Journal of the International Neuropsychological Society (2017), 23, 1–10. Copyright © INS. Published by Cambridge University Press, 2017. doi:10.1017/S1355617717000716
Socioeconomic Status and Race Outperform Concussion History and Sport Participation in Predicting Collegiate Athlete Baseline Neurocognitive Scores
Zac Houck,1 Breton Asken,1 James Clugston,2 William Perlstein,1 AND Russell Bauer1 1 2
Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida Department of Community Health and Family Medicine, University of Florida, Gainesville, Florida
(RECEIVED November 7, 2016; FINAL REVISION June 14, 2017; ACCEPTED June 21, 2017)
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 (β = −0.164; p = .001) and attention deficit–hyperactivity disorder (β = −0.102; p = .038) significantly predicted worse memory and speed performance, respectively. Older age predicted better speed performance (β = .176; p < .001). Black/African American race predicted worse memory (β = −0.113; p = .026) and speed performance (β = −.242; p < .001). In football players, higher maternal SES predicted better memory performance (β = 0.308; p = .007); older age predicted better speed performance (β = 0.346; p = .001); while Black/African American race predicted worse speed performance (β = −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, 2017, 23, 1–10) Keywords: Neurocognitive, Baseline, Concussion, Socioeconomic Status, Neurobiopsychosocial, Collegiate student-athletes
abilities. In the event of a concussion, sports medicine clinicians assess for deficits and track recovery by administering the same battery of tests and assessing change. Accurate interpretation of neurocognitive scores is often difficult due to the number of factors associated with poorer performance at baseline. Male gender (Covassin et al., 2006), previous concussion (Collins et al., 1999), history of learning disability (LD), and attention deficit–hyperactivity disorder (ADHD) (Collins et al., 1999; Elbin et al., 2013), psychological distress such as anxiety and depression (Bailey, Samples, Broshek, Freeman, & Barth, 2010), as well as education level and language (Jones et al., 2014) have all been associated with lower baseline neurocognitive performance. However, a large amount of neurobiopsychosocial variance, which is the combination of neurological,
INTRODUCTION In response to increased public awareness and concern about sport-related concussion over the past decade, many sports organizations nation-wide have attempted to improve athlete safety by requiring concussion management protocols. At the collegiate level, concussion management protocols typically require athletes to undergo a battery of assessments that serve as a measure of baseline status representing pre-injury function. A baseline assessment often includes measures of concussion-related symptoms, balance, and neurocognitive
Correspondence and reprint requests to: Zac Houck, University of Florida, 1225 Center Drive, Room 3151, Gainesville, FL, 32610. E-mail: [email protected]fl.edu 1
Downloaded from https://www.cambridge.org/core. Florida Atlantic University, on 04 Oct 2017 at 02:04:02, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1355617717000716
2 biological, and psychosocial factors, in baseline neurocognitive functioning has yet to be explained. Early exposure to contact sports has recently been associated with cognitive deficits in later life (Stamm et al., 2015); however, the effects of contact sport exposure have not been assessed in the context of baseline neurocognitive performance. The effects of various socioeconomic status (SES) factors considered influential in other applications of neuropsychological assessment are yet to be investigated in the context of baseline neurocognitive assessment. SES factors potentially influence biological and psychosocial determinants of baseline performance and, thus, may be critical for interpreting deviant but valid baseline results, particularly when being compared to post-injury results in the evaluation and management of patients with concussion (McCrea, Broshek, & Barth, 2015). The current study takes a first step in filling this gap in the concussion literature. The influence of SES on cognition has been widely studied in the educational field. Children from disadvantaged SES backgrounds perform worse on intellectual testing than do children from higher SES backgrounds (Andersson, Sommerfelt, Sonnander, & Ahlsten, 1996; Duncan, Brooks-Gunn, & Klebanov, 1994). Children from lower-SES families have less access to recreational and learning materials from infancy to adolescence, which likely mediates the association between SES and cognitive development (Bradley, Convyn, Burchinal, McAdoo, & Coll, 2001). Further supporting the influence of resource access, cognitive stimulation in the home environment explained one third to one half of the variance in verbal abilities and math skills in economically disadvantaged children (Korenman, Miller, & Sjaastad, 1995). The negative effects of disadvantaged SES backgrounds during childhood that are associated with impoverished learning environments lead to poorer cognitive performance in adulthood (Kaplan et al., 2001). This suggests that children’s developmental environment heavily influences their cognitive ability and warrants consideration when interpreting neurocognitive performance across their lifespan. This is particularly necessary when interpreting baseline scores of those at increased risk of sustaining a head injury, in