Attention Mechanisms in Children with Anxiety

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Attention Mechanisms in Children with Anxiety Disorders and in Children with Attention Deficit Hyperactivity Disorder: Implications for Research and Practice Adam S. Weissman

a b

c

c

, Brian C. Chu , Linda A. Reddy & Jan Mohlman

d

a

Child/Adolescent Treatment Center, Institute for Behavior Therapy; Judge Baker Children's Center, Harvard Medical School b

Department of Psychology, Harvard University

c

Graduate School of Applied and Professional Psychology, Rutgers University

d

Department of Psychology, Rutgers University

Available online: 14 Mar 2012

To cite this article: Adam S. Weissman, Brian C. Chu, Linda A. Reddy & Jan Mohlman (2012): Attention Mechanisms in Children with Anxiety Disorders and in Children with Attention Deficit Hyperactivity Disorder: Implications for Research and Practice, Journal of Clinical Child & Adolescent Psychology, 41:2, 117-126 To link to this article: http://dx.doi.org/10.1080/15374416.2012.651993

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Journal of Clinical Child & Adolescent Psychology, 41(2), 117–126, 2012 Copyright # Taylor & Francis Group, LLC ISSN: 1537-4416 print=1537-4424 online DOI: 10.1080/15374416.2012.651993

ANXIETY

Attention Mechanisms in Children with Anxiety Disorders and in Children with Attention Deficit Hyperactivity Disorder: Implications for Research and Practice Adam S. Weissman Downloaded by [Rutgers University] at 09:56 15 March 2012

Child=Adolescent Treatment Center, Institute for Behavior Therapy; Judge Baker Children’s Center, Harvard Medical School and Department of Psychology, Harvard University

Brian C. Chu and Linda A. Reddy Graduate School of Applied and Professional Psychology, Rutgers University

Jan Mohlman Department of Psychology, Rutgers University

Inattention is among the most commonly referred problems for school-aged youth. Research suggests distinct mechanisms may contribute to attention problems in youth with anxiety disorders versus youth with attention deficit hyperactivity disorder (ADHD). This study compared children (8–17 years) with anxiety disorders (n ¼ 24) and children (8–16 years) with ADHD (n ¼ 23) on neurocognitive tests of both general and emotion-based attention processes. As hypothesized, children with ADHD demonstrated poorer selective and sustained attention, whereas youth with anxiety disorders demonstrated greater attentional bias toward threatening faces on a visual probe task. Findings suggest the neuropsychological differentiation of attention problems in anxious and ADHD children, despite potentially similar phenotypes.

Inattentive behavior (e.g., difficulty concentrating in class, distractibility, restlessness, difficulty following directions) has at least 38 different causes (Goodman & Poillion, 1992) and is among the most commonly referred problems for children at risk for and diagnosed with emotional and behavioral disorders (e.g., anxiety, depression, This research was supported in part by student grant awards from the New Jersey Psychological Association and the Association for Behavioral and Cognitive Therapies Child and Adolescent Anxiety Special Interest Group. We thank Jeffrey T. Vietri for his contribution to the Faces Dot Probe Task, Christopher Dudek for his assistance with data collection, and Marsha E. Bates and Mark Cooperberg for their comments on an earlier version of this manuscript. In addition, we gratefully acknowledge the children and parents who took part in this study. Correspondence should be addressed to Adam S. Weissman, Director, Child=Adolescent Treatment Center, Institute for Behavior Therapy, 517 Almena Avenue, Ardsley, NY 10502. E-mail: [email protected]

attention-deficit=hyperactivity disorder [ADHD]; American Psychiatric Association [APA], 2000; Jarrett & Ollendick, 2008; Reddy & Hale, 2007). Inattention is a core criterion in the diagnosis of ADHD (APA, 2000). However, ADHD-related attention symptoms are also present in up to 24% of youth diagnosed with an anxiety disorder (Angold, Costello, & Erkanli, 1999), and significant attention problems secondary to anxiety may occur in many more (Jarrett & Ollendick, 2008; Tannock, 2000). As would be expected from these findings, there is a high rate of comorbidity between ADHD and anxiety disorders in youth, with 25% to 50% of ADHD youth meeting criteria for an anxiety disorder (Biederman, Faraone, Keenan, Steingard, & Tsuang, 1991; Bowen, Chavira, Bailey, Stein, & Stein, 2008; Jarrett & Ollendick, 2008; Jensen, Martin, & Cantwell, 1997; Reynolds & Lane, 2009; Schatz & Rostain, 2006). There

