516490

research-article2014

JADXXX10.1177/1087054713516490Journal of Attention DisordersGropper et al.

Special Section On Working Memory

Working Memory Training in College Students With ADHD or LD

Journal of Attention Disorders 2014, Vol. 18(4) 331­–345 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054713516490 jad.sagepub.com

Rachel J. Gropper1, Howell Gotlieb2, Reena Kronitz2, and Rosemary Tannock1,3

Abstract Objective: The objective of this study was to determine the feasibility and effectiveness of working memory (WM) training in college students with ADHD or learning disabilities (LD). Method: A total of 62 students (21 males, 41 females) were randomized to a 5-week intensive WM training program or a wait-list control group. Participants were evaluated before treatment, 3 weeks after completion, and at 2-month follow-up. The criterion measures were standardized tests of auditory-verbal and visual-spatial WM. Near transfer measures included other cognitive tasks; far transfer measures included academic tasks and behavioral rating scales. Results: Intent-to-treat analysis revealed that participants receiving WM training showed significantly greater improvements on the criterion WM measures and self-reported fewer ADHD symptoms and cognitive failures. The follow-up assessment indicated that gains in WM were maintained, as were improvements in cognitive failures. Conclusion: Computerized WM training is a feasible and possibly viable approach for enhancing WM in college students with ADHD or LD. (J. of Att. Dis. 2014; 18(4) 331-345) Keywords ADD/ADHD, working memory, learning disabilities, college students, academic performance

Introduction ADHD is one of the most common neurobehavioural disorders in childhood, with worldwide prevalence rates estimated at 5.3% (Polanczyk & Jensen, 2008). ADHD often co-occurs with learning disabilities (LD) with comorbidity estimates ranging from 40% to 80% (Pastor & Reuben, 2008; Sexton, Gelhorn, Bell, & Classi, 2011). Moreover, individuals with both disorders are at greater risk of poorer academic outcomes than individuals with either disorder alone (Frazier, Youngstrom, Glutting, & Watkins, 2007; Willcutt et al., 2007). Research has shown that both ADHD and LD symptoms persist into young adulthood, a time when many are attending college and university. Students with either ADHD or LD (or both) at the post-secondary education level constitute an emergent subgroup that has received far less attention in the literature compared with children, adolescents and employed adults. The exact prevalence of ADHD, LD, or the comorbid condition, in college populations is unknown; however, it has been estimated that 2% to 8% of students pursuing post-secondary education have been diagnosed with ADHD (DuPaul, Weyandt, O’Dell, & Varejao, 2009) and 1% to 5% have been diagnosed with an LD (Horn & Nevill, 2006; Houck, Asselin, Troutman, & Arrington, 1992). Students with LD or ADHD constitute the largest population of college students with disabilities (Gregg, 2009).

Cognitive and academic problems experienced by college students with ADHD or LD are inadequately understood. The limited available research on college students with ADHD suggests that they obtain lower grade point averages (GPAs) compared with controls, report more academic problems and are more likely to receive academic probation (Frazier et al., 2007; Heiligenstein, Guenther, Levy, Savino, & Fulwiler, 1999). Socially and emotionally, these students also exhibit lower levels of adjustment, social skills and self-esteem compared with matched controls (Shaw-Zirt, Popali-Lehane, Chaplin, & Bergman, 2005). College students with LD also manifest academic problems, are more likely to pursue 2-year rather than 4-year programs, and to drop out before they have completed their degrees (Gregg, 2007; Murphy, Barkley, & Bush, 2002). Current research suggests that both ADHD and LD may share common risk factors in cognitive processes, such as core deficits in processing speed and executive functions, especially working memory (WM; Jensen, Martin, & Cantwell, 1997; McGrath et al., 2011; Willcutt et al., 2010). 1

Ontario Institute for Studies in Education, Toronto, Canada Jewish Vocational Services, Toronto, Ontario, Canada 3 SickKids Hospital, Toronto, Ontario, Canada 2

Corresponding Author: Rosemary Tannock University of Toronto, 252 Bloor Street West, Toronto, Ontario M5S1V6, Canada. Email: [email protected]

332 WM, a “mental workspace” that allows information to be held in mind briefly for a few seconds and manipulated during problem solving, is impaired in those with ADHD (Hervey, Epstein, & Curry, 2004; Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Martinussen & Tannock, 2006; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). WM is an important cognitive function that is necessary for academic learning and reasoning (Baddeley, Gathercole, & Papagno, 1998; Gathercole, Brown, & Pickering, 2003). In addition, WM problems are also evident in adults with ADHD, including those in post-secondary education (Gropper & Tannock, 2009). WM deficits are associated with both ADHD and LD, and some researchers report a differential pattern of impairment in WM in these two clinical conditions, but findings are inconsistent (e.g., Gathercole, Alloway, Willis, & Adams, 2006; Swanson, 2006). Initially, WM was regarded as a fixed trait; however, recent investigations indicate that WM can be improved by intensive training (Klingberg, 2010). Several studies have investigated the efficacy of this training program and found improvements in WM tasks that were not part of the training program, suggesting improvements in WM capacity (Beck, Hanson, Puffenberger, Benninger, & Benninger, 2010; Holmes, Gathercole, & Dunning, 2009, 2010; Holmes, Gathercole, Place, et al., 2010; Klingberg et al., 2005; Klingberg, Forssberg, & Westerberg, 2002; Thorell, Lindqvist, Bergman Nutley, Bohlin, & Klingberg, 2009; Westerberg et al., 2007). Beneficial effects on WM were sustained for several months after training (Holmes et al., 2009; Klingberg et al., 2005). Three of these studies, which were controlled trials conducted in children and adolescents with ADHD, found that WM training also led to a decrease in inattentive ADHD symptoms, as reported by parents (Beck et al., 2010; Klingberg et al., 2005; Klingberg, Forssberg, & Westerberg, 2002). One study reported that WM training was associated with academic improvements, specifically in mathematical skills, several months after training was completed (Holmes et al., 2009). However, this was not a randomized controlled trial (RCT) and so could distinguish between the effects of training and the effects of educational instruction. To date, there has been one RCT study published on WM training in LD (Gray et al., 2012). In this sample of adolescents with severe LD and comorbid ADHD, WM training conducted in a semi-residential school setting, led to improvements on non-trained WM measures. Moreover, those who showed the greatest compliance and effort in the WM training were rated as less inattentive/hyperactive by their parents, who saw their youngsters only at weekends. There have yet to be any studies on WM training in a postsecondary population, specifically those students with ADHD, LD, or the comorbid condition. The primary purpose of the present study was to determine the effectiveness of intensive WM training in college students with ADHD and/or LD using a randomized

