The most recent National Athletic Trainers' Association (NATA) position statement on the management of sport-related concussion reaffirms the need for regular baseline concussion testing that includes an evaluation of neurocognitive function.1 A variety of written, computerized, and verbal tools are available for testing athletes' working memory, concentration, and delayed recall, such as those included in the Standardized Assessment of Concussion. Software programs such as the Concussion Vital Signs (CNS Vital Signs, LLC, Morrisville, NC) and Immediate Post-Concussion Assessment (ImPACT) (ImPACT Applications, Inc., San Diego, CA) conduct tests and store scores so that comparisons between baseline and post-concussion can be easily made. Although these tests are only one aspect of a concussion evaluation, it is important that the assessment tool be reliable.
Test–rest reliability refers to a test's ability to produce consistent scores under the same circumstances. For example, if a healthy athlete were to take a neurocognitive test twice during a given period of time (ie, weeks or months), he or she would get similar scores unless something had changed between the two evaluations. The two scores can be correlated statistically (intraclass, Pearson correlations, or Cronbach's alpha) to determine whether the test is reliable. This simple way of establishing reliability is limited by the fact that the test conditions must be the same each time. A correlation coefficient of 0.70 is considered to be adequate reliability for neuropsychological assessment tools.2 There are numerous non-concussive explanations for changes in neurocognitive function in young adults, such as fatigue3 or stimulant use.4 The presence of attention deficit/hyperactivity disorder (ADHD) or a learning disability may also affect the reliability of such tests.
The reliability of the ImPACT has important implications for the return-to-play guidelines used by certified athletic trainers. If scores vary for non-concussive reasons, such as ADHD or a learning disability, then the utility of the test is greatly limited. Current evidence suggests that the test–retest reliability of the ImPACT is below acceptable levels in healthy participants without ADHD or a learning disability.
A systemic review of ImPACT reliability concluded that the tool has poor to moderate test–retest reliability.5 A variety of test–retest time intervals have been considered (Table 1). The best reliabilities were found at 30 days,6 45 days,7 and 1 year.8 The ImPACT previously recommended a 2-year interval for high school athletes,9 whereas the NATA position statement calls for annual baseline testing for adolescents and those with prior concussions.1 The type of intraclass coefficient analysis used also affects results, making comparisons difficult. Schatz,10 Schatz and Ferris,6 Elbin et al.,8 and Nakayama et al.7 calculated intraclass coefficients using the average measures format and found substantially better results than Broglio et al.,11 Resch et al.,12 Bruce et al.,13 Register-Mihalik et al.,14 and Echemendia et al.15 According to Alsalaheen et al.,5 the single measure format is more appropriate than average measures. Most recently, Womble et al.16 reported both the single and average intraclass coefficients to highlight the differences. Iverson et al.17 used Pearson correlations, which are not comparable to intraclass coefficients. A Pearson coefficient of 0.80 would suggest a moderately strong relationship between two test scores, but it is not the best analysis when tests are given multiple times by different clinicians.5
Test–Retest Reliabilities by Time Interval
ADHD is defined by the Diagnostic and Statistical Manual of Mental Disorders, 5th edition as a pattern of behavior present in multiple settings (eg, school and home) that can result in performance issues in social, educational, or work-related tasks.18 Symptoms of ADHD can be grouped into two categories: inattention/hyperactivity and impulsivity. Symptoms include difficulty staying focused, fidgeting, inability to pay attention to details, and failure to remain seated in appropriate situations.18 Individuals with a learning disability exhibit persistent difficulties in reading, writing, and mathematical reasoning in school. Symptoms may include effortful but slow reading, writing that lacks clarity, and the inability to remember and recall numerical facts.19
Student-athletes with ADHD or a learning disability have been known to score lower than peers without ADHD or a learning disability on computerized cognitive assessments,8,17,20 whereas Covassin et al.9 found no significant differences between the two. Differences may be due to the need to recall and organize information multiple times. An individual with ADHD would have difficulty maintaining focus, whereas an individual with a learning disability may organize and recall information more slowly. Additionally, baseline concussion testing is typically done in a group format, thereby creating environmental distractions.
It is important for the certified athletic trainer to be aware of the presence of ADHD or a learning disability. Two studies have found a relationship between having ADHD and a longer recovery time for concussions.7,21 Current evidence suggests that the test–retest reliability of the ImPACT is below acceptable levels in healthy participants without ADHD or a learning disability. Previous authors7–9,17,20 have commented that poor test reliability may be a greater concern for athletes with ADHD or a learning disability. However, a study comparing the ImPACT's reliability in high school athletes with and without ADHD or a learning disability has not been conducted.
