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Developmental Cognitive Neuroscience 4 (2013) 16– 28
Contents lists available at SciVerse ScienceDirect
Developmental Cognitive Neuroscience
j o ur nal ho me p age : ht tp : / /www.e lsev ier .com/ locat e/dcn
reliminary data suggesting the efficacy of attention training forchool-aged children with ADHD
eanne Tamma,∗, Jeffery N. Epsteina, James L. Peugha, Paul A. Nakoneznyb,arroll W. Hughesb
CCHMC, 3333 Burnet Avenue, ML 10006, Cincinnati, OH 45229, USAUniversity of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., MC 8589, Dallas, TX 75390-8589, USA
r t i c l e i n f o
rticle history:eceived 11 July 2012eceived in revised form 1 October 2012ccepted 3 November 2012
eywords:DHDttention trainingognitive trainingxecutive functioning
a b s t r a c t
A pilot randomized clinical trial was conducted to examine the initial efficacy of PayAttention!, an intervention training sustained, selective, alternating, and divided atten-tion, in children diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD). Aftera diagnostic and baseline evaluation, school-aged children with ADHD were randomizedto receive 16 bi-weekly sessions of Pay Attention! (n = 54) or to a waitlist control group(n = 51). Participants completed an outcome evaluation approximately 12 weeks after theirbaseline evaluation. Results showed significant treatment effects for parent and clinicianratings of ADHD symptoms, child self-report of ability to focus, and parent ratings ofexecutive functioning. Child performance on neuropsychological tests showed significant
ntervention treatment-related improvement on strategic planning efficiency, but no treatment effectswere observed on other neuropsychological outcomes. Treatment effects were also notobserved for teacher ratings of ADHD. These data add to a growing body of literature sup-porting effects of cognitive training on attention and behavior, however, additional research
is warranted.. Introduction
Attention training, also known as cognitive training, isn intervention in which the various components of atten-ion are viewed as skills that can be enhanced by training.ttention in this conceptualization is defined as theppropriate allocation of processing resources to relevant
timuli and is thought to comprise several sub-processesCoull, 1998). Specifically, attention can be fractionatednto (1) attention orientation (direction of attention to a∗ Corresponding author at: Division of Behavioral Medicine and Clinicalsychology, Cincinnati Children’s Hospital Medical Center, 3333 Burnetvenue, MLC 10006, Cincinnati, OH 45229, USA. Tel.: +1 513 803 3176;
ax: +1 513 636 0755.E-mail addresses: [email protected] (L. Tamm),
[email protected] (J.N. Epstein), [email protected]. Peugh), [email protected] (P.A. Nakonezny),[email protected] (C.W. Hughes).
878-9293/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.dcn.2012.11.004
© 2012 Elsevier Ltd. All rights reserved.
stimulus), (2) selective attention (prioritizing one stimulusin favor of another), (3) sustained attention (attending toone stimulus over time), and (4) divided attention (divid-ing attention between two plus stimuli) (Coull, 1998).These sub-processes are thought to be the building blocksunderlying executive functioning and to have unique neu-ronal correlates (Coull, 1998). Theoretically, more efficientattentional sub-processes should give rise to enhancedexecutive processes (Paelecke-Habermann et al., 2005),such as inhibition, task management, planning, monitor-ing/working memory, and coding. Further, more efficientexecutive functioning should translate into improvementsin behavior – for example, improved inhibition might leadto better self-regulation.
The rationale for attention training is based on the
concept that efficiency increases after repetitive practiceof specific cognitive operations of attention (Posner andRaichle, 1994), theoretically because practice producesadaptations in the underlying neuroanatomical networks
Cognitiv
L. Tamm et al. / Developmentallinked to these processes (Kerns et al., 1999). Initial neu-roimaging research comparing brain activation before andafter different forms of cognitive training supports thiscontention (Beauregard and Levesque, 2006; Brefczynski-Lewis et al., 2007; Erickson et al., 2007; Kim et al., 2008;Thimm et al., 2006). Furthermore, controlled trials investi-gating the efficacy of attention training programs reportgains in both trained and untrained assays of attentioncomponents such as sustained attention and executivefunction (Ethier et al., 1989; Finlayson et al., 1987; Grayand Robertson, 1989; Mateer and Mapou, 1996; Penkman,2004; Sohlberg and Mateer, 1987; Thompson and Kerns,1995).
In the last decade, researchers have turned toinvestigate attention training as an intervention forAttention-Deficit/Hyperactivity Disorder (ADHD) sinceADHD involves impairment in attention and related exec-utive functions. There is a growing literature providingsupport for attention training in individuals with ADHD(Epstein and Tsal, 2010; Kerns et al., 1999; Klingberget al., 2002, 2005; O’Connell et al., 2006; Semrud-Clikemanet al., 1999; Shalev et al., 2004; Tamm et al., 2007, 2010;Williams, 1989). These studies report improvements inthe cognitive skill being trained directly but also improve-ments in untrained cognitive skills (Epstein and Tsal, 2010;Tamm et al., 2010; Williams, 1989), although effect sizesare typically small to moderate (Epstein and Tsal, 2010).Furthermore, studies of attention training in ADHD alsoprovide initial support for improvements on untrainedmeasures of attention and academic efficiency (Semrud-Clikeman et al., 1999; Williams, 1989), as well as reductions(although not consistently) in teacher and parent ratingsof ADHD symptoms (Kerns et al., 1999; Klingberg et al.,2005; Semrud-Clikeman et al., 1999; Shalev et al., 2004;Tamm et al., 2010; Thomson et al., 1984) and restless-ness/head movements (Klingberg et al., 2002). There is alsopreliminary neuroimaging data to suggest that cognitivetraining impacts brain function in ADHD (Hoekzema et al.,2010, 2011). After 10 days of cognitive training, childrenwith ADHD showed increases in orbitofrontal, superiorfrontal, middle temporal, and inferior frontal cortex onan inhibition task and increased cerebellar activity on anattentional task, while the control group showed no suchactivation differences (Hoekzema et al., 2010). Further-more, these activation differences were complemented byfocal volumetric gray matter increases in bilateral mid-dle frontal cortex and right inferior–posterior cerebellum(Hoekzema et al., 2011). Taken together, these studiessuggest that attention training is a promising treatmentapproach for ADHD. However, to date there have been fewrandomized clinical trials investigating attention trainingin ADHD. This is critical given that replication studies havenot always been successful and, more critically, studies uti-lizing blinded evaluators do not always find a positive effectfor attention training.
