return to play, and upon completion of collegiate athletic participation.BackgroundSport-related concussion is a major public health concern currently. Yet, the diagnosis is all done clinically, without a standardized objective measurement that could definitively implicate the presence of a concussion. Previous studies have shown blood brain biomarkers to be useful in determining the diagnosis of traumatic brain injury. Indeed, few studies have shown biomarkers that are highly sensitive and specific for detecting concussion in the general population and the FDA has approved such a biokit for public use. This biokit is used to determine if a brain CT scan is needed for an alert patient that presents to the emergency department following head trauma. However, much more work is needed to for concussion diagnosis in collegiate athletes.Design/MethodsA retrospective study is being conducted to analyze the blood biomarkers and head acceleration data collected from a pilot project. Four different groups are being used in this study: (1) nonimpact, (2) vigorous athletic controls (swimming, running, and baseball), (3) non- concussed football player (active controls), and (4) concussed football players.ResultsPreliminary results indicate significant differences in the means for the aforementioned groups (F = 3.85, df = 5.69 p = 0.0070, n = 74). Serum S100B levels are also significantly different for pre- and post-concussion groups (F = 4.51, p = 0.0405, df = 37).ConclusionsThere is a statistical difference in the blood biomarker levels in concussed versus non-concussed players. Current work is being undertaken to correlate head acceleration data to serum biomarker findings of concussion at baseline, post injury and completion of collegiate athletic participation to further study biomarker as a diagnostic tool in athletes.

Over four thousand data sets of 6 DOF (degree of freedom) (3 linear + 3 angular) accelerations measured at the head CG (center of gravity) were obtained from 19 college football players who voluntarily participated. Of these impacts, one hundred cases with high angular velocity were selected, and the set of 6 DOF head accelerations was applied to a detailed human head brain FE model. This study predicted the maximum 1 principal strain and CSDM (Cumulative Strain Damage Measure) for each set and found significant correlations between CSDM and a proposed criterion with angular velocity and acceleration of the head CG.

This study reports the results of 38 infraorbital maxilla impacts performed on male cadavers. Impacts were performed using an unpadded, cylindrical impactor (3.2 kg) at velocities between 1 and 5 m/s. The peak force and acoustic emission data were used to develop a statistical relationship of fracture risk as a function of impact force. Acoustic emission sensors were used to provide a noncensored measure of the maxilla tolerance and were essential due to the increase in impactor force after fracture onset. Parametric and nonparametric techniques were used to estimate the risk of fracture tolerance. The nonparametric technique produced an estimated 50% risk of fracture between 970 and 1223 N. The results obtained from the parametric and nonparametric techniques were in good agreement. Peak force values achieved in this study were similar to those of previous work and were unaffected by impactor velocity. The results of this study suggest that an impact to the infraorbital maxilla is a load-limited event due to compromise of structural integrity.

The current understanding of the tolerance of the frontal bone to blunt impact is limited. Previous studies have utilized vastly different methods, which limits the use of statistical analyses to determine the tolerance of the frontal bone. The purpose of this study is to determine the tolerance of the frontal bone to blunt impact. Acoustic emission sensors were used to provide a noncensored measure of the frontal bone tolerance and were essential due to the increase in impactor force after fracture onset. In this study, risk functions for fracture were developed using parametric and nonparametric techniques. The results of the statistical analyses suggest that a 50% risk of frontal bone fracture occurs at a force between 1885 N and 2405 N. Subjects that were found to have a frontal sinus present within the impacted region had a significantly higher risk of sustaining a fracture. There was no association between subject age and fracture force. The results of the current study suggest that utilizing peak force as an estimate of fracture tolerance will overestimate the force necessary to create a frontal bone fracture.