Abstract

Children with intellectual and developmental disabilities (IDD) experience significant difficulties in attention, learning, executive functions, and behavioral regulation. Emerging evidence suggests that computerized cognitive training may remediate these impairments. In a double blind controlled trial, 76 children with IDD (4–11 years) were randomized to either an attention training (n = 38) or control program (n = 38). Both programs were completed at home over a 5-week period. Outcome measures assessed literacy, numeracy, executive functioning, and behavioral/emotional problems, and were conducted at baseline, post-training, and 3-month follow-up. No training effects were observed at post-training; however, children in the training group showed greater improvements in numeracy skills at the 3-month follow-up. These results suggest that attention training may be beneficial for children with IDD; however, the modest nature of the intervention effects indicate that caution should be taken when interpreting clinical significance.

Executive functions encompass a vast range of skills that are needed to control and coordinate behavior. There is general agreement that there are three core interrelated executive functions that are orchestrated by activity within the prefrontal cortex (see Diamond, 2013): inhibition/attention (e.g., being able to control ones attention), working memory (e.g., mentally holding and manipulating information), and cognitive flexibility (e.g., switching between information/tasks). Substantial evidence highlights that the presence of even a few persistent executive function difficulties in childhood can be seen as a developmental risk factor, and may predict later difficulties in learning (Alloway & Alloway, 2010; Clark et al., 2014; LeFevre et al., 2013; Steele, Karmiloff-Smith, Cornish, & Scerif, 2012). Findings from a recent 21-year longitudinal study indicated that difficulties in core executive functions, such as attention, at the age of 4 significantly predicted long-term educational attainment (McClelland, Acock, Piccinin, Rhea, & Stallings, 2013).

During the past decade, there has been an exponential increase in interventions that aim to improve executive functions via targeted training. A distinct literature demonstrates that executive functions and their associated brain regions are dynamic in childhood and highly responsive to environmental influences (Jolles & Crone, 2012; Klingberg, 2010). Based on these principles, investigators have reported that cognitive training can strengthen underlying neural circuits, which may translate into gains in cognitive and behavioral processes (Astle, Barnes, Baker, Colclough, & Woolrich, 2015). These cognitive training programs commonly involve repetition, feedback, and a constant increase in cognitive load. Investigations of the efficacy of these programs in typically developing (TD) children and clinical populations, such as attention deficit hyperactivity disorder (ADHD), have revealed promising results in targeted executive functions such as working memory (Alloway, Bibile, & Lau, 2013; Holmes, Gathercole, & Dunning, 2009; Klingberg et al., 2005; Klingberg, Forssberg, & Westerberg, 2002) and attention (Rabiner, Murray, Skinner, & Malone, 2010; Rueda, Checa, & Combita, 2012). However, generalization to broader skills such as inhibitory control, reasoning, and behavioral problems has been more elusive (Cortese et al., 2015; Rapport, Orban, Kofler, & Friedman, 2013; Redick et al., 2013).

Despite the strong links between executive functions and learning, few studies have assessed whether executive function training impacts academic outcomes (see Titz & Karbach, 2014, for a review). Most studies have utilized working memory training programs, and have generally shown no improvements in literacy or numeracy-based academic skills after a period of intensive training (Dunning, Holmes, & Gathercole, 2013; Holmes et al., 2009; van der Donk, Hiemstra-Beernink, Tjeenk-Kalff, van der Leij, & Ramon, 2015). However, some training-based gains in literacy skills, such as spelling (Alloway et al., 2013) and reading comprehension (Dahlin, 2011; Karbach, Strobach, & Schubert, 2015), have been documented in young TD children and children with ADHD. In addition, limited improvements in numeracy skills (Dahlin, 2013) have also been observed in children with attention deficits, such as ADHD, after working memory training. Although these findings indicate that executive function training may have the potential to yield benefits in literacy and numeracy skills, it is important to note that a number of these studies have methodological limitations, such as the lack of an appropriate control/comparison group (Dahlin, 2011; Dahlin, 2013). Consequently, it is difficult to ascertain whether the reported improvements are attributable to cognitive training or simply the result of general developmental change. Further research using randomized controlled designs are necessary to fully establish the potential impact of targeted executive function training programs on learning and academic outcomes.

