Scott L. Bruce

Background/Objectives: Concussions produce a wide array of symptoms that are often subtle and difficult to quantify. One such symptom involves reaction or response time (RT), consisting of perceptual latency time (LT) and movement time (MT). This pilot study examined the relationship between concussion history, mental health, and perceptual-motor performance among military veterans using a virtual reality (VR)-based assessment. The primary outcome was intraindividual variability (IIV), defined as the standard deviation of an individual’s responses across repeated trials. Methods: Of 78 veterans who volunteered, 29 (22 males, 7 females) provided complete VR data. Participants completed surveys assessing concussion and combat history, mental health issues, and suicide ideation. During VR testing, participants responded to 40 trials requiring neck rotation, arm reach, and a step toward left or right virtual targets. Associations between predictors (e.g., concussion, mental health) and VR outcomes (RT, LT, IIV) were evaluated using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) values. Results: Concussion history was the strongest predictor of performance deficits. IIV metrics were sensitive indicators of both concussion and mental health issues. Univariable analyses yielded AUC values of 0.944–0.806 all of which were statistically significant (p ≤ 0.001), and multivariable analyses produced AUCs of 0.950–0.870 all of which were also statistically significant (p ≤ 0.001). Incongruent movements and longer LT values were especially discriminative. Conclusions: Veterans with concussion and mental health histories demonstrated quantifiable perceptual-motor impairments in VR environments. Findings support VR assessment as a feasible, sensitive tool for detecting subtle residual effects of concussion.

A novel phone application (SOBER-EYE) was developed to quantify measures of pupillary light reflex (PLR) that extends beyond traditional PLR dichotomous measurement techniques. Prior to implementation into clinical practice, the test–retest reliability must be evaluated. The primary outcome measure was the intraclass correlation coefficient. Intraclass correlation coefficients for two PLR trials included: constriction amplitude (.825), constriction velocity (.693), initial pupil diameter (.888), latency (.962), and minimum size (.851). Clinical utility was found as all intraclass correlation coefficient values were >.60. The SOBER-EYE phone app has the potential to be used in the clinical environment to assess PLR.

Background/Objectives: Suicides among U.S. military veterans are alarmingly high, driven by factors such as mental health issues, combat exposure, and history of mild traumatic brain injury (mTBI)/concussion. This study aims to examine the relationship between concussion history, combat experience, and their effects on mental health issues and suicide ideation among military veterans. Additionally, this study investigates the impact of post-traumatic stress disorder (PTSD) on these variables. Methods: A total of 78 veterans (62 males, 16 females) participated in this study. Participants completed a demographic survey and the Global Well-being Index (GWI) to assess concussion history and residual symptoms. A licensed social worker interviewed the veterans using the 9-Item Patient Health Questionnaire (PHQ-9) for depression, the Generalized Anxiety Disorder survey (7 Items) (GAD-7) for anxiety, and the Columbia-Suicide Severity Rating Scale (C-SSRS) for suicide ideation. A 2 × 2 cross-tabulation analysis examined the relationships between concussion history, combat experience, and outcomes of anxiety, depression, and suicide ideation. PTSD was also assessed as both a predictor and an outcome. Statistical analyses yielded odds ratios (OR) with 95% confidence intervals (CI), Chi-square, and Cramer’s V (V) correlations along with associated p-values. Results: The combination of concussion history and combat experience strongly predicted either anxiety, depression, or suicide ideation (OR = 7.97, 95% CI: 1.70, 37.44; V = 0.334, (p = 0.003)), more than either factor alone. Combat experience was the strongest predictor of PTSD (OR = 11.12, 95% CI: 3.30, 37.47; V = 0.485, p ≤ 0.001), both individually and when combined with concussion history. PTSD strongly influenced mental health issues and suicide ideation (OR = 8.16, 95% CI: 1.74, 38.25; V = 0.339, p = 0.003). Stratification by PTSD status (positive or negative) affected the relationships between independent and dependent variables. Small cell counts resulted in a wide 95% CI for some ORs, though some statistically significant Fisher’s Exact Test results were observed. Credibility analysis using the critical prior interval (CPI) metric confirmed the intrinsic credibility of the results. Conclusions: This study provides insights into the relationships between concussion history, combat experience, and their impacts on mental health issues and suicide ideation among military veterans.

