Abstract

Effects of attention deficit hyperactivity disorder (ADHD) and stimulant medications on concussion measures are unclear. The objectives of this study were to (i) examine consistency of performance in an unmedicated ADHD group and a control group on concussion measures, (ii) assess performance differences between the two groups, and (iii) assess the effect of stimulant medication on performance in the ADHD group. College-aged participants (22 ADHD and 22 matched controls) were administered a symptom checklist and a computerized neurocognitive test (CNS Vital Signs, CNSVS) 3 times (1 week apart): Sessions 1 and 2 were unmedicated for all participants; Session 3 was medicated for the ADHD group. The reliability of the measures (intraclass correlation coefficients, ICC2,1) was consistent for both groups. When unmedicated, the ADHD group performed worse than controls on psychomotor speed [F(1,40) = 15.19, p < 0.001], and worse than when medicated on reaction time [F(1,39) = 6.34, p = 0.02]. The ADHD group performed better and comparable with controls when medicated. Clinicians should take medication status into account when interpreting scores.

Introduction

Concussion is a serious injury affecting millions of athletes annually in the United States alone (Langlois, Rutland-Brown, & Wald, 2006). A concussion is “a complex pathophysiological process affecting the brain, induced by biomechanical forces” (McCrory et al., 2009). Concussion evaluation and diagnosis should use a multifaceted approach, taking into account symptoms, neurocognition, and balance (McCrory et al., 2013). In college athletes, symptoms and neurocognitive deficits typically persist longer than balance deficits following sport-related concussion (Nelson, Janecek, & McCrea, 2013). Current standards recommend testing athletes on measures commonly affected by concussion prior to athletic participation, in order to establish a baseline for comparison in the event that the athlete sustains a concussion (Guskiewicz et al., 2004; McCrory et al., 2009). One reason behind the use of baseline testing is to provide an individualized measure of an athlete's performance in the absence of injury, to control for “extraneous variables” that may influence performance, such as attention disorders (Guskiewicz et al., 2004).

One of the most common attention disorders is attention deficit hyperactivity disorder (ADHD), a behavioral syndrome primarily characterized by hyperactivity, impulsivity, and inattention (National Institute for Health and Clinical Excellence, 2008). Although ADHD is commonly diagnosed in children, it often persists into adulthood (Wolf, 2001), and it has been shown to adversely affect cognitive processes, such as executive functioning, verbal memory, visual memory, working memory, sustained attention, and reaction time (Brown, 2008; Gropper & Tannock, 2009; Solomon & Haase, 2008; Valera et al., 2007). About 10% of NCAA Division I athletes report a diagnosis of ADHD (Alosco, Fedor, & Gunstad, 2014). These athletes are more likely to sustain concussions (Alosco et al., 2014), but concussion assessment in these individuals is often complicated by several factors. For example, there is an overlap of ADHD and concussion symptoms (White, Harris, & Gibson, 2014), altered neurocognitive performance in athletes with ADHD (Brown, 2008; Gropper & Tannock, 2009; Solomon & Haase, 2008; Valera et al., 2007), and increased variability in neurocognitive test scores over time in this group (Tamm et al., 2012). Additionally, stimulant medications commonly used to treat ADHD (Huang, Wang, & Chen, 2012; Mikami et al., 2009; Pasini et al., 2013) and exercise or physical activity (Choi, Han, Kang, Jung, & Renshaw, 2015; Ziereis & Jansen, 2015) have been shown to improve cognition in individuals with ADHD. Therefore, in order for symptom scales and neurocognitive tests to be useful in evaluating concussions and clinical decision making, their psychometric properties must be well established in both healthy and pathological populations. Specifically, the assessments must be valid, reliable, and sensitive to the effects of concussive injuries (Broglio, Ferrara, Macciocchi, Baumgartner, & Elliott, 2007).

Reliability is especially important in the assessment of concussion, because symptom scales and neurocognitive tests are often readministered in a short time period following concussion. These serial assessments are conducted to aid in return to school and work decisions (McCrory et al., 2013) and ultimately return to play decisions (Johnson, Kegel, & Collins, 2011). When these assessments are repeatedly administered in such a short time period, such as 1 week, their reliability has performed only moderately well (Broglio, Zhu, Sopiarz, & Park, 2009; Johnson et al., 2011). Therefore, clinicians should use reliable change indices (RCIs) to account for practice effects and identify meaningful score changes due to pathology (e.g., concussion) (Lovell & Solomon, 2013; Schatz & Sandel, 2013). RCIs can account for expected change over time, by providing an estimate of how much and in what direction an individual's test score has changed, and whether the change is clinically significant. The reliability of concussion symptom checklists (Lovell et al., 2006; McLeod & Leach, 2012; Sady, Vaughan, & Gioia, 2014) and neurocognitive assessments (Broglio et al., 2007; Bruce, Echemendia, Meeuwisse, Comper, & Sisco, 2014; Elbin, Schatz, & Covassin, 2011; Nakayama, Covassin, Schatz, Nogle, & Kovan, 2014; Register-Mihalik et al., 2012) has been established in healthy populations, but these properties have not been examined in individuals with ADHD. Response time variability has been demonstrated in individuals with ADHD (Hervey et al., 2006; Johnson et al., 2007; Vaurio, Simmonds, & Mostofsky, 2009) and is likely due to disengagement or distraction (Tamm et al., 2012). This intraindividual response time variability could lead to changes in scores over time, resulting in altered reliability of assessments in individuals with ADHD. This could complicate interpretation of scores on concussion assessment tools, if it is not taken into account.

Another factor that could affect changes in symptoms and neurocognitive performance over time in college students with ADHD is the use of stimulant medications, which are the most commonly used pharmacological treatment for ADHD (Chang et al., 2014). The theory behind stimulant medications is increased arousal and alertness of the central nervous system through stimulation of norepinephrine and dopamine (Rowe, Robinson, & Gordon, 2005). Stimulants also suppress the locus coeruleus, which reduces stimulation of the thalamic reticular nucleus, ultimately improving cortical arousal (Rowe et al., 2005). One of the main effects of stimulant medications is reduced impulsivity (Advokat, 2010). Adults with ADHD have experienced increased working memory abilities (Kinsbourne, De Quiros, & Tocci Rufo, 2001), improved motor speed and processing speed, and decreases in distractibility (Riordan et al., 1999) after taking stimulant medications. It is likely that individuals with ADHD perform better on clinical measures of concussion while on stimulant medications than off medication, but no previous studies have investigated this theory. This is important from a clinical standpoint, because a patient's medication status could change from one administration of the test to the next. If stimulant medications influence scores, clinicians must take medication status into account when interpreting scores. Furthermore, it is especially important to examine the effects of stimulant medications on concussion assessments tools in individuals who are physically active, because exercise has also been shown to improve cognition in individuals with ADHD (Choi et al., 2015; Ziereis & Jansen, 2015), and may be useful as a treatment alone or in combination with a stimulant medication (Pontifex, Saliba, Raine, Picchietti, & Hillman, 2013; Robinson & Bucci, 2014).

Although clinical outcome measures provide clinicians with valuable information to use during the evaluation and management of concussion and offer quantitative values for use in making return to play decisions, the effects of ADHD on the reliability of, and performance on, these assessments is unclear. In addition, the effects of stimulant medications on concussion assessments tools are unclear. Therefore, the objectives of this study were to (i) examine differences in reliability between an unmedicated ADHD group and a control group on a concussion symptom scale reporting, and performance on a computerized neurocognitive concussion tool, CNS Vital Signs, across two testing sessions separated by 1 week, (ii) assess differences between the control group and an unmedicated ADHD group on concussion symptom reporting and performance on CNS Vital Signs, and (iii) assess the effect of stimulant medications on concussion symptom reporting and performance on CNS Vital Signs in the ADHD group. We hypothesized that (i) the ADHD group would have smaller intraclass correlation coefficients (ICCs) than the control group, (ii) the ADHD group would report more cognitive symptoms and demonstrate decreased performance on complex attention, cognitive flexibility, executive functioning, reaction time, psychomotor speed, and processing speed compared with the control group, and (iii) stimulant medication would decrease cognitive symptoms and improve performance on complex attention, cognitive flexibility, executive functioning, reaction time, psychomotor speed, and processing speed in the ADHD group.

Methods

Participants

Participants consisted of a convenience sample that was recruited via a campus-wide email, or was approached in person in a class, or in the campus student health center. Forty-four physically active, healthy college students participated in this study. Physically active college students were selected for this study, because exercise has been shown to affect cognition (Leckie et al., 2014; Voss et al., 2011; Wu et al., 2011), especially in individuals with ADHD (Choi et al., 2015; Ziereis & Jansen, 2015). Twenty-two participants were in the ADHD group, and 22 participants were in the matched control group. Participants were matched on age (21.29 ± 2.05 years), previous number of concussions (0.64 ± 0.70 concussions), and sex (11 men and 11 women in each group). All participants consistently completed at least 30 min of cardiovascular and/or resistive training at least four times per week for the 5 months preceding study enrollment. Individuals reporting a history of three or more previous concussions, use of medication to treat any other psychiatric conditions, or self-report diagnosis of any other psychiatric conditions, neurological conditions, or learning disabilities were excluded from the study.