Journal of Attention Disorders 18(4) controlled design. Our rationale was based on the evidence of WM as a shared risk factor for ADHD and LD. As WM correlates with both ADHD symptoms and academic performance, we speculated that if WM training does improve WM, then concomitant improvements would be expected in both ADHD symptoms and academic outcomes, possibly as later-onset benefits. Specific objectives were to determine (a) the feasibility of WM training in this population (i.e., whether college students with ADHD and/or LD would be able to comply with the required intensity and duration of training); (b) whether WM training would lead to improvements in WM performance on cognitive tasks that did not resemble the training activities, as well as those that did; (c) whether the effects of WM training would generalize to daily-life activities involving WM, such as academic performance and selfregulation of attention; and (d) whether any gains in functioning would persist (i.e., for at least 2 months) beyond the end of the training period. We hypothesized that WM training would enhance auditory-verbal and visual-spatial WM and that beneficial effects would be maintained beyond the end of the training period. It was also anticipated that WM training would be accompanied by improvements in academic areas that are dependent upon WM (e.g., reading comprehension and math reasoning), and improved selfregulation in everyday life (albeit perhaps as later-onset outcomes discernible at follow-up only).

Method Participants Participants were recruited through university and college student disability services located in a major urban center in Canada. Information sessions were also held on campus at two local universities. Inclusion criteria were as follows: (a) current enrollment in a post-secondary program at the college or university level; (b) a previous diagnosis of ADHD, LD, or comorbid ADHD + LD; (c) registration with respective college disability services that requires documented evidence of a confirmed current diagnosis of ADHD or LD or both; and (d) access to a personal computer with Internet connection (students were loaned a laptop if necessary). Exclusion criteria were as follows: (a) uncorrected sensory impairment, (b) major neurological dysfunction and psychosis, (c) current use of sedating or mood altering medication other than stimulants provided for ADHD, and (d) motor or perceptual handicap that would prevent using the computer program. To register with student disability services at these universities, the student must provide a professional evaluation completed within the last 3 years and documentation that includes relevant psychoeducational testing, identification of specific diagnosis, and recommendations for academic accommodations.

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Gropper et al. Table 1.  Characteristics of the Sample.

Age Gender ASRS total score Nelson Denny comprehension standard score Cognitive failures total score WAIS-Digit Span scaled score CANTAB Spatial Span Woodcock–Johnson Reading Fluency Woodcock–Johnson Math Fluency Woodcock–Johnson Applied Problems

ADHD (n = 26)

LD (n = 30)

ADHD + LD (n = 6)

28.1 Female = 17, male = 9 46.89 (9.65) 204.80 (29.91) 58.59 (16.0) 7.67 (3.18) 6.80 (1.16) 96.04 (20.96) 86.67 (17.46) 94.92 (8.89)

27.8 Female = 20, male = 10 36.39 (11.64) 197.50 (33.53) 51.53 (13.38) 7.43 (2.92) 6.41 (1.34) 87.93 (20.12) 86.0 (18.19) 92.61 (11.91)

30 Female = 4, male = 2 49.20 (10.85) 210.20 (17.51) 59.20 (7.69) 9.40 (3.78) 5.80 (.84) 96.80 (20.56) 90.60 (17.81) 95.20 (12.42)

Note. LD = learning disabilities; ASRS = Adult ADHD Self-Report Scale; WAIS = Wechsler Adult Intelligence Scale; CANTAB = Cambridge Neuropsychological Testing Automated Battery.

A total of 62 college students participated (21 males, 41 females) aged 19 to 52 years (M = 28.04, SD = 7.2), all of whom were registered with student disability services. Some were registered with a previously confirmed diagnosis of ADHD (n = 26), others with LD (n = 30), and some with both (n = 6). However, examination of test scores revealed that most of the students with a confirmed diagnosis of one of these disorders also manifested at least sub-threshold levels of the other (see Table 1). For instance, 62% of those with an LD diagnosis reported clinically significant ADHD symptoms on the Adult ADHD Self-Report Scale (ASRS v1.1; Adler et al., 2006; Kessler, Adler, & Ames, 2005). Conversely, 54% of the ADHD group scored below the 25th percentile on the Nelson Denny Reading Comprehension test (Brown, Fishco, & Hanna, 1993) or on the Woodcock– Johnson Math Fluency subtest (Woodcock, Mather, & McGrew, 2001). Thus, this sample of college students had higher levels of ADHD symptoms and learning difficulties than would be expected on the basis of their “official” diagnoses. Accordingly, we opted to aggregate data from the participants irrespective of their given diagnoses, based on the following reasons: (a) marked overlap of symptoms, (b) shared risk factors for LD and ADHD, and (c) small number of participants in each purported subgroup (ADHD, LD, ADHD + LD) which would preclude meaningful analysis. Thus, the acronym ADHD/LD will be used in the rest of the manuscript to refer to this combined sample. Consistent with previous research on WM impairments in children and adolescents with ADHD (Lambek et al., 2011), only a subset of this sample of college students with ADHD/LD exhibited scores on the WM tests that were below the 25th percentile. For instance, at baseline 56% of the sample (49% of experimental participants and 68% of the waitlist [WL] participants) had impaired auditory-verbal WM (below the 25th percentile) based on their Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) Digit Span scores. Moreover, 31% of participants had scores in

the Average range (i.e., between the 25th and 74th percentiles); and 13% had scores in the High Average or Superior range (i.e., greater than 75th percentile). An examination of their CANTAB (Cambridge Neuropsychological Testing Automated Battery) Spatial Span scores, which measure visual-spatial WM, revealed that 23% scored below the 25th percentile; 46% had average scores (i.e., 25th-74th percentiles); and almost one third (31%) scored above the 75th percentile. Among the sample, 26% (n = 16) were being treated with stimulant medication, as prescribed by a community physician. Medication status and dose remained the same throughout the assessment and training periods. Students were randomized to experimental and WL control groups, using unequal randomization (5:3 assignment, in favor of the WM training program) to enhance recruitment and minimize drop-out rates (Dumville, Hahn, Miles, & Torgerson, 2006). A total of 39 students (63% of sample; 16 males, 23 females) comprised the experimental group. A total of 23 students (37% of sample; 5 males, 18 females) comprised the control group. Once participants in the experimental group completed the training, the WL group received the opportunity to complete the WM training program; 56% opted to do so.