The purpose of this pilot study was to examine the reliability of the ImPACT in high school student-athletes with and without ADHD or a learning disability within actual clinical practice. Data were compiled retrospectively from the ImPACT databases of two high schools. A secondary aim was to test for differences in composite scores between the two groups. It was hypothesized that the ImPACT would be less reliable in student-athletes with ADHD or a learning disability and that the composite scores would be lower than those of student-athletes without ADHD or a learning disability.
This retrospective observational study used the ImPACT database of two high schools in southwestern New Hampshire. Access to medical records was provided by the athletic training service provider, who paid for the software and trained their staff to conduct the screenings. Baseline ImPACT testing was conducted every 2 years per the manufacturer's guidelines at the time of the study.23 The study was approved by the college's institutional review board.
Thirty (28 males, 2 females) student-athletes who participated in football, soccer, basketball, wrestling, and cheerleading between 2008 and 2014 were included in this study. Their files met the inclusion criteria of having two baseline ImPACT assessments and no history of concussion. The average age was 14.47 and 16.53 years at the first and second assessments, respectively. The sample was overwhelmingly non-Hispanic, white, and male. Demographic information is presented in Table 2.
The ImPACT is an online computerized tool for neurocognitive screening that takes approximately 25 minutes to complete. The assessment has seven composite areas and each composite uses one to two modules to test neurocognitive function.23 These modules challenge the athletes to remember the placement of shapes after being distracted with organizing numbers in sequence as quickly as possible. Verbal memory and processing speed is tested by memorizing a series of words, whereas visual memory asks for recall of shapes and figures. Visual memory, impulse control, and reaction time are calculated by combining memory scores with time needed to complete the composite.23 A scale assesses any symptoms that could be exacerbated by the examination. Baseline scores are compared to scores obtained post-concussion. At the time of the study, it was recommended that baseline assessments be completed every 2 years.22 Resulting published intraclass coefficients for the composite areas varied greatly, ranging from .015 to 0.88 (Table 1).
Assessments were conducted during the preseason of each sport for first-year and third-year high school participants. Standard procedure was to place a group of 15 to 20 student-athletes in a classroom equipped with a computer. The athletic training staff would log each student in so that there were limited errors prior to the assessment. Student-athletes were told the assessment was not an intelligence test and were asked to take the test seriously. The student-athletes were monitored during the 30 to 45 minutes of test taking.
An initial review of the database found 341 files with two valid ImPACT tests. Administration of the examination took place at the beginning of preseason for all sports. The time between assessment 1 and assessment 2 was 2 years, given that the student-athlete continued with the same sport. Prior to cognitive testing, the ImPACT program asked student-athletes whether they had had a concussion, had been diagnosed as having ADHD or a learning disability, and/or took medication for ADHD or a learning disability. The initial 341 files were filtered as “yes” for ADHD/learning disability or “yes” for ADHD/learning disability medications (n = 22). Seven files were excluded due to presence of a post-concussion ImPACT evaluation or reported history of concussions, leaving 15 participants total. Student-athletes were not excluded if the use of medication differed from assessment 1 to assessment 2. Names were removed and files were assigned individual identification numbers. Next, a comparison group of student-athletes who did not have ADHD or a learning disability (n = 15) was selected by matching the ages and genders of the ADHD/learning disability group participants. Comparison student-athletes reporting a prior concussion or having a post-concussion ImPACT score on file were excluded.
Composite scores for verbal memory, visual memory, visual motor speed, reaction time, and impulse control for each assessment were entered into SPSS software (version 21; IBM Corporation, Armonk, NY). Group assignment of either ADHD/learning disability or no ADHD/learning disability was based on self-report of having ADHD or a learning disability at one of the ImPACT assessments.
The intraclass coefficient (single measures) for each ImPACT composite area was determined using the test–retest method. Because all student-athletes were reportedly concussion free, their ImPACT performance should have been constant over the two assessments. Intraclass coefficients range from 0.00 to 1.00 and higher coefficients indicated greater test reliability (Table 3).
Single Measure Intraclass Coefficient
A repeat-measures analysis of variance test was used for the secondary analysis of group × time differences between the ADHD/learning disability and non-ADHD/learning disability groups for individual composite scores. A Bonferroni adjustment was employed for multiple comparisons. P values were corrected within SPSS and then displayed relative to a significance level of .05.