Participants were only included in the ADHD group if they met the following inclusion criteria: (i) declared they had been diagnosed with ADHD at least 6 months prior to enrollment in the study, (ii) completed an ADHD rating scale adapted from part of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) to confirm they met the ADHD criteria, and (iii) provided proof of a prescription for an immediate release (short acting) stimulant medication and stated that they had been taking the same medication for at least 6 months prior to enrollment in the study. The ADHD rating scale identifies three separate subtypes of ADHD based on responses to a series of questions regarding inattentiveness and hyperactivity. It has been used to aid in diagnosing ADHD. In order to participate, each participant in the ADHD group had to meet the criteria of one of the three subtypes identified by the ADHD rating scale (10 participants were identified as Predominantly Inattentive, 7 as Predominantly Hyperactive/Impulsive, and 5 as Combined Type). All participants in the ADHD group took either methylphenidate (e.g., Ritalin or Concerta) or dextromethylphenidate (e.g., Focalin), with doses between 5 and 20 mg, which have a half-life of ∼1–3 h (Connor, 2005).

Materials

The concussion symptom scale used in this study is a unique concussion symptom scale, which our clinical research center has found to be the most sensitive. It includes 18 concussion symptoms, which are rated on a 7-point Likert scale, including headache, nausea, vomiting, dizziness, poor balance, sensitivity to noise, ringing in the ear, sensitivity to light, blurred vision, difficulty concentrating, feeling mentally foggy, difficulty remembering, trouble falling asleep, drowsiness, fatigue, sadness, irritability, and neck pain. Participants were asked to score themselves on the 18 concussion symptoms based on how they felt at the time of evaluation. The scale includes 0 (none), 1 and 2 (mild), 3 and 4 (moderate), and 5 and 6 (severe). We stratified the symptoms into three categories, using Piland et al.'s (Piland, Motl, Guskiewicz, McCrea, & Ferrara, 2006) and Patel et al.'s (Patel, Mihalik, Notebaert, Guskiewicz, & Prentice, 2007) structural validity frameworks as a reference: somatic (headache, nausea, vomiting, dizziness, poor balance, sensitivity to noise, ringing in the ears, sensitivity to light, blurred vision, and neck pain), cognitive (difficulty concentrating, feeling mentally foggy, and difficulty remembering), and neurobehavioral (sadness, irritability, trouble falling asleep, drowsiness, and fatigue). For each symptom cluster, a symptom score was derived by adding the severity scores (0–6) for each of the symptoms endorsed within the cluster.

The CNS Vital Signs (CNS Vital Signs, LLC, Chapel Hill, NC) battery consists of eight subtests, which are computerized versions of well-established traditional tests of Verbal Memory and Visual Memory, Finger Tapping, Symbol Digit Coding, Stroop Test, a Shifting Attention Test, a Continuous Performance Test, and a Non-Verbal Reasoning Test. CNS Vital Signs assesses many cognitive domains sensitive to most causes of mild cognitive dysfunction including traumatic brain injury (Gualtieri & Johnson, 2006). Portions of the test battery are available commercially as a computerized concussion assessment test, Concussion Vital Signs. CNS Vital Signs uses scores from the various subtests to create scores for the following nine clinical domains: Verbal Memory, Visual Memory, Processing Speed, Executive Function, Psychomotor Speed, Reaction Time, Complex Attention, Cognitive Flexibility, and Reasoning. Detailed explanations of the clinical domains have been described elsewhere (Gualtieri & Johnson, 2006), and brief descriptions are included in Table 1. Standard scores are calculated for each composite score, by placing all outcomes on the same scale, based on a normative dataset that matches participants by sex and age. Standard scores of >109 indicate high functioning, whereas scores from 91 to 109 represent normal function and normal capacity, scores from 70 to 90 indicate a likely deficit or impairment, and scores <70 are considered very low.

Table 1.

Description of subtests and calculation of domain scores on CNS vital signs

CNS Vital Signs subtest Description of subtest Subtest is included in the following domains 
Verbal Memory Fifteen words are presented, one by one, on the screen; a new word is presented every 2 s. The participant is asked to remember these words. Then, a list of 30 words is presented. The 15 target words are mixed randomly among 15 new/distractor words. When the participant recognizes a word from the original list, he or she presses the space bar. After this trial of 30 words, the participant goes on to do the next six tests. At the end of the battery, ∼20 min later, the 15 target words appear again, mixed with 15 new nontarget words Verbal Memory 
Visual Memory This test is identical to Verbal Memory only instead of using words, this test uses geometric figures Visual memory 
Finger Tapping Test (FTT) Participants are asked to press the space bar with their right index finger as many times as they can in 10 s. They do this once for practice, and then there are three test trials. The test is repeated with the left hand. The score is the average number of taps, right and left Psychomotor Speed 
Symbol Digit Coding (SDC) Symbol Digit Coding is based on the Symbol Digit Modalities Test, itself a variant of the Wechsler digit symbol substitution test. There is a grid on the participant's screen, which has a series of symbols, with empty boxes underneath of them. There is a key at the top of the screen, with each of the symbols and the corresponding numbers to go along with them. Patients are asked to fill in the empty boxes underneath the symbols, in the order in which they appear, with the corresponding number for the symbol (found in the key). Patients are given 90 s to correctly fill in as many numbers as possible Psychomotor Speed, Processing Speed 
Stroop Test The test has three parts. In the first part, the words RED, YELLOW, BLUE, and GREEN (printed in black ink) appear at random on the screen. The participant presses the space bar as soon as he or she sees the word. In the second part, the same words appear on the screen, printed in color. The participant is asked to press the space bar when the color of the word matches what the word says (i.e., RED in red ink). In the third part, the participant is asked to press the space bar when the color of the word does not match what the word says (i.e., RED in blue ink) Reaction Time, Complex Attention, Cognitive Flexibility 
Shifting Attention Test (SAT) Participants are instructed to match geometric objects either by shape or by color. Three figures appear on the screen, one on top and two on the bottom. The top figure is either a square or a circle. The bottom figures are a square and a circle. The figures are either red or blue; the colors are mixed randomly. The participant is asked to match one of the bottom figures to the top figure, based on either shape or color. The rules change at random. The goal is to make as many correct matches as one can in 90 s Complex Attention, Cognitive Flexibility, Executive Function 
Continuous Performance Test (CPT) The participant is asked to respond to target stimulus “B” but not to any other letter. In 5 min, the test presents 200 letters. Forty of the stimuli are targets (the letter “B”) and 160 are nontargets (other letters). The stimuli are presented at random, with the target stimulus is “blocked” so it appears eight times during each minute of the test Complex Attention 
Non-verbal Reasoning Test (NVRT) The Reasoning test includes 15 presentations with a 14-s response time. The test runs continuously for ∼5 min. It consists of a series of puzzles, or visual analogies, similar to those in Raven's Progressive Matrices. The puzzles are progressively more difficult. The participant identifies the correct response from a field of possible answers by selecting a number to match the answer Reasoning 
CNS Vital Signs subtest Description of subtest Subtest is included in the following domains 
Verbal Memory Fifteen words are presented, one by one, on the screen; a new word is presented every 2 s. The participant is asked to remember these words. Then, a list of 30 words is presented. The 15 target words are mixed randomly among 15 new/distractor words. When the participant recognizes a word from the original list, he or she presses the space bar. After this trial of 30 words, the participant goes on to do the next six tests. At the end of the battery, ∼20 min later, the 15 target words appear again, mixed with 15 new nontarget words Verbal Memory 
Visual Memory This test is identical to Verbal Memory only instead of using words, this test uses geometric figures Visual memory 
Finger Tapping Test (FTT) Participants are asked to press the space bar with their right index finger as many times as they can in 10 s. They do this once for practice, and then there are three test trials. The test is repeated with the left hand. The score is the average number of taps, right and left Psychomotor Speed 
Symbol Digit Coding (SDC) Symbol Digit Coding is based on the Symbol Digit Modalities Test, itself a variant of the Wechsler digit symbol substitution test. There is a grid on the participant's screen, which has a series of symbols, with empty boxes underneath of them. There is a key at the top of the screen, with each of the symbols and the corresponding numbers to go along with them. Patients are asked to fill in the empty boxes underneath the symbols, in the order in which they appear, with the corresponding number for the symbol (found in the key). Patients are given 90 s to correctly fill in as many numbers as possible Psychomotor Speed, Processing Speed 
Stroop Test The test has three parts. In the first part, the words RED, YELLOW, BLUE, and GREEN (printed in black ink) appear at random on the screen. The participant presses the space bar as soon as he or she sees the word. In the second part, the same words appear on the screen, printed in color. The participant is asked to press the space bar when the color of the word matches what the word says (i.e., RED in red ink). In the third part, the participant is asked to press the space bar when the color of the word does not match what the word says (i.e., RED in blue ink) Reaction Time, Complex Attention, Cognitive Flexibility 
Shifting Attention Test (SAT) Participants are instructed to match geometric objects either by shape or by color. Three figures appear on the screen, one on top and two on the bottom. The top figure is either a square or a circle. The bottom figures are a square and a circle. The figures are either red or blue; the colors are mixed randomly. The participant is asked to match one of the bottom figures to the top figure, based on either shape or color. The rules change at random. The goal is to make as many correct matches as one can in 90 s Complex Attention, Cognitive Flexibility, Executive Function 
Continuous Performance Test (CPT) The participant is asked to respond to target stimulus “B” but not to any other letter. In 5 min, the test presents 200 letters. Forty of the stimuli are targets (the letter “B”) and 160 are nontargets (other letters). The stimuli are presented at random, with the target stimulus is “blocked” so it appears eight times during each minute of the test Complex Attention 
Non-verbal Reasoning Test (NVRT) The Reasoning test includes 15 presentations with a 14-s response time. The test runs continuously for ∼5 min. It consists of a series of puzzles, or visual analogies, similar to those in Raven's Progressive Matrices. The puzzles are progressively more difficult. The participant identifies the correct response from a field of possible answers by selecting a number to match the answer Reasoning 