Intervention Participants completed 25 training sessions (about 45 min per session) of “Cogmed QM” (the adult version of Cogmed; Pearson Education, 2011) over 5 weeks. The program was provided on a CD and used on a personal computer. During each session participants completed a set of auditory-verbal and visual-spatial WM tasks, and responded by clicking on displays with the computer mouse. All tasks involved the manipulation and storage of visuo-spatial information (i.e., reproducing a sequence of moving shapes) or auditory WM information (remembering strings of digits), feedback and rewards based on performance of each trial, and progressive

334 adaptation of difficulty level as a function of individual performance so that an optimal level of challenge was maintained and participants were always working at their WM capacity. Training plans were individualized and modified based on current performance, but the typical plans included 12 different WM training exercises; participants completed 8 tasks every day, 15 trials of each task. The average training time each day was about 45 min (excluding breaks). Responses to each trial were logged to a file and participants uploaded new data files via the Internet to a secure server. Compliance and training performance was verified by a certified Cogmed training coach (psychologist from licensed community agency) who was completely independent from the research team. The coach was in touch with study participants on a weekly basis, via email or telephone calls and monitored participants’ data on the server, as well as advising them about the next week’s training.

Procedure The study was approved by the Institutional Research Ethics Board. After a complete description of the study was provided to the participants, informed written consent was obtained before the study began. Participants were assessed individually in a university lab space at three different time points: prior to training (T1), 3 weeks after completing training (T2), and at 2-month follow-up (T3). On all three occasions, participants completed the same battery of tests and rating scales. Once every week, the certified training coach called or emailed the participants to inquire about any training difficulties and to give feedback regarding training scheduling and performance. The Cogmed WM training was completed on the participants’ personal computers, which provided them the flexibility to train where and whenever they wanted, at their convenience. A flow diagram of participant recruitment and progress through the study is shown in the schematic, in Figure 1.

Outcome Measures Outcome measures were categorized as (a) “feasibility measures” (number of training sessions and compliance with the required training intensity as quantified by the Cogmed Improvement Index score), (b) “criterion measures” (closely resembled tasks trained in WM program), (c) “near transfer” measures (indices of other cognitive functions or measures of WM that differed from the trained tasks), and (d) “far transfer” measures (indices of academic achievement and self-report questionnaires). These measures were selected based on their use in previous studies and the theoretical support (Klingberg et al., 2005; Westerberg et al., 2007). Feasibility measures.  The Cogmed Improvement Index score was computed automatically from two training indices

Journal of Attention Disorders 18(4) (Start Index, Max Index) and provided for every user. The Start Index is calculated with the results from Days 2 and 3, and the Max Index is calculated with the results from the two best days during the training period. The Improvement Index is calculated by subtracting the Start Index from the Max Index. The mean Improvement Index for individuals aged 18 to 65 years is 29 (normal range = 15-41). Higher Index scores signify good compliance and effort with the training (Cogmed Cognitive Medical Systems, 2009). Criterion WM tasks.  The Digit Span subtest from the WAISIV was used to assess auditory-verbal WM (Wechsler, 2008). This is a composite score consisting of digit span forward, backward, and sequencing. The Digit Span raw score was converted to an age-adjusted scaled score, with a mean of 10 (SD of 3). The Cambridge Neuropsychological Testing Automated Battery Spatial Span Forward task was used to assess visual-spatial WM (CANTAB; Fray, Robbins, & Sahakian, 1996). Spatial span scores ranged from 0 to 9, with the score representing the highest level at which the participant reproduces at least one correct sequence. Near transfer measures of WM.  The Paced Auditory Serial Addition Test (PASAT) was used as a secondary measure of auditory-verbal WM (Gronwall, 1977). It is a well-validated test of sustained attention and speed of mental computation, as well as indexing the updating function of WM. The PASAT consists of sets of numbers stated in serial fashion, presented verbally via computer. The participant was required to add each successive pair of numbers and report the sum to the examiner. For example, when presented with the numbers 3 . . . 4 . . . 1 . . . 6, the correct responses would be 7 (i.e., 3 + 4), 5 (i.e., 4 + 1), and 7 (i.e., 1 + 6). Thus, the task requires continuous updating of WM as well as inhibition and “forgetting” the sum of the previous calculation. Two different sets were administered: Set A, in which numbers were presented at a rate of 2.4 s between each number; and Set B, in which numbers were presented at a rate of 1.6 s between each number. The longer the time interval between target stimuli, the greater the challenge for WM, because WM will only hold information for 1 to 2 s. The total number of errors for each set constituted the score for the test. An age-adjusted T-score was generated for each set. Higher T-scores indicated better performance. The CANTAB Spatial Working Memory task was used to differentiate and quantify WM strategy skills. Based on the self-ordered pointing task (Petrides & Milner, 1982), it differs from WM span tasks because it is not affected by varying levels of dopamine in the dorsolateral prefrontal cortex (Diamond, Briand, Fossella, & Gehlbach, 2004). It was administered on a computer with a touch screen monitor. Participants are required to update information about spatial locations in WM: They must search boxes to find the hidden token, but must not return to the same box where a

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Gropper et al.

Assessed for eligibility (n= 67)

Excluded (n= 5) ♦

Not meeting inclusion criteria (n=1)

♦

Declined to participate (n=4)

♦

Other reasons (n= 3)

Randomized (n= 62 )

Allocated to experimental group (n= 39) ♦ Completed the intervention and posttesting (n=34; 87%) ♦ Attrition at post-test (n=5)

Completed follow-up testing (n=24; 62%)

ANALYSIS Pre-/Post Intent to Treat (n= 39; LOCF=5) Pre-/Post/Follow-up As Treated (n=24)

Allocated to waitlist control group (n=23) ♦ Completed control condition and posttesting (n=23; 100%) ♦ Attrition at post-test, n=0

Completed follow-up testing (n= 21; 91%)

ANALYSIS Pre/Post Intent to Treat (n=23; LOCF=0) Pre-/Post/Follow-up As Treated (n= 21)

Figure 1.  Flow diagram of participant recruitment and participation in controlled trial of WM training in college students with ADHD/LD. Note. WM = working memory; LD = learning disabilities.