Reliabilities were low for all composite areas regardless of group (Table 3). The only composite area greater than 0.70 was visual memory in the ADHD/learning disability group (intraclass coefficient = 0.78). Reliability of visual motor speed in the non-ADHD/learning disability group was close to acceptable at 0.68. Composite reliabilities were lower in the ADHD/learning disability group than the non-ADHD/learning disability group, except for visual memory and cognitive efficiency.
The mean composite scores for each group by assessment are listed in Table 4. There were no significant changes in scores from assessment 1 to assessment 2. There were significant group differences in verbal memory (F = 5.25, P = .03), visual motor speed (F = 8.04, P = .01), and reaction time (F = 7.51, P = .01). Follow-up t tests revealed that visual motor speed and reaction time were only different at assessment 2. Student-athletes with ADHD or a learning disability had significantly lower visual motor speeds (t = −4.49, P = .00) and significantly better reaction times (t = 2.55, P = .02) compared to student-athletes without ADHD or a learning disability. Verbal memory was not significantly different at either assessment according to the t test. The effect sizes ranged from medium to large: verbal memory (d = 0.65), visual motor speed (d = 1.27), and reaction time (d = 0.85).24
Composite Mean ± SD
There was a significant group × time interaction for visual motor speed (F = 4.12, P = .05), meaning that the presence of ADHD or a learning disability significantly affected the scores (Figure 1). An additional post hoc analysis of variance test was conducted looking for composite score differences by gender. No significant differences were found between females and males.
Visual motor speed scores by group and assessment. ADHD/LD = attention deficit/hyperactivity disorder or a learning disability
The ImPACT data analyzed here show a marked lack of reliability for student-athletes with and without ADHD or a learning disability. For the cohort (N = 30), none of the composite areas met the 0.70 criteria. When looking at the groups individually, only visual memory in the ADHD/learning disability group achieved adequate reliability (Table 2). In the non-ADHD/learning disability group, visual motor speed approached the cut-off point. As we hypothesized, reliability was generally lower in the ADHD/learning disability group than in the non-ADHD/learning disability group.
Table 1 shows the range of composite intraclass coefficients that have been previously reported. The current cohort and non-ADHD/learning disability group results are similar to other studies using the single-measure intraclass coefficient.11–15 Except for visual memory, the coefficients for the non-ADHD/learning disability group match the 2-year results of Echemendia et al.15 Most of the ADHD/learning disability reliabilities were substantially lower than prior reports. For example, Broglio et al.11 had the worst findings of all single measure intraclass coefficient studies, with coefficients between 0.23 and 0.39. Reliabilities of the ADHD/learning disability group were even lower, except for the visual memory composite (0.78). Interestingly, the reliability for visual memory in the non-ADHD/learning disability group was much lower (0.39). It is possible that the use of shapes rather than words or numbers is a better method to test recall in those with ADHD or a learning disability. Further research is needed to see whether this result is true for most student-athletes with ADHD or a learning disability or whether it was unique to this study.
This study used a 2-year interval between baseline assessments. This was the guideline in place at the high schools between 2008 and 2014 prior to the new NATA position statement.1 Although the non-ADHD/learning disability reliabilities are in line with other 2-year studies,6,15 they are not as good as 1-year studies.8,13 Based on the current study and prior research, baseline intervals shorter than 2 years may improve the ImPACT's reliability. Shorter periods between tests appear to increase the reliability coefficients, shown in Table 1. The highest reliability results from intervals between 5 and 45 days. These timeframes are not clinically practical and possibly represent a learning effect. Although the current guideline for annual testing appears to be supported in student-athletes without ADHD or a learning disability, future studies are needed to determine whether more frequent testing is beneficial for those with ADHD or a learning disability.
Little research has been conducted on student-athletes with ADHD or a learning disability. Three studies suggest that those with ADHD tend to score lower than their peers on the ImPACT.7–9 The current study also found worse scores in the ADHD/learning disability group, except for reaction time. Not all differences were statistically significant, likely due to the small sample size. Mean differences in verbal memory, cognitive efficiency, and impulse control and a significant difference in visual motor speed support those earlier reports. Contrary to prior studies, the ADHD/learning disability group performed significantly better (lower score) in reaction times in assessment 2. A review of functional magnetic resonance imaging studies suggested that children and adults with ADHD have greater activation of brain regions associated with motor, visual, and spatial processing, and may compensate for difficulties in verbal processing and attention with enhanced visual–spatial skills.25 The current study only partially supports this idea because participants with ADHD or a learning disability outperformed healthy peers in reaction time but were significantly slower in visual motor speed.
