Abstract: A recent Consensus Conference proposed subtyping concussive injuries into 5 categories. We propose adding a neuromotor subtype. Abnormal motion is a key feature of brain injury, as concussions can impact the neurological systems controlling gait. Neuromotor function can remain abnormal after symptom resolution and may be superior to self-report for tracking recovery. Neuromotor function can also define co-occurring orthopedic injuries and reveal vulnerabilities which could reduce injury risk.
Brevity is critical to the broad adoption of any screening measure. We examine the efficacy of a short Conners Continuous Performance Test (CCPT 3) to screen for concussion symptoms.
Data were from 20 U.S. sites, including university/schools conducting baseline testing (N = 817) and post-concussion assessments in medical settings (N = 108) from March 2018 to March 2020. Nine hundred twenty-five participants (57.3% female) aged 8–66 (M = 19.4, SD = 6.1) completed the computerized CCPT 3, Balance Error Scoring System, NIH 4-meter gait test, and 31-item Centers for Disease Control and Prevention (CDC) concussion symptom checklist.
Short CCPT 3 scores correlate highly with full CCPT 3, with coefficients of .70–.96 (M = .88). Short CCPT 3 explains 16.8% variance in CDC concussion symptoms (F(8, 910) = 23.01, p < .001; Cohen’s d = .90) and provides incremental validity (15% variance; d = .78) over behavioral measures (Fchange (8, 842) = 20.11, p < .001). Predictive validity of the short CCPT 3 was greater for those having a recent concussion (22.1% variance, d = 1.07; F(8, 97) = 3.45, p= .002). Scores also predict concussion history.
The short CCPT 3 yields large effect sizes when predicting CDC concussion symptoms, compares favorably to other concussion measures, and shows no trade-off from the full CCPT, which has previously predicted concussion symptoms and severity. Short CCPT 3 scores can objectively quantify cognitive functioning to serve as a screener and inform return-to-play decisions even for post-acute presentations in children, adolescents, and adults.
Dangers of sports-related concussion are well documented, and those participating in sports involving significant contact are at an even higher risk relative to the general population. Despite extensive concussion education, athletes still make decisions that would be considered unsafe, such as underreporting and continuing to play despite experiencing symptoms. Although baseline testing is an increasingly common practice at all levels of sport, little is known about its ability to improve player safety perceptions.
The current study examines whether taking part in a standardized baseline concussion assessment changes athletes’ knowledge, attitudes, or perceptions of concussion safety decisions.
A total of 229 club and National Collegiate Athletic Association athletes completed a modified Rosenbaum Concussion Knowledge and Attitudes Survey–Student Version (RoCKAS-ST), which was used to evaluate knowledge, attitudes, and perceptions of concussion safety decisions in hypothetical scenarios. Athletes were randomly assigned to either complete baseline concussion testing prior to the RoCKAS-ST or complete baseline testing after the RoCKAS-ST.
Athletes randomly assigned to complete baseline testing before the RoCKAS-ST demonstrated greater agreement with favorable concussion safety decisions in hypothetical scenarios relative to athletes completing baseline testing after the RoCKAS-ST. The two conditions did not differ with respect to concussion knowledge or attitudes.
Baseline testing appears to have an added benefit of resulting in more favorable perceptions toward making safe decisions following suspected concussions.
Baseline testing may provide an effective means of improving a broader constellation of concussion safety behavior, particularly in club athletes, who are typically underserved in terms of concussion-related resources and care.
Abstract: Machine learning (ML) techniques can help harness insights from data that complement and extend those that can be attained by traditional statistical methods. The current article introduces clinicians to concepts underlying ML and explores how it can be applied within the domain of neuropsychology. Specifically, we illustrate an application of ML to a dataset that includes a battery of standardized measures designed to provide diagnostic support for concussions, including standardized neurocognitive (CPT 3) and neurobehavioral (BESS, NIH 4 meter gait) measures, gait sensor data, and a CDC concussion symptom checklist. These variables were used to predict the decision-making of a pediatric neurologist evaluating a group of child/adolescent patients. With a sample of 111 cases, ML (using a general linear model and deep learning as illustrations) achieved accuracies of 91% and 84.8% and AUCs of 1.0 and .947, respectively, when predicting the neurologist’s binomial decision-making (safe/remove). In presenting the data and various considerations for interpretation, we attempt to balance both the promise and perils of ML.
Abstract: Effective screening for concussion is increasingly important, and medical professionals play a critical role in diagnostic and return-to-play decisions. However, few well-validated measures are available to assist in those decisions. This study aims to determine whether previously validated measures assessing neurocognitive and neurobehavioral abilities can predict Centers for Disease Control (CDC) concussion symptom endorsement in a sample of child or youth athletes.
Abstract: Sports-related concussions are particularly dangerous injuries due to their complex nature and difficulties associated with diagnostic and return-to-play decisions. Some of the most commonly employed assessment tools have been shown to be unreliable, leading to misdiagnoses. Guidelines for selecting more effective concussion assessment instruments and the use of such tests in urgent care settings are here suggested as an optimal framework for improved care.