Executive function training is considered most effective in individuals who have significant weaknesses in the domains being trained (e.g., Rabiner et al., 2010). Therefore, individuals with intellectual and developmental disabilities (IDD), who are characterized by reduced global cognitive capacities, executive function deficits, and learning impairments may find cognitive training particularly beneficial (see Kirk, Gray, Riby, & Cornish, 2015). The few studies of executive functioning in individuals with IDD provide some evidence for mental-age-appropriate performance on executive function tasks (Van der Molen, Van Luit, Jongmans, & Van der Molen, 2007), but indicate that performance levels are lower than TD comparison groups matched for chronological age (Danielsson, Henry, Rönnberg & Nilsson, 2010). Specific impairments in working memory are frequently reported in IDD, such as autism spectrum disorder (ASD; Steele, Minshew, Luna & Sweeney, 2007) and Down syndrome (DS; Lanfranchi, Cornoldi, & Vianello, 2004), and the most common reported concern for children with IDD is attention difficulties (Fombonne, 2009). Indeed the prevalence of ADHD is particularly high in children with DS (43.9%; Ekstein, Glick, Weill, Kay, & Berger, 2011) and ASD (50%; Leyfer et al., 2006). Recent evidence indicates that, despite seemingly homogenous behavioral deficits (e.g., hyperactivity and inattentiveness) in children with IDD, the cognitive pathways that have led to these behavioral end points are unique to specific developmental disorders (Kirk, Gray, Riby, Taffe, & Cornish, 2016). For instance, children with DS have been shown to have specific weaknesses in selective attention abilities (Kirk et al., 2016), whereas children with ASD have particular problems in inhibitory control (Agam, Joseph, Barton, & Manoach, 2010). It is these unique cognitive weaknesses in children with IDD that may benefit the most from training programs that target underlying difficulties rather than focusing solely on reducing behavioral symptomatology.

Despite the potential benefits of cognitive training for children with IDD, few studies have utilized training-based interventions to improve the core weaknesses prevalent in this clinical population, and have yielded mixed results regarding the benefits of training (Bennett, Holmes, & Buckley, 2013; Ottersen & Grill, 2015; Soderqvist, Nutley, Ottersen, Grill, & Klingberg, 2012; Van der Molen, Van Luit, Van der Molen, Klugkist, & Jongmans, 2010). Soderqvist et al. (2012) assessed working memory training in 41 children with intellectual disabilities between the ages of 6 to 12 years, and identified gains in visuospatial and verbal working memory after 5 weeks of training. In contrast, a larger study of 93 older children with intellectual disabilities (13 to 16 years), observed no improvements in visuospatial or verbal working memory after a comparable training duration (Van der Molen et al., 2010). In addition to the conflicting results in trained domains, widespread improvements in untrained executive functions, such as attention, inhibition, planning, shifting, or reasoning, have not been observed (Bennett et al., 2013; Soderqvist et al., 2012; Van der Molen et al., 2010) or have been limited (Ottersen & Grill, 2015). Moreover, no gains in either numeracy or literacy abilities have been observed in children with IDD after executive function training (i.e., working memory training; Van der Molen et al., 2010). The absence of consistent gains in both trained and untrained domains suggest that this intervention, in its current form, may not be a viable treatment option for children with IDD.