No significant differences were found with student-athletes aged 14 years in assessment 1. However, visual motor speed and reaction time differed between the groups 2 years later. There is limited research on age differences for ImPACT scores. Register-Mihalik et al.14 found that college student-athletes had greater cognitive efficiency than high school student-athletes, but no other differences were found. A study by Hunt and Ferrara26 also supported improvements with age. The neurocognitive function of high school football athletes was assessed using pencil-and-paper tests and compared by grade level, and eleventh and twelfth graders scored significantly better than ninth graders. Both studies used participants without ADHD or a learning disability. Differences between participants with and without ADHD or a learning disability are important to consider, but the poor reliability of visual motor speed (0.57) and reaction time (0.36) in this study bring the actual presence of differences into question.
This study joins others in documenting the poor reliability of the ImPACT composite scores. It expands the literature by demonstrating that the reliability of this test is worse for athletes who have ADHD or a learning disability. More research is needed to verify these findings and to develop neurocognitive tests that are reliable in an ADHD/learning disability population. The role of age and ADHD or learning disability medications on composite scores and test reliability should also be investigated.
This study has several limitations. The diagnosis of ADHD or a learning disability was entirely self-reported and taken from a quick response questionnaire within the ImPACT program. Additionally, the prevalence of ADHD or a learning disability in this sample was much lower than expected. Only 4.3% of the student-athletes with two baseline ImPACT scores reported a diagnosis of ADHD or a learning disability. In 2011, the prevalence of ADHD or a learning disability in New Hampshire was 10.1% of children aged 4 to 17 years.27 It is likely that more student-athletes with ADHD or a learning disability existed and that some were included in the non-ADHD/learning disability comparison group. Another limitation was that the sample was small and predominately male and white. The 15 student-athletes with ADHD or a learning disability were matched to control student-athletes without ADHD or a learning disability based only on age and gender. Although the two schools were located in the same county, differences in income, parental education, and resources for students with ADHD or a learning disability may have existed. Furthermore, consistency of medication use and testing is important at baseline and post-injury assessment for reliable results. Two participants with ADHD or a learning disability who reported taking medication at baseline were not excluded. At the time of the study, keeping as many identified participants with ADHD or a learning disability as possible outweighed the potential confounding variable. Considerably more research is needed to see what effect medications such as methylphenidate and amphetamine/dextroamphetamine have on neurological test scores. Also, differences in type of sport (eg, collision, contact, or non-contact) were not considered.
Implications for Clinical Practice
A strength of the current study is that the data were collected in a clinical setting using procedures that would be typical in a high school setting. The reliability demonstrated by the ImPACT was poor and certified athletic trainers should be cautious about interpreting changes in scores from baseline to post-concussion. Alternative tests should be used in athletes with known ADHD or learning disabilities. For example, the Symbol Digit Modalities and Stroop tests have better reliability (0.72 and 0.69).14 Individual testing in a quiet environment is also suggested. Neurocognitive testing is only one part of concussion evaluation. The NATA position statement recommends that return-to-play decisions should be guided by multiple assessments and review of the student-athlete's symptoms during a period of gradually increasing physical activity.1
It may be difficult to identify student-athletes with ADHD or a learning disability because self-disclosure is required and baseline testing is often done in group settings early in the season. Questions about ADHD and learning disabilities should be included on health history forms and this information should be requested annually because changes in status and medications are likely in adolescence. Certified athletic trainers should emphasize that reporting of ADHD or a learning disability is important. Participants with ADHD or a learning disability tend to score lower and there is evidence that recovery from a concussion may take longer.7,8,21
- Broglio S, Cantu R, Gioia G, et al. National Athletic Trainers' Association. National Athletic Trainers' Association position statement: management of sport concussion. J Athl Train. 2014;49:245–265. doi:10.4085/1062-6050-49.1.07 [CrossRef]
- Strauss E, Sherman E, Spreen O. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary. Oxford, England: Oxford University Press; 2006.
- Alhola P, Polo-Kantola P. Sleep deprivation: impact on cognitive performance. Neuropsychiatr Dis Treat. 2007;3:553–567.