token has already been found. There are four trials for each of three load conditions (four, six, and eight boxes). Performance was measured according to the number of errors made. At the end of the task, participants received a strategy score: A low score indicates efficient strategy use, whereas a high score reflects poor strategy use. The error score and the strategy score were converted to standardized Z-scores, which were used in the analyses. Far transfer measures.  The Ruff 2 & 7 Selective Attention Task (Ruff, Niemann, Allen, Farrow, & Wylie, 1992) was used to measure two aspects of visual attention: sustained attention (ability to maintain consistent performance level

over time) and selective attention (ability to select relevant stimuli while ignoring distracters). The test consists of a series of 20 trials of a visual search and cancellation task. The respondent detects and marks through all occurrences of the two target digits: “2” and “7.” In the 10 Automatic Detection trials, the target digits are embedded among alphabetical letters that serve as distracters. In the 10 Controlled Search trials, the target digits are embedded among other numbers that serve as distracters. The Controlled Search trials are more demanding of attentional resources. Accordingly, WM training would be expected to improve performance on the Controlled Search tasks. Correct hits and errors are counted for each trial and serve as the basis

336 for scoring the test. Speed scores reflect the total number of correctly identified targets (hits). Accuracy scores evaluate the number of targets identified in relation to the number of possible targets. Participants were also administered several academic testing measures. The first, the Nelson Denny Reading Test (Brown et al., 1993), is a standardized test of reading fluency and comprehension that predicts college students’ academic outcomes and is linked with WM (Aldridge, Keith, Sloas, & Mott-Murphree, 2010; Ehrlich, Brebion, & Tardieu, 1994). Participants had 20 min to read seven different passages and answer questions about each passage. Performance yielded a score for reading comprehension, which was converted to a standard score. Participants were also given the Applied Problems subtest from the Woodcock–Johnson–III Tests of Achievement (WJ-III) as a measure of mathematical reasoning ability (Woodcock et al., 2001). The Math Fluency and Reading Fluency tasks from the WJ-III were only administered at baseline to determine automaticity of identifying words and recalling math facts. Scores on these subtests provided quantifiable information about the nature of learning difficulties in this population. Dependent variables were the number of correctly completed items in 3 min. All subtest raw scores were converted to standard scores with a mean of 100, SD = 15. Each participant was also required to complete two questionnaires to assess current symptom impairment. The ASRS v1.1 is a reliable and valid scale for evaluating ADHD in adults (Adler et al., 2006; Murphy & Adler, 2004). The ASRS v1.1 is an instrument consisting of 18 questions based on the criteria used for diagnosing ADHD in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000). Questions focus on how often ADHD symptoms occur (0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = very often). Scores for each item were added to calculate a total score. Higher scores indicate greater frequency of symptoms. Post hoc analyses were conducted to examine the relative impact of Cogmed WM training on inattention versus hyperactive/impulsive symptoms. The Cognitive Failures Questionnaire (CFQ) measures self-reported failures in perception, memory, and motor function in everyday life. This 25-item scale has good reliability, as test–retest scores are consistent over a period of 16 months (Broadbent, Cooper, FitzGerald, & Parkes, 1982). Furthermore, CFQ scores reliably predict performance on laboratory measures of attention (Forster & Lavie, 2007; Kanai, Yuan Dong, Bahrami, & Rees, 2011). Questions require participants to rank how often these mistakes occur (0 = never, 1 = very rarely, 2 = occasionally, 3 = quite often, 4 = very often). Scores for each item were added to calculate a total score. In addition, the CFQ has

Journal of Attention Disorders 18(4) been found to yield four separate factor scores, memory (i.e., Do you read something and find you haven’t been thinking about it and must read it again?), distractibility (i.e., Do you daydream when you ought to be listening to something?), blunders (errors in task execution; that is, Do you drop things?), and (memory for) names (Wallace, Kass, & Stanny, 2002). Therefore, four subscale scores were also computed. Previous research has found that scores on the CFQ are predicted by gray matter volume in the left superior parietal cortex (Kanai et al., 2011). It is this region of the brain that is responsible for directing attention to relevant stimuli. Given the link between WM and controlled attention, it would follow that WM training would have a differential impact on the four subscales and would be more likely to improve the distractibility subscale score.

Statistical Analysis Data were first checked for outliers and the shape of distribution of scores. A Winsorizing technique described in Tabachnick and Fidell (2007) was required to minimize the effect of one outlier in the variable Ruff 2 & 7 Total Accuracy T-Score and two outliers in the Nelson Denny Reading Rate Standard Score. We used a multi-step approach to analysis. First, we tested whether the WM training did improve WM. To do so, we used an Intent-toTreat analysis (ITT), using the Last Observation Carried Forward (LOCF) for missing data, to test for training effects on the criterion measures (e.g., WAIS-IV Digit Span and CANTAB Spatial Span) at post-test (conducted 3 weeks after training had been completed). However, given the substantial proportion of participants who were female, and the number being treated with stimulant medication we first conducted separate ANCOVA ITT analyses using sex and medication as covariates. We found that males performed better than females on the following measures: CANTAB Spatial Span, WAIS-IV Digit Span and Arithmetic, and PASAT. However, there were no interaction effects with the intervention, so the reported analyses do not include gender as a between-subjects factor. Similarly, there were no effects of medication, except on the CFQ, where those on medication reported fewer cognitive failures, and so reported analyses did not include medication as a factor. Thus, reported ITT analyses used ANCOVA with baseline as a covariate, and Group (experimental, WL control) as a between-subjects factor. The dependent variables were post-test scores on target indices. Given significant findings, we then proceeded to the second step, using the ITT/LOCF for the near and far transfer measures. LOCF was used for five experimental participants’ data and two WL controls. In the third step, we tested for sustained or late-emerging benefits of WM training at the 2-month follow-up assessment. We were unable to use an ITT approach for follow-up data because of substantial missing data (38% from

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Gropper et al. experimental group and 9% from WL). Attrition was due primarily to scheduling conflicts with exams or increasing academic demands during the semester. Thus, analysis of follow-up data was based on an As-Treated approach, using repeated-measures ANOVA, with Group (experimental, WL control) as a between-subjects factor and time (pre, post, follow-up) as a within-subjects repeated measure. Partial eta squared (η2) values were used to obtain a rough estimate of effect sizes of treatment outcomes: η2 is interpreted as the percentage of variance in the outcome measure that was explained by the treatment variable. An η2 of .01 corresponds to a small effect, η2 = .10 corresponds to a medium effect and η2 = .25 represents a large effect (Vacha-Haase & Thompson, 2004). All analyses were conducted using SPSS v.19. The fourth step in our analytic plan consisted of supplementary regression analyses to test potential predictors of gains following WM training. We tested participant characteristics (severity of ADHD, reading/math ability, baseline WM ability) as well as compliance and effort with training (WM Improvement Index) as predictors of the significant treatment outcomes (i.e., change scores for WAIS-IV Digit Span, CANTAB Spatial span, and CFQ). Regression analyses involving the WM Improvement Index were based on all participants who completed WM training: The available sample of 47 included the 10 participants originally assigned to the WL group who subsequently completed WM training, but did not include the 5 participants in the experimental group who dropped out and for whom we used LOCF for the main ITT analyses.