Procedures

Following the university's institutional review board approval, participants reported to our clinical research center. Participants completed a questionnaire to ensure that all inclusion and exclusion criteria were met. All participants were administered the symptom checklist followed by CNS Vital Signs on three separate occasions, 7–9 days apart (ADHD group: 7.12 ± 0.33 days between sessions; control group: 7.24 ± 0.56 days between sessions). Using a short test–retest interval (7–9 days) is helpful in identifying when scores stabilize over time and may also help to identify if a second test administration is needed to stabilize scores on specific measures. It also provides a framework for the reliability of the tests in a time frame that is often used in postinjury evaluations. Each participant was instructed to sustain their attention throughout the entire test to read all instructions and to respond quickly and accurately to each item. All participants were administered the test in a quiet controlled setting. The test took ∼30 min for each participant to complete.

All three testing sessions for the control group were conducted in the absence of any medication that could influence mental function. In the ADHD group, the first two testing sessions were conducted in the absence of any medication and for the third session, only participants were taking their prescribed stimulant medication. For Sessions 1 and 2, participants in the ADHD group were asked to refrain from taking a dose of their stimulant medication within 24 h of the scheduled testing session to ensure their system had depleted the dose prior to testing. For Session 3, participants in the ADHD group were asked to take a dose of their stimulant medication 1–3 hr prior to the testing session. All testing sessions were held at approximately the same time of day (within 2 hr of prior testing sessions). Participants reporting in the morning did so prior to their first class. Participants reporting in the afternoon or evening did so at least 2 hr after the conclusion of their last class and had no more than 3 hr of class that day. Participants were instructed to keep their sleeping habits, nutritional intake, and physical activity consistent throughout the study. Information about hours slept, nutrition, and physical activity was collected at both testing sessions as a means of assessing the internal validity of the study design (i.e., compliance with instructions), and not analyzed to address our study purpose.

Statistical Analyses

All data were analyzed using SPSS Version 19.0 (SPSS Inc., Chicago, IL). Symptom scores for each symptom cluster and standard scores for each CNS Vital Signs neurocognitive domain were used for analyses. Any neurocognitive scores or symptom scores that were 2.5 SDs above or below their group's mean for each specific dependent variable at each time point were excluded from analyses. An a priori α level was set at 0.05.

Reliability

Reliability measures were only computed between Session 1 and Session 2, because medication status changed in the ADHD group between Session 2 and Session 3, which could influence changes in scores. The strength of the linear relationship between variables was determined through Pearson product–moment correlations (r) between Session 1 and Session 2. Additionally, ICC2,1 and standard error of measurements (SEM) were calculated to determine the consistency of the participants' performance in Session 1 and Session 2 for each of the three symptom clusters and each of the nine neurocognitive domain outcome measures of interest. We used Fleiss’ (1986) recommendations for interpreting ICC2,1 values: >0.75 = excellent, 0.40–0.75 = fair to good, and <0.40 = poor. RCIs (Chelune, Naugle, Luders, & Awad, 1991; Hinton-Bayre, 2000; Hinton-Bayre, Geffen, Geffen, McFarland, & Friis, 1999; Iverson, Lovell, & Collins, 2003) were calculated to determine whether changes between repeated assessments were meaningful. We calculated 80% RCIs, which are the most clinically conservative, because they expect the least amount of change. This means that patients must score closer to their baseline scores to not be considered pathological.

The formulas we used for calculating SEM and RCI are included subsequently: 

SEM1=SD11r12
SEM1 is calculated by multiplying the standard deviation from Time 1 by the square root of 1 minus the test–retest coefficient. 
SEM2=SD21r12
SEM2 is calculated by multiplying the standard deviation from Time 2 by the square root of 1 minus the test–retest coefficient. 
Sdiff=SEM12+SEM22
Sdiff is calculated by summing the squared SEMs for each testing occasion. 
80%RCI=Sdiff×1.282
RCIs are calculated by multiplying the Sdiff by the z score associated with the desired confidence level.

Effect of ADHD and stimulant medications

We conducted a series of 2 (group) × 2 (Sessions 2 and 3) mixed model repeated measures analyses of covariance (ANCOVAs) to determine whether the unmedicated ADHD group performed differently than the control group on symptoms or performance on CNS Vital Signs and to assess the effect of stimulant medication on performance in the ADHD group. Sex was included as a covariate, because it has been shown to affect some components of neurocognitive performance (Iverson, Brooks, & Ashton Rennison, 2014), especially in an ADHD population (Fisher et al., 2014). Tukey post hoc analyses were employed when the omnibus tests for interaction effects were significant. Each difference was compared using the Tukey critical value (dcrit). Dependent variables included total symptom scores (sum of responses for each symptom within the symptom cluster) for somatic symptoms, neurobehavioral symptoms, and cognitive symptoms and the following CNS Vital Signs neurocognitive domain standard scores: Verbal Memory, Visual Memory, Processing Speed, Executive Function, Psychomotor Speed, Reaction Time, Complex Attention, Cognitive Flexibility, and Reasoning. The standard scores are based on a normative dataset that matches participants by age and places all outcomes on the same scale, allowing for easier clinical interpretation (e.g., a higher score is always better). Effect size measures (Cohen's d) were also determined to indicate the standardized difference between the means, and Cohen's guidelines (Cohen, 1977) for interpreting Cohen's ds (small effect, d = 0.2; medium effect, d = 0.5; large effect, d = 0.8) were followed.

Results

Results of the ICCs, SEMs, Pearson product–moment correlations (r values), and RCIs are presented in Table 2. The ICCs for the control group on the symptom checklist ranged from 0.05 (neurobehavioral symptoms) to 0.38 (somatic symptoms), and the ICCs for the ADHD group ranged from 0.53 (cognitive symptoms) to 0.71 (somatic symptoms). For CNS Vital Signs, the ICCs for the control group ranged from 0.18 (Verbal Memory) to 0.84 (Psychomotor Speed), and the ICCs for the ADHD group ranged from 0.44 (Complex Attention and Cognitive Flexibility) to 0.74 (Processing Speed).

Table 2.