A recent literature review suggests that the lack of reported training effects in this clinical population may be attributable to past training programs being inappropriate for those with IDD (Kirk et al., 2015). Although training programs have proven valuable for children with other causes of executive function and academic difficulties, such as ADHD (Klingberg et al., 2005; Mezzacappa & Buckner, 2010), using the same programs for children with IDD is likely to be ineffective because they require cognitive skills that are already severely compromised. For example, past training studies in children with IDD have unanimously focused on improving working memory as a gateway to improving other executive functions and learning. However, evidence indicates that working memory abilities are in fact supported by underlying attentional capacities that act as a filter to limit the amount of information entering the memory store (Darowski, Helder, Zacks, Hasher, & Hambrick, 2008; Gazzaley & Nobre, 2012; Zanto & Gazzaley, 2009). Additionally, attention has been highlighted as a particularly salient predictor of academic and developmental outcomes in children with IDD (Cornish, Steele, Monteiro, Karmiloff-Smith, & Scerif, 2012). Therefore, it may be reasonable to assume that, in children who have widespread deficits in both working memory and attention as a result of IDD, targeting attention may be a more viable approach. We recently reported the first evidence that computerized attention training enhances aspects of core attentional process in IDD populations (i.e., selective attention; the ability to locate relevant items whilst ignoring irrelevant items), compared to a control group (Kirk, Gray, Ellis, Taffe, & Cornish, 2016). These findings are promising given that past training studies in this population have largely been unsuccessful in promoting benefits, even within trained domains. Based on the observed training-based improvements in selective attention, the current study explores whether these gains may transfer to untrained domains.

The aim of this study was to investigate the efficacy of computerized attention training on untrained outcomes, such as executive functions, literacy and numeracy skills, and behavioral/emotional problems in children with intellectual and developmental disabilities. It was hypothesized that the attention training program would promote improvements in executive functioning, but that transfer to behavioral and academic domains would be limited.

Method

Trial Design

Participants were enrolled in a double-blind, parallel group, randomized controlled trial that commenced in November 2013 and ended in December 2014 (not all children started at the same time; this was the duration of the trail from the first participant to the last). The design of the trial was a 2 (group: attention training, control) x 3 (occasion: baseline, post-training, 3-month follow-up) mixed design. The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN 12613001180707). No important changes to the trial design were made after trial commencement; the only alteration was the removal of the Automated Working Memory Assessment (AWMA; Alloway, 2007). The short form of the AWMA, which consists of four subtests assessing verbal short term memory (STM), verbal working memory (WM), visuospatial STM, and visuospatial WM, was originally included; however, after the first five children enrolled in the trial were unable to complete the task due to verbal demands being too high and the instructions too complex, it was removed. Although this task has previously been used in children with developmental disabilities such as DS (Bennett et al., 2013), the cognitive capacity and age of these participants (7 to 12 years) was considerably higher than the current sample. The Consolidated Standards of Reporting Trials (CONSORT) checklist for this trial is available in the article Appendix.

Participants

Participants were recruited from mainstream and specialist schools, as well as early intervention centers. In addition, participants were recruited through disorder-specific associations, such as the Down Syndrome Association, the Fragile X Association, Amaze (Autism), and the Williams Syndrome Family Support Group. This study was conducted within a predominantly urban community. Eligible participants were children aged 4 to 11 years with English as their first language, a diagnosis of intellectual disability (ID; IQ < 75) and elevated attention difficulties. Diagnosis of ID was confirmed using reports from psychologists, which included standardized assessments of intelligence using either the Wechsler Intelligence Scales for Children (WISC-IV; Wechsler, 2003), or the Wechsler Preschool and Primary Scales of Intelligence (WPPSI-III; Wechsler, 2002), and standardized assessments of adaptive functioning using the Vineland Adaptive Behaviour Scale-II parent/caregiver rating form (VABS-II; Sparrow, Cicchetti, & Balla, 2005). Twelve children were unable to complete standardized assessments of intelligence due to task demands; however, a clinical diagnosis of ID was provided based on behavioral reports and adaptive behavior scores. In addition, only children with elevated attention difficulties on the Conners 3 Parent Rating Scale (CPRS; Conners, 2008) were eligible for enrollment in the trial. Exclusion criteria included any visual, auditory, or motor impairment that would prevent children from understanding or executing the requirements of the assessment measures or the training program. Participants were not excluded if they were taking medication for attention problems, however, parents were asked to maintain a stable dosage throughout the trial. Participant's baseline demographic and clinical characteristics are outlined in Table 1.