Results Post-Intervention Outcomes Feasibility measures. Overall, attrition was low; almost all participants (92%) completed the required 25 training sessions. Most (97.5%) obtained an Improvement Index score within the normal range for age (i.e., a score of 15-41, M = 25.72, SD = 6.54) suggesting good effort and compliance with WM training. Criterion WM measures. As hypothesized, significant effects of WM training were found for the WAIS-IV Digit Span subtest and for CANTAB Spatial Span (see Table 2 and Figures 2 and 3). As indicated by the magnitude of the effect sizes (η2), WM training accounted for 22% of the variance in the post-test scores for CANTAB Spatial Span (i.e., large effect size [ES]) but only 8% for post-test WAISDigit Span scores (small to moderate ES). The Binomial Effect Size Display (BESD) calculation provides another way of appraising the magnitude of change (Rosnow, Rosenthal, & Rubin, 2000): Those who received WM training experienced a 47% greater improvement in CANTAB

Spatial Span and a 28% greater improvement in Digit Span compared with participants in the WL control group. Post hoc analyses conducted for Digit Span Forward, Backward, and Sequencing revealed that at post-test, there was a significant group difference on Digit Span Backward (p < .05), with experimental participants performing significantly better M = 10.87 (0.64) than WL controls M = 8.77 (0.74). There were no effects of WM training on the Digit Span Forwards or Sequencing subscales. Near transfer WM measures.  With respect to the PASAT, analyses revealed no significant effects of WM training for either Set A or Set B. Similarly, on the CANTAB Spatial WM task there were no training effects on either the Between Errors Standard Score or the Strategy Score. Far transfer measures.  There were no training effects on the Ruff 2 & 7 Total Accuracy T-Score and the Total Speed T-Score. Nor were there any training effects on the Nelson Denny Comprehension Standard Score. Furthermore, no differences between the two groups were found on the Woodcock–Johnson Applied Problems or on the WAIS-IV Arithmetic subtest. By contrast, the ITT ANCOVAs were significant for the ASRS and CFQ questionnaires, with the WM training group reporting significantly fewer ADHD symptoms and significantly fewer everyday cognitive failures at postintervention compared with controls. The treatment effects sizes were of small magnitude for both outcome variables. BESD calculations indicated that the WM training group reported a 28% greater reduction in ADHD symptoms and 25% fewer cognitive difficulties in everyday life post-intervention compared with controls (see Table 2). Post hoc analyses were conducted to ascertain whether WM training had a differential effect on (a) inattention versus hyperactive/impulsive symptoms on the ASRS, and (b) on the four subscale scores of the CFQ (memory, distractibility, blunders, memory for names). There were no differential treatment effects on any of these subscales.

Follow-Up Outcomes Follow-up analyses, which were based on an As-Treated approach, included 24 participants who received WM training (62% of participants who completed pre-/post-testing) and 21 WL controls (91% of participants who completed pre-/post-testing). A one-way ANOVA revealed no significant differences at pre-test on key measures of WM and self-report questionnaires in participants from the experimental group who completed follow-up testing versus those who did not. Tests of repeated-measures ANOVA were performed for Time (pre-test, post-test, follow-up) and Group (experimental, WL). Data are reported in Table 3 and Figures 2 and 3.

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Journal of Attention Disorders 18(4)

Table 2.  Post-Treatment Effects on WM, Academic and Symptom Measures for College Students With ADHD/LD in the WM Training or Waitlist Control Group. Baseline Measures Criterion measures   CANTAB Spatial Span   WAIS-IV Digit Span Near transfer measures   CANTAB Spatial WM between errors   CANTAB Spatial WM Strategy Score   PASAT Set A T-Score   PASAT Set B T-Score

Group

M

Experimental Control Experimental Control

0.167 −0.077 8.32 6.90

0.799 1.08 2.76 3.45

0.789 −0.157 10.24 7.77

0.791 0.954 3.06 2.79

Experimentala Controlb Experimental Control Experimental Control Experimental Control

−0.105 −0.802 −0.203 −0.079 42.60 31.83 41.71 35.24

1.30 1.66 0.825 1.11 12.49 11.09 14.40 10.98

0.185 −0.205 0.151 0.049 50.27 42.74 57.98 49.93

Far transfer measures   Cognitive Failures Questionnaire            

Post-treatment

Experimental Control Adult ADHD Self-Report Scale Experimental Control Ruff 2 & 7 Total Speed T-Score Experimental Control Ruff 2 & 7 Total Accuracy T-Score Experimental Control Woodcock–Johnson Applied Problems Standard Score Experimental Control WAIS-IV Arithmetic Standard Score Experimental Control Nelson Denny Reading Comprehension Standard Score Experimental Control

55.75 56.13 42.02 42.29 52.03 50.3 49.15 47.39 96.59 90.52 9.49 8.21 207.1 197.7

SD

13.08 15.13 12.12 11.67 11.22 12.40 9.40 11.07 10.40 10.35 3.13 3.46 29.72 28.85

M

48.75 55.65 36.15 41.35 55.89 54.87 49.15 46.57 98.13 92.74 9.87 8.08 213.1 207.9

SD

F(1, 59)

ES

8.72***

0.22

4.99*

0.08

0.96 1.18 0.925 1.19 18.19 18.26 12.89 15.72

0.087

0.001

0.801

0.013

0.188

0.003

1.70

0.029

17.72 15.11 14.72 11.96 11.66 12.86 9.40 13.62 10.04 8.87 3.45 2.78 27.48 27.10

4.39*

0.070

5.62*

0.087

0.053

0.001

0.06

0.001

0.128

0.02

2.29

0.037

0.009

0.000

Note. The F values reported are for group (experimental vs. control) by time (baseline vs. post-treatment) interactions. WM = working memory; LD = learning disabilities; ES = effect size; CANTAB = Cambridge Neuropsychological Testing Automated Battery (Fray, Robbins, & Sahakian, 1996); WAISIV = Wechsler Adult Intelligence Scale–Fourth Edition (Wechsler, 2008); PASAT = Paced Auditory Serial Addition Test (Gronwall, 1977). a n = 39 for the experimental group. b n = 23 for the control group. *p < .05. **p < .01. ***p < .0001.