One potentially promising intervention is Pay Atten-tion! (www.lapublishing.com), an intervention targeting
sustained, selective, divided, and alternating attention,constructs which have been shown to be compromisedin individuals with ADHD (Manly et al., 2001). A smallrandomized trial of the effects of Pay Attention! in sevene Neuroscience 4 (2013) 16– 28 17
children with ADHD reported significant benefits of atten-tion training in measures of sustained attention, executivefunctioning, and effortful processing compared to chil-dren in a video game control group (Kerns et al., 1999).However, the sample size for this study was small andparticipant diagnosis was not verified by the researchteam. We conducted an open trial of Pay Attention! with23 children diagnosed with ADHD and showed effectson executive functioning measures (cognitive flexibility,working memory, parent ratings), as well as improve-ments in ADHD symptomatology (Tamm et al., 2010).However, we did not utilize a control group as the primaryemphasis was to determine feasibility of the interven-tion in a clinically diagnosed group and in our outpatientsetting.
The current trial was initiated to investigate if thePay Attention! intervention was effective in improvingtrained and untrained executive functions as well asparent, teacher, and clinician ratings of attention andbehavior when compared to a waitlist control groupthat did not receive the intervention. Consistent withthe theoretical underpinnings of attention training, wehypothesized that by training attention (sustained, selec-tive, divided, and alternating), we would see proximaltreatment-related improvements in executive functioningand distal treatment-related improvements in attentionand behavior (see Fig. 1).
2. Method
The study was approved by the University of Texas,Southwestern Medical Center at Dallas InstitutionalReview Board and informed parental consent and partic-ipant assent were obtained from all participants prior toinitiating any procedures.
2.1. Participants
Participants (n = 132) were recruited from outpatientclinics at Children’s Medical Center at Dallas, the com-munity, and Shelton School, a private school for learningdifferences; 105 participants were randomized (see Fig. 2,CONSORT diagram). Participants ranged in age from 7to 15 years (M = 9.3, SD = 1.4) and were predominantlyCaucasian. Demographic and baseline characteristics arepresented in Table 1. Randomization was stratified bygender, ADHD subtype, and medication status. Partici-pants were asked to maintain a stable medication status(i.e., continue taking medication as directed and not ini-tiate new medication during the study). Exclusion criteriaincluded the following: estimated full scale IQ < 85, historyof head injury, history of prenatal drug exposure, diagnosiswith other congenital or acquired neurological conditions,and participating in other non-pharmacological treatmentinterventions for ADHD (e.g., neurofeedback, cognitive-behavioral therapy, etc.).
2.2. Design and procedure
Families meeting eligibility criteria by phone screenwere invited to participate in a baseline evaluation.
18 L. Tamm et al. / Developmental Cognitive Neuroscience 4 (2013) 16– 28
Distal Ou tcomesBehavior & Attention (Inattention, H yperactivity-Impuls ivity, Impairment as
measured by the SNA P, BASC, and CGI )
Proximal Ou tco mesExecutive Functioning
(Working Memory, Cognitive Flexibility, Planning/Problem Solv ing, Response Inhibit ion, Metacognition as measured by subtests of the DKEFS ,
WISC, WJ -III , Qu otient, and parent BRI EF rat ings)Attention
(Sus tained, Se lec tive ,Alternating, Divi ded
Attention as measured by TEA-Ch)
Training
Fig. 1. Model of training targets and outcomes.
Assessed for e ligibi lity(n = 132 )
Excluded (n = 27)
Not mee tin g inclus ion crit eria(n = 27)
Randomized (n = 1 05)
Allocated to inter vention(n = 54)
Rece ive d al located intervention (n = 51)
Did not rece ive al located interv enti on (n = 3)
Parent di d not respond to calls to sch edule inter vention
Allo
catio
nE
nrol
lmen
t
Allocated to waitlis t c ontr ol(n = 51)
Rece ive d al located interventi on (n = 51)
Did not rece ive al located interventi on (n = 0)
Out
com
e Attended Outco me (n = 45)
Lost to follow up (n =6)
Attended Outco me (n = 46)
Lost t o fol low up (n=5)
Fig. 2. CONSORT diagram.
L. Tamm et al. / Developmental Cognitive Neuroscience 4 (2013) 16– 28 19
Table 1Demographic and baseline characteristics.
Intervention (n = 54) Waitlist control (n = 51) Statistical comparison
Age (mean, SD) 9.1 (1.2) 9.5 (1.5) t(103) = 1.29, n.s.Race (n) �2(4) = 3.7, n.s.
Caucasian 40 (74%) 34 (67%)African American 1 (1%) 4 (7%)Hispanic 7 (13%) 5 (10%)Asian 3 (6%) 2 (4%)Mixed race 3 (6%) 6 (12%)
Gender (n) �2(1) = .05, n.s.Male 36 (66%) 35 (69%)Female 18 (34%) 16 (31%)
ADHD subtype (n) �2(2) = .003, n.s.Combined type 32 (59%) 30 (59%)Inattentive type 21 (39%) 20 (39%)Not otherwise specified 1 (2%) 1 (2%)
Comorbid Dx (n) n/aOppositional defiant disorder 1 (2%) 2 (4%)Anxiety disorders 1 (2%) 4 (8%)Elimination disorder 6 (11%) 2 (4%)Depressive disorders 0 1 (2%)Learning disorder 1 (2%) 0
2
Medicated for ADHD (n) 35 (65%)Estimated IQ (mean, SD) 107.4 (11.5)Intervention Sessions Attended (mean, SD) 13.5 (3.2)
A semi-structured clinical interview, the Kiddie-SADS-Present and Lifetime Version or K-SADS-PL (Kaufman et al.,1997), was conducted with the primary caregiver, andwith the child/adolescent separately, to determine ADHDdiagnosis. Information regarding family functioning, fam-ily psychiatric history, and developmental and medicalhistories was also collected. In addition, the clinical inter-viewers provided ratings of ADHD on the Swanson, Nolan,and Pelham DSM-IV ADHD rating scale (SNAP-IV) basedon the interviews, and impairment/functioning usingthe Clinical Global Impressions (CGI) rating scale. Par-ents completed several rating scales assessing attention,executive function, and behavior, and participants wereadministered several tests assessing a variety of execu-tive functions including planning, behavioral inhibition,visual–spatial abilities, and working memory.