Clinical prediction models are useful in addressing several orthopedic conditions with various cohorts. American football provides a good population for attempting to predict injuries due to their relatively high injury rate. Physical performance can be assessed a variety of ways using an assortment of different tests to assess a diverse set of metrics, which may include reaction time, speed, acceleration, and deceleration. Asymmetry, the difference between right and left performance has been identified as a possible risk factor for injury. The purpose of this study was to determine the whole-body reactive agility metrics that would identify Division I football players who were at elevated risk for core, and lower extremity injuries (CLEI). This cohort study utilized 177 Division I football players with a total of 57 CLEI suffered who were baseline tested prior to the season. Single-task and dual-task whole-body reactive agility movements in lateral and diagonal direction reacting to virtual reality targets were analyzed separately. Receiver operator characteristic (ROC) analyses narrowed the 34 original predictor variables to five variables. Logistic regression analysis determined the three strongest predictors of CLEI for this cohort to be: lateral agility acceleration asymmetry, lateral flanker deceleration asymmetry, and diagonal agility reaction time average. Univariable analysis found odds ratios to range from 1.98 to 2.75 for these predictors of CLEI. ROC analysis had an area under the curve of 0.702 for any combination of two or more risk factors produced an odds ratio of 5.5 for risk of CLEI. These results suggest an asymmetry of 8–15% on two of the identified metrics or a slowed reaction time of ≥0.787 s places someone at increased risk of injury. Sixty-three percent (36/57) of the players who sustained an injury had ≥2 positive predictors In spite of the recognized limitation, these finding support the belief that whole-body reactive agility performance can identify Division I football players who are at elevated risk for CLEI.

Current literature indicates loss of consciousness occurs in about 10% of concussions. Posturing presentations represent brain injuries and a loss of consciousness. The purpose of this study was to observe video evidence of football-game-related concussions to determine the rate in which a posturing presentation occurs in reported concussion. Over the course of three National Football League and three National Collegiate Athletic Association football seasons, 103 videos of 805 reported concussions met the inclusion criteria; 35 videos demonstrated a posturing presentation, for a rate of 33.98%. Our study indicates that the published statistic regarding loss of consciousness (occurring only about 10% of the time) may be too conservative.

There is an absence of literature pertaining to the reliability of core muscular endurance tests. The purpose of this study was to assess the test-retest and interrater reliability of four core muscular endurance tests. Participants were physically active, college students. Data were gathered during three trials for each core test. Participants were timed by two test administrators (raters) until the participant could no longer hold the test position. Test-retest reliability values ranged from 0.57–0.85 for all three trials, and from 0.80–0.89 for the latter two trials. Interrater reliability values ranged from 0.99–1.00 for all three trials of all four tests. Although the participants were not athletes, we were able to demonstrate good test-retest and interrater reliability for the core muscular endurance tests assessed.

Context: A common goal of professional education programs is to recruit the students best suited for the professional career. Selection of students can be a difficult process, especially if the number of qualified candidates exceeds the number of available positions. The ability to predict academic success in any profession has been a challenging proposition. No studies to date have examined admission predictors of professional master's athletic training programs (PMATP).Objective: The purpose of this study was to identify program applicant characteristics that are most likely to predict academic success within a PMATP.Design: Cohort-based.Setting: University professional PMATP.Patients or Other Participants: A cohort of 119 students who attended a PMATP for at least 1 year.Intervention(s): Common application data from subjects' applications to the university and the PMATP were gathered and used to create the prediction models.Main Outcome Measure(s): Sensitivity, specificity, odds ratio, and relative frequency of success were used to determine the strongest set of predictors.Results: Multiple logistic regression analyses yielded a 3-factor model for prediction of success in the PMATP (undergraduate grade point average ≥ 3.18; Graduate Record Examination quantitative [percentile rank] ≥ 141.5 [≥12]; taking calculus as an undergraduate). A student with ≥2 predictors had an odds ratio of 17.94 and a relative frequency of success of 2.13 for being successful in the PMATP. This model correctly predicted 90.5% of PMATP success.Conclusions: It is possible to predict academic success in a PMATP based on common application data.