Table 1

Baseline Demographic and Clinical Characteristics by Group

Baseline Demographic and Clinical Characteristics by Group
Baseline Demographic and Clinical Characteristics by Group

Note. Standardized scores are displayed for all measures. VABS = Vineland Adaptive Behavior Scale; CPRS = Conner's 3 Parent Rating Scale; SRS = Social Responsiveness Scale; IQ = intelligence quotient; ID = intellectual disability.

a

Two parents did not complete the SRS: attention training (n = 1); control (n = 1).

b

Twelve children could not complete formal IQ assessments: attention training (n = 8); control (n = 4).

c

Four children were taking methylphenidates and one child was taking atomoxetine.

d

Williams syndrome, Fragile X syndrome, Kabuki syndrome, Coffin-Siris syndrome, 1p36 deletion syndrome, DiGeorge syndrome and XYY syndrome.

Attention Training Program: The Training Attention and Learning Initiative

The Training Attention and Learning Initiative (TALI) is a computerized home-based attention training program designed specifically for children with IDD (Kirk et al., 2016). The program is named after the interactive guides “TALIs,” who instruct and motivate children throughout four touchscreen training activities. Each training session lasts for approximately 20 minutes including transition sequences. The first activity is a visual search task that aims to train selective attention skills; the second is a vigilance task that targets sustained attention skills; the third is a conflict resolution task that focuses on attentional control; and the fourth is a response inhibition task that also trains aspects of attentional control. Each activity lasts for 4 minutes and the difficulty of these training activities is automatically adjusted based on the child's performance. A reward system is built into the training program to promote participant motivation—children receive virtual toys for completing each activity and can view these rewards at the end of each training session.

Active Control Program

The control program consisted of four computerized activities designed to have minimal attentional requirements. These activities were delivered on the same touchscreen software as the TALI program, included the same interactive TALIs as guides, and also incorporated a reward system. The four control activities were nonadaptive, so children completed the same basic level each day. These tasks required children to: (1) touch, (2) drag, (3) pinch, and (4) rotate shapes using their fingers.

Measures

Screening and demographic measures

The Conners 3 Parent Rating Scale–Long Form (Conners, 2008) was completed prior to enrollment in the study. This standardized screening instrument was used to assess attention difficulties and has good internal consistency (Cronbach's α = .71 to .98; Conners, 2008). Parents rated their child's behavior on 108 items on a scale of 0 “never” to 3 “very often.” Children who had a total standard score above 60 (elevated range) on either of the subscales relating to inattentive behavior (i.e., inattention; Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition [DSM-IV] inattentive) were deemed eligible for inclusion in the trial. Higher scores indicate greater inattentive behaviors.

The Vineland Adaptive Behavior Scale - II Parent/Caregiver Rating Form (VABS-II; Sparrow et al., 2005) was completed at the baseline assessment to assess personal and social skills needed for everyday living. This standardized measure is designed for individuals from birth to age 90 and has excellent internal consistency (Cronbach's α = .93 to .97; Sparrow et al., 2005). Parents rated their child's behavior on a scale of 0 “never performed” to 2 “usually performs” across four domains: communication, daily living, motor skills, and socialization. A total standardized score was obtained (Adaptive Behavior Composite) to give an overview of adaptive functioning for use as a covariate in analysis. Higher scores indicate greater adaptive behavior skills.

The Social Responsiveness Scale (SRS; Constantino & Gruber, 2005) was completed at the baseline assessment to assess autism symptomology and social reciprocity. The SRS is designed for use in children 4 to 18 years old and has excellent internal consistency (Cronbach's α = .91 to .97; Bolte, Poustka, & Constantino, 2008). Parents completed 65 items across five subscales: social awareness, social cognition, social motivation, social anxiety, and autistic mannerisms. A standardized total score for social ability/functioning was generated from these subscales. Higher scores indicate greater difficulties in social reciprocity.

Academic outcome measures

Cardinality understanding was assessed using a version of the “give-a-number” (GAN) protocol originally designed by (Wynn, 1990). Children were given an empty bowl and 15 small fish. The experimenter held up a penguin puppet and explained to the child that they needed to feed the hungry penguin. Children were asked to give the experimenter small (1 to 3) and large (7 to 9) numbers of fish, three times for each requested number. This task contained 18 trials and a total raw score was calculated by summing all correct responses.