Following the baseline evaluation, participants wererandomized to either receive the intervention or to awaitlist control group. Participants randomized to theintervention attended twice-weekly 30-min sessions for 8consecutive weeks (for a total of 16 sessions). Individualsrandomized to the waitlist control condition were asked tonot begin any new treatment for ADHD during the waitperiod and were offered the opportunity to receive theintervention at the end of the wait period. All participantsand their parents were invited to attend an outcome eval-uation. There was variability between subjects as to howlong it took them to complete the 16 treatment sessions dueto parent and interventionist schedules; thus we opted tore-test all participants on the same schedule (i.e., 12 weeksafter baseline). During the outcome evaluation, parents andchildren were re-interviewed using the K-SADS-PL, clini-cians completed SNAP-IV and CGI improvement ratings,
and participants completed the same battery of execu-tive functioning measures as at baseline. Parents were alsogiven a packet of ratings to give to teachers to completeat baseline and outcome, although few teachers returned37 (73%) � (1) = .73, n.s.105.9 (13.3) t(100) = 0.63, n.s.n/a
the forms (n = 33 at baseline and n = 27 at outcome for theintervention group; n = 25 at baseline and n = 27 at outcomefor the waitlist control group).
2.3. Intervention
The Pay Attention! materials are designed to train sus-tained, selective, alternating, and divided attention usingvisual and auditory stimuli. The visual stimuli include aset of cards depicting drawings of children and adults thatcan be distinguished by various features including age,gender, hair color, and other physical qualities; and a setof home layouts that include several rooms with objectsthat can be sorted by color, shape, and other character-istics. The auditory stimuli include lists of words playedon a CD with the participant required to press a buzzerwhenever a specific word is heard. The tasks become pro-gressively more difficult (e.g., a distracting overlay is placedover the visual stimuli, a distracting sound is played duringtask completion, or participants are asked to complete twotasks simultaneously). All participants completed an initialsession to establish performance levels and orient themto the materials. Participants then progressed throughthe four modules, beginning with the simplest sustainedattention tasks. After criterion was reached (e.g., gains inspeed while maintaining overall accuracy), the next mod-ule was started. Not all participants completed all fourmodules since they progressed at different rates, althoughthe majority completed at least the sustained and selec-tive attention modules. Participants were given immediatefeedback regarding their performance and interventionistsspent time each session discussing how the targeted atten-tional skill could be applied in a home or school setting.
Pay Attention! is designed to be flexibly delivered so thatthe interventionist can tailor the treatment to the specificneeds of the participant. Parents were also provided withreading materials about the attention skills being trained
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nd met with the child and interventionist for a few min-tes after each session to discuss the activities practiced atach session, which skill was being trained, and how par-nts could support the child’s implementation of the skilln home and school activities.
.4. Measures
.4.1. Rating scales
Swanson, Nolan, and Pelham (SNAP-IV) DSM-IV ADHDRating Scale (Swanson, 1992): Clinicians (based on par-ent and child interview), parents and teachers rated howwell each ADHD symptom described the child on a fourpoint Likert scale (0 = not at all, 1 = just a little, 2 = quitea bit, 3 = very much). We examined average scoresfor Inattention symptoms and Hyperactivity/Impulsivitysymptoms. Although psychometric properties have notbeen investigated for the SNAP-IV, adequate reliabilityand validity has been established for similar DSM-IVchecklists [e.g., ADHD Rating Scale-IV (DuPaul et al.,1998)].Behavioral Assessment System for Children, Second Edi-tion (BASC-II) (Reynolds and Kamphaus, 2004): Parentsand teachers completed the BASC-II describing a vari-ety of behaviors. For analytic purposes, we analyzedsubscales relevant to ADHD (i.e., Externalizing, Hyper-activity, Attention Problems and Behavioral SymptomsIndex). The scale has been demonstrated to have ade-quate reliability and validity (Reynolds and Kamphaus,2004).Clinical Global Impressions (CGI) (Leon et al., 1993):Un-blinded clinicians rated the severity of the child’simpairment related to ADHD at baseline and outcomeon an 7-point scale ranging from Not At All Ill to VerySeverely Ill, and the level of improvement at outcome onan 7-point Likert scale ranging from Very Much Improvedto Very Much Worse (4 being “no change”). The scale hasadequate reliability and validity (Leon et al., 1993).Attentional Control Scale (ATTC) (Derryberry and Reed,2002): Participants rated themselves on their ability tofocus and shift their attention on the ATTC. The ATTC con-sists of 20 items that are rated on a four-point Likert scalefrom 1 (almost never) to 4 (always). The ATTC yields twoprimary scales (attention shifting and attention focus-ing). This measure was added later in the study, and thuswas only available for 20 participants. Analyses of con-tent, internal and construct validity as well as reliabilityprovided evidence of the scale’s significant convergentand discriminant validity (Fajkowska and Derryberry,2010).Behavior Rating Inventory of Executive Function (BRIEF)(Gioia et al., 2000): Parents and teachers completed thisrating scale assessing executive function behaviors inthe home and school environments, yielding T-scoreson several subscales, including Inhibit, Shift, Working
Memory, Emotion Regulation, Organization, Planning,Initiate, Monitor, Behavioral Regulation Index, Metacog-nitive Index, and a General Executive Composite. Thescale has adequate reliability and validity (Gioia et al.,2000).e Neuroscience 4 (2013) 16– 28
2.4.2. Neuropsychological measures• Test of Everyday Attention for Children (TEA-Ch) – The
TEA-Ch is an objective measure of attention (Manly et al.,2001). The battery has alternate forms (A and B) to allowfor re-test. Subtests utilized for this study included: SkySearch, Score!, Creature Counting, Sky Search DT, ScoreDT, and Code Transmission. The TEA-Ch was designedto more directly assess sustained, alternating, selective,and divided attention constructs while at the same time,minimizing demands on memory, reasoning, task com-prehension, motor speed, verbal ability, and perceptualability (Manly et al., 2001). Thus, the TEA-Ch tasks moredirectly map onto the constructs being trained in PayAttention! and was included as an assessment of whetherthe intervention was successful in training the desiredtarget (and thus was only collected for the interventiongroup). Form A of the TEA-Ch has adequate reliabilityalthough Form B is not as well established; validity stud-ies are lacking (Strauss et al., 2006).