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WEISSMAN, CHU, REDDY, MOHLMAN

is also a high degree of symptom overlap between the two types of disorders (Jarrett & Ollendick, 2008; Reddy, Hale, Weissman, Lukie, & Schneider, in press; Tannock, 2000). Children with generalized anxiety disorder (GAD), for example, often display symptoms of irritability, restlessness, and difficulty concentrating, symptoms commonly associated with ADHD (Reddy & Hale, 2007). However, GAD youth exhibit these symptoms due to excessive and uncontrollable worry (APA, 2000) and attentional biases toward threat in the environment (see Puliafico & Kendall, 2006, for a review), in lieu of the poor inhibitory control, selective=sustained attention, and hyperactivity commonly seen in children with ADHD (Pliszka & Olvera, 1999). Accumulating evidence has begun to identify distinct cognitive mechanisms that may contribute to attention problems in anxiety and ADHD (Barkley, 1997a; Beck & Clark, 1988; Jarrett & Ollendick, 2008; Kendall, 2000; Puliafico & Kendall, 2006; Schatz & Rostain, 2006). This research supports specific, emotion-based cognitive (e.g., worry, negative interpretation bias, attentional bias toward perceived threat=danger), physiological (e.g., rapid heartbeat), and behavioral (e.g., avoidance) interference in anxiety disorders that may interfere with a child’s attention (e.g., Beck & Clark, 1988; Beck, Emery, & Greenberg, 1985; Kendall, 2000; Puliafico & Kendall, 2006; Weissman, Antinoro, & Chu, 2008). This is in contrast to more general, neurobiologically based attention deficits in ADHD, such as abnormalities in the prefrontal cortex and related circuitry (Durston, 2003) and associated deficits in executive function (Barkley, 1997a). At a clinical level, research suggests that in cases where the behavioral symptoms of ADHD and anxiety disorders are not easily differentiated, differential diagnosis can be informed by assessing the presence and type of thoughts and worries that accompany the behavior (e.g., fear of speaking in a group, test anxiety, preoccupation with problems at home; Reddy & Hale, 2007). However, especially in younger children, these thoughts and worries may be difficult to assess. Thus, when both behavioral and cognitive symptoms are difficult to detect or differentiate, the use of neurocognitive methods may be appropriate to help delineate more general ADHD-related attention deficits from more specific, emotion-based attentional biases in anxious youth (e.g., Reddy et al., in press). Although the majority of experimental research conducted with independent samples of children with anxiety disorders or ADHD support these distinctions (Barkley, Grodzinsky, & DuPaul, 1992; Homack & Riccio, 2003; Roy et al., 2008; Waters, Mogg, Bradley, & Pine, 2008), direct comparison of attention processing in children with the two types of disorders has not been conducted. Toward this end, the goal of the current study was to examine and distinguish neurocognitive processes of inattention in youth with anxiety disorders and youth

with ADHD, using both general and emotion-based attention measures. Delineating these underlying attention mechanisms may inform differential diagnosis and intervention planning (e.g., medication and behavioral therapies), which may be challenging for those working with youths with these disorders, particularly when symptoms of inattention predominate. Neurocognitive studies have consistently documented impairments in general attention processing (e.g., selective=sustained attention, attentional shifting) in youth with ADHD. Relative to typically developing children, children with ADHD have demonstrated poorer performance on all four Stroop Color-Word Test (SCWT; Stroop, 1935) subscales—Stroop Word (i.e., word reading), Stroop Color (i.e., color naming), Stroop Color-Word (i.e., selective attention, response inhibition), and Stroop Interference Scores (i.e., a calculated variable reflecting performance cost resulting from the presentation of interfering or competing stimuli; Barkley et al., 1992; Ozonoff & Jensen, 1999; Reeve & Schandler, 2001). In addition, a meta-analytic review of 33 empirical studies (1984–2002) examining Stroop performance in youth with ADHD revealed attentional=inhibitory impairment across all four Stroop scores in children with ADHD (Homack & Riccio, 2003). Research has also demonstrated deficits in sustained attention and attentional shifting in children with ADHD relative to typically developing youth using variations of the Continuous Performance Test (Conners, 2004; Gordon, 1983; Rosvold, Mirsky, Sarason, Bransome, & Beck, 1956). Studies have documented elevated rates of omission (i.e., nonresponse to a target; sustained attention, vigilance) and commission (i.e., response to a nontarget; sustained attention, response inhibition) errors, and lower CPT Attention (d’) scores (i.e., a sensitivity measure of how well the child can discriminate between targets and nontargets; attentional shifting ability), across multiple samples of youth with ADHD (Barkley et al., 1992; Fischer, Barkley, Smallish, & Fletcher, 2005). In addition, Perugini et al. (2000) demonstrated a lower mean CPT Overall Index score (Conners, 2004), a composite index of 11 performance measures including omission and commission errors, hit reaction time, and several measures of performance variability, in youth with ADHD relative to a nonclinical control group. Thus, youth with ADHD exhibit general attentional deficiencies (i.e., selective=sustained attention, attentional shifting), relative to typically developing peers. However, studies identifying emotion-based cognitive distortions or attentional biases in children with ADHD are absent from the literature. In contrast, the majority of cognitive research with youth with anxiety disorders has focused on emotion-based misperceptions of environmental threat=social evaluation, aversive imagery, negative self-talk, negative interpretation bias, and the selective