which premorbid deficits are often interpreted as brain injury effects (Larrabee, Binder, Rohling, & Ploetz, 2013). Exposure to contact sports, such as American football, during key neurodevelopmental periods has recently been described as being associated with cognitive deficits later in life (Stamm et al., 2015). While the repetitive concussive and sub-concussive impacts sustained throughout one’s career may moderate long-term cognition in this population, other factors, such as developmental environment, have yet to be considered. American football players come from a wide range of socioeconomic backgrounds. In 2014, 52.9% of American football players at Division 1 Football Bowl Subdivision (FBS) level were African-American, a larger percentage than all other sports except men’s basketball (“NCAA College Sport Racial and Gender Report Card,” 2015). Previous research indicates that African American children have a significantly increased likelihood of being exposed to
Z. Houck et al. persistently low SES backgrounds that have influential effects on childhood cognitive abilities (Duncan et al., 1994). This suggests a large percentage of collegiate football players are at an increased risk of being exposed to low SES backgrounds that result in being cognitively disadvantaged. This could lead to lower cognitive reserve at the time of concussive/subconcussive exposure and throughout their lifetime. Athletes with lower cognitive reserve may be at an increased risk of prolonged recovery at the time of concussive injury or of long-term cognitive deficits following repetitive concussive and sub-concussive impacts. However, no studies to date have assessed the role of SES in conjunction with previously studied variables in collegiate football players. Developmental environment and SES have been inconsistently defined from a methodological perspective but the most commonly used measures of SES are income, education, and occupation, either individually or in some combination (Braveman et al., 2005; Duncan & Magnuson, 2003; Krieger, Williams, & Moss, 1997). The Hollingshead Four Factor Index (Hollingshead, 1975), which was used to represent parental SES in the current study, describes SES as a composite of educational and occupational variables and is considered a reliable indicator of SES (Gottfried, 1985). The purpose of this study was to assess the contribution of SES along with neurodevelopmental/demographic factors and exposure to sport as predictors of baseline neurocognitive functioning in collegiate athletes. We hypothesized that SES factors would significantly predict baseline neurocognitive performance above and beyond previously studied variables, such as race (Kontos, Elbin, Covassin, & Larson, 2010), gender, previous concussion history, years playing primary sport, neurodevelopmental and psychological history. Understanding the relative importance of these factors on baseline neurocognitive performance may aid in the interpretation of deviant, or lower than expected, scores as well as identifying athletes at risk for poor outcomes after brain trauma. In a separate analysis, we aimed to assess the contribution of multivariate predictors on baseline neurocognitive functioning in collegiate football players. We hypothesized that SES would be a better predictor of baseline neurocognitive performance than years exposed to football, which is an indirect measure of the cumulative exposure to repetitive brain trauma, and previous concussion history. Understanding the contribution of SES on baseline neurocognitive functioning in collegiate football players may aid in the interpretation of neurocognitive scores. Additionally, understanding the contribution of SES may be a first step in identifying a risk factor for student-athletes that may be at an increased susceptibility to acute and long-term cognitive deficits associated with repetitive brain trauma.
METHODS Data were obtained from the National Collegiate Athletic Association (NCAA) and Department of Defense
Downloaded from https://www.cambridge.org/core. Florida Atlantic University, on 04 Oct 2017 at 02:04:02, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1355617717000716
SES factors and neurocognitive performance Concussion Assessment, Research and Education (CARE) Consortium for secondary analysis by a participating institution. The CARE Consortium, initiated in 2014, collects concussion-related assessment data, including baseline and post-injury concussion test results for student-athletes at multiple NCAA institutions. Before the start of their respective seasons, student athletes at each participating CARE institution annually undergo a battery of assessments that serve as a baseline measure of performance. The battery includes, at a minimum, measurements of concussion-related symptoms, balance, and neurocognitive abilities. In addition to these assessments, all participating studentathletes complete a Clinical Reporting Form (CRF), which provides comprehensive background information on sport participation history, academic history, self- and familymedical history, parental education and occupation, and family income. A more detailed description of the CARE consortium methodology is described elsewhere (Broglio et al., 2017). For the purpose of this initial investigation, we limited analyses to University of Florida student-athlete data only. The University of Florida Institutional Review Board (IRB-01) approved the reception of the data from CARE and the analysis of the data.