• Wechsler Intelligence Scale for Children – Fourth Edi-tion (WISC-IV) (Wechsler, 2004): Matrix Reasoning (fluidreasoning/cognitive flexibility), Digit Span and LetterNumber Sequencing (working memory). Vocabulary andBlock Design were administered to provide an estimateof Full Scale IQ (Sattler and Dumont, 2004) at baselineonly. Psychometric properties for this measure are wellestablished (Wechsler, 2004).
• Woodcock Johnson Tests of Achievement – Third Edi-tion (WJ-III) (Woodcock et al., 2001): UnderstandingDirections (auditory working memory, listening compre-hension). The subtest has high reliability (>.8) (Woodcocket al., 2001).
• Delis–Kaplan Executive Functioning System (D-KEFS)(Delis et al., 2001): Tower Test (problem solving) andColor-Word Interference (CWI; inhibition/cognitive flex-ibility). Additional measures generated from the Towersubtest include Mean First Move Time (initial planningtime as measured by the time spent planning the firstmove), Time Per Move Ratio (overall planning time asmeasured by the time spent planning all moves subse-quent to the first move), Move Accuracy Ratio (accuracyand planning efficiency of problem solving strategy asmeasured by the total number of moves), and RuleViolations Per Item Ratio (establishing and maintainingcognitive set as measured by the number of rule viola-tions). Reliability of the DKEFS subtests is in the moderateto high range and validity has been established with anumber of executive functioning measures (Delis et al.,2004).
• Quotient ADHD system – The Quotient is a continuousperformance test assessing a participant’s ability to payattention which also objectively measures a patient’sability to sit still (Sumner, 2010). Children are asked topress a button each time they see an eight-sided starbut to withhold their response to five-sided stars. Amotion-tracking device directed at sensors on the anklesand forehead records movement simultaneously withperformance of the task. Variables analyzed included
omission errors (percentage of missed targets), com-mission errors (percentage of inaccurate responses tonon-targets), latency (average amount of time to respond
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L. Tamm et al. / Developmentalcorrectly), and variability (variation in response time tothe correct target). Reliability and validity for this mea-sure has not yet been established, although the task isvery similar to the Connor’s Continuous Performance Testwhich has adequate reliability and validity (Conners andJeff, 1999).
2.5. Analytic strategy
Demographic and baseline clinical characteristics of thetwo groups were reported using mean (SD) for continu-ous variables and frequency (percentage) for categoricalvariables. To identify any differences between the charac-teristics of the 2 groups, we used the 2-independent samplet test for continuous variables and the �2 test for categoricalvariables.
As a manipulation check of whether the Pay Attention!intervention impacted sustained, selective, divided, and/oralternating attention, we conducted repeated measuresANOVAs in SPSS for each of the 6 subtests administered ofthe TEA-Ch (note, alternate forms were used for this mea-sure) for the intervention group only; this measure was notavailable for the waitlist control group.
To address whether the intervention improved behav-ioral and executive functioning ratings, and child per-formance on executive function tasks, multivariate andunivariate generalized estimating equation (GEE) analy-ses were performed to test for significant mean differencesbetween the treatment intervention and waitlist controlgroups at outcome using baseline scores and participantage (for non-normed measures, e.g., SNAP-IV, CGI) as con-trol covariates (e.g., Feldon et al., 2011). The baselinescores were included as covariates to address potentialgroup differences at baseline contributing to the patternof findings at outcome. Multivariate GEE analyses wereused to test for group mean differences among corre-lated subtests from the same response variable measure(e.g., BRIEF subscales), while univariate GEE analyses wereused to test for mean differences among summative sub-scales or total scores from a response variable measureseparately to avoid collinearity (e.g., BRIEF Index scores).These GEE analyses were conducted using Mplus version6.12 (Muthén and Muthén, 1998–2010) to: (a) handlemissing data via maximum likelihood estimation assum-ing missing at random (MAR) (e.g., Enders, 2010), (b) testfor significant response variable mean differences directlyusing the ‘Model Constraint’ and ‘New’ commands, and(c) ensure that analysis assumptions of homogeneity ofcovariance matrices and homogeneity of covariate regres-sion slopes were met by imposing the necessary parameterestimate constraints prior to analysis. Testing for groupdifferences on the ATTC attention focusing and attentionshifting scales was done via separate analyses of covariancein SPSS (baseline scores and age were entered as covari-ates) due to the reduced sample size (N = 20) availablefor those analyses. Given the number of response variablemean difference computations involved, the false discov-
ery rate procedure (Benjamini and Hochberg, 1995) wasused to ensure that no more than 5% of the total num-ber of significant findings reported represent Type-I errors.The false discovery rate has been shown to produce fewere Neuroscience 4 (2013) 16– 28 21
Type-I errors than per-comparison critical alpha correc-tions, and has been shown to better maintain statisticalpower compared to experiment-wise critical alpha cor-rections (Benjamini and Hochberg, 1995; Maxwell andDelaney, 2004). The p-values listed in Tables 2–4 arethose generated from the false discovery rate proce-dure and are thus labeled “corrected p-value”. We alsoreport effect sizes which can be particularly illuminat-ing when there is limited power (e.g., teacher ratings,child self-report ratings), and where one wants to com-pare them with other research in the literature. Cohen’sd effect size estimates were computed for all group meandifferences.