- Scholey AB, Kennedy DO. Cognitive and physiological effects of an “energy drink”: an evaluation of the whole drink and of glucose, caffeine and herbal flavouring fractions. Psychopharm. 2004;176:320–330. doi:10.1007/s00213-004-1935-2 [CrossRef]
- Alsalaheen B, Stockdale K, Pechumer D, Broglio SP. Measurement error in the Immediate Postconcussion Assessment and Cognitive Testing (ImPACT): systematic review. J Head Trauma Rehabil. 2016;31:242–251. doi:10.1097/HTR.0000000000000175 [CrossRef]
- Schatz P, Ferris C. One-month test-retest reliability of the ImPACT test battery. Arch Clin Neuropsychol. 2013:28:499–504. doi:10.1093/arclin/act034 [CrossRef]
- Nakayama Y, Covassin T, Schatz P, Nogle S, Kovan J. Examination of the test-retest reliability of a computerized neurocognitive test battery. Am J Sports Med. 2014;42:2000–2005. doi:10.1177/0363546514535901 [CrossRef]
- Elbin R, Kontos A, Kegel N, Johnson E, Burkhart S, Schatz P. Individual and combined effects of LD and ADHD on computerized neurocognitive concussion test performance: evidence for separate norms. Arch Clin Neuropsychol. 2013;28:476–484. doi:10.1093/arclin/act024 [CrossRef]
- Covassin T, Elbin RJ, Deitrick JM, Whalen DJ. Effects of attention deficit hyperactivity disorder on neurocognitive performance and symptoms in concussed athletes. Athletic Training Sports & Health Care. 2013;5:254–260. doi:10.3928/19425864-20131030-03 [CrossRef]
- Schatz P. Long-term test-retest reliability of baseline cognitive assessments using ImPACT. Am J Sports Med. 2010;38:47–53. doi:10.1177/0363546509343805 [CrossRef]
- Broglio SP, Ferrara MS, Macciocchi SM, Baumgartner TA, Elliot R. Test-retest reliability of computerized concussion assessment programs. J Athl Train. 2007;42:509–514.
- Resch J, Driscoll A, McCaffrey N, et al. ImPACT test-retest reliability: reliably unreliable?J Athl Train. 2013;48:506–511. doi:10.4085/1062-6050-48.3.09 [CrossRef]
- Bruce J, Echemendia R, Meeuwisse W, Comper P, Sisco A. 1 year test-retest reliability of ImPACT in professional ice hockey players. Clin Neuropsychol. 2014;28:14–25. doi:10.1080/13854046.2013.866272 [CrossRef]
- Register-Mihalik JK, Kontos DL, Guskiewicz KM, Mihalik JP, Conder R, Shields E. Age-related differences and reliability on computerized and paper-and-pencil neurocognitive assessment batteries. J Athl Train. 2012;47:297–305. doi:10.4085/1062-6050-47.3.13 [CrossRef]
- Echemendia RJ, Bruce JM, Meeuwisse W, Comper P, Aubry M, Hutchison M. Long-term reliability of ImPACT in professional ice hockey. Clin Neuropsychol. 2016;30:328–337. doi:10.1080/13854046.2016.1158320 [CrossRef]
- Womble MN, Reynolds E, Schatz P, Shah KM, Kontos AP. Test-retest reliability of computerized neurocognitive testing in youth ice hockey players. Arch Clin Neuropsychol. 2016;31:305–312. doi:10.1093/arclin/acw011 [CrossRef]
- Iverson GL, Lovell MR, Collins MW. Interpreting change on ImPACT following sport concussion. Clin Neuropsychol. 2003;17:460–467. doi:10.1076/clin.17.4.460.27934 [CrossRef]
- American Psychological Association. Attention-deficit/hyperactivity disorder. In: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Washington, DC: Author; 2013:59.
- American Psychological Association. Changes in diagnostic criteria for specific learning disabilities. In: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Washington, DC: Author; 2013:66.
- Zuckerman SL, Lee YM, Odom MJ, Solomon GS, Sills AK. Baseline neurocognitive scores in athletes with attention deficit-spectrum disorders and/or learning disability: clinical article. J Neurosurg Pediatr. 2013;12:103–109. doi:10.3171/2013.5.PEDS12524 [CrossRef]
- Mautner K, Sussman WI, Axtman M, Al-Farsi Y, Al-Adawi S. Relationship of attention deficit hyperactivity disorder and postconcussion recovery in youth athletes. Clin J Sport Med. 2015;25:355–360. doi:10.1097/JSM.0000000000000151 [CrossRef]
- Guskiewicz KM, Bruce SL, Cantu RC, et al. National Athletic Trainers' Association position statement: management of sport-related concussion. J Athl Train. 2004;39:280–297.