ATTENTION MECHANISMS IN ANXIOUS AND ADHD YOUTH

allocation of attention toward threatening information (Beck et al., 1985; Kendall, 2000; Mogg & Bradley, 1998; Puliafico & Kendall, 2006). These symptoms and processes have been associated with cognitive (e.g., worry), physiological (e.g., arousal), and behavioral (e.g., avoidance) distress unique to the pathology, function, and expression of youth with anxiety disorders (Derryberry & Reed, 2002; Lonigan & Vasey, 2009; Puliafico & Kendall, 2006; Weissman et al., 2008). A number of visual dot probe paradigms have been used to examine selective attentional processing of threat in youth with anxiety disorders, consistently demonstrating attentional biases toward threat cues, including threatening words (Dalgleish et al., 2003; Taghavi, Neshat-Doost, Moradi, Yule, & Dalgleish, 1999) and angry faces (Roy et al., 2008; Telzer et al., 2008; Waters et al., 2008). Waters et al. (2008) found that children (9–12 years) with severe GAD, grouped by parent-reported anxiety severity, had a significant attention bias toward both angry and happy faces on a pictorial dot probe paradigm using facial cues (Mogg & Bradley, 1999), whereas youth with low-level GAD and nonclinical controls showed no attention bias for emotional faces. In addition, Roy et al. (2008) compared 101 youth with GAD, social phobia, and=or separation anxiety (7–18 years) enrolled in a multisite anxiety treatment study and 51 nonclinical youth recruited separately (9–18 years), in performance on the same visual probe task. Again, children with an anxiety disorder demonstrated a greater attentional bias toward threatening facial stimuli relative to controls. Of note, threat bias in the anxious group did not vary significantly across anxiety disorders. Although these studies lend consistent support to an emotion-based attentional bias toward threat cues in youth with anxiety disorders, research targeting more general attention processes in youth with anxiety but without ADHD is limited and mixed. For example, Ribordy, Tracy, and Bernotas (1981) reported more Stroop Color-Word errors in children high in test anxiety relative to youth low in anxiety. However, Kusche, Cook, and Greenberg (2005) found no SCWT differences between youth high on anxiety and somatization and a nonclinical control group. Based on this review of the literature, no published studies have compared clinically anxious and ADHD youth directly on measures of general (or emotion-based) attention processing. The present investigation aims to clarify and differentiate attention mechanisms in anxious and ADHD youth by comparing anxious (ANX) and ADHD children (ADHD) on neurocognitive measures of both general and emotion-based attention processes. Based on the emerging neurocognitive research in this area, it was hypothesized that youth with ADHD would demonstrate greater impairment than ANX children on tests of general attention processes (i.e., SCWT, CPT), whereas ANX youth would show greater impairment

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on a faces dot probe paradigm measuring attentional bias toward threatening facial cues (i.e., Faces Dot Probe).

