Preseason Neurocognitive Testing in Athletes with Academic Problems
Grant L. Iverson
University of British Columbia & British Columbia Mental Health & Addiction Services
Michael W. Collins
University of Pittsburgh Medical Center
Marie-Claude Roberge
British Columbia Mental Health & Addiction Services
Mark R. Lovell
University of Pittsburgh Medical Center
Author Notes: Drs. Mark Lovell and Michael Collins have a proprietary interest in ImPACT. Please address correspondence to
Grant Iverson, Ph.D., Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, B.C.,
Canada V6T 2A1. Phone: (604) 822-7588; Fax: (604) 822-7756; Email: giverson@interchange.ubc.ca.
Abstract
Objective: Baseline preseason neurocognitive testing is recommended for amateur and professional
athletes. Then, if an athlete sustains a concussion, it can be determined more precisely when he or she
returns to normal neurocognitive functioning. Baseline testing is particularly important if an athlete has a
developmental condition, such as ADHD or a learning disability, because these conditions might have an
adverse effect on cognitive functioning. However, the effect of learning problems and disabilities on test
batteries used in athletics is unknown. The purpose of this study was to examine the effect of academic
problems on preseason testing in amateur athletes.
Participants and Methods: Forty-one student athletes with academic problems (e.g., those who receive
special education services or have repeated a grade) were compared to 41 randomly selected controls on
ImPACT, a 20-minute computerized neurocognitive test battery. The two groups were compared on the
five composite scores using MANOVA followed by univariate ANOVAs.
Results: The multivariate effect was nonsignificant [Wilks’ Lambda =.88; F(5, 76) = 3.1, p < .075, eta
squared=.12]. The ANOVA results revealed significantly worse test scores for students with academic
problems on the Verbal Memory (p<.014, Cohen’s d=.55) and Processing Speed (p<.033, d=.48)
composites. The groups did not differ on the Visual Memory, Reaction Time, or Impulse Control
composites. The students with academic problems also reported significantly more subjective symptoms
on the Post-Concussion Scale (p<.006, Cohen’s d=.70).
Conclusions: Knowing the pre-injury symptom reporting and neurocognitive test performance of student
athletes with academic problems will facilitate the interpretation of post-injury evaluations and return-toplay decision making.
Introduction
Neurocognitive testing is widely cited and recommended as a component of a comprehensive concussion
management program in athletics (1-7). 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 (8-24). Baseline,
preseason neurocognitive testing has become increasingly feasible given the availability of brief
Presented at the annual conference of the International Neuropsychological Society, February, 2008, Hawaii, USA.
computerized batteries (11; 25-27). 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 (9; 19; 28-30). 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 is because normative data for
neurocognitive tests, as a rule, are not available for youth with these conditions. In fact, having a learning
disability or ADHD 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 postconcussion results from that test in an athlete with a pre-existing condition will be particularly difficult to
interpret.
In fact, there is a large literature indicating that youth with learning disabilities perform more poorly on
some neuropsychological tests (e.g., 31-35). The most common subtypes of learning disabilities, in the
literature, are reading (dyslexia), writing (dysgraphia), and mathematics (dyscalculia). Researchers have
reported that adults with reading disabilities can have slowed speed of processing on verbal and visual
tasks, slowed naming speed, poor temporal processing, and reduced working memory, listening
comprehension, and general knowledge (36-39). Some aspects of executive functioning have been
reported to be adversely affected, including impaired sequence learning (40) and reduced ability to inhibit
distracters and sequence events (41). Differences in short-term verbal memory, phonological awareness,
and backwards counting abilities have been reported in individuals with a familial pattern of dyslexia
(42).
A learning disability in mathematics, often referred to as developmental dyscalculia, involves problems
with memorization of numerical facts, ability to solve simple mathematical questions, and the learning of
simple arithmetic skills (43). Mathematics disabilities occur in 5 to 6% of the school-aged population
(44). Boys with ADHD and a comorbid learning disability in mathematics have pronounced cognitive
deficits on a number of neuropsychological tests (45). One theory from research on children with early
mathematical disabilities (i.e., identifiable as early as grade 1) suggests that there is a problem with the
central executive (46; 47). Baddeley (46; 47) theorized that children with mathematics disabilities have
problems with the “central executive.” The central executive, which oversees cognitive processing, is
comprised of two components that allow for information to be held and manipulated: the phonological
loop (verbal, auditory information) and the visual sketchpad (nonverbal, visuospatial information).
Researchers have reported that children with mathematics disabilities have problems with phonological
processing, working memory, reading, short-term memory, and vocabulary (48-50), which are believed to
be mediated by, and subsequently overload, the central executive.
Adults with learning disabilities in mathematics also show neurocognitive deficits in other domains,
although the research appears to be limited in this area. Greiffenstein and Baker (51) reported that adults
with arithmetic deficiency had problems with nonverbal reasoning and constructional problems.