Finally, additional analyses were conducted to investi-gate if there were any moderators of treatment responseafter controlling for baseline score variation. Specifically,for each response variable, separate treatment group by (1)age, (2) gender, (3) IQ, (4) ADHD subtype, and (5) medica-tion status interaction variables were computed and testedfor significance. Results of these analyses were also cor-rected for Type-1 error inflation via the false discovery rateprocedure.
3. Results
3.1. Manipulation check
Compared to their pre-intervention performance, thegroup that received intervention improved significantly onthe TEA-Ch Score!, Sky Search Completion Time and Atten-tion (combined accuracy and timing score), and CreatureCounting Completion Time after the intervention (Table 2).
3.2. Parent, clinician, and teacher behavioral ratings
Parents and clinicians reported significantly fewerADHD symptoms on the SNAP-IV Inattention and Hyper-activity/Impulsivity ratings for the intervention groupcompared to the waitlist control group (Table 3). Onthe BASC-II, parents rated children in the interventiongroup as having fewer Attention Problems comparedto the waitlist control group. Teacher ratings were notsignificant for the SNAP-IV or BASC-II. Clinician rat-ings on the CGI indicated lower severity and greaterimprovement for the intervention group than the wait-list control group. Participants rated themselves as havingsignificantly improved ability to focus their attention[F(3, 19) = 6.6, p < .05, Cohen’s d = 1.11] and shift theirattention [F(3, 19) = 5.5, p < .05, Cohen’s d = .51] on theATTC.
Examination of effect sizes revealed that teachers werealso reporting reductions in Hyperactivity/Impulsivity rat-ings (medium effect) for the intervention group comparedto the control group. On the BASC-II, both parents andteachers reported improvements on the Externalizing,Hyperactivity, and Behavioral Symptoms Index, and tea-
chers reported improvements on the Attention Problemssubscale, for the intervention group compared to the con-trol group. These effect sizes ranged from small to large(Table 3).
22 L. Tamm et al. / Developmental Cognitive Neuroscience 4 (2013) 16– 28
Table 2Intervention group results on Test of Everyday Attention for Children (TEA-Ch).
Pre-interventionMean (SD)
Post-interventionMean (SD)
F Corrected p
Sustained attention/divided attentionScore! 7.2 (2.4) 8.6 (3.3) 8.02 .007**
Sky Search Dual Task 7.0 (4.0) 7.8 (3.8) .06 .80Score Dual Task 8.8 (3.4) 9.2 (3.5) .47 .50Code Transmission 7.6 (2.8) 8.1 (3.3) 1.31 .26
Selective attentionSky Search Accuracy 10.2 (3.1) 10.5 (2.6) .57 .46Sky Search Completion Time 8.8 (2.3) 10.1 (2.7) 12.3 .001**
Sky Search Attention (combined accuracy/time) 8.9 (2.9) 9.9 (2.7) 5.5 .024*
Alternating attentionCreature Counting Accuracy 9.3 (3.3) 9.9 (2.8) .73 .39
**
3
aftIf
rpBsmtc
3
cTico
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3
aeoBp
Creature Counting Completion Time 8.5 (2.6)
* p < .05.** p < .01.
.3. Parent and teacher ratings of executive functioning
Significant improvements were reported by parents forll the BRIEF subscales, with the exception of Inhibit (trendor improvement) and Emotion Regulation for the interven-ion group compared to the waitlist control group (Table 4).n contrast, no significant effect of treatment was observedor any subscale on the teacher BRIEF ratings.
Examination of effect sizes for the teacher ratingsevealed improvements for the intervention group com-ared to the waitlist control group on the teacher ratedRIEF Shift, Emotion Regulation, Initiate, Working Memoryubscales, and Behavioral Regulation Index, and a decre-ent (moderate effect size) for Organization (Table 4),
hough none of these effects reached statistical signifi-ance.
.4. Child measures of neuropsychological functioning
Children in the intervention group performed signifi-antly better than the waitlist control group on the DKEFSower Time per Move Ratio (Table 5). No other signif-cant differences between the intervention and waitlistontrol group were observed on the other neuropsychol-gical measures.
Examination of effect sizes revealed a small effect favor-ng the intervention group on the DKEFS Inhibition, DKEFSnhibition/Switching Errors, DKEFS Tower Mean First Moveime, DKEFS Tower Move Accuracy Ratio, WISC-IV Matrixeasoning, Quotient Latency, Quotient Variability, and WJ-
II Understanding Directions measures, but these did noteach statistical significance. The intervention group alsoade more errors of omission on the Quotient compared
o the waitlist control group.
.5. Potential treatment moderators
There were no significant interactions between groupnd IQ, ADHD subtype, medication status, or gender. How-
ver, we observed an interaction between age and groupn the BRIEF Shift subscale (b = 3.33, Wald Z = 3.26, p < .01),RIEF Behavioral Regulation Index (b = 1.95, Wald Z = 2.67,< .01), and the CGI Severity rating (b = .23, Wald Z = 2.56,
9.8 (3.0) 9.4 .004
p < .01) at outcome. For all three interactions, older mem-bers of the treatment group had lower scores on thoseresponse variables.
4. Discussion
The results of this pilot randomized clinical trialof Pay Attention! in children with ADHD suggest thatthe intervention was successful in impacting attentionalsub-processes targeted by training (i.e., we observedimprovement on tasks assessing aspects of sustained,selective, divided and alternating attention like visualdetection, response speed, etc.). Further, our data showthat the intervention improved ADHD symptomatologyby parent and clinician report, reduced impairment byclinician report, improved executive functioning includinginhibition, shifting, planning, and self-monitoring by par-ent report, improved child self-report of ability to focus andshift attention, and improved child performance on a taskmeasuring planning efficiency. Although non-significant,examination of effect sizes revealed small to moderateimprovements for the intervention group on teacher ADHDand executive function (Shift, Emotion Regulation, Initiate,and Working Memory) ratings, and child performance onexecutive function measures of inhibition, planning, com-prehension and memory of verbal instructions, cognitiveflexibility, and response latency. As with other studies,these promising findings are tempered by the fact that wedid not have blinded evaluators, parents may have beenaffected by expectancy bias or Hawthorne Effect, and thevast majority of the most objective measures administeredto both groups (child neuropsychological tests) were notstatistically significant despite having small to moderateeffect sizes. Further, teacher ratings were not significant.