- Applications, Inc. ImPACT Version 2.1 User's Manual. Hilton Head, SC: Author; 2003.
- Cohen J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1998.
- Fassbender C, Schweitzer JB. Is there evidence for neural compensation in attention deficit hyperactivity disorder? A review of the functional neuroimaging literature. Clin Psychol Review. 2006;26:445–465. doi:10.1016/j.cpr.2006.01.003 [CrossRef]
- Hunt T, Ferrara M. Age-related differences in neuropsychological testing among high school athletes. J Athl Train. 2009;44:405–409. doi:10.4085/1062-6050-44.4.405 [CrossRef]
- Centers for Disease Control and Prevention (CDC). Attention-Deficit/Hyperactivity Disorder. Parent-Reported Diagnosis of ADHD by a Health Care Provider and Medication Treatment Among Children 4–17 Years: National Survey of Children's Health – 2003 to 2011 – Data Profiles by State. https://www.cdc.gov/ncbddd/adhd/features/key-findings-adhd72013.html.
Test–Retest Reliabilities by Time Interval
|Time Between Tests||Measurement|
|Verbal Memory||Visual Motor||Visual Motor Speed||Reaction Time||Symptom Scale|
| Echemendia et al. (2016)a(n = 118)||0.29||0.42||0.69||0.39||–|
| Echemendia et al. (2016)a(n = 118)||0.35||0.46||0.57||0.54||–|
| Schatz (2010)b(n = 95)||0.46||0.65||0.74d||0.68||0.44|
| Echemendia et al. (2016)a(n = 199)||0.44||0.55||0.66||0.33||–|
| Elbin et al. (2011)b(n = 369)||0.62||0.70d||0.85d||0.76d||0.57|
| Bruce et al. (2014)a(n = 305)||0.54||0.38||0.74d||0.52||–|
| Womble et al. (2016)a(n = 200)||0.48||0.59||0.75d||0.62||0.63|
| Nakayama et al. (2014)b(n = 85)||0.76d||0.72d||0.87d||0.67||–|
| Resch et al. (2014)a(n = 45)||0.45||0.52||0.76d||0.57||–|
| Broglio et al. (2007)a(n = 118)||0.23||0.32||0.38||0.39||–|
| Schatz & Ferris (2013)b(n = 25)||0.79d||0.60||0.88d||0.77d||0.81|
|5 to 7 days|
| Iverson et al. (2003)c(n = 56)||0.70||0.67||0.86d||0.79||0.65|
|1 to 2 days|
| Register-Mihalik et al. (2012)a(n = 40)||0.29||0.45||0.71d||0.60||–|
|Characteristic||ADHD/LD (n = 15)||Non-ADHD/LD(n = 15)|
| Non-Hispanic, white||1||1|
| Age at assessment 1||16||16|
| Age at assessment 2||18||18|
| Medication at assessment 1||0||0|
| Medication at assessment 2||0||0|
| Non-Hispanic, white||12||14|
| Non-Hispanic, black||1||0|
| Age at assessment 1||14.4||14.4|
| Age at assessment 2||16.4||16.5|
| Medication at assessment 1||1a||0|
| Medication at assessment 2||1a||0|
Single Measure Intraclass Coefficient
|Variable||Cohort (N = 30)||ADHD/LD (n = 15)||Non-ADHD/LD (n = 15)|
|Visual motor speed||0.57||0.46||0.68|
Composite Mean ± SD
|Variable||ADHD/LD Time 1||Non-ADHD/LD Time 1||ADHD/LD Time 2||Non-ADHD/LD Time 2|
|Verbal memory||82.6 ± 10.42||88.0 ± 7.26||80.53 ± 10.67||86.53 ± 6.73|
|Visual memory||75.27 ± 12.4||73.93 ± 11.2||74.53 ± 11.54||80.67 ± 11.59a|
|Visual motor speed||32.77 ± 12.42||37.38 ± 6.9||31.02 ± 6.79||41.75 ± 6.30|
|Reaction time||.65 ± .084||.60 ± .065||.68 ± .11b||.57 ± .12|
|Cognitive efficiency||.32 ± .136||.36 ± .098||.28 ± .09||.36 ± .15|
|Impulse control||8.1 ± 6.1||6.87 ± 3.91||6.27 ± 3.53||7.07 ± 4.65|
|Symptom scale||5.07 ± 5.47||3.00 ± 3.7||5.07 ± 5.47||3.0 ± 3.70|