Individuals with this difficulty also perform poorly on the Tower of London test, which measures visual
attention, working memory, and planning (52).
Therefore, it is quite clear that amateur and professional athletes with learning disabilities might perform
differently on neurocognitive testing than athletes without learning disabilities. This has not been
adequately studied, however. The purpose of this study was to determine if student athletes with selfreported learning problems perform more poorly on a commonly used computerized battery called
Immediate Postconcussion Assessment and Cognitive Testing (ImPACT). It was hypothesized that
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athletes with learning problems would perform more poorly on at least one of the five composite scores
derived from ImPACT.
Methods
Participants
All participants were young men who were participating in high school athletics. The vast majority were
football players (90%). Their average age was 15.3 years (SD = 1.5). Their average number of completed
years of education was 9.1 (SD = 1.2).
Learning problems were operationally-defined as (a) past or present special education services, or (b) past
grade failure. Forty-one student athletes with academic problems (e.g., those who receive special
education services or have repeated a grade) were compared to 41 randomly selected controls on
ImPACT, a 20-minute computerized neurocognitive test battery. The two groups were compared on the
five composite scores using MANOVA followed by univariate ANOVAs.
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. 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/no-go 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 (53; 54) and concurrent validity (55; 56) of the
cognitive composite scores and the Post-Concussion Scale (57-59), and the sensitivity of the battery to the
acute effects of concussion (21-24; 29; 54; 60-62), 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 not significant,
indicating that the covariance matrices did not differ across the dependent variables. However, a few of
the individual variables within groups had significant departures from normality as assessed by Shapiro
Wilk test for normality. No neurocognitive variables had unequal error variances between groups (i.e.,
Page 3 of 8
based on Levene’s Test). MANOVA and ANOVA tend to be quite robust to relatively minor violations of
underlying general linear model assumptions. Therefore, the results will be reported.
The multivariate effect was nonsignificant [Wilks’ Lambda =.88; F(5, 76) = 3.1, p < .075, eta
squared=.12]. The ANOVA results revealed significantly worse test scores for students with academic
problems on the Verbal Memory (p<.014, Cohen’s d=.55) and Processing Speed (p<.033, d=.48)
composites. The groups did not differ on the Visual Memory, Reaction Time, or Impulse Control
composites. The students with academic problems also reported significantly more subjective symptoms
on the Post-Concussion Scale (p<.006, Cohen’s d=.70).
Table 1. Descriptive statistics, univariate F tests, and effect sizes for the ImPACT composite scores.
Composite Score
Verbal Memory
Visual Memory
Processing Speed
Reaction Time
Impulse Control
Total Symptoms
Group
Mean
SD
p
.014
Eta
Squared
.073
Control
Learning Problems
Control
Learning Problems
Control
Learning Problems
Control
Learning Problems
Control
Learning Problems
Control
Learning Problems
87.7
82.9
77.8
73.1
35.6
32.3
.56
.57
10.5
13.3
3.1
8.5
8.5
8.9
12.2
14.5
7.0
6.9
.07
.07
8.2
10.2
3.8
11.4
Cohen’s d
.55
.116
.031
.35
.033
.056
.48
.695
.002
.09
.179
.022
.30
.006
--
.70
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).
Discussion
To our knowledge, this is the first published study examining computerized neurocognitive test
performance in student athletes with learning problems. Students with learning problems performed more
poorly on tests measuring verbal memory and processing speed. These findings are consistent with the
literature, in general, that suggest that children and adults with learning disabilities perform more poorly
on some neuropsychological tests.
This study has significant limitations. The primary limitation is the self-report method of identifying
young people with learning problems. Using self-report with no independent verification is not ideal.
Moreover, the operational definition of learning problems was (a) involvement in special education, or (b)
repeating a grade. It is not known whether these students met formal criteria for a learning disability, or
the type of learning disability they might have. Moreover, it is possible, although unlikely, that a small
number of subjects in the control group could have had a learning disability. Clearly, there could be
uncontrolled heterogeneity in these groups.
This study has important implications for the use of neurocognitive testing with athletes who have
learning problems or disabilites. There are very few published studies relating to preseason
neurocognitive testing in student athletes with learning disabilities, or in outcome from concussion in
athletes with this pre-existing condition. Athletes with pre-existing conditions, such as learning
disabilities and/or ADHD, might need to be assessed and managed somewhat differently. In a previous
Page 4 of 8
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 (9). Having baseline preseason
testing on athletes with 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.
Knowing the pre-injury symptom reporting and neurocognitive test performance of student athletes with
academic problems will facilitate the interpretation of post-injury evaluations and return-to-play decision
making.
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
learning disabilities, ADHD, or both, as well as whether these pre-existing conditions may be a risk factor
for recurrent concussive injury.
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