Participants The dataset obtained from the CARE Consortium contained baseline assessments for 494 University of Florida studentathletes from the 2014–2015 and 2015–2016 seasons. After exclusion of student-athletes due to insufficient SES data (N = 64), incomplete sport participation variables (N = 6), incomplete race data (N = 14), and invalid ImPACT data (N = 7), 403 student-athletes were included in the final analyses. Student-athletes from 9 male and 12 female sports were included in the sample, as depicted in Table 1. Studentathletes’ year of athletic eligibility ranged from the first year to the fifth year, as depicted in Table 2.
Predictor Variables Demographic Self-reported age and gender for the 403 participants [mean age = 19.67, standard deviation (SD) = ±1.3 years; range, 18–23 years old; male, n = 202] were included in the analysis. Race, which is one commonly used proxy of SES, was included in the analysis due to the unreliable nature of selfreported SES. Race was self-reported by the student-athletes and defined as White/Caucasian, Black/African American, or Other. The “other” category included Asian, Indian-Alaskan, and student-athletes of multiple races, which, due to small numbers, were combined. Fourteen student-athletes were excluded due to incomplete race data. Race variables were dummy-coded before entry into the regression model. Ethnicity was defined as Hispanic/Latino, Not Hispanic/ Latino, or Unknown. Table 2 shows the sample distribution of student-athletes’ race and ethnicity.
3 Table 1. Sample Distribution of Student-Athletes’ Primary Sport Sport
Male N (%)
Female N (%)
Baseball Basketball Cross-country/track Diving Field event Football Golf Gymnastics Lacrosse Soccer Softball Swimming Tennis Volleyball Total
29 (7.0) 12 (2.9) 35 (8.4) 2 (0.5) 2 (0.5) 87 (20.9) 7 (1.7) — — — — 32 (7.7) 7 (1.7) — 202 (50.1)
— 10 (2.4) 32 (7.7) 7 (1.7) 4 (1.0) — 8 (1.9) 11 (2.6) 40 (9.6) 29 (7.0) 19 (4.6) 21 (5.0) 7 (1.7) 16 (3.8) 201 (49.9)
Neurodevelopmental History and Psychological Distress Self-reported neurodevelopmental disorders and psychological distress were collected as part of the baseline assessments. Self-reported history of diagnosed attention deficithyperactivity disorder (ADHD) and LD, as well as current depressive, anxiety, and somatic symptoms, were included in the regression models. Neurodevelopmental history was obtained from the medical history portion of the CRF. Data were not available on the stimulant usage of ADHD diagnosed participants. Psychological distress was assessed with the Brief Symptom Inventory-18 (BSI-18; Derogatis, 2000), an 18-item questionnaire that assesses general feelings of anxiety (mean = 0.72; SD = ±1.49), depression (mean = 0.69; SD = ±1.68), and somatization (mean = 1.09; SD = ±1.90) representative of the preceding 7 days. The BSI-18 includes six questions for each of the three domains assessed on a 5-point Likert scale, where “0” indicates that the symptom has not been present, “1” indicates that the symptom has been present “A Little Bit,” “2” indicates that the symptom has been “Moderately” present, “3” indicates that the symptom has been present “Quite A Bit,” and “4” indicates that the symptom has been “Extremely” present, for a possible score of 0–24 on each domain. Table 2 shows the sample distribution of student-athlete’s neurodevelopmental history.