The Test of Early Mathematics Ability–Third Edition (TEMA-3; Ginsburg & Baroody, 2003) was used to assess informal and formal numerical concepts, such as numbering skills, counting, number comparisons, calculation skills, and understanding concepts. The TEMA-3 is a standardized measure designed for children between the ages of 3 and 8 years old and has excellent internal consistency (Cronbach's α = .94 to .96; Ginsberg & Baroody, 2003). This measure contains 72 items and a total raw score was obtained by summing correct items.

The Peabody Picture Vocabulary Task–Fourth Edition (PPVT-4; Dunn & Dunn, 2007) was used to measure receptive vocabulary. This standardized measure is appropriate for children aged 2 years 6 months through 90 years and has excellent internal consistency (Cronbach's α = .96 to .97; Dunn & Dunn, 2007). This test contains 228 items and a total raw score was calculated for use in analyses.

The letter knowledge and rhyme detection subscales of the Phonological Abilities Test (PAT; Muter, Hulme, & Snowling, 1997) were used to assess early phonological abilities. For the letter knowledge subtest, participants were presented with all 26 lowercase letters from the alphabet in a random order and asked to give the name or sound associated with each letter. One point was scored for each correct letter. The rhyme detection subtest involved presenting participants with four images, reading aloud the names of the images, and then asking the participant which of the pictures rhymed. A total of 10 trials were completed and children scored one point for each correct answer. A total phonological ability raw score was derived by summing correct items across the subtests.

Executive functioning and behavioral/emotional outcome measures

The Behavior Rating Inventory of Executive Function questionnaire (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000) was used to assess parent-rated executive functioning. This measure is suitable for children aged 5 to 18 years, has good internal consistency (Cronbach's α = .80 to .98; Gioa et al., 2000), and has been used in children with IDD (Memisevic & Sinanovic, 2014). Parents rated 86 items across eight distinct subscales: inhibit, shift, emotional control, initiate, working memory, plan/organize, organization of materials, and monitor. These subscales form an overall raw score reflecting executive ability; higher scores indicate greater executive functioning difficulties.

The Working Memory Rating Scale (WMRS; Alloway, Gathercole, & Kirkwood, 2008) was used to assess teacher-rated working memory skills. This measure is suitable for children between the ages of 5 to 11 years, and has excellent internal consistency (split-test reliability of .97; Alloway et al., 2008). Teachers ranked how typical each of the 20 items were of the student on a scale of 0 “not typical at all” to 3 “very typical.” A total raw score was calculated by totalling these items; higher scores indicate greater working memory difficulties.

The Developmental Behavior Checklist–Parent (DBC-P; Einfeld & Tonge, 1995) was used to assess parent-rated behavioral and emotional problems. This measure was designed for individuals aged 4 to 18 years with IDD and has good internal consistency (Cronbach's α = .78; Dekker, Nunn, & Koot, 2002). The DBC-P consists of 96 items on a scale of 0 to 2 (with 0 being “not true as far as you know” and 2 being “very true or often true”). The DBC-P comprises five subscales of behavioral disturbance: disruptive, self-absorbed, communication disturbance, anxiety, and social relating. A total raw score was calculated across these subscales to give an overall measure of behavioral disturbance; higher scores are indicative of greater behavioral/emotional problems.

Procedures

All participants were screened for eligibility at initial phone contact with a parent or other primary caregiver. Once initial inclusion criteria was met, participants were sent a consent form along with further screening measures via post. If all trial inclusion criteria were met, participants were invited to attend a baseline session. Participants were then randomly assigned to either the attention training or control program by a biostatistician who was independent to the study, using a computerized algorithm (ratio 1:1, blocks of 10). During the baseline session, the outcome measures were conducted by a researcher blinded to group allocation. Parents completed the questionnaires as per their standard instructions while their child was assessed. In the same week, teachers also completed the questionnaire. Immediately after the baseline session, participants were given a 7-inch touch screen android tablet in a sealed box that had been loaded with either the attention training or control program by an independent research assistant. Participants were also given a comprehensive instruction manual corresponding to their allocated program. Participants in both groups were asked to complete the allocated program once a day, 5 times a week, for a 5-week period (total of 25 sessions), under the supervision of a parent/guardian. During this training period, a research assistant contacted participating families to monitor progress and resolve any technical issues. Outcome measures were obtained at 5–6 weeks post-randomization, and at a follow-up assessment 3 months post-randomization by a researcher blinded to group allocation (see CONSORT diagram in Figure 1). On completion of the 3-month follow-up, researchers opened a sealed opaque envelope that revealed group assignment to participants.