Reliability measures (ICC, SEM, r, and RCIs) for each symptom cluster and CNS Vital Signs Domain by group

Outcome variable Group n Session 1—mean (95% CI) Session 2—mean (95% CI) ICC SEM1 SEM2 r 80% RCI 
Symptoms 
 Somatic Control 20 0.55 (0.16, 0.94) 0.65 (0.12, 1.18) 0.38 0.65 0.89 .38 1.41 
ADHD 19 1.74 (0.69, 2.79) 1.37 (0.49, 2.25) 0.71 1.15 0.97 .72 1.93 
 Neurobehavioral Control 21 2.19 (1.27, 3.11) 1.48 (0.73, 2.22) 0.05 1.97 1.59 .05 3.25 
ADHD 19 3.37 (1.50, 5.24) 2.79 (1.39, 4.19) 0.53 2.63 1.96 .54 4.20 
 Cognitive Control 21 0.71 (0.26, 1.17) 1.86 (0.33, 3.39) 0.20 0.79 2.64 .39 3.53 
ADHD 21 5.95 (4.23, 7.68) 4.43 (2.76, 6.09) 0.54 2.47 2.38 .58 4.39 
CNS Vital Signs 
 Verbal Memory Control 21 102.95 (97.96, 107.95) 101.29 (93.35, 109.22) 0.18 9.90 15.72 .19 23.81 
ADHD 22 99.55 (92.52, 106.58) 94.95 (87.74, 102.17) 0.46 11.63 11.93 .46 21.36 
 Visual Memory Control 21 105.67 (99.63, 111.70) 105.95 (98.97, 112.94) 0.57 8.73 10.11 .57 17.13 
ADHD 21 105.57 (101.05, 110.09) 100.05 (93.96, 106.14) 0.47 6.77 9.11 .54 14.55 
 Psychomotor Speed Control 22 105.64 (98.01, 113.26) 108.18 (101.43, 114.93) 0.84 6.66 5.90 .85 11.40 
ADHD 21 100.62 (96.36, 104.88) 99.48 (95.98, 102.97) 0.62 5.73 4.70 .63 9.50 
 Reaction Time Control 21 95.81 (88.57, 103.05) 102.95 (96.94, 108.97) 0.42 11.64 9.67 .46 19.41 
ADHD 20 95.50 (91.21, 108.70) 98.05 (92.61, 103.49) 0.49 9.21 8.31 .49 15.90 
 Complex Attention Control 21 99.95 (91.21, 108.7) 104.48 (96.74, 112.21) 0.77 8.85 7.83 .79 15.14 
ADHD 20 88.05 (79.45, 96.65) 99.75 (93.57, 105.93) 0.44 12.04 8.66 .57 19.02 
 Cognitive Flexibility Control 21 100.33 (95.62, 105.05) 108.05 (102.60, 113.49) 0.54 5.98 6.90 .67 11.71 
ADHD 21 90.71 (84.32, 97.11) 99.52 (94.52, 104.52) 0.44 9.37 7.32 .56 15.24 
 Processing Speed Control 22 106.86 (101.81, 111.92) 109.64 (102.24, 117.04) 0.69 5.77 8.44 .74 13.11 
ADHD 22 101.09 (94.90, 107.28) 101.68 (96.62, 106.75) 0.74 7.02 5.74 .75 11.63 
 Executive Functioning Control 21 101.10 (96.63, 105.56) 109.48 (104.51, 114.45) 0.49 5.84 6.50 .65 11.21 
ADHD 21 93.57 (87.78, 99.36) 101.24 (96.41, 106.06) 0.45 8.61 7.17 .54 14.37 
 Reasoning Control 22 98.27 (92.56, 103.99) 103.82 (97.57, 110.07) 0.59 7.87 8.61 .63 14.96 
ADHD 21 104.81 (99.63, 109.99) 103.76 (96.66, 110.86) 0.53 7.66 10.50 .55 16.66 
Outcome variable Group n Session 1—mean (95% CI) Session 2—mean (95% CI) ICC SEM1 SEM2 r 80% RCI 
Symptoms 
 Somatic Control 20 0.55 (0.16, 0.94) 0.65 (0.12, 1.18) 0.38 0.65 0.89 .38 1.41 
ADHD 19 1.74 (0.69, 2.79) 1.37 (0.49, 2.25) 0.71 1.15 0.97 .72 1.93 
 Neurobehavioral Control 21 2.19 (1.27, 3.11) 1.48 (0.73, 2.22) 0.05 1.97 1.59 .05 3.25 
ADHD 19 3.37 (1.50, 5.24) 2.79 (1.39, 4.19) 0.53 2.63 1.96 .54 4.20 
 Cognitive Control 21 0.71 (0.26, 1.17) 1.86 (0.33, 3.39) 0.20 0.79 2.64 .39 3.53 
ADHD 21 5.95 (4.23, 7.68) 4.43 (2.76, 6.09) 0.54 2.47 2.38 .58 4.39 
CNS Vital Signs 
 Verbal Memory Control 21 102.95 (97.96, 107.95) 101.29 (93.35, 109.22) 0.18 9.90 15.72 .19 23.81 
ADHD 22 99.55 (92.52, 106.58) 94.95 (87.74, 102.17) 0.46 11.63 11.93 .46 21.36 
 Visual Memory Control 21 105.67 (99.63, 111.70) 105.95 (98.97, 112.94) 0.57 8.73 10.11 .57 17.13 
ADHD 21 105.57 (101.05, 110.09) 100.05 (93.96, 106.14) 0.47 6.77 9.11 .54 14.55 
 Psychomotor Speed Control 22 105.64 (98.01, 113.26) 108.18 (101.43, 114.93) 0.84 6.66 5.90 .85 11.40 
ADHD 21 100.62 (96.36, 104.88) 99.48 (95.98, 102.97) 0.62 5.73 4.70 .63 9.50 
 Reaction Time Control 21 95.81 (88.57, 103.05) 102.95 (96.94, 108.97) 0.42 11.64 9.67 .46 19.41 
ADHD 20 95.50 (91.21, 108.70) 98.05 (92.61, 103.49) 0.49 9.21 8.31 .49 15.90 
 Complex Attention Control 21 99.95 (91.21, 108.7) 104.48 (96.74, 112.21) 0.77 8.85 7.83 .79 15.14 
ADHD 20 88.05 (79.45, 96.65) 99.75 (93.57, 105.93) 0.44 12.04 8.66 .57 19.02 
 Cognitive Flexibility Control 21 100.33 (95.62, 105.05) 108.05 (102.60, 113.49) 0.54 5.98 6.90 .67 11.71 
ADHD 21 90.71 (84.32, 97.11) 99.52 (94.52, 104.52) 0.44 9.37 7.32 .56 15.24 
 Processing Speed Control 22 106.86 (101.81, 111.92) 109.64 (102.24, 117.04) 0.69 5.77 8.44 .74 13.11 
ADHD 22 101.09 (94.90, 107.28) 101.68 (96.62, 106.75) 0.74 7.02 5.74 .75 11.63 
 Executive Functioning Control 21 101.10 (96.63, 105.56) 109.48 (104.51, 114.45) 0.49 5.84 6.50 .65 11.21 
ADHD 21 93.57 (87.78, 99.36) 101.24 (96.41, 106.06) 0.45 8.61 7.17 .54 14.37 
 Reasoning Control 22 98.27 (92.56, 103.99) 103.82 (97.57, 110.07) 0.59 7.87 8.61 .63 14.96 
ADHD 21 104.81 (99.63, 109.99) 103.76 (96.66, 110.86) 0.53 7.66 10.50 .55 16.66 

Notes: ADHD = attention deficit hyperactivity disorder; ICC = intraclass correlation coefficient; SEM = standard error of measurements; RCI = reliable change index; CI = confidence interval.

The results of the ANCOVAs can be found in Table 3. We observed a main effect of time for all symptoms clusters, with participants reporting lower symptom scores at Session 3 compared with Session 2. The control group reported lower somatic symptom scores than the ADHD group. We did not observe any significant interaction effects for any other symptom clusters (p > .05). We observed a significant interaction effect for the Psychomotor Speed and Reaction Time domain standard scores on CNS Vital Signs. The control group performed better than the unmedicated ADHD group at Session 2 (off medication) on Psychomotor Speed (dcrit = 5.53). Additionally, the ADHD group performed better at Session 3, when they were on their stimulant medication, than at Session 2, when they were unmedicated on Psychomotor Speed and Reaction Time (dcrit = 6.34). There were no differences between the groups on any measures at Session 3.

Table 3.

Effect of ADHD and stimulant medications on symptoms and CNS Vital Signs (2 × 2 ANCOVA with sex as covariate)