Concussion History and Years Participating in Sport Self-reported concussion history, diagnosed and suspected, was obtained from the medical history portion of the CRF. Number of years participating in primary sport (mean = 11.1; SD = ±3.7 years) was defined as the number of years in which the athlete participated in an organized sports league. In the secondary analysis, number of years participating in football (mean = 10.2; SD = ± 4.0 years) was defined as the
Downloaded from https://www.cambridge.org/core. Florida Atlantic University, on 04 Oct 2017 at 02:04:02, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1355617717000716
4
Z. Houck et al.
Table 2. Sample Distribution of Student-Athlete’s Year of Athletic Eligibility, Race, Ethnicity, Neurodevelopmental Disorder History, and Concussion History Factor Eligibility year 1st year 2nd year 3rd year 4th year 5th year Redshirt Unknown Race White/Caucasian Black/African American Other Ethnicity Non-Hispanic Hispanic Unknown Neurodevelopmental history No ADHD/LD LD only ADHD only Combined LD/ADHD Concussion history 0 1 2 3+ Total
Overall sample N (%)
Football N (%)
All other sports N (%)
χ2
p-Value
— 147 (36.5) 94 (23.3) 83 (20.6) 49 (12.2) 15 (3.7) 9 (2.2) 6 (1.4) — 299 (74.2) 76 (18.9) 28 (6.9) — 315 (78.2) 40 (9.9) 48 (11.9) — 335 (83.1) 15 (3.7) 40 (9.9) 13 (3.2) — 294 (73.0) 77 (19.1) 22 (5.5) 10 (2.4) 403 (100)
— 34 (40.0) 19 (22.4) 10 (11.8) 9 (10.6) 9 (10.6) 4 (4.7) 0 (0) — 43 (50.6) 35 (41.2) 7 (8.2) — 67 (78.8) 7 (8.2) 11 (12.9) — 62 (72.9) 5 (5.9) 15 (17.6) 3 (3.5) — 49 (57.6) 28 (32.9) 4 (4.7) 4 (4.7) 85 (100)
— 113 (35.5) 75 (23.6) 73 (23.0) 40 (12.6) 6 (1.9) 5 (1.6) 6 (1.6) — 256 (80.5) 41 (12.9) 21 (6.6) — 248 (78.0) 33 (10.4) 37 (11.6) — 273 (77.0) 10 (3.1) 25 (7.9) 10 (3.1) — 245 (77.0) 49 (15.4) 18 (5.7) 6 (1.9) 318 (100)
22.90 — — — — — — — 36.80 — — — .412 — — — 9.20 — — — — 16.51 — — — — —
.002 — — — — — — —
Socioeconomic Status and Race Outperform Concussion History and Sport Participation in Predicting Collegiate Athlete Baseline Neurocognitive Scores
Zac Houck,1 Breton Asken,1 James Clugston,2 William Perlstein,1 AND Russell Bauer1 1 2
Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida Department of Community Health and Family Medicine, University of Florida, Gainesville, Florida
(RECEIVED November 7, 2016; FINAL REVISION June 14, 2017; ACCEPTED June 21, 2017)
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 (β = −0.164; p = .001) and attention deficit–hyperactivity disorder (β = −0.102; p = .038) significantly predicted worse memory and speed performance, respectively. Older age predicted better speed performance (β = .176; p < .001). Black/African American race predicted worse memory (β = −0.113; p = .026) and speed performance (β = −.242; p < .001). In football players, higher maternal SES predicted better memory performance (β = 0.308; p = .007); older age predicted better speed performance (β = 0.346; p = .001); while Black/African American race predicted worse speed performance (β = −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, 2017, 23, 1–10) Keywords: Neurocognitive, Baseline, Concussion, Socioeconomic Status, Neurobiopsychosocial, Collegiate student-athletes
abilities. In the event of a concussion, sports medicine clinicians assess for deficits and track recovery by administering the same battery of tests and assessing change. Accurate interpretation of neurocognitive scores is often difficult due to the number of factors associated with poorer performance at baseline. Male gender (Covassin et al., 2006), previous concussion (Collins et al., 1999), history of learning disability (LD), and attention deficit–hyperactivity disorder (ADHD) (Collins et al., 1999; Elbin et al., 2013), psychological distress such as anxiety and depression (Bailey, Samples, Broshek, Freeman, & Barth, 2010), as well as education level and language (Jones et al., 2014) have all been associated with lower baseline neurocognitive performance. However, a large amount of neurobiopsychosocial variance, which is the combination of neurological,
INTRODUCTION In response to increased public awareness and concern about sport-related concussion over the past decade, many sports organizations nation-wide have attempted to improve athlete safety by requiring concussion management protocols. At the collegiate level, concussion management protocols typically require athletes to undergo a battery of assessments that serve as a measure of baseline status representing pre-injury function. A baseline assessment often includes measures of concussion-related symptoms, balance, and neurocognitive
Correspondence and reprint requests to: Zac Houck, University of Florida, 1225 Center Drive, Room 3151, Gainesville, FL, 32610. E-mail: [email protected]fl.edu 1
Downloaded from https://www.cambridge.org/core. Florida Atlantic University, on 04 Oct 2017 at 02:04:02, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1355617717000716
