Preseason Neurocognitive Testing 1
Preseason Neurocognitive Testing in Athletes with ADHD
Grant L. Iverson
University of British Columbia
British Columbia Mental Health & Addiction Services
Michael W. Collins & Mark R. Lovell
University of Pittsburgh Medical Center
Acknowledgements: This study was presented at the National Association of School
Psychologists, Dallas, Texas, April 1, 2004. The authors thank Carrie Strangway for her
comments and assistance with an earlier draft of this manuscript, and Jennifer Bernardo for
assistance with manuscript preparation.
Competing Interests: Drs. Lovell and Collins have a proprietary interest in ImPACT.
Correspondences: Please address correspondence to Grant Iverson, Ph.D., Department of
Psychiatry, 2255 Wesbrook Mall, University of British Columbia, Vancouver, B.C. Canada, V6T
2A1. Phone: 822-7588; Fax: 822-7756; Email: giverson@interchange.ubc.ca.
Preseason Neurocognitive Testing 2
Abstract
Neurocognitive testing is widely cited and recommended as a component of a comprehensive
concussion management program in athletics. Baseline testing is especially important for athletes
who might have a developmental condition, such as a learning disability or attention-deficit
hyperactivity disorder (ADHD). This is because normative data for neurocognitive tests, as a
rule, are not available for youth with these conditions. The purpose of this study was to examine
the effects of ADHD on the baseline preseason test performance of student athletes on a
computerized neuropsychological screening battery (Immediate Postconcussion Assessment and
Cognitive Testing; ImPACT). Participants were 38 adolescent athletes with ADHD and 38
control subjects matched for age, education, gender, and history of concussion. Student athletes
with ADHD performed more poorly on visual memory (p < .006, Cohen’s d = .65, medium
effect size), processing speed (p < .004, d = .69), and right-left orientation and inhibition (p <
.003, d = .93, large effect size). These results support the recommendation of baseline testing for
athletes, especially those with ADHD.
Key Words: ADHD, Concussion, Neurocognitive Testing
Preseason Neurocognitive Testing 3
Introduction
Neurocognitive testing is widely cited and recommended as a component of a
comprehensive concussion management program in athletics (Aubry et al., 2002; Collins &
Hawn, 2002; Guskiewicz et al., 2004; McCrory et al., 2005; Putukian, 2006; Randolph, 2001).
This, of course, is because neurocognitive testing is sensitive to the acute effects of concussion
and can be used to serially monitor an athlete’s recovery (Barr & McCrea, 2001; Broglio,
Macciocchi, & Ferrara, 2007; Collins et al., 1999; Delaney, Lacroix, Gagne, & Antoniou, 2001;
Echemendia, Putukian, Mackin, Julian, & Shoss, 2001; Erlanger et al., 2003; Erlanger et al.,
2001; Guskiewicz, Ross, & Marshall, 2001; Iverson, Brooks, Collins, & Lovell, 2006;
Macciocchi, Barth, Alves, Rimel, & Jane, 1996; Makdissi et al., 2001; Matser, Kessels, Lezak, &
Troost, 2001; McClincy, Lovell, Pardini, Collins, & Spore, 2006; McCrea, Kelly, Randolph,
Cisler, & Berger, 2002; Schatz, Pardini, Lovell, Collins, & Podell, 2006; Van Kampen, Lovell,
Pardini, Collins, & Fu, 2006; Warden et al., 2001). Baseline, preseason neurocognitive testing
has become increasingly feasible given the availability of brief computerized batteries (Cernich,
Reeves, Sun, & Bleiberg, 2007; Collie, Makdissi, Maruff, Bennell, & McCrory, 2006; Covassin
et al., 2006; Erlanger et al., 2003). Regardless of whether computerized or traditional
neurocognitive tests are used, preseason testing is frequently recommended as a clinical
methodology in which the athlete serves as his or her own comparison if injured in the future
(Collins et al., 1999; Echemendia et al., 2001; Lovell, Collins, Iverson, Johnston, & Bradley,
2004; McCrea et al., 2003; Moser, Schatz, & Jordan, 2005). Following a concussion, an athlete
can be monitored to determine when he or she recovers to baseline levels of functioning.