Symptom cluster/neurocognitive domain Group Session 2—ADHD group off medication, mean (95% CI) Session 3—ADHD group on medication, mean (95% CI) Time × group interaction
effect
F(df)
p-value 
Time main effect
F(df)
p-value 
Group main effect
F(df)
p-value 
Symptoms 
 Somatic Control 0.50 (0.03, 0.97) 0.35 (0.08, 0.62) F(1,36) = 0.39
p = .54 
F(1,36) = 5.28
p = .03* 
F(1,36) = 5.72
p = .02* 
ADHD 1.32 (0.50, 2.14) 0.89 (0.27, 1.51) 
 Neurobehavioral Control 1.50 (0.72, 2.28) 1.30 (0.57, 2.03) F(1,38) = 1.41
p = .24 
F(1,38) = 5.70
p = .02* 
F(1,38) = 3.25
p = .08 
ADHD 3.00 (1.70, 4.30) 2.14 (0.98, 3.31) 
 Cognitive Control 1.90 (0.38, 3.43) 1.10 (0.11, 2.08) F(1,39) = 2.91
p = .10 
F(1,39) = 6.28
p = .02* 
F(1,39) = 4.01
p = .05 
ADHD 4.29 (2.70, 5.87) 1.86 (0.75, 2.97) 
CNS Vital Signs 
 Verbal Memory Control 103.05 (95.75, 110.34) 102.62 (95.31, 109.93) F(1,40) < 0.01
p = .98 
F(1,30) = 0.30
p = .59 
F(1,40) = 4.44
p = .04* 
ADHD 94.95 (87.74, 102.17) 94.05 (87.16, 100.93) 
 Visual Memory Control 105.77 (99.12, 112.42) 100.45 (92.69, 108.22) F(1,40) = 1.78
p = .19 
F(1,40) = 0.09
p = .77 
F(1,40) = 0.55
p = .47 
ADHD 100.05 (93.96, 106.14) 101.90 (95.92, 107.89) 
 Psychomotor Speed Control 108.18 (101.43, 114.93) 106.64 (99.33, 113.94) F(1,40) = 15.19
p < .001* 
F(1,40) = 4.63
p = .04* 
F(1,40) = 0.52
p = .48 
ADHD 99.48 (95.98, 102.97) 109.14 (104.46, 113.83) 
 Reaction Time Control 102.95 (96.94, 108.97) 101.86 (97.25, 106.47) F(1,39) = 6.34
p = .02* 
F(1,39) = 1.38
p = .25 
F(1,39) = 0.08
p = .78 
ADHD 97.52 (92.25, 102.80) 105.00 (98.65, 111.35) 
 Complex Attention Control 104.05 (96.64, 111.46) 103.50 (97.26, 109.74) F(1,39) = 1.33
p = .26 
F(1,39) = 0.04
p = .84 
F(1,39) = 0.04
p = .84 
ADHD 101.25 (96.14, 106.36) 104.70 (100.70, 108.70) 
 Cognitive Flexibility Control 108.05 (102.60, 113.49) 108.62 (104.16, 113.08) F(1,40) = 0.69
p = .41 
F(1,40) = 0.01
p = .92 
F(1,40) = 4.93
p = .03* 
ADHD 98.36 (93.03, 103.69) 103.27 (95.33, 111.22) 
 Processing Speed Control 109.64 (102.24, 117.04) 113.18 (105.98, 120.38) F(1,41) = 1.16
p = .29 
F(1,41) = 9.53
p = .004* 
F(1,41) = 2.63
p = .11 
ADHD 101.68 (96.62, 106.75) 108.95 (102.69, 115.22) 
 Executive Functioning Control 109.48 (104.51, 114.45) 109.86 (105.85, 113.87) F(1,38) = 3.31
p = .08 
F(1,38) = 0.37
p = .54 
F(1,38) = 2.86
p = .11 
ADHD 101.70 (96.71, 106.69) 108.85 (105.85, 113.87) 
 Reasoning Control 103.82 (97.57, 110.07) 104.45 (97.63, 111.28) F(1,40) = 0.03
p = .86 
F(1,40) < 0.01
p = .99 
F(1,40) = 0.13
p = .72 
ADHD 103.76 (96.66, 110.86) 105.71 (99.39, 112.04) 
Symptom cluster/neurocognitive domain Group Session 2—ADHD group off medication, mean (95% CI) Session 3—ADHD group on medication, mean (95% CI) Time × group interaction
effect
F(df)
p-value 
Time main effect
F(df)
p-value 
Group main effect
F(df)
p-value 
Symptoms 
 Somatic Control 0.50 (0.03, 0.97) 0.35 (0.08, 0.62) F(1,36) = 0.39
p = .54 
F(1,36) = 5.28
p = .03* 
F(1,36) = 5.72
p = .02* 
ADHD 1.32 (0.50, 2.14) 0.89 (0.27, 1.51) 
 Neurobehavioral Control 1.50 (0.72, 2.28) 1.30 (0.57, 2.03) F(1,38) = 1.41
p = .24 
F(1,38) = 5.70
p = .02* 
F(1,38) = 3.25
p = .08 
ADHD 3.00 (1.70, 4.30) 2.14 (0.98, 3.31) 
 Cognitive Control 1.90 (0.38, 3.43) 1.10 (0.11, 2.08) F(1,39) = 2.91
p = .10 
F(1,39) = 6.28
p = .02* 
F(1,39) = 4.01
p = .05 
ADHD 4.29 (2.70, 5.87) 1.86 (0.75, 2.97) 
CNS Vital Signs 
 Verbal Memory Control 103.05 (95.75, 110.34) 102.62 (95.31, 109.93) F(1,40) < 0.01
p = .98 
F(1,30) = 0.30
p = .59 
F(1,40) = 4.44
p = .04* 
ADHD 94.95 (87.74, 102.17) 94.05 (87.16, 100.93) 
 Visual Memory Control 105.77 (99.12, 112.42) 100.45 (92.69, 108.22) F(1,40) = 1.78
p = .19 
F(1,40) = 0.09
p = .77 
F(1,40) = 0.55
p = .47 
ADHD 100.05 (93.96, 106.14) 101.90 (95.92, 107.89) 
 Psychomotor Speed Control 108.18 (101.43, 114.93) 106.64 (99.33, 113.94) F(1,40) = 15.19
p < .001* 
F(1,40) = 4.63
p = .04* 
F(1,40) = 0.52
p = .48 
ADHD 99.48 (95.98, 102.97) 109.14 (104.46, 113.83) 
 Reaction Time Control 102.95 (96.94, 108.97) 101.86 (97.25, 106.47) F(1,39) = 6.34
p = .02* 
F(1,39) = 1.38
p = .25 
F(1,39) = 0.08
p = .78 
ADHD 97.52 (92.25, 102.80) 105.00 (98.65, 111.35) 
 Complex Attention Control 104.05 (96.64, 111.46) 103.50 (97.26, 109.74) F(1,39) = 1.33
p = .26 
F(1,39) = 0.04
p = .84 
F(1,39) = 0.04
p = .84 
ADHD 101.25 (96.14, 106.36) 104.70 (100.70, 108.70) 
 Cognitive Flexibility Control 108.05 (102.60, 113.49) 108.62 (104.16, 113.08) F(1,40) = 0.69
p = .41 
F(1,40) = 0.01
p = .92 
F(1,40) = 4.93
p = .03* 
ADHD 98.36 (93.03, 103.69) 103.27 (95.33, 111.22) 
 Processing Speed Control 109.64 (102.24, 117.04) 113.18 (105.98, 120.38) F(1,41) = 1.16
p = .29 
F(1,41) = 9.53
p = .004* 
F(1,41) = 2.63
p = .11 
ADHD 101.68 (96.62, 106.75) 108.95 (102.69, 115.22) 
 Executive Functioning Control 109.48 (104.51, 114.45) 109.86 (105.85, 113.87) F(1,38) = 3.31
p = .08 
F(1,38) = 0.37
p = .54 
F(1,38) = 2.86
p = .11 
ADHD 101.70 (96.71, 106.69) 108.85 (105.85, 113.87) 
 Reasoning Control 103.82 (97.57, 110.07) 104.45 (97.63, 111.28) F(1,40) = 0.03
p = .86 
F(1,40) < 0.01
p = .99 
F(1,40) = 0.13
p = .72 
ADHD 103.76 (96.66, 110.86) 105.71 (99.39, 112.04) 

Notes: ADHD = attention deficit hyperactivity disorder; CI = confidence interval.

*Statistically significant difference at the 0.05 alpha level.

The majority of Cohen's d values for reliability were small for both groups, indicating that the effect of time on scores was small; Cohen's d was medium for neurobehavioral symptoms in the control group (d = 0.52) and cognitive symptoms in the ADHD group (d = 0.2). Cohen's d values for the effect of ADHD (ADHD Session 2—off medication, compared with Control Session 2) were very small or small for Visual Memory, Reaction Time, Complex Attention, and Reasoning; medium for all three symptom clusters, Verbal Memory, Psychomotor Speed, Processing Speed, and Executive Functioning; and large for Cognitive Flexibility. The Cohen's d values for the effect of stimulant medication (ADHD Session 3—on medication, compared with ADHD Session 2—off medication) were very small or small for the somatic and neurobehavioral symptom clusters, Verbal Memory, Visual Memory, Complex Attention, Cognitive Flexibility, and Reasoning; medium for Reaction Time, Processing Speed, and Executive Functioning, and large for cognitive symptoms and Psychomotor Speed. Effect sizes are presented in Table 4.

Table 4.