2 biological, and psychosocial factors, in baseline neurocognitive functioning has yet to be explained. Early exposure to contact sports has recently been associated with cognitive deficits in later life (Stamm et al., 2015); however, the effects of contact sport exposure have not been assessed in the context of baseline neurocognitive performance. The effects of various socioeconomic status (SES) factors considered influential in other applications of neuropsychological assessment are yet to be investigated in the context of baseline neurocognitive assessment. SES factors potentially influence biological and psychosocial determinants of baseline performance and, thus, may be critical for interpreting deviant but valid baseline results, particularly when being compared to post-injury results in the evaluation and management of patients with concussion (McCrea, Broshek, & Barth, 2015). The current study takes a first step in filling this gap in the concussion literature. The influence of SES on cognition has been widely studied in the educational field. Children from disadvantaged SES backgrounds perform worse on intellectual testing than do children from higher SES backgrounds (Andersson, Sommerfelt, Sonnander, & Ahlsten, 1996; Duncan, Brooks-Gunn, & Klebanov, 1994). Children from lower-SES families have less access to recreational and learning materials from infancy to adolescence, which likely mediates the association between SES and cognitive development (Bradley, Convyn, Burchinal, McAdoo, & Coll, 2001). Further supporting the influence of resource access, cognitive stimulation in the home environment explained one third to one half of the variance in verbal abilities and math skills in economically disadvantaged children (Korenman, Miller, & Sjaastad, 1995). The negative effects of disadvantaged SES backgrounds during childhood that are associated with impoverished learning environments lead to poorer cognitive performance in adulthood (Kaplan et al., 2001). This suggests that children’s developmental environment heavily influences their cognitive ability and warrants consideration when interpreting neurocognitive performance across their lifespan. This is particularly necessary when interpreting baseline scores of those at increased risk of sustaining a head injury, in which premorbid deficits are often interpreted as brain injury effects (Larrabee, Binder, Rohling, & Ploetz, 2013). Exposure to contact sports, such as American football, during key neurodevelopmental periods has recently been described as being associated with cognitive deficits later in life (Stamm et al., 2015). While the repetitive concussive and sub-concussive impacts sustained throughout one’s career may moderate long-term cognition in this population, other factors, such as developmental environment, have yet to be considered. American football players come from a wide range of socioeconomic backgrounds. In 2014, 52.9% of American football players at Division 1 Football Bowl Subdivision (FBS) level were African-American, a larger percentage than all other sports except men’s basketball (“NCAA College Sport Racial and Gender Report Card,” 2015). Previous research indicates that African American children have a significantly increased likelihood of being exposed to
Z. Houck et al. persistently low SES backgrounds that have influential effects on childhood cognitive abilities (Duncan et al., 1994). This suggests a large percentage of collegiate football players are at an increased risk of being exposed to low SES backgrounds that result in being cognitively disadvantaged. This could lead to lower cognitive reserve at the time of concussive/subconcussive exposure and throughout their lifetime. Athletes with lower cognitive reserve may be at an increased risk of prolonged recovery at the time of concussive injury or of long-term cognitive deficits following repetitive concussive and sub-concussive impacts. However, no studies to date have assessed the role of SES in conjunction with previously studied variables in collegiate football players. Developmental environment and SES have been inconsistently defined from a methodological perspective but the most commonly used measures of SES are income, education, and occupation, either individually or in some combination (Braveman et al., 2005; Duncan & Magnuson, 2003; Krieger, Williams, & Moss, 1997). The Hollingshead Four Factor Index (Hollingshead, 1975), which was used to represent parental SES in the current study, describes SES as a composite of educational and occupational variables and is considered a reliable indicator of SES (Gottfried, 1985). The purpose of this study was to assess the contribution of SES along with neurodevelopmental/demographic factors and exposure to sport as predictors of baseline neurocognitive functioning in collegiate athletes. We hypothesized that SES factors would significantly predict baseline neurocognitive performance above and beyond previously studied variables, such as race (Kontos, Elbin, Covassin, & Larson, 2010), gender, previous concussion history, years playing primary sport, neurodevelopmental and psychological history. Understanding the relative importance of these factors on baseline neurocognitive performance may aid in the interpretation of deviant, or lower than expected, scores as well as identifying athletes at risk for poor outcomes after brain trauma. In a separate analysis, we aimed to assess the contribution of multivariate predictors on baseline neurocognitive functioning in collegiate football players. We hypothesized that SES would be a better predictor of baseline neurocognitive performance than years exposed to football, which is an indirect measure of the cumulative exposure to repetitive brain trauma, and previous concussion history. Understanding the contribution of SES on baseline neurocognitive functioning in collegiate football players may aid in the interpretation of neurocognitive scores. Additionally, understanding the contribution of SES may be a first step in identifying a risk factor for student-athletes that may be at an increased susceptibility to acute and long-term cognitive deficits associated with repetitive brain trauma.
