Abstract:
OBJECTIVES: The purpose of this study was to assess the contribution of socioeconomic status (SES) and other multivariate predictors to baseline neurocognitive functioning in collegiate athletes. METHODS: Data were obtained from the Concussion Assessment, Research and Education (CARE) Consortium. Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) baseline assessments for 403 University of Florida student-athletes (202 males; age range: 18-23) from the 2014-2015 and 2015-2016 seasons were analyzed. ImPACT composite scores were consolidated into one memory and one speed composite score. Hierarchical linear regressions were used for analyses. RESULTS: In the overall sample, history of learning disability (beta=-0.164; p=.001) and attention deficit-hyperactivity disorder (beta=-0.102; p=.038) significantly predicted worse memory and speed performance, respectively. Older age predicted better speed performance (beta=.176; p<.001). Black/African American race predicted worse memory (beta=-0.113; p=.026) and speed performance (beta=-.242; p<.001). In football players, higher maternal SES predicted better memory performance (beta=0.308; p=.007); older age predicted better speed performance (beta=0.346; p=.001); while Black/African American race predicted worse speed performance (beta=-0.397; p<.001). CONCLUSIONS: Baseline memory and speed scores are significantly influenced by history of neurodevelopmental disorder, age, and race. In football players, specifically, maternal SES independently predicted baseline memory scores, but concussion history and years exposed to sport were not predictive. SES, race, and medical history beyond exposure to brain injury or subclinical brain trauma are important factors when interpreting variability in cognitive scores among collegiate athletes. Additionally, sport-specific differences in the proportional representation of various demographic variables (e.g., SES and race) may also be an important consideration within the broader biopsychosocial attributional model. (JINS, 2018, 24, 1-10).