Effect sizes for reliability, ADHD effect, and medication effect

Dependent variable Control reliability: Control Session 1 versus Control Session 2 ADHD reliability: ADHD Session 1 versus ADHD Session 2 ADHD effect: ADHD Session 2 versus Control Session 2 Medication effect: ADHD Session 2 versus ADHD Session 3 
Somatic Symptoms 0.08 0.18 0.59 0.29 
Neurobehavioral Symptoms 0.52 0.05 0.64 0.32 
Cognitive Symptoms 0.05 0.47 0.70 0.81 
Verbal Memory 0.06 0.07 0.50 0.06 
Visual Memory 0.01 0.10 0.40 0.14 
Psychomotor Speed 0.04 0.01 0.72 1.06 
Reaction Time 0.11 0.05 0.44 0.58 
Complex Attention 0.07 0.20 0.20 0.35 
Cognitive Flexibility 0.09 0.14 0.81 0.32 
Processing Speed 0.01 0.02 0.56 0.57 
Executive Function 0.10 0.12 0.72 0.67 
Reasoning 0.07 0.04 <0.01 0.13 
Dependent variable Control reliability: Control Session 1 versus Control Session 2 ADHD reliability: ADHD Session 1 versus ADHD Session 2 ADHD effect: ADHD Session 2 versus Control Session 2 Medication effect: ADHD Session 2 versus ADHD Session 3 
Somatic Symptoms 0.08 0.18 0.59 0.29 
Neurobehavioral Symptoms 0.52 0.05 0.64 0.32 
Cognitive Symptoms 0.05 0.47 0.70 0.81 
Verbal Memory 0.06 0.07 0.50 0.06 
Visual Memory 0.01 0.10 0.40 0.14 
Psychomotor Speed 0.04 0.01 0.72 1.06 
Reaction Time 0.11 0.05 0.44 0.58 
Complex Attention 0.07 0.20 0.20 0.35 
Cognitive Flexibility 0.09 0.14 0.81 0.32 
Processing Speed 0.01 0.02 0.56 0.57 
Executive Function 0.10 0.12 0.72 0.67 
Reasoning 0.07 0.04 <0.01 0.13 

Discussion

Overall, the reliability of performance on the neurocognitive domains was very similar between the two groups. Neurobehavioral and cognitive symptom reporting was less reliable in the control group than in the ADHD group (ICCs: neurobehavioral—0.05 vs. 0.53, cognitive—0.20 vs. 0.54). One potential reason the ADHD group was more reliable in their symptom reporting is because they are likely asked about how they feel and asked to rate themselves on similar symptoms on a more regular basis than the control group.

The ICCs for neurocognitive domains were comparable between both groups. We hypothesized that the ADHD group would have lower ICCs than the control group, because of findings of intraindividual variability in reaction times on computerized tasks (Tamm et al., 2012). The lack of differences in reliability could have been due to the speed at which stimuli appeared on the screen during CNS Vital Signs. Response time variability often disappears when the stimuli are presented quickly (Tamm et al., 2012). Therefore, it seems as though the reliability on CNS Vital Signs is comparable in healthy young adults and young adults with ADHD. From a clinical standpoint, the 80% RCIs were also very similar between the two groups. The largest difference between the two groups for the 80% RCI was 3.88 on Complex Attention (control group: 80% RCI = 15.14, ADHD: 80% RCI = 19.02). This suggests that general population RCIs can be carefully used to determine changes in scores on CNS Vital Signs in athletes with ADHD, as long as medication status stays consistent.

The unmedicated ADHD group performed worse than the control group on Psychomotor Speed. This suggests that baseline testing is especially important in individuals with ADHD, especially those who are unmedicated, because they may not perform comparable with normative data on measures of psychomotor speed. However, the ADHD group improved on Psychomotor Speed and Reaction Time when on their medication. In fact, when the ADHD group was on their medication, there were no differences between the two groups on any of the measures. This suggests that individuals with ADHD who take immediate release stimulant medications may perform comparable with normative values on computerized concussion assessment tools. We hypothesized that there would also be differences in performance between groups on complex attention, cognitive flexibility, executive functioning, reaction time, and processing speed, based on previous findings that these cognitive domains are affected by ADHD (Brown, 2008; Gropper & Tannock, 2009; Solomon & Haase, 2008; Valera et al., 2007). One reason we may not have observed these differences is because the individuals in our group were all physically active, which has been shown to improve cognition in individuals with ADHD (Choi et al., 2015; Ziereis & Jansen, 2015).

Some previous studies have failed to control for activity levels of the participants (Ruiz et al., 2010). However, previous studies examining athletes have conflicting findings. Elbin et al. (2013) demonstrated that college athletes and younger athletes with ADHD perform worse than controls on visual memory, visual motor speed, and reaction time, whereas other studies demonstrated that younger athletes with ADHD performed significantly higher (or better) than controls on reaction time and impulse control (Zuckerman, Lee, Odom, Solomon, & Sills, 2013). National Football League athletes with a history of learning disabilities or ADHD performed worse than controls on verbal memory and visual memory, but not on processing speed or reaction time (Solomon & Haase, 2008). Some possibilities for discrepancies in findings are lack of control for medication status (Nielsen & Wiig, 2011) and comorbidities often associated with ADHD (Elbin et al., 2013). However, our findings are consistent with previous studies in which medication status was taken into account. When unmedicated, children and adults with ADHD who had not taken their medication were slower on portions of the Stroop interference task (Schwartz & Verhaeghen, 2008), efficiency, and simple processing-speed measures compared with peers, but performed comparable with peers when on their medication (Nielsen & Wiig, 2011).

We acknowledge there are some limitations of our study. By design, our study was very specific, only examining physically active college-aged individuals, and individuals in the ADHD group were all prescribed an immediate release stimulant medication for the treatment of ADHD. Although these parameters strengthened the internal validity of our study, they limit the generalizability of our findings. Individuals taking nonstimulant medications could respond differently than our sample of ADHD participants. Both the type and dose of medication could influence the effects of medication. Also, the time since ADHD diagnosis, amount of time taking current prescribed stimulant medication (Hautmann, Rothenberger, & Dopfner, 2013), and ADHD subtype (Nikolas & Nigg, 2013) could all influence the performance on concussion assessment tools. We attempted to control for these variables by ensuring that all ADHD participants had been previously diagnosed with ADHD and had taken their current stimulant medication for at least 6 months prior to their first testing session. Although we administered the ADHD rating scale to the ADHD group, we did not administer it to the control group, and we did not control for socioeconomic status. We also used a unique symptom scale; however, it is an adapted version of several developed symptom scales (Guskiewicz et al., 2004; Patel et al., 2007; Piland et al., 2006). Our study had a relatively small sample size and could have potentially benefitted from a larger sample size, as we observed low effect sizes for some of the dependent variables (Table 4). This may have limited our ability to detect statistically significant group by session interaction effects for a few variables.

There are several other factors that could affect the performance of individuals with ADHD on concussion assessment tools. Therefore, future research should examine the influence of type of medication and dose of medication on the effects of concussion assessment tools. It has recently been demonstrated that higher doses of medication produce greater improvements than lower doses for certain tasks, such as vigilance and working memory, but not for other tasks, such as cognitive flexibility or executive control (Pietrzak, Mollica, Maruff, & Snyder, 2006). Future research should examine differences between two groups of individuals with ADHD, one with a higher dose of medication and the other group with a lower dose of medication. It is possible that the different groups would improve on different areas of the tests when they are on their medication. In addition, future studies could examine the effect of ADHD subtype on scores of concussion assessment tools.

Conclusions

Our findings are consistent with current literature demonstrating that stimulant medications used to treat ADHD have been shown to positively affect some aspects of cognitive function (Agay, Yechiam, Carmel, & Levkovitz, 2010; Brams, Moon, Pucci, & Lopez, 2010; Cornforth, Sonuga-Barke, & Coghill, 2010). Athletes with ADHD performed similar to controls only when under the influence of stimulant medication, suggesting that it may be especially important to obtain a baseline measure of neurocognitive performance in individuals with ADHD. Sports medicine professionals should be mindful that athletes with ADHD who do not have baseline neurocognitive scores and are evaluated postinjury without taking a prescribed medication should be cautiously compared with normative data. Additionally, because stimulant medications have been shown to have an effect on select measures of concussion, clinicians should record athlete's medication statuses prior to any neurocognitive assessment. Ideally, clinicians should remind their patients to take all medications as prescribed prior to reporting for any type of baseline or postinjury assessments.

Conflict of Interest

None declared.

Acknowledgements

CNS Vital Signs provides its test platform to the investigators; none of the investigators have any financial interest in the product.