Figure 1. 

CONSORT flow diagram.

Figure 1. 

CONSORT flow diagram.

Data Analysis

Baseline demographic characteristics were assessed using independent samples one-way ANOVAs and chi-squared analysis. An intent-to-treat approach was used to assess intervention effects. Data were analyzed using Stata Statistical Software (StataCorp, 2015). Random effects linear regression was used to model each outcome measure (cardinality, numeracy skills, vocabulary, phonological abilities, executive functioning, working memory, and behavioral/emotional problems) as a function of occasion (post-training, follow-up), group (attention training, control), and occasion by group interaction. This type of modeling is appropriate for longitudinal designs where there is random attrition in data over time. Performance at baseline, chronological age, gender, adaptive behavior skills, and medication status were included in all regressions as covariates.

Results

Baseline Characteristics

There were no significant differences between groups on adaptive behavior skills, degree of inattentive behavior, autism symptomology, or full scale IQ (see Table 1). A significant difference in age indicated that participants randomly assigned to the attention training group had lower chronological age than participants in the control group.

Compliance

Of the 38 participants assigned to the attention training group, 100% attended each assessment session (0% drop out; see CONSORT diagram in Figure 1), and 34 (90%) met compliance criteria (defined a priori as ≥ 15 of 25 training sessions within 5 weeks). There were no significant differences between training “compliers” and “noncompliers” on age, adaptive behavior skills, inattentive behavior, autism symptomology, gender, or medication status (p < .05). Comparisons of the number of training sessions completed by the attention training group (M = 20.63, SD = 5.81) and the control group (M = 19.00, SD = 7.71), revealed no significant difference in training frequency, t (1, 73) = 1.08, p = .303.

Intervention Effects

Mean scores and standard deviations for the outcome measures at each occasion (baseline, post-training, 3-month follow-up) are presented in Table 2. The regression coefficients for all outcome measures as functions of occasion, group, and occasion by group interaction, co-varying for age, adaptive behavior skills, gender, and medication status are reported in Table 3. There were no significant differences between groups on any of the outcome measures at baseline.

Table 2

Means (M) and Standard Deviations (SD) for Academic, Executive Functioning, and Behavioral Outcomes at Each Occasion

Means (M) and Standard Deviations (SD) for Academic, Executive Functioning, and Behavioral Outcomes at Each Occasion
Means (M) and Standard Deviations (SD) for Academic, Executive Functioning, and Behavioral Outcomes at Each Occasion

Note. Raw scores are displayed for all measures. GAN = Give a Number; TEMA = Test of Everyday Mathematical Abilities–Third Edition; PPVT = Peabody Picture Vocabulary Test–Fourth Edition; PAT = Phonological Abilities Test; BRIEF = Behavioral Rating Inventory of Executive Functions; WMRS = Working Memory Rating Scale; DBC = Developmental Behavioral Checklist.

Table 3

Regressions of Academic Skills, Executive Functioning, and Behavioral/Emotional Problems on Occasion, Group, and Occasion by Group Interaction

Regressions of Academic Skills, Executive Functioning, and Behavioral/Emotional Problems on Occasion, Group, and Occasion by Group Interaction
Regressions of Academic Skills, Executive Functioning, and Behavioral/Emotional Problems on Occasion, Group, and Occasion by Group Interaction

Note. GAN = Give a Number; TEMA = Test of Everyday Mathematical Abilities–Third Edition; PPVT = Peabody Picture Vocabulary Test–Fourth Edition; PAT = Phonological Abilities Test; BRIEF = Behavioral Rating Inventory of Executive Functions; WMRS = Working Memory Rating Scale; DBC = Developmental Behavioral Checklist; VABS = Vineland Adaptive Behavior Scale.