Although not statistically significant in most cases,the direction of group means were, however, generallyconsistent with those of the previous small randomizedclinical trial of Pay Attention! (Kerns et al., 1999) whichreported gains on measures of planning, sustained auditory
attention, sustained visual attention, behavioral inhibi-tion/executive function/selective attention, and academicefficiency. There was also a trend observed for improvedteacher ADHD ratings. In this trial, which utilized different
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Table 3Group differences at outcome: parent, clinician and teacher behavior ratings.
InterventionMean (SD)Baseline
WaitlistMean (SD)Baseline
InterventionMean (SD)Outcome
WaitlistMean (SD)Outcome
Wald Z Corrected p Cohen’s d
SNAP-IVInattention – P 2.13 (0.5) 2.14 (0.5) 1.42 (0.5) 2.15 (0.5) −7.42 <.001 1.65 L
Hyperactivity/Impulsivity – P 1.30 (0.7) 1.42 (0.8) 0.93 (0.6) 1.30 (0.7) −3.19 .007 0.65 M
Inattention – C 2.37 (0.4) 2.42 (0.4) 1.84 (0.5) 2.39 (0.5) −6.85 <.001 1.41 L
Hyperactivity/Impulsivity – C 1.60 (0.6) 1.58 (0.7) 1.27 (0.6) 1.51 (0.7) −3.44 .003 0.68 M
Inattention–T 1.96 (0.7) 1.46 (0.8) 1.84 (.6) 1.68 (0.7) −0.20 .98 0.07Hyperactivity/Impulsivity – T 1.62 (1.0) 0.94 (0.8) 1.35 (0.9) 1.18 (0.9) −1.23 .37 0.49 M
BASC-IIExternalizing – P 54.52 (9.4) 56.47 (10.1) 51.83 (9.1) 54.96 (10.6) −1.21 .37 0.25S
Behavioral Symptoms Index–P 57.26 (9.8) 59.24 (9.2) 53.67 (10.2) 57.8 (9.8) −1.56 .29 0.35 S
Hyperactivity – P 59.87 (12.1) 63.24 (12.9) 54.50 (9.6) 60.43 (13.7) −2.13 .10 0.46 M
Attention Problems – P 65.06 (7.7) 67.90 (5.4) 60.21 (8.3) 66.00 (7.4) −3.00 .01 0.66 M
Externalizing – T 60.64 (11.1) 50.52 (7.7) 57.97 (10.3) 54.77 (10.3) −1.72 .21 0.66 M
Behavioral Symptoms Index – T 65.30 (10.6) 55.04 (9.0) 61.00 (8.4) 58.72 (11.7) −0.94 .53 0.33 S
Hyperactivity – T 66.79 (14.1) 54.78 (12.2) 63.83 (14.5) 60.67 (14.6) −1.37 .33 0.78 L
Attention Problems – T 65.12 (5.8) 58.35 (8.4) 62.97 (6.1) 62.78 (8.1) −1.39 .32 0.33 S
CGISeverity 4.33 (0.5) 4.35 (0.5) 3.51 (0.9) 4.19 (0.6) −5.45 <.001 1.04 L
Improvement n/a n/a 2.80 (0.8) 3.66 (0.8) −5.54 <.001 1.14 L
Note: S = small effect size (.2–.4); M = medium effect size (.5–.8); L = large effect size (>.8) (Cohen, 1992); SNAP-IV = Swanson, Nolan, and Pelham ADHD Rating Scale; BASC-II = Behavioral Assessment System forChildren, Second Edition; CGI = Clinician Global Impairment rating; P = parent; T = teacher; C = clinician.
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Table 4Group differences at outcome: parent and teacher ratings of executive functioning.
InterventionMean (SD)Baseline
WaitlistMean (SD)Baseline
InterventionMean (SD)Outcome
WaitlistMean (SD)Outcome
Wald Z Corrected p Cohen’s d
BRIEF – parentInhibit 60.34 (12.4) 63.00 (14.0) 56.31 (11.9) 62.32 (14.0) −2.33 .06 0.51 M
Shift 56.92 (11.6) 56.66 (13.0) 52.08 (9.0) 56.98 (13.1) −3.06 .009 0.63 M
Emotion Regulation 52.13 (10.8) 53.54 (11.0) 49.04 (10.0) 52.68 (12.9) −1.84 .18 0.38 S
Initiate 61.83 (10.1) 60.26 (9.2) 53.94 (11.2) 60.26 (8.1) −4.72 <.001 0.98 L
Working Memory 70.02 (8.3) 71.68 (7.4) 61.17 (10.3) 71.30 (8.2) −5.29 <.001 1.16 L
Planning 66.85 (10.0) 66.88 (9.9) 58.69 (11.4) 67.02 (10.1) −4.65 <.001 1.00 L
Organization 60.60 (9.1) 62.14 (8.7) 56.23 (10.3) 61.00 (8.7) −2.50 .04 0.53 M
Monitor 60.83 (9.8) 63.06 (9.1) 54.98 (10.0) 61.83 (11.3) −3.37 .004 0.70 M
Behavioral Regulation Index 57.26 (10.7) 58.94 (11.8) 52.83 (10.3) 58.45 (12.5) −3.00 .01 0.63 M
Metacognitive Index 67.15 (8.9) 68.00 (7.9) 58.71 (10.5) 67.45 (8.9) −5.17 <.001 1.13 L
General Executive Composite 64.19 (8.8) 65.64 (8.9) 56.85 (10.1) 65.06 (9.9) −4.79 <.001 1.03 L
BRIEF – teacherInhibit 69.29 (16.7) 64.76 (19.9) 70.24 (19.0) 67.35 (20.7) −0.37 .91 0.12Shift 61.76 (11.9) 61.42 (13.5) 59.93 (11.4) 61.29 (19.1) −0.49 .82 0.15 S
Emotion Regulation 60.21 (11.9) 55.80(12.0) 61.52 (15.5) 57.52 (17.7) −0.81 .60 0.23 S
Initiate 65.85 (10.8) 67.16 (11.1) 65.10 (10.4) 67.35 (10.8) −0.80 .60 0.20 S
Working Memory 73.32 (14.6) 72.60 (14.9) 70.45 (11.7) 72.19 (12.5) −0.71 .66 0.20 S
Planning 68.29 (12.5) 66.92 (10.9) 67.55 (11.6) 68.12 (12.6) −0.18 .98 0.05Organization 66.65 (18.3) 67.08 (19.1) 70.69 (16.7) 66.38 (20.9) 1.54 .29 0.52 M
Monitor 69.79 (13.0) 65.04 (16.0) 69.14 (12.7) 66.69 (17.4) −0.12 .98 0.03Behavioral Regulation Index 65.76 (12.6) 62.79 (15.6) 66.07 (15.5) 62.96 (19.6) −0.58 .76 0.22 S
Metacognitive Index 70.97 (13.7) 69.60 (13.5) 70.41 (11.5) 70.64 (14.8) −0.21 .98 0.07General Executive Composite 70.47 (13.2) 68.87 (14.7) 70.24 (12.3) 68.91 (17.6) 0.05 .98 0.02
Note: S = small effect size (.2–.4); M = medium effect size (.5–.8); L = large effect size (>.8); BRIEF = Behavior Rating Inventory of Executive Function.