References

Advokat
C.
(
2010
).
What are the cognitive effects of stimulant medications? Emphasis on adults with attention-deficit/hyperactivity disorder (ADHD)
.
Neuroscience Biobehavioral Review
 ,
34
,
1256
1266
.
Agay
N.
,
Yechiam
E.
,
Carmel
Z.
,
Levkovitz
Y.
(
2010
).
Non-specific effects of methylphenidate (Ritalin) on cognitive ability and decision-making of ADHD and healthy adults
.
Psychopharmacology (Berl)
 ,
210
,
511
519
.
Alosco
M. L.
,
Fedor
A. F.
,
Gunstad
J.
(
2014
).
Attention deficit hyperactivity disorder as a risk factor for concussions in NCAA division-I athletes
.
Brain Injury
 ,
28
,
472
474
.
Brams
M.
,
Moon
E.
,
Pucci
M.
,
Lopez
F. A.
(
2010
).
Duration of effect of oral long-acting stimulant medications for ADHD throughout the day
.
Current Medical Research & Opinion
 ,
26
,
1809
1825
.
Broglio
S. P.
,
Ferrara
M. S.
,
Macciocchi
S. N.
,
Baumgartner
T. A.
,
Elliott
R.
(
2007
).
Test-retest reliability of computerized concussion assessment programs
.
Journal of Athletic Training
 ,
42
,
509
514
.
Broglio
S. P.
,
Zhu
W.
,
Sopiarz
K.
,
Park
Y.
(
2009
).
Generalizability theory analysis of balance error scoring system reliability in healthy young adults
.
Journal of Athletic Training
 ,
44
,
497
502
.
Brown
T. E.
(
2008
).
ADD/ADHD and impaired executive function in clinical practice
.
Current Psychiatry Reports
 ,
10
(5)
,
407
411
.
Bruce
J.
,
Echemendia
R.
,
Meeuwisse
W.
,
Comper
P.
,
Sisco
A.
(
2014
).
1 year test-retest reliability of ImPACT in professional ice hockey players
.
Clinical Neuropsychology
 ,
28
,
14
25
.
Chang
Z.
,
Lichtenstein
P.
,
Halldner
L.
,
D'Onofrio
B.
,
Serlachius
E.
,
Fazel
S.
et al
. (
2014
).
Stimulant ADHD medication and risk for substance abuse
.
Journal of Child Psychology and Psychiatry
 ,
55
,
878
885
.
Chelune
G. J.
,
Naugle
R. I.
,
Luders
H.
,
Awad
I. A.
(
1991
).
Prediction of cognitive change as a function of preoperative ability status among temporal lobectomy patients seen at 6-month follow-up
.
Neurology
 ,
41
,
399
404
.
Choi
J. W.
,
Han
D. H.
,
Kang
K. D.
,
Jung
H. Y.
,
Renshaw
P. F.
(
2015
).
Aerobic exercise and attention deficit hyperactivity disorder: Brain research
.
Medical & Science in Sports and Exercise
 ,
47
,
33
39
.
Cohen
J.
(
1977
).
Statistical power analysis for the behavioral sciences
 .
New York: Academic
.
Connor
D. F.
(
2005
).
Psychostimulants in Attention Deficit Hyperactivity Disorder: Theoretical and Practical Issues for the Community Practitioner
.
Attention Deficit Hyperactivity Disorder
 , pp.
487
527
.
Humana Press
.
Cornforth
C.
,
Sonuga-Barke
E.
,
Coghill
D.
(
2010
).
Stimulant drug effects on attention deficit/hyperactivity disorder: A review of the effects of age and sex of patients
.
Current Pharmaceutical Design
 ,
16
,
2424
2433
.
Elbin
R. J.
,
Kontos
A. P.
,
Kegel
N.
,
Johnson
E.
,
Burkhart
S.
,
Schatz
P.
(
2013
).
Individual and combined effects of LD and ADHD on computerized neurocognitive concussion test performance: Evidence for separate norms
.
Archives of Clinical Neuropsychology
 ,
28
,
476
484
.
Elbin
R. J.
,
Schatz
P.
,
Covassin
T.
(
2011
).
One-year test-retest reliability of the online version of ImPACT in high school athletes
.
American Journal of Sports Medicine
 ,
39
,
2319
2324
.
Fisher
B. C.
,
Garges
D. M.
,
Yoon
S. Y.
,
Maguire
K.
,
Zipay
D.
,
Gambino
M.
(
2014
).
Sex differences and the interaction of age and sleep issues in neuropsychological testing performance across the lifespan in an ADD/ADHD sample from the years 1989 to 20091
.
Psychological Reports
 ,
114
,
404
438
.
Fleiss
J. L.
(
1986
).
The design and analysis of clinical experiments
 .
New York
:
John Wiley & Sons
.
Gropper
R. J.
,
Tannock
R.
(
2009
).
A pilot study of working memory and academic achievement in college students with ADHD
.
Journal of Attention Disorders
 ,
12
6
,
574
581
.
Gualtieri
C. T.
,
Johnson
L. G.
(
2006
).
Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs
.
Archives of Clinical Neuropsychology
 ,
21
,
623
643
.
Guskiewicz
K. M.
,
Bruce
S. L.
,
Cantu
R. C.
,
Ferrara
M. S.
,
Kelly
J. P.
,
McCrea
M.
et al
. (
2004
).
National Athletic Trainers’ Association position statement: Management of sport-related concussion
.
Journal of Athletic Training
 ,
39
,
280
297
.
Hautmann
C.
,
Rothenberger
A.
,
Dopfner
M.
(
2013
).
Daily symptom profiles of children with ADHD treated with modified-release methylphenidate: An observational study
.
Journal of Attention Disorders
 . .
Hervey
A. S.
,
Epstein
J. N.
,
Curry
J. F.
,
Tonev
S.
,
Eugene Arnold
L.
,
Keith Conners
C.
et al
. (
2006
).
Reaction time distribution analysis of neuropsychological performance in an ADHD sample
.
Child Neuropsychology
 ,
12
,
125
140
.
Hinton-Bayre
A.
(
2000
).
Reliable change formula query
.
Journal of the International Neuropsychological Society
 ,
6
,
362
365
.
Hinton-Bayre
A. D.
,
Geffen
G. M.
,
Geffen
L. B.
,
McFarland
K. A.
,
Friis
P.
(
1999
).
Concussion in contact sports: Reliable change indices of impairment and recovery
.
Journal of Clinical and Experimental Neuropsychology
 ,
21
,
70
86
.
Huang
Y. S.
,
Wang
L. J.
,
Chen
C. K.
(
2012
).
Long-term neurocognitive effects of methylphenidate in patients with attention deficit hyperactivity disorder, even at drug-free status
.
BMC Psychiatry
 ,
12
,
194
.
Iverson
G. L.
,
Brooks
B. L.
,
Ashton Rennison
V. L.
(
2014
).
Minimal gender differences on the CNS vital signs computerized neurocognitive battery
.
Applied Neuropsychology Adult
 ,
21
,
36
42
.
Iverson
G. L.
,
Lovell
M. R.
,
Collins
M. W.
(
2003
).
Interpreting change on ImPACT following sport concussion
.
Clinical Neuropsychology
 ,
17
,
460
467
.
Johnson
E. W.
,
Kegel
N. E.
,
Collins
M. W.
(
2011
).
Neuropsychological assessment of sport-related concussion
.
Clinical Sports Medicine
 ,
30
,
73
88
,
viii–ix.
Johnson
K. A.
,
Kelly
S. P.
,
Bellgrove
M. A.
,
Barry
E.
,
Cox
M.
,
Gill
M.
et al
. (
2007
).
Response variability in attention deficit hyperactivity disorder: Evidence for neuropsychological heterogeneity
.
Neuropsychologia
 ,
45
,
630
638
.
Kinsbourne
M.
,
De Quiros
G. B.
,
Tocci Rufo
D.
(
2001
).
Adult ADHD. Controlled medication assessment
.
Annals of the New York Academy of Sciences
 ,
931
,
287
296
.
Langlois
J. A.
,
Rutland-Brown
W.
,
Wald
M. M.
(
2006
).
The epidemiology and impact of traumatic brain injury: A brief overview
.
Journal of Head Trauma Rehabilitation
 ,
21
,
375
378
.
Leckie
R. L.
,
Oberlin
L. E.
,
Voss
M. W.
,
Prakash
R. S.
,
Szabo-Reed
A.
,
Chaddock-Heyman
L.
et al
. (
2014
).
BDNF mediates improvements in executive function following a 1-year exercise intervention
.
Frontiers in Human Neuroscience
 ,
8
,
985
.
Lovell
M. R.
,
Iverson
G. L.
,
Collins
M. W.
,
Podell
K.
,
Johnston
K. M.
,
Pardini
D.
et al
. (
2006
).
Measurement of symptoms following sports-related concussion: Reliability and normative data for the post-concussion scale
.
Applied Neuropsychology
 ,
13
,
166
174
.
Lovell
M. R.
,
Solomon
G. S.
(
2013
).
Neurocognitive test performance and symptom reporting in cheerleaders with concussions
.
Journal of Pediatrics
 ,
163
,
1192
1195. e1
.
McCrory
P.
,
Meeuwisse
W.
,
Aubry
M.