Baseline testing is especially important for athletes who might have a developmental
condition, such as a learning disability or attention-deficit hyperactivity disorder (ADHD). This
Preseason Neurocognitive Testing 4
is because normative data for neurocognitive tests, as a rule, are not available for youth with
these conditions. In fact, having ADHD or a learning disability typically is an exclusion criterion
for being included in a normative sample. Thus, if ADHD or a learning disability has an adverse
effect on a particular neurocognitive test, then the post-concussion results from that test in an
athlete with a pre-existing condition will be particularly difficult to interpret.
The neuropsychological problems associated with ADHD in children and adolescents
have been well documented, and typically are characterized as core deficits in attention and
executive functioning (e.g., Barkley, 2000; Halperin et al., 1990; Konrad, Gauggel, Manz, &
Scholl, 2000; Kupietz, 1990; Loge, Staton, & Beatty, 1990; Seidel & Joschko, 1990; van der
Meere & Sergeant, 1988). Those youth with comorbid learning disabilities often have more
pronounced neuropsychological deficits (Korkman & Pesonen, 1994; Purvis & Tannock, 1997;
Seidman et al., 1995; Seidman, Biederman, Monuteaux, Doyle, & Faraone, 2001). The purpose
of this study was to determine if student athletes with self-reported ADHD perform more poorly
on a commonly used computerized battery called Immediate Postconcussion Assessment and
Cognitive Testing (ImPACT). It was hypothesized that athletes with ADHD would perform more
poorly on at least one of the five composite scores derived from ImPACT.
Method
Participants
Thirty-eight adolescent athletes with a self-reported diagnosis of ADHD were compared
to 38 carefully matched adolescent athletes with no self-reported ADHD, learning, or speechrelated problems. All participants were selected from the normative sample for ImPACT
(Immediate Post-Concussion Assessment and Cognitive Testing; N = 555). The students with
suspected ADHD were not diagnosed through structured interviewing or testing; a psychologist
Preseason Neurocognitive Testing 5
or psychiatrist did not evaluate them. This is a sample of convenience, derived from a normative
database. The two groups were matched precisely on age, education, gender, and number of
previous concussions (self-reported number of lifetime concussions is collected as part of the
health history section of ImPACT). All participants in this study were student athletes
undergoing preseason testing. The average age of the students was 15.5 years (Range = 13-19)
and their average number of completed years of education was 9.1 (all were in grades 8-12). The
majority of the participants were boys (92%). Approximately half of the subjects with ADHD
reported that they had repeated a grade in school, received special education services, and/or had
learning problems (58%). The specific breakdown for each of these was as follows: repeated a
grade = 21%, special education services = 24%; and learning problems = 29%. None of the
control subjects reported educational problems. Most of the student athletes were football players
(87% of each group). The number of previous concussions was not medically verified. These
were self-reported past concussions. The breakdown for the control group was as follows: none =
76.3%, 1 = 15.8%, 2 = 5.3%, and 3 or more = 2.6%. The breakdown of past concussions for the
ADHD group was none = 73.7%, 1 = 15.8%, 2 = 5.8%, and 3 or more = 5.2%. Based on the
literature, we did not anticipate a need to match on number of previous concussions because 1-2
past sport-related concussions are not believed to have a lingering effect (e.g., Iverson, Brooks,
Lovell, & Collins, 2006). However, because this information was collected, it was decided to use
it as a matching variable to have a methodologically-tighter study.
Measure
Version 2.0 of ImPACT is a brief (20-25 minutes) computer-administered
neuropsychological test battery that consists of six individual test modules that measure aspects
of neurocognitive functioning including attention, memory, reaction time, and processing speed.