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Table 5Group differences at outcome: child performance on executive functioning measures.
InterventionMean (SD)Baseline
WaitlistMean (SD)Baseline
InterventionMean (SD)Outcome
WaitlistMean (SD)Outcome
Wald Z Corrected p Cohen’s d
DKEFS Color Word InterferenceInhibition Scaled Score 9.88 (3.0) 9.96 (3.0) 11.56 (2.7) 10.98 (2.5) 1.24 .37 0.25 S
Inhibition/Switching Scaled Score 10.43 (2.7) 10.41 (2.9) 11.31 (2.2) 11.17 (2.6) 0.14 .98 0.03Inhibition Errors 9.19 (2.6) 9.88 (3.7) 10.33 (2.4) 10.45 (2.6) 0.01 .99 0.01Inhibition/Switching Errors 9.71 (2.9) 9.98 (3.4) 11.83 (13.4) 10.3 (2.7) 0.79 .60 0.17 S
DKEFS TowerTower Scaled Score 10.04 (2.4) 10.51 (2.7) 11.53 (2.6) 11.57 (2.0) 0.28 .97 0.06Tower Mean First Move Time 12.35 (1.5) 12.35 (1.3) 13.12 (1.0) 12.85 (0.9) 1.4 .32 0.29 S
Tower Time Per Move Ratio 10.89 (1.9) 11.29 (1.7) 12.80 (1.1) 12.36 (1.2) 2.61 .03 0.55 M
Tower Move Accuracy Ratio 7.46 (3.0) 8.27 (2.6) 7.73 (3.0) 7.13 (2.6) 1.26 .37 0.28 S
Tower Rule Violations Per Item 9.65 (2.1) 9.39 (2.5) 10.82 (1.1) 10.8 (1.0) 0.05 .98 0.01WJ-III
Understanding Directions 101.70 (10.1) 102.1 (11.9) 106.20 (10.8) 103.3 (13.1) 1.79 .19 0.35 S
WISC-IVMatrix Reasoning 10.56 (2.4) 11.51 (2.5) 11.51 (3.1) 11.51 (2.8) 1.43 .32 0.30 S
Digit Span 10.63 (2.4) 10.30 (3.0) 10.69 (2.4) 10.26 (3.1) 0.24 .98 0.05Letter Number Sequencing 10.15 (2.7) 9.68 (3.3) 10.47 (2.9) 10.04 (2.9) 0.05 .98 0.01
QuotientOmission Errors 13.07 (14.4) 10.17 (11.3) 16.2 (19.1) 10.27 (12.4) 1.43 .32 0.29 S
Commission Errors 34.68 (20.4) 29.07 (16.7) 24.34 (21.1) 21.00 (17.2) 0.14 .98 0.03Latency 508.2 (89.8) 519.9 (82.6) 568.6 (86.8) 558.3 (108.5) 1.19 .37 0.23 S
Variability 206.7 (85.2) 189.8 (71.5) 219.4 (99.3) 192.0 (72.5) 1.21 .37 0.26 S
Note: WISC-IV = Wechsler Intelligence Scale for Children, Fourth Edition; WJ-III = Woodcock Johnson Tests of Achievement, Third Edition; DKEFS = Delis–Kaplan Executive Functioning System.
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ssessment instruments, we also showed significantmprovements on planning efficiency, and non-significantains in behavioral inhibition/executive function/selectivettention, and auditory attention/memory. It should beoted that Kerns (Kerns et al., 1999) utilized raw scores
n her analyses to address issues related to not being ableo detect treatment effects due to a child remaining inhe same “normative” group and to detect a child’s ownains compared to their own performance. We opted tose scaled scores allowing us better ability to compare theurrent results to those on the open trial which utilizedhe same measures.
The findings from our RCT are also essentially consis-ent with attention, behavior, and executive functioningmprovements we observed in our open trial (Tamm et al.,010). While we did not replicate our previous find-
ngs of significantly improved cognitive flexibility on theKEFS Inhibition/Switching subtest with the Pay Atten-
ion! intervention (Tamm et al., 2010), we did observereater improvement on the WISC Matrix Reasoning andKEFS Inhibition subscales for the intervention group com-ared to the waitlist group (small effects). Thus, we stillee evidence of enhanced fluid reasoning or cognitive flex-bility with Pay Attention! which may not have achievedignificance due to the relatively small sample size. Themprovement in these domains, if replicated in futuretudies, is clinically relevant for individuals diagnosedith ADHD who have neuropsychological deficits in these
onstructs which are associated with impaired brain func-ioning (Tamm and Juranek, 2012).
Despite significant improvements on parent ratings oforking memory, improvements on child working mem-
ry subtests were not observed. There was, however, amall, non-significant effect of treatment on the WJ-IIInderstanding Directions subtest which does involve aorking memory component in that it requires listening
nd mapping a series of sequential directions onto theental structure under construction and maintaining the
equence in immediate awareness until a new directivehanges the sequence (Gernsbacher, 1991). It may be thatay Attention! improves listening abilities and selectivettention more than the construct of working memory.