METHODS Data were obtained from the National Collegiate Athletic Association (NCAA) and Department of Defense
Downloaded from https://www.cambridge.org/core. Florida Atlantic University, on 04 Oct 2017 at 02:04:02, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1355617717000716
SES factors and neurocognitive performance Concussion Assessment, Research and Education (CARE) Consortium for secondary analysis by a participating institution. The CARE Consortium, initiated in 2014, collects concussion-related assessment data, including baseline and post-injury concussion test results for student-athletes at multiple NCAA institutions. Before the start of their respective seasons, student athletes at each participating CARE institution annually undergo a battery of assessments that serve as a baseline measure of performance. The battery includes, at a minimum, measurements of concussion-related symptoms, balance, and neurocognitive abilities. In addition to these assessments, all participating studentathletes complete a Clinical Reporting Form (CRF), which provides comprehensive background information on sport participation history, academic history, self- and familymedical history, parental education and occupation, and family income. A more detailed description of the CARE consortium methodology is described elsewhere (Broglio et al., 2017). For the purpose of this initial investigation, we limited analyses to University of Florida student-athlete data only. The University of Florida Institutional Review Board (IRB-01) approved the reception of the data from CARE and the analysis of the data.
Participants The dataset obtained from the CARE Consortium contained baseline assessments for 494 University of Florida studentathletes from the 2014–2015 and 2015–2016 seasons. After exclusion of student-athletes due to insufficient SES data (N = 64), incomplete sport participation variables (N = 6), incomplete race data (N = 14), and invalid ImPACT data (N = 7), 403 student-athletes were included in the final analyses. Student-athletes from 9 male and 12 female sports were included in the sample, as depicted in Table 1. Studentathletes’ year of athletic eligibility ranged from the first year to the fifth year, as depicted in Table 2.
Predictor Variables Demographic Self-reported age and gender for the 403 participants [mean age = 19.67, standard deviation (SD) = ±1.3 years; range, 18–23 years old; male, n = 202] were included in the analysis. Race, which is one commonly used proxy of SES, was included in the analysis due to the unreliable nature of selfreported SES. Race was self-reported by the student-athletes and defined as White/Caucasian, Black/African American, or Other. The “other” category included Asian, Indian-Alaskan, and student-athletes of multiple races, which, due to small numbers, were combined. Fourteen student-athletes were excluded due to incomplete race data. Race variables were dummy-coded before entry into the regression model. Ethnicity was defined as Hispanic/Latino, Not Hispanic/ Latino, or Unknown. Table 2 shows the sample distribution of student-athletes’ race and ethnicity.