METHODS Participants ADHD group. Youth (n ¼ 23; 8–16 years old, M ¼ 11.57, SD ¼ 2.31) with a primary Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev. [DSM–IV–TR]; APA, 2000) ADHD, combined type diagnosis were recruited from families seeking services at a university research clinic specializing in ADHD. Seventy percent were boys; 87% were Caucasian, 9% were African American, and 4% were Hispanic=Latino (see Table 1). Criteria for inclusion were as follows: (a) independently diagnosed by a licensed psychologist and pediatric neurologist with ADHD, combined type using a modified version of Barkley’s (1997b) clinician’s manual, a semistructured parent interview that follows the criteria of the DSM–IV–TR; (b) enrolled full-time in school; and (c) exhibited elevated scores on the ADHD Index of the Conners’ Parent Rating Scale–Revised (T score 60; Mildly Atypical; Conners, 2001). In addition, two children with an ADHD Index T score 60 (i.e., 61 and 74). Exclusion criteria included (a) parents who were separated or divorced within the past 12 months; (b) children who had experienced a significant loss (e.g., death of parent, sibling) within the past 12 months; (c) children who had been physically and=or sexually abused within the past 18 months; (d) children diagnosed with a brain injury or seizure disorder; (e) children diagnosed with an anxiety disorder, bipolar disorder, schizophrenia, mental retardation, or a pervasive developmental disorder, and=or with a T score 60 on any Revised Children’s Anxiety and Depression Scale (RCADS) anxiety or depression subscale; and (f) children prescribed nonstimulant (e.g., Strattera) and antidepressant medications TABLE 1 Demographic Characteristics of Sample Variable Age Gender % Men Ethnicity % Caucasian % African American % Asian American % Hispanic=Latino

ANX

n

ADHD

n

12.83 2.62

24

11.57 2.31

23

29%

24

70%

23

75% 8% 17% 0%

24 24 24 24

87% 9% 0% 4%

23 23 23 23

Note: ANX ¼ anxiety disorder; ADHD ¼ attention deficit hyperactivity disorder.

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(e.g., Wellbutrin). For children prescribed stimulant medications (70%; 16=23), parents were instructed to not give their children medication the day before and the day of the testing. Anxiety group. Youth (n ¼ 24; 8–17 years, M ¼ 12.83, SD ¼ 2.62) with a primary DSM–IV–TR (APA, 2000) anxiety diagnosis were recruited from families seeking services at a university research clinic specializing in anxiety and mood disorders. Seventy-one percent were girls; 75% were Caucasian, 17% were Asian American, and 8% were African American (see Table 1). Principal anxiety diagnoses included GAD (n ¼ 12), social phobia (n ¼ 4), specific phobia (n ¼ 4), separation anxiety disorder (SAD; n ¼ 3), and panic disorder with agoraphobia (n ¼ 1) based on a clinician-administered Anxiety Disorder Interview Schedule (Anxiety Disorders Interview Schedule–IV–Child=Parent version; Silverman & Albano, 2000). Secondary anxiety diagnoses included social phobia (n ¼ 9), specific phobia (n ¼ 6), SAD (n ¼ 4), GAD (n ¼ 2), and obsessive-compulsive disorder (n ¼ 1). Five participants were diagnosed with two anxiety disorders and eight children met criteria for three or more. Nine youth with an anxiety disorder were diagnosed with a comorbid depressive spectrum disorder (i.e., major depressive disorder, dysthymia, or minor depression), two had a co-occurring disruptive behavior disorder (i.e., conduct disorder or oppositional defiant disorder), and one child was diagnosed with a concurrent eating disorder (i.e., anorexia nervosa). Four of the 24 youth met subthreshold criteria for an anxiety disorder (a Clinician Severity Rating of 3 on the Anxiety Disorders Interview Schedule) but were retained in the sample to increase statistical power. No significant differences were found between the four subclinical youth and the 20 clinically anxious children on any neurocognitive subtest. Children with a comorbid DSM–IV diagnosis of ADHD, bipolar disorder, schizophrenia, mental retardation, or a pervasive developmental disorder were excluded from the study. Measures The Conners Continuous Performance Test–II (CPT–II; Conners, 2004) is a neurocognitive test of sustained attention, attentional shifting, and response inhibition. It was administered in its standard computerized format. The CPT–II features good retest reliability (omission errors: r ¼ .84; commission errors: r ¼ .65; Conners, 2004) and takes approximately 14 min to complete. The test features six blocks, each comprising 20-trial sub-blocks, and requires participants to press a specified key (i.e., space bar) for any letter other than ‘‘X.’’ Each letter is displayed for 250 ms, and the interstimulus intervals vary between 1, 2, and 4s and are different for each block. T scores for CPT–II errors of omission (CPT-OmT; how many