Criterion WM measures. For the WAIS-IV Digit Span, the repeated-measures ANOVA yielded significant main effects for Time and Group, which were qualified by a Group by Time interaction of large effect size. Post hoc analyses indicated that the WM training group showed greater improvements in Digit Span than controls at post-test and follow-up (p < .001), but no group differences at baseline. This pattern of findings indicated that the gains were related to WM training and that these treatment-related gains in this component of WM were sustained for at least 2 months. This significant finding was followed up with post hoc analyses to ascertain any differential effects of WM training on the Forwards, Backwards, and Sequencing

subscales. At follow-up, there was a significant group difference on Digit Span Sequencing, with experimental participants M = 9.10 (2.29) showing significantly greater improvement than WL controls M = 6.94 (3.27). On the CANTAB Spatial Span, there was a significant main effect of Time but not Group. Of key interest was the significant Group by Time interaction of large effect size. Post hoc analyses revealed significant group differences at both post-test and follow-up, indicating that compared with the WL group, those receiving WM training showed greater improvements in WM spatial span at post-test and those gains were maintained for at least 2 months after completing the training.

339

Gropper et al. CANTAB Spatial Span 2.5 2

Experimental Control

1.5 1 0.5 0 -0.5

T1

T2

T3

-1 -1.5

Figure 2.  Mean values for Cambridge Neuropsychological Testing Automated Battery spatial span at Baseline (T1), PostIntervention (T2), and Follow-Up (T3).

baseline. On the WAIS-IV Arithmetic subtest, there were no Time, Group, or Group by Time effects. Analysis of the 2-month follow-up data for the CFQ revealed a significant effect of Time and a Group by Time interaction of medium effect size. Post hoc between-group comparisons showed no significant differences at any time point. However, the experimental group showed significant reduction in scores between baseline and post-test (p < .05) and baseline and follow-up (p < .01), suggesting trainingrelated gains in everyday cognitive functioning that were maintained for at least 2 months. By contrast, the WL control group did not show any significant changes across time in CFQ scores. There were no significant group differences on the four different subscale scores. On the ASRS, there was a main effect of Time, not Group, and a non-significant trend for the experimental group to show sustained reduction in ADHD symptoms from post-test to follow-up. However, there were no significant group differences on the Inattention and Hyperactivity/Impulsivity subscales.

Supplemental Analyses

Figure 3.  Mean values for Wechsler Adult Intelligence Scale– Fourth Edition Digit Span at Baseline (T1), Post-Intervention (T2), and Follow-Up (T3).

Near and far transfer measures.  We found significant main effects for Time, but not for Group and no Group by Time interaction for the following measures: CANTAB SWM Strategy Score, CANTAB SWM Between Errors Scores, the Ruff 2 & 7 Total Speed and Total Accuracy Scores, and the Woodcock–Johnson Applied Problems subtest. Thus, both groups obtained better scores on these measures across time regardless of whether participants received WM training. Significant main effects for time and group (but not the group by time interaction) were found for scores on PASAT Set A and B. This pattern of findings indicated that scores of both groups increased over time and that the group assigned to WM training scored higher overall on these measures than the control group, despite randomization. Note that the primary ITT analysis controlled for the group differences at

To test the hypothesis that WM training effects on WM were a function of four key variables (participants’ WM scores at pre-tests, ADHD severity, LD scores, sex, age), separate hierarchical multiple regressions were conducted on WM pre–post change scores for WAIS-DS and CantabSpatial Span. Composite scores of reading (WJ-Reading Fluency, ND-Comprehension) and math (WJ-Math Fluency, WJ-Applied Problems) were computed to index LD. Selection of predictor variables was based on inspection of correlations between possible predictor variables and the dependent variable. To test predictors of training-related gains in WAIS-DS, sex was entered first, followed by the pre-test WM score (WAIS-DS-T1), next, ADHD severity (ASRS), and finally LD-Reading score. Results of the regression analysis provided partial support for the hypothesized predictors. Beta coefficients for the predictors were: Sex, β = −.320, t =−2.55, p = .014; WAIS-DS-T1, β = −.404, t = −3.14, p < .003; ASRS-T1, β = .191, t = 1.54, p > .1; LD-Reading, β = .051, t = 0.41, p > .1. The best fitting model for predicting gains in WAIS-DS was a linear combination of sex and pretest WAIS-DS scores, R = .438, R2 = .183, F(1, 54) = 8.926, p < .004, indicating that males and those with lower WAIS-DS at pre-test made the greatest gains in WAIS-DS scores after WM training. Addition of variables indexing ADHD severity (R2 change = .036, F = 2.401, p = .127) and composite reading score (R2 change = .003, F = 0.169, p = .683), did not significantly improve prediction. We then reran this regression to determine whether including participants’ effort in WM training (as indexed by the Cogmed WM Improvement Index) into the model would improve prediction: It did not.

340

Journal of Attention Disorders 18(4)

Table 3.  Changes From Pre-Treatment to Follow-Up on WM, Academic and Symptom Measures for College Students With ADHD/ LD Who Received WM Training or a Control Condition. Pre-treatment Measures Criterion measures   CANTAB Spatial Span   WAIS-IV Digit Span Near transfer measures   CANTAB spatial WM between errors   CANTAB Spatial WM Strategy Score   PASAT Set A T-Score   PASAT Set B T-Score

         

Follow-up

Group

M

SD

M

SD

M

SD

F(1, 43)

ES

Experimentala Controlb Experimental Control

0.067 0.156 8.21 7.15

0.830 0.935 2.40 3.44

0.907 0.023 10.21 8.00

0.844 0.953 2.99 2.77

1.04 −0.183 10.79 7.95

0.822 0.766 2.36 3.10

12.5***

0.243

3.82*

0.083

Experimental Control Experimental Control Experimental Control Experimental Control