Analysis of processing variables necessary for DKEFSower subtest completion revealed a significant effect onhe Time Per Move Ratio. The Time Per Move Ratio rep-esents the mean amount of time that was spent on eachove – it is a ratio of the total number of seconds spent
olving a problem relative to the total number of movessed for that problem (Yochim et al., 2009). Interest-
ngly, the intervention group also significantly improvedn the TEA-Ch subtests involving timing (Sky Search,reature Counting), which, taken together with small non-ignificant improvements on Mean First Move Time (speed)nd increased latency (i.e., slowing) on the Quotient contin-ous performance subtest, suggests that one impact of Payttention! training is on the child’s efficiency and timing.ertainly, the intervention itself included a significant focus
n timing, with children encouraged to improve speednd efficiency for each subsequent administration of thearious tasks. This finding, if replicated, could have signifi-ant implications for reducing impairment in ADHD, sincee Neuroscience 4 (2013) 16– 28
recent research suggests that individuals with ADHD havespecific deficits in timing functions which are postulated tobe closely associated with impulsiveness, defined as pre-mature, impatient and delay averse with associated lack oftemporal foresight (Hart et al., 2012; Zelaznik et al., 2012).
Analyses investigating potential treatment moderatorswere not significant for gender, IQ, medication status, orADHD subtype. Age interacted with group on two parent-rated variables (BRIEF Shift and Behavioral RegulationIndex) and one clinician-rated variable (CGI) but not withany of the child neuropsychological variables. We con-trolled for age in the primary analyses for the CGI butnot for the BRIEF which is an age-normed measure. It istempting to conclude that older children may have ben-efited more from the intervention, but given that thiseffect was observed on so few variables, it may be thatthese few variables are more sensitive to the effects ofmaturation.
One of the issues with psychosocial interventions whichhas not received much attention in the literature is whethertreatment can have emanative (Whalen et al., 1985) orunintended negative effects. It is often assumed that behav-ioral or cognitive interventions are either benign or have noimpact, versus potentially negatively impacting outcomes.Although the findings were not statistically significant,there was a small effect for the intervention group to makemore errors of omission on the Quotient continuous per-formance task and a moderate non-significant effect for theintervention group to be rated as less organized by tea-chers than the waitlist control group. Thus, it appears thatdespite potentially improving inhibition and planning effi-ciency, the Pay Attention! intervention might be havingan unintended negative effect in that the child might beslowing too much and therefore failing to respond quicklyenough resulting in somewhat increased errors of omis-sion – again, this effect was not statistically significantbut warrants attention. Replication is necessary, given thelarge number of statistical tests and possibility of chancefindings.
There are a number of strengths to this study includ-ing that it is amongst the largest randomized clinical trialsinvestigating attention training in ADHD. However, thesample size was still relatively limited, at least for detec-ting effects on the neuropsychological variables. It shouldbe noted that when we did a post hoc power analysis toexamine what sample size would be needed to reject thenull hypothesis for the neuropsychological measures, nec-essary sample sizes were much larger than our sample size(e.g., DKEFS Inhibition, N = 314; WISC-IV Matrix Reason-ing, N = 178). The use of a waitlist control group and theresulting lack of blinding to condition could have resultedin a Hawthorne effect or inflated ratings by parent, child,and clinician; replication is necessary with an attentioncontrol group. Another limitation was the relative lack ofteacher data (approximately 50% of the sample had teacherdata available for outcome). Finally, we observed signifi-cant variability on some outcome variables suggesting the
possibility that participants differed in their response totreatment. Further work is necessary to investigate indi-vidual differences in response to treatment and to identifypotential moderators.
Cognitiv
L. Tamm et al. / DevelopmentalOverall, our study adds to the growing body of litera-ture suggesting cognitive training may improve executivefunction and behavior in children with ADHD. However,before Pay Attention! can be considered for use in clinicalsettings additional research is needed. The optimal designwould be an RCT which includes a larger sample of, ideallyun-medicated, children with ADHD, blinded evaluators, acontrol group that is designed to reduce expectancy biases,more teacher ratings, and tests that are selected to measureboth proximal (areas trained) and distal (generalization tofunctional impairment) gains. Nonetheless, our results sug-gest that Pay Attention! did improve sustained, selective,and alternating attention directly, and specifically timingefficiency. Further, it may be effective in reducing ADHDsymptoms as well as improving parental ratings of exec-utive functioning. Effect size examination reveals smallalthough non-significant improvements on teacher rat-ings (small n) and child neuropsychological measures ofexecutive functioning including planning, cognitive flexi-bility, inhibition, auditory working memory, and responselatency/variability. Caution is warranted, however, giventhe large number of statistical comparisons conducted.
Conflict of interest
All authors declare that there are no actual or poten-tial conflicts of interest including any financial, personalor other relationships with other people or organizationswithin 3 years of beginning the submitted work that couldinappropriately influence, or be perceived to influence,their work. The funding sources did not have a role in studydesign, collection, analysis and interpretation of data, writ-ing of the manuscript, nor the decision to submit the articlefor publication.
Acknowledgements
This research project was supported in part by theNational Center for Research Resources, National Institutesof Health Grant Number UL1 RR024982. Its contents aresolely the responsibility of the authors and do not neces-sarily represent the official views of the NIH. Additionalfunding for the project was provided by a gift from theSparrow Foundation for the Center for Advanced ADHDResearch, Treatment, and Education (CAARTE). We grate-fully acknowledge the Pay Attention! interventionists,Aleksandra Foxwell, Jarrette Moore, Lauren Smith, JeanneRintelmann, Amanda Gray, Laure Ames, Maryanne Het-rick, Amy Rollo, Cathy Bass, Ana Arenivas, Deidre Edwards,Sarah Swart, Shelley Williamson, Gina Bolanos, and KyleClayton, and Conrad Barnes for his data management.We also thank Joyce Pickering, Hum.D. for her supportand the use of space at the Shelton School. We appreci-ate the families who participated in the evaluations andintervention.
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