times a child fails to respond to a target; i.e., sustained attention, vigilance) and commission (CPT-ComR=T; how many times the child responds to a nontarget; i.e., sustained attention, impulsivity) and CPT Attention (d’) T scores (CPT-AttT), a sensitivity measure of the child’s ability to distinguish between targets and nontargets (i.e., attentional shifting), were used for statistical analysis. The SCWT (Stroop, 1935), commonly used with children 6 years and older, assesses selective attention, response inhibition, and controlled cognitive processing. In its standard format, the SCWT features three subtests: Stroop Word (word reading), Stroop Color (color naming), and Stroop Color-Word (SCW; selective attention, response inhibition). The SCW prompts the child to name (i.e., selectively attend to) the ink colors of a series of unmatched color words (e.g., the word, ‘‘red,’’ printed in blue ink) while suppressing prepotent responding to the lexical feature of the words (i.e., reading the words). The SCW was administered to all participants in the current study. SCW age-corrected T scores (SCW-T) and the number of SCW errors (SCW-Err) were used for statistical analysis (Weissman & Bates, 2010). The Stroop Word was administered prior to the SCW to prime participants for word reading to increase interference on the SCW. All three Stroop subtests comprise 100 items, and scoring is based on the number of items read or named correctly within a 45-s time interval. All three subtests have demonstrated good retest reliability (r > .80; Connor, Franzen, & Sharp, 1988). Additional psychometric properties are available by the test authors or in other investigations (Ozonoff & Jensen, 1999; Reeve & Schandler, 2001; Savitz & Jansen, 2003; Weissman & Bates, 2010). The Faces Dot Probe Task (FDP; Weissman, Chu, Reddy, & Mohlman, 2010) consists of 20 practice trials and 96 test trials (four blocks, 24 trials per block) and takes approximately 7 min to complete. Following a 500-ms fixation cross at the center of the screen, two faces appear for 500 ms. In practice trials, both faces are neutral, whereas in test trials, one face is neutral and the other is happy, angry, or sad. Once the pictures are presented, a small dot probe (i.e., an up or down arrow) appears in the location of one of the faces (i.e., either the right or left side of the screen). The child is prompted to press the up arrow key in response to an up arrow and the down arrow key in response to a down arrow. Once the child responds, there is a brief intertrial waiting period that varies randomly between 750 and 1,250 ms. On practice trials, a 1,500 ms feedback display appears following the probe indicating a correct or incorrect response or a failure to respond. The FDP was developed for the current study using E-Prime Version 1.0 (2002) and was presented with a refresh rate of 85 Hertz per minute. Participants were seated approximately 2 ft from the computer screen, and all stimuli (i.e., faces, arrows, fixation crosses, and instructions) were presented in black on a white background.


ATTENTION MECHANISMS IN ANXIOUS AND ADHD YOUTH

Facial stimuli were approximately 2 in. high 1.5 in. wide; arrows were approximately 1.5 in. high 0.5 in. wide. The FDP measures the time (in milliseconds) it takes each participant to respond to the dot probe. On trials where the arrow appears in the location of an emotion face, faster response times (RTs) indicate an attentional bias toward emotionally valenced faces. Likewise, on trials where the probe appears in the location opposite an emotion face, slower response latencies indicate an attention bias toward the emotionally charged stimuli. Because children do not respond directly to the emotion cue, the FDP minimizes the possibility that intergroup effects are attributed to a response bias (i.e., an interference in RT) rather than a selective attention processing bias (MacLeod, Mathews, & Tata, 1986). The current FDP uses line drawings as stimuli, and thus may feature less ecological validity than photographic images (Mohlman, Carmin, & Price, 2007). However, research suggests that facial expression may be more easily recognized in cartoon than photographic depictions, as cartoon expressions may eliminate distracting idiosyncratic facial features (e.g., freckles, unusual hairline) and allow exaggeration of expression beyond what a human face is capable of (Calder et al., 2000; Mohlman et al., 2007). The ADIS–IV–Child=Parent version (ADIS-IV-C=P; Silverman & Albano, 2000) features independent parent and child interviews that have shown good interrater (k ¼ .98, parent interview; k ¼ .93, child interview; Silverman & Nelles, 1988) and retest reliability (r ¼ .76, parent interview; Silverman & Eisen, 1992). A composite diagnosis or Clinician Severity Rating (0 ¼ none to 8 ¼ incapacitating) of 4 or higher constitutes a clinical diagnosis. In the present study, psychology doctoral students were trained by watching ‘‘gold standard’’ DVDs. Interviewers were considered reliable when they matched expert ratings of diagnosis and Clinician Severity Rating (Cohen’s j 0.80). Actual mean interrater reliability was j ¼ 0.94 (range ¼ 0.85–0.99). The RCADS–Parent Version (RCADS–P; Chorpita, Yim, Moffitt, Umemoto, & Francis, 2000) is a 47-item parent-report scale that closely corresponds to DSM–IV anxiety and depressive disorders. Factor analysis has yielded subscales that are associated with the diagnoses of interest in the current study. The subscales feature good factorial validity, internal consistency (SAD: a ¼ .78; social phobia: a ¼ .87; obsessive-compulsive disorder: a ¼ .82; panic disorder: a ¼ .88; GAD: a ¼ .84; major depressive disorder a ¼ .87), 1-week retest reliability, and discriminant validity (Chorpita, Moffitt, & Gray, 2005; Chorpita et al., 2000). In addition, the RCADS has demonstrated good convergent validity with other leading anxiety measures (e.g., Revised Children’s Manifest Anxiety Scale). The Conners Parent Rating Scale–Revised (CPRS–R; Conners, 2001) features 80 questions assessing childhood psychopathology (3–17 years) and ADHD-related