−0.152 −0.497 −0.226 −0.008 44.46 33.55 41.53 35.60

1.34 1.45 0.836 1.09 10.23 14.46 12.01 12.24

0.168 0.032 0.197 0.174 52.47 41.58 56.98 47.76

1.12 0.878 1.09 1.09 14.50 21.38 11.43 15.90

0.257 −0.021 0.596 0.167 54.74 46.60 58.90 51.36

1.18 1.04 1.36 1.15 13.68 17.50 11.76 13.07

0.222

0.006

2.07

0.049

0.418

0.010

0.953

0.022

23.69 18.35 18.02 12.40 14.57 7.09 9.28 14.40 12.16 10.47 3.45 3.30 28.75 25.39

3.10*

0.067

2.61

0.057

1.09

0.025

0.010

0.000

0.085

0.002

1.74

0.039

0.848

0.019

Far transfer measures   Cognitive Failures Questionnaire  

Post-treatment

Experimentala 55.86 55.62 Controlb Adult ADHD Self-Report Scale Experimental 42.07 Control 41.89 Ruff 2 & 7 Total Speed T-Score Experimental 52.88 Control 50.85 Ruff 2 & 7 Total Accuracy T-Score Experimental 45.92 Control 43.65 Woodcock–Johnson Applied Problems Experimental 96.53 Standard Score Control 91.38 WAIS-IV Arithmetic Standard Score Experimental 9.50 Control 8.45 Nelson Denny Reading Comprehension Experimental 200.33 Standard Score Control 198.05

14.03 15.18 13.45 11.78 11.80 13.15 10.22 16.14 12.01 10.16 3.30 3.35 30.24 30.22

47.00 55.52 35.29 40.33 56.96 55.65 48.79 46.35 98.17 93.48 10.25 8.45 210.15 208.00

19.78 15.29 16.29 11.31 11.54 13.36 10.57 14.57 10.87 8.85 3.25 2.65 29.36 28.13

46.17 53.52 35.46 40.19 61.46 55.85 49.13 46.55 98.70 94.38 10.54 8.50 211.05 216.05

Note. The F and p values reported are for group (experimental vs. control) by time (baseline/post-treatment/follow-up) interactions. WM = working memory; LD = learning disabilities; ES = effect size; CANTAB = Cambridge Neuropsychological Testing Automated Battery (Fray, Robbins, & Sahakian, 1996); WAIS-IV= Wechsler Adult Intelligence Scale–Fourth Edition (Wechsler, 2008); PASAT = Paced Auditory Serial Addition Test (Gronwall, 1977). a n = 24 for the experimental group. b n = 21 for the control group. *p < .05. **p < .01. ***p < .0001.

Similarly, to test predictors of training-related gains in CANTAB-Spatial-Span, we entered Sex first, followed by the pre-test WM score (Cantab-SP-T1), and then ADHD severity (ASRS), and finally composite Math score. Results of the regression again provided partial support for the hypothesized predictors. Beta coefficients for the predictors were: Sex, β = −.155, t = −1.34, p = .186; CANTAB-T1, β = −.512, t = −4.65, p < .000; ASRS-T1, β = −.052, t = −.45, p = .633; LD-Math, β = .381, t = 3.26, p < .002. The best fitting model for participants’ baseline characteristics predicting gains in WAIS-DS was a linear combination of pretest CANTAB Spatial Span standardized score, and their composite Math score at pre-test, R = .646, R2 = .417, F(1,

52) = 10.645, p < .002, indicating that males and participants with lower WM spatial span scores, but higher math scores at pre-test, made the greatest gains in CANTAB Spatial Span after WM training. Addition of sex or ADHD severity (ASRS, R2 change = −.013, F = .014, p = .907) did not significantly improve prediction. We reran this regression to determine whether including participants’ effort in WM training (as indexed by the Cogmed WM Improvement Index) into the model would improve prediction: It did. WM Index Improvement yielded a β = .465, t = 3.86, p < .000. Thus, overall the best fitting model for predicting WM training effects on visual-spatial WM (CANTAB-SpatialSpan change score) was a linear combination of sex,

341

Gropper et al. pre-test scores on CANTAB Spatial Span, composite Math, and WM Improvement Index, R = .808, R2 = .653, F(1, 41) = 14.91, p < .000.

Participant Perspectives Before beginning the Cogmed WM training, participants created a set of training goals in conjunction with their coaches. After completing the training program, the coaches asked the participants to reflect back to their original goals and think about how the training affected their daily lives. Based on their accounts, a number of consistent improvements were noted. The majority noticed improvements in their ability to retrieve verbal information (i.e., phone numbers, people’s names, appointments). Furthermore, with respect to academics, this improved recall of verbal information enabled them to learn and retain information from lectures and books, without having to re-read multiple times. In addition, several participants reported that they were able to sustain attention and feel more alert for longer periods of time. A number of students said they used to go into a room and forget why, as well as misplace things (i.e., keys, wallet, and cell phone); after completing Cogmed, this became less of an issue. One participant who wrote a column for a magazine felt he was able to maintain his attention and write for sustained amounts of time, making it easier to meet his deadlines. Finally, and perhaps because of all of the aforementioned improvements, these young adults reported that they felt more confident, “on top of things,” motivated, and focused on getting things done. When asked about areas where they did not see improvements, some of the participants indicated that they hoped to improve time management and organization skills, but they had not noticed any substantial changes. Overall, the feedback from the participants was positive. However, a causal effect between Cogmed and the improvements noted cannot be assumed: This information is anecdotal self-report and not based on feedback from significant others.

Discussion This study provides the first investigation of the effectiveness of an intensive WM training program for college students with ADHD/LD. These college students constitute a unique subset of the ADHD/LD population. On one hand, they are, by definition, a high-achieving subgroup of individuals with ADHD/LD who were successful enough to pursue post-secondary education; on the other hand, they continue to manifest cognitive, academic, and other functional impairments compared with their peers. One major objective of this study was to determine the feasibility of carrying out this program with this population. Remarkably, the majority of students completed the training as well as the post-test assessment and reached the optimal