Preseason Neurocognitive Testing 6
Each test module may contribute scores to multiple composite scores. Five composite scores
were used for this study. The Verbal Memory composite score represents the average percent
correct for a word recognition paradigm, a symbol number match task, and a letter memory task
with an accompanying interference task. These tests are conceptually similar to traditional verbal
learning (word list) tasks and the auditory consonant trigrams test (i.e., the Brown-Peterson
short-term memory paradigm), although the information is presented visually on the computer,
not auditorily by an examiner. The Visual Memory composite score is comprised of the average
percent correct scores for two tasks; a recognition memory task that requires the discrimination
of a series of abstract line drawings, and a memory task that requires the identification of a series
of illuminated X’s or O’s after an intervening task (mouse clicking a number sequence from 25
to 1). The first test taps immediate and delayed memory for visual designs and the second test
measures short-term spatial memory (with an interference task). The Reaction Time composite
score represents the average response time (in milliseconds) on a choice reaction time, a go/nogo task, and the previously mentioned symbol match task (which is similar to a traditional digit
symbol task). The Processing Speed composite represents the weighted average of three tasks
that are done as interference tasks for the memory paradigms. The Impulse Control composite
score represents the total number of errors of omission or commission on the go/no-go test and
the choice reaction time test.
In addition to the cognitive measures, ImPACT also contains a Post-Concussion Scale
that consists of 22 commonly reported symptoms (e.g., headache, dizziness, “fogginess”). The
dependent measure is the total score derived from this 22-item scale. The reliability (Iverson,
Lovell, & Collins, 2003; Iverson, Lovell, Collins, & Norwig, 2002) and concurrent validity
(Iverson, Franzen, Lovell, & Collins, 2004; Iverson, Lovell, & Collins, 2005) of the cognitive
Preseason Neurocognitive Testing 7
composite scores and the Post-Concussion Scale (Iverson & Gaetz, 2004; Janusz, Gioia, Gilstein,
& Iverson, 2004; Lovell et al., 2006), and the sensitivity of the battery to the acute effects of
concussion (Broglio et al., 2007; Collins et al., 2003; Iverson, Gaetz, Lovell, & Collins, 2002;
Iverson et al., 2003; Lovell et al., 2003; Lovell et al., 2004; McClincy et al., 2006; Schatz et al.,
2006; Van Kampen et al., 2006), have been examined in a number of studies.
Results
The two groups were compared on the five neuropsychological composite scores using
mulivariate analysis of variance (MANOVA) followed by univariate ANOVAs. Box’s M Test
was significant (p < .001), indicating that the covariance matrices differed across the dependent
variables, thus violating an assumption of MANOVA. Moreover, some of the individual
variables within groups had significant departures from normality as assessed by the
Kolmogorov Smirnoff procedure, and one variable had unequal error variances between groups
(based on Levene’s Test). MANOVA and ANOVA tend to be quite robust to these violations of
underlying general linear model assumptions. Therefore, the results will be reported. There was a
significant multivariate effect [Wilks’ Lambda = .71; F (5, 70) = 5.7, p < .001, eta squared =
.29]. The univariate ANOVA results revealed significantly worse neuropsychological test scores
for students with ADHD on the Visual Memory, Processing Speed, and Impulse Control
composites. The groups did not differ on the Verbal Memory or Reaction Time composites. The
students with ADHD reported more subjective symptoms on the Post-Concussion Scale. The
effect sizes for the significant differences were medium to large (See Table 1). Given the
violations of assumptions of the general linear model, nonparametric pairwise analyses for the
five neuropsychological composite scores and the total score for the Post-Concussion Scale were
Preseason Neurocognitive Testing 8
conducted using Mann Whitney U tests. The results of the nonparametric analyses were identical
to the parametric analyses.
_____________________
Insert Table 1 About Here
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Discussion
To our knowledge, this is the first published study examining ImPACT performance in
student athletes with ADHD. The results of this study are largely consistent with the
neuropsychological theories and empirical studies on ADHD in adolescents and adults. Athletes
with self-reported histories of ADHD performed more poorly on computerized tests of
concentration and memory, processing speed, inhibition, and in total subjective symptoms. The
results of this study can be compared to a meta-analysis of neuropsychological studies in
children with ADHD. Effect sizes on specific tests such as Digit Span (.52 to .76), Trails A (.26
to .54), Trails B (.46 to .72), Stroop Interference (.46 to .66), Digit Symbol Coding (.72 to .92),
and WCST Categories completed (.19 to .39) were mostly in the medium to large range (Frazier,
Demaree, & Youngstrom, 2004). The ImPACT tests rely heavily on attention, working memory,
and speed of processing. These core abilities partially underlie each composite score. The effect
sizes for ImPACT (i.e., .65 to .93) were comparable or larger than the average effect sizes
reported in the ADHD literature.