,
Cantu
B.
,
Dvorak
J.
,
Echemendia
R.
et al
. (
2013
).
Consensus statement on Concussion in Sport—The 4th International Conference on Concussion in Sport held in Zurich, November 2012
.
Physical Therapy in Sport
 ,
14
,
e1
e13
.
McCrory
P.
,
Meeuwisse
W.
,
Johnston
K.
,
Dvorak
J.
,
Aubry
M.
,
Molloy
M.
et al
. (
2009
).
Consensus statement on concussion in sport: The 3rd International Conference on Concussion in Sport held in Zurich, November 2008
.
Journal of Athletic Training
 ,
44
,
434
448
.
McLeod
T. C.
,
Leach
C.
(
2012
).
Psychometric properties of self-report concussion scales and checklists
.
Journal of Athletic Training
 ,
47
,
221
223
.
Mikami
A. Y.
,
Cox
D. J.
,
Davis
M. T.
,
Wilson
H. K.
,
Merkel
R. L.
,
Burket
R.
(
2009
).
Sex differences in effectiveness of extended-release stimulant medication among adolescents with attention-deficit/hyperactivity disorder
.
Journal of Clinical Psychology in Medical Settings
 ,
16
,
233
242
.
Nakayama
Y.
,
Covassin
T.
,
Schatz
P.
,
Nogle
S.
,
Kovan
J.
(
2014
).
Examination of the Test-Retest Reliability of a Computerized Neurocognitive Test Battery
.
American Journal of Sports Medicine
 ,
42
,
2000
2005
.
National Institute for Health and Clinical Excellence
. (
2008
).
Attention deficit hyperactivity disorder: Diagnosis and management of ADHD in children, young people and adults
 .
NICE clinical guideline 72. Retrieved June 10, 2015, from www.nice.org.uk/CG72 [NICE guideline]
.
Nelson
L. D.
,
Janecek
J. K.
,
McCrea
M. A.
(
2013
).
Acute clinical recovery from sport-related concussion
.
Neuropsychology Review
 ,
23
,
285
299
.
Nielsen
N. P.
,
Wiig
E. H.
(
2011
).
AQT cognitive speed and processing efficiency differentiate adults with and without ADHD: A preliminary study
.
International Journal of Psychiatry in Clinical Practice
 ,
15
,
219
227
.
Nikolas
M. A.
,
Nigg
J. T.
(
2013
).
Neuropsychological performance and attention-deficit hyperactivity disorder subtypes and symptom dimensions
.
Neuropsychology
 ,
27
,
107
120
.
Pasini
A.
,
Sinibaldi
L.
,
Paloscia
C.
,
Douzgou
S.
,
Pitzianti
M. B.
,
Romeo
E.
et al
. (
2013
).
Neurocognitive effects of methylphenidate on ADHD children with different DAT genotypes: A longitudinal open label trial
.
European Journal of Paediatric Neurology
 ,
17
,
407
414
.
Patel
A. V.
,
Mihalik
J. P.
,
Notebaert
A. J.
,
Guskiewicz
K. M.
,
Prentice
W. E.
(
2007
).
Neuropsychological performance, postural stability, and symptoms after dehydration
.
Journal of Athletic Training
 ,
42
,
66
75
.
Pietrzak
R. H.
,
Mollica
C. M.
,
Maruff
P.
,
Snyder
P. J.
(
2006
).
Cognitive effects of immediate-release methylphenidate in children with attention-deficit/hyperactivity disorder
.
Neuroscience and Biobehavioral Reviews
 ,
30
,
1225
1245
.
Piland
S. G.
,
Motl
R. W.
,
Guskiewicz
K. M.
,
McCrea
M.
,
Ferrara
M. S.
(
2006
).
Structural validity of a self-report concussion-related symptom scale
.
Medicine & Science in Sports & Exercise
 ,
38
,
27
32
.
Pontifex
M. B.
,
Saliba
B. J.
,
Raine
L. B.
,
Picchietti
D. L.
,
Hillman
C. H.
(
2013
).
Exercise improves behavioral, neurocognitive, and scholastic performance in children with attention-deficit/hyperactivity disorder
.
Journal of Pediatrics
 ,
162
,
543
551
.
Register-Mihalik
J. K.
,
Kontos
D. L.
,
Guskiewicz
K. M.
,
Mihalik
J. P.
,
Conder
R.
,
Shields
E. W.
(
2012
).
Age-related differences and reliability on computerized and paper-and-pencil neurocognitive assessment batteries
.
Journal of Athletic Training
 ,
47
,
297
305
.
Riordan
H. J.
,
Flashman
L. A.
,
Saykin
A. J.
,
Frutiger
S. A.
,
Carroll
K. E.
,
Huey
L.
(
1999
).
Neuropsychological correlates of methylphenidate treatment in adult ADHD with and without depression
.
Archives of Clinical Neuropsychology
 ,
14
,
217
233
.
Robinson
A. M.
,
Bucci
D. J.
(
2014
).
Individual and combined effects of physical exercise and methylphenidate on orienting behavior and social interaction in spontaneously hypertensive rats
.
Behavioral Neuroscience
 ,
128
,
703
712
.
Rowe
D. L.
,
Robinson
P. A.
,
Gordon
E.
(
2005
).
Stimulant drug action in attention deficit hyperactivity disorder (ADHD): Inference of neurophysiological mechanisms via quantitative modelling
.
Clinical Neurophysiology
 ,
116
,
324
335
.
Ruiz
J. R.
,
Ortega
F. B.
,
Castillo
R.
,
Martin-Matillas
M.
,
Kwak
L.
,
Vicente-Rodriguez
G.
et al
. (
2010
).
Physical activity, fitness, weight status, and cognitive performance in adolescents
.
Journal of Pediatrics
 ,
157
,
917
922. e1-5
.
Sady
M. D.
,
Vaughan
C. G.
,
Gioia
G. A.
(
2014
).
Psychometric characteristics of the postconcussion symptom inventory in children and adolescents
.
Archives of Clinical Neuropsychology
 ,
29
,
348
363
.
Schatz
P.
,
Sandel
N.
(
2013
).
Sensitivity and specificity of the online version of ImPACT in high school and collegiate athletes
.
American Journal of Sports Medicine
 ,
41
,
321
326
.
Schwartz
K.
,
Verhaeghen
P.
(
2008
).
ADHD and Stroop interference from age 9 to age 41 years: A meta-analysis of developmental effects
.
Psychological Medicine
 ,
38
,
1607
1616
.
Solomon
G. S.
,
Haase
R. F.
(
2008
).
Biopsychosocial characteristics and neurocognitive test performance in National Football League players: An initial assessment
.
Archives of Clinical Neuropsychology
 ,
23
,
563
577
.
Tamm
L.
,
Narad
M. E.
,
Antonini
T. N.
,
O'Brien
K. M.
,
Hawk
L. W.
Jr.
,
Epstein
J. N.
(
2012
).
Reaction time variability in ADHD: A review
.
Neurotherapeutics
 ,
9
,
500
508
.
Valera
E. M.
,
Faraone
S. V.
,
Murray
K. E.
,
Seidman
L. J.
(
2007
).
Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder
.
Biological Psychiatry
 ,
61
,
1361
1369
.
Vaurio
R. G.
,
Simmonds
D. J.
,
Mostofsky
S. H.
(
2009
).
Increased intra-individual reaction time variability in attention-deficit/hyperactivity disorder across response inhibition tasks with different cognitive demands
.
Neuropsychologia
 ,
47
,
2389
2396
.
Voss
M. W.
,
Chaddock
L.
,
Kim
J. S.
,
Vanpatter
M.
,
Pontifex
M. B.
,
Raine
L. B.
et al
. (
2011
).
Aerobic fitness is associated with greater efficiency of the network underlying cognitive control in preadolescent children
.
Neuroscience
 ,
199
,
166
176
.
White
R. D.
,
Harris
G. D.
,
Gibson
M. E.
(
2014
).
Attention deficit hyperactivity disorder and athletes
.
Sports Health
 ,
6
,
149
156
.
Wolf
L. E.
(
2001
).
College students with ADHD and other hidden disabilities. Outcomes and interventions
.
Annals of the New York Academy of Sciences
 ,
931
,
385
395
.
Wu
C. T.
,
Pontifex
M. B.
,
Raine
L. B.
,
Chaddock
L.
,
Voss
M. W.
,
Kramer
A. F.
et al
. (
2011
).
Aerobic fitness and response variability in preadolescent children performing a cognitive control task
.
Neuropsychology
 ,
25
,
333
341
.
Ziereis
S.
,
Jansen
P.
(
2015
).
Effects of physical activity on executive function and motor performance in children with ADHD
.
Research in Developmental Disabilities
 ,
38C
,
181
191
.
Zuckerman
S. L.
,
Lee
Y. M.
,
Odom
M. J.
,
Solomon
G. S.
,
Sills
A. K.
(
2013
).
Baseline neurocognitive scores in athletes with attention deficit-spectrum disorders and/or learning disability
.
Journal of Neurosurgery Pediatrics
 ,
12
,
103
109
.