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problem behaviors and takes approximately 15 min to complete. The CPRS–R comprises seven DSM–IVderived symptom subscales (i.e., oppositional, cognitive problems=inattention, hyperactivity, anxious-shy, perfectionism, social problems, psychosomatic) and an overall ADHD index that contains a set of items for distinguishing ADHD children from nonclinical youth. Children meeting DSM–IV–TR criteria for ADHD who scored a T score 60 on the ADHD Index (Mildly Atypical; Conners, 2001) were included in the present ADHD sample. The CPRS–R has shown strong internal consistency (a ¼ .75–.90) and 6- to 8-week retest reliability (r ¼ .60–.90), as well as good convergent, divergent, and discriminant validity. Procedure All families completed a brief telephone-screening interview, and all children completed a half-hour in-person cognitive assessment battery (i.e., CPT–II, SCWT, FDP). The children in the ANX group and their parents completed a 3-hr clinician-administered diagnostic interview (i.e., ADIS-C=P) as part of the usual clinic assessment procedures. All families in the ADHD group completed brief parent-report measures of child psychopathology (i.e., RCADS–P, CPRS–R) and were independently diagnosed by a licensed psychologist and pediatric neurologist with ADHD, Combined Type using a modified version of Barkley’s (1997b) clinician’s manual. Breaks were provided for children in both clinical groups, as needed, either at the request of the child or if the child showed signs of fatigue or excessive off-task behavior. Written informed consent was obtained from each parent, and written assent was obtained from each child following careful description of study procedures in developmentally appropriate language. All families received a $5 Blockbuster or Target gift card for their participation, along with a copy of the computerized CPT–II testing report and verbal feedback about their child’s performance on the CPT–II. Participants in the ADHD group received an additional $20, as their assessment was not part of typical clinic procedures. The study was approved by the Institutional Review Board of Rutgers University.

RESULTS All data were analyzed using SPSS Version 16.0 for Windows. Skew, kurtosis, outliers, and normality were examined for all dependent variables. Distributional properties for all dependent measures approximated the normal distribution and represented ranges consistent with the literature. There were no missing data with the exception of two anxious children who chose not to


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complete the CPT due to its length. In addition, initial correlations of age, gender, and ethnicity with all cognitive variables were conducted. Age and gender were found to significantly correlate with several dependent measures, and thus were included as covariates in all subsequent analyses. Age correlated with FDP Happy Bias scores (r ¼ .50, p < .001). Gender correlated with SCW T scores (r ¼ .31, p ¼ .037), such that girls performed better than boys. To examine the effects of gender on cognitive outcomes, a series of analyses of covariance (ANCOVAs) were performed to compare boys and girls on all cognitive measures, with age included as a covariate. No significant gender effects were found, indicating gender did not differentially impact cognitive outcomes across group. In addition, all analyses were completed separately for boys and girls, and the same pattern of results was found within each sex group. Finally, partial eta-squared (g2p ) were computed as an estimate of effect size to assess the clinical or practical differences between groups. To aid interpretation, the following convention was used for interpreting effect size (