This study has a number of limitations. The primary limitation is the self-report method
of identifying young people with ADHD. Using a single diagnostic indicator such as a self-report
with no cross-validation of history or symptoms from multiple settings or informants is not ideal.
However, given the lack of a gold standard method for diagnosing ADHD many researchers
Preseason Neurocognitive Testing 9
have been forced to rely on an adolescent’s or adult’s self-report of ADHD symptoms in a
clinical interview or on an individual’s report of a prior diagnosis (e.g., Epstein, Conners,
Sitarenios, & Erhardt, 1998; Epstein, Johnson, Indira, & Conners, 2001; Murphy, 2002a, 2002b;
Nigg, Butler, Huang-Pollock, & Henderson, 2002). In the present study, this problem is
mitigated, to a certain degree, because this sample ranged in age from 13-19. There is a strong
likelihood that the present sample received a diagnosis of ADHD within the few years prior to
the study.
Because this was a sample of convenience, derived from a normative database, the
individuals with ADHD were not separated into DSM-IV-TR subtypes. This is a common
limitation in previous studies (e.g., Epstein et al., 2001; Kovner et al., 1998; Nigg et al., 2002;
Walker, Shores, Trollor, Lee, & Sachdev, 2000), and most research to date has not consistently
identified different cognitive profiles among the ADHD subtypes (e.g., Barkley, Grodzinsky, &
DuPaul, 1992; Carlson, Lahey, & Neeper, 1986; Epstein et al., 1998; Murphy, Barkley, & Bush,
2001; Trommer, Hoeppner, Lorber, & Armstrong, 1988). A final limitation relates to stimulant
medication. In many previous ADHD studies, participants were not taking or were taken off
stimulant medications prior to testing, or the studies implemented statistical procedures to
control for the possible cognitive effects of medication. In the present study, the effects of
medication could not be examined because medication information was not available in the
database. Despite these methodological limitations, relatively large differences were found
between groups. The obvious limitations were likely offset by the methodological strengths of
the study, especially the relatively narrow age band of subjects (all in grades 8 - 12) and the
precise matching on age, education, gender, and number of previous concussions.
Preseason Neurocognitive Testing 10
This study has important implications for the use of neurocognitive testing with athletes
who have ADHD. To our knowledge, there are no published studies relating to preseason
neurocognitive testing in student athletes with ADHD, or in outcome from concussion in athletes
with this pre-existing condition. Athletes with pre-existing conditions, such as ADHD and/or
learning disabilities, might need to be assessed and managed somewhat differently. In a previous
study, Collins and colleagues reported that college football players with diagnosed learning
disabilities were more likely to (a) sustain a concussion, and (b) have slow recovery (Collins et
al., 1999). Having baseline preseason testing on athletes with ADHD and/or learning disabilities
is important because there is no normative data for athletes with pre-existing conditions. Thus, it
is can be difficult to interpret their post-injury test performance. In the future, it might be helpful
to create neurocognitive normative data for athletes with pre-existing conditions. In addition,
future research should focus on post-injury recovery curves in athletes with ADHD, learning
disabilities, or both, as well as whether these pre-existing conditions may be a risk factor for
recurrent concussive injury.
Preseason Neurocognitive Testing 11
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Preseason Neurocognitive Testing 19
Table 1. Descriptive statistics, t-tests, and effect sizes for the ImPACT composite scores.
Composite Score
Group
Mean
Std.
p
Cohen’s d
.122
.37
.006
.65
.004
.69
.486
.16
.001
.93
.002
.73
Deviation
Verbal Memory
Visual Memory
Processing Speed
Reaction Time
Impulse Control
Total Symptoms
Control
86.1
9.0
ADHD
82.4
11.2
Control
79.2
12.4
ADHD
70.7
13.8
Control
37.5
7.2
ADHD
33.2
5.3
Control
.570
.069
ADHD
.583
.096
Control
6.1
6.6
ADHD
14.6
11.7
Control
5.0
8.9
ADHD
12.0
10.3
Note: By convention, effect sizes are interpreted as follows: .2 = small, .5 = medium, and .8 =
large. A modified Bonferonni procedure would set the experimentwise alpha at .008 (.05/6).