index score of improvement. However, several participants did not return for follow-up testing 2 months after the completion of the training program. Approximately, 62% of the experimental group participants and 91% of the WL group participants completed follow-up testing. Another major objective was to determine whether WM training would be effective with this college population of young adults with ADHD/LD. The most robust findings were training-related effects on two WM span measures that resembled but were not identical to the trained tasks: a test of auditory-verbal WM (WAIS-IV Digit Span) and a short-term visual-spatial storage measure (CANTAB Spatial Span Forward). Training-related effects were largest for the measure of visual-spatial WM. Beneficial effects on these aspects of WM, particularly on visual-spatial WM, were still discernible at 2-month follow-up. These findings are consistent with previous research (Klingberg et al., 2005; Klingberg et al., 2002). Results also indicated that participants in the WM training group self-reported fewer ADHD symptoms and fewer cognitive failures in their everyday lives at post-test. At follow-up, they continued to endorse a decrease in cognitive failures. As we utilized a WL control group, participants were not blind to training conditions, thus we cannot rule out the possibility of participant bias in their self-reports. In contrast to predictions, we did not find transfer effects to other measures of WM (e.g., the PASAT) or other aspects of cognitive function (e.g., as indexed by the Ruff 2 & 7.) Similarly, treatment-related improvements in WM were not accompanied by gains on academic measures. There are several plausible explanations, one to do with the measurement and the other to do with the training program. For example, rating scales of ADHD symptoms may be insensitive to training effects on concentration and so on because behavior is rated across broader time frames and contexts. By contrast, when ADHD symptoms have been assessed in the context of completing tasks demanding WM, training effects have been found (Green et al., 2012). Moreover, a recent modification to the adaptive training algorithm has resulted in a larger and broader array of benefits (Gibson et al., 2013).

Limitations One important limitation of this study is that we used a nocontact WL control group as opposed to an active control group. Thus, both groups were aware of which treatment condition they were in, which might have biased their ratings on self-report scales. For example, participants in the WL group might underreport any positive changes over the study period in case that might preclude the opportunity to then receive the WM training. Use of an active control condition, such as the non-adaptive Cogmed placebo control program or an alternative computer based program is recommended (as in Gray et al., 2012).

342 A second limitation is that many of our key outcome measures, specifically transfer of training, relied on selfreport and thus introduced the possibility of informant bias. Inclusion of reports from significant others (e.g., the student’s parents, siblings, partner, etc.) is recommended in future studies to provide unbiased outcome measures. A third limitation is that there was differential attention to participants in the two groups. That is, participants in the experimental group received weekly calls from the training coach; however, the WL control group did not receive the same level of attention. Thus, we cannot disentangle any non-specific effects of attention from specific effects of WM training. One possibility is to provide telephone-based coaching to the WL group to control for the addition attention accorded to the experimental group by the Cogmed coach. A fourth limitation is that the study may have lacked statistical power to detect some sustained or later-emerging benefits from WM training because a substantial portion (38%) of the sample did not return for follow-up testing. Due to increasing work demands across the semester and exams, it was difficult to get many of the students to come back 2 months after they completed training. Specifically, it may take time for certain skills to transfer and generalize (i.e., reading comprehension). Thus, longitudinal studies may be more successful at understanding the impact of training on academic functioning. Finally, this was a heterogeneous sample of young adults with prior diagnoses of ADHD or an LD or both disorders. In addition, some but not all had WM deficits. Moreover, females greatly outnumbered males, although the statistics suggest that these are predominantly male disorders. This is likely due to females volunteering more than males for research studies. Therefore, the heterogeneity of the current sample limits the conclusions that can be extrapolated to college populations of ADHD, LD, or ADHD + LD per se. However, the supplemental regression analyses failed to find evidence that the severity of ADHD symptoms influenced the treatment outcomes, although math scores did: those with higher math scores showed greater gains on visual-spatial WM. Over and above the impact of ADHD or academic achievement, the effort students put into WM training (as indexed by the WM Improvement Index) had the largest positive impact on treatment outcomes.

Clinical and Research Implications Notwithstanding the aforementioned limitations, our findings provide some support for the effectiveness of intensive, computerized WM training for improving WM, at least as measured by neuropsychological tests. Effects were maintained at a 2-month follow-up, suggesting that WM training may have long-term beneficial effects. Previous

Journal of Attention Disorders 18(4) studies have found training-related reduction in ADHD symptoms, as measured by parent-completed rating scales (Beck et al., 2010; Holmes et al., 2009; Klingberg et al., 2005; Klingberg et al., 2002). In this study, participants self-reported a reduction in core ADHD symptoms posttreatment; however, our use of a WL control group and lack of an independent corroborating report does not allow us to disentangle training-related effects from participant bias in self-report. It is worth noting that despite the significant time requirement of this program, most students were able to comply with the treatment demands. One advantage of the Cogmed intervention is that individuals are in control of their training schedule (i.e., when and where they do the training). When younger children complete Cogmed, their parents/ teachers are actively involved in the training process. It is possible that having this encouragement may provide greater motivation and better training outcomes; however, it comes at the cost of less flexibility. By providing “distance training” to these college students, they were able to complete the training at their convenience and schedule coach calls when they were available, which likely contributed to the impressive compliance rates. Nonetheless, consistent with findings from the recent WM training literature, we found no robust evidence of transfer of training effects on real-life outcomes (e.g., academic performance). At this point, it is unclear whether the problem lies with measurement or with the training itself or both. Further research is required to investigate whether modifications to the WM training program plus the use of more sensitive measurement will detect more practical and meaningful transfer of gains in WM to daily functioning. Acknowledgment We would like to thank the Cogmed coaches from Jewish Vocational Services (JVS), Rachel Goldberg and Wendy Greenbaum, as well as all of our participants.

Declaration of Conflicting Interests The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Pearson-Cogmed provided the software licenses required for the study with no cost, and they (but not the authors) could potentially benefit if findings were positive. Dr Tannock is on the advisory board for Eli Lilly, a consultant for Purdue Pharma Canada, and in 2010 participated in an ADHD meeting sponsored by Janssen-Cilag.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this project was provided in part by a Banting and Best Award from Canadian Institutes of Health Research (R.J.G.),

Gropper et al. the Canada Research Chairs Program (R.T.), and The Provincial Centre of Excellence for Children and Youth (RT: Research Grant (RG) Reference Number: #RG-684).

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Author Biographies Rachel J. Gropper was a doctoral candidate at the time of the study. She has since received her PhD from the University of Toronto and is currently completing supervised practice.

345 Howell Gotlieb is a Senior Psychological Associate at JVS Toronto. Reena Kronitz is the Chief Psychologist and Senior Director of JVS Toronto. Rosemary Tannock, PhD, is a Senior Scientist in the Neurosciences & Mental Health Research Program, Research Institute of The Hospital for Sick Children, and Professor Emerita (Special Education), University of Toronto.