3 Table 1. Sample Distribution of Student-Athletes’ Primary Sport Sport
Male N (%)
Female N (%)
Baseball Basketball Cross-country/track Diving Field event Football Golf Gymnastics Lacrosse Soccer Softball Swimming Tennis Volleyball Total
29 (7.0) 12 (2.9) 35 (8.4) 2 (0.5) 2 (0.5) 87 (20.9) 7 (1.7) — — — — 32 (7.7) 7 (1.7) — 202 (50.1)
— 10 (2.4) 32 (7.7) 7 (1.7) 4 (1.0) — 8 (1.9) 11 (2.6) 40 (9.6) 29 (7.0) 19 (4.6) 21 (5.0) 7 (1.7) 16 (3.8) 201 (49.9)
Neurodevelopmental History and Psychological Distress Self-reported neurodevelopmental disorders and psychological distress were collected as part of the baseline assessments. Self-reported history of diagnosed attention deficithyperactivity disorder (ADHD) and LD, as well as current depressive, anxiety, and somatic symptoms, were included in the regression models. Neurodevelopmental history was obtained from the medical history portion of the CRF. Data were not available on the stimulant usage of ADHD diagnosed participants. Psychological distress was assessed with the Brief Symptom Inventory-18 (BSI-18; Derogatis, 2000), an 18-item questionnaire that assesses general feelings of anxiety (mean = 0.72; SD = ±1.49), depression (mean = 0.69; SD = ±1.68), and somatization (mean = 1.09; SD = ±1.90) representative of the preceding 7 days. The BSI-18 includes six questions for each of the three domains assessed on a 5-point Likert scale, where “0” indicates that the symptom has not been present, “1” indicates that the symptom has been present “A Little Bit,” “2” indicates that the symptom has been “Moderately” present, “3” indicates that the symptom has been present “Quite A Bit,” and “4” indicates that the symptom has been “Extremely” present, for a possible score of 0–24 on each domain. Table 2 shows the sample distribution of student-athlete’s neurodevelopmental history.
Concussion History and Years Participating in Sport Self-reported concussion history, diagnosed and suspected, was obtained from the medical history portion of the CRF. Number of years participating in primary sport (mean = 11.1; SD = ±3.7 years) was defined as the number of years in which the athlete participated in an organized sports league. In the secondary analysis, number of years participating in football (mean = 10.2; SD = ± 4.0 years) was defined as the
Downloaded from https://www.cambridge.org/core. Florida Atlantic University, on 04 Oct 2017 at 02:04:02, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1355617717000716
4
Z. Houck et al.
Table 2. Sample Distribution of Student-Athlete’s Year of Athletic Eligibility, Race, Ethnicity, Neurodevelopmental Disorder History, and Concussion History Factor Eligibility year 1st year 2nd year 3rd year 4th year 5th year Redshirt Unknown Race White/Caucasian Black/African American Other Ethnicity Non-Hispanic Hispanic Unknown Neurodevelopmental history No ADHD/LD LD only ADHD only Combined LD/ADHD Concussion history 0 1 2 3+ Total
Overall sample N (%)
Football N (%)
All other sports N (%)
χ2
p-Value
— 147 (36.5) 94 (23.3) 83 (20.6) 49 (12.2) 15 (3.7) 9 (2.2) 6 (1.4) — 299 (74.2) 76 (18.9) 28 (6.9) — 315 (78.2) 40 (9.9) 48 (11.9) — 335 (83.1) 15 (3.7) 40 (9.9) 13 (3.2) — 294 (73.0) 77 (19.1) 22 (5.5) 10 (2.4) 403 (100)
— 34 (40.0) 19 (22.4) 10 (11.8) 9 (10.6) 9 (10.6) 4 (4.7) 0 (0) — 43 (50.6) 35 (41.2) 7 (8.2) — 67 (78.8) 7 (8.2) 11 (12.9) — 62 (72.9) 5 (5.9) 15 (17.6) 3 (3.5) — 49 (57.6) 28 (32.9) 4 (4.7) 4 (4.7) 85 (100)
— 113 (35.5) 75 (23.6) 73 (23.0) 40 (12.6) 6 (1.9) 5 (1.6) 6 (1.6) — 256 (80.5) 41 (12.9) 21 (6.6) — 248 (78.0) 33 (10.4) 37 (11.6) — 273 (77.0) 10 (3.1) 25 (7.9) 10 (3.1) — 245 (77.0) 49 (15.4) 18 (5.7) 6 (1.9) 318 (100)
22.90 — — — — — — — 36.80 — — — .412 — — — 9.20 — — — — 16.51 — — — — —
.002 — — — — — — —