*

p < .05; **p < .01; ***p < .001.

Academic outcomes

Attention training had no immediate effect on cardinality, numeracy skills, vocabulary, or phonological abilities. However, at the 3-month follow-up, children in the attention training group showed a significantly greater improvement in numeracy skills as measured by the TEMA-3 than children in the control group (significant occasion by group interaction, small effect size, d = 0.15, 95% CI 0.01, 0.29, see Table 3). There was no evidence of difference between the attention training and control group in improvements in cardinality, vocabulary, or phonological abilities at follow-up (no significant interaction; see Table 3). Analysis of academic performance on average over groups and occasion indicated that scores on all academic outcome measures were positively associated with chronological age and adaptive behavior skills. Therefore, as age and adaptive behavior skills increased, so did scores on measures of literacy and numeracy.

Executive functioning outcomes

There was no evidence of difference between groups in improvements across occasions in either parent-rated executive functions, or teacher-rated working memory (i.e., no significant occasion by group interaction; see Table 3). Parent-rated executive functions were negatively associated with adaptive behavior skills on average across groups and occasions; as adaptive behavior skills increased, executive function difficulties decreased. Teacher-rated working memory was not associated with any of the demographic variables. Analyses were repeated across the subscales of the parent-rated measure of executive functions (BRIEF). There was no evidence to indicate that the attention training group had greater improvements on these subscales than the control group at either post-training or follow-up (see Table 4).

Table 4

Regressions of Executive Functioning on Occasion, Group, and Occasion by Group Interaction

Regressions of Executive Functioning on Occasion, Group, and Occasion by Group Interaction
Regressions of Executive Functioning on Occasion, Group, and Occasion by Group Interaction

Note. BRIEF = Behavioral Rating Inventory of Executive Functions; VABS = Vineland Adaptive Behavior Scale.

*

p < .05; **p < .01; ^p < .001.

Behavioral/Emotional outcomes

There was evidence that children in both groups showed lower levels of parent-rated behavioral/emotional problems at post-training and follow-up than at baseline. However, there was no significant interaction of occasion by group on the total scores of the DBC (see Table 3) or any of its subscales (see Table 5). Parent-rated behavioral/emotional problems were negatively associated with adaptive behavior skills on average across groups and occasions; as adaptive behavior skills increased, behavioral/emotional problems decreased.

Table 5

Regressions of Behavioral/Emotional Problems on Occasion, Group, and Occasion by Group Interaction

Regressions of Behavioral/Emotional Problems on Occasion, Group, and Occasion by Group Interaction
Regressions of Behavioral/Emotional Problems on Occasion, Group, and Occasion by Group Interaction

Note. DBC = Developmental Behavioral Checklist; VABS = Vineland Adaptive Behavior Scale.

*

p < .05; **p < .01; ^p < .001.

Discussion

This study provides the first evaluation of the efficacy of attention training in promoting widespread improvements in untrained academic skills, executive functions, and behavioral/emotional problems in children with intellectual and developmental disabilities (IDD). The core finding was that children who received the attention training program showed greater improvements in numeracy skills at the 3-month follow-up, as compared to children who received an active control program. However, there was no evidence to suggest that attention training improved other academic skills such as receptive vocabulary, phonological abilities, or cardinality. Despite reductions in executive function difficulties and behavioral/emotional problems in children who received the attention training program, similar improvements were seen in the control group. These observed gains can therefore not be attributed to the attention training program. Given the limited number of studies that have investigated the potential of training programs in children with IDD, the current results have important implications for future work in this field.

The intervention-related effects on numeracy abilities are largely inconsistent with past training studies that have struggled to promote change in this domain, in both typically developing children (Alloway et al., 2013; Dunning et al., 2013; Karbach et al., 2015; Titz & Karbach, 2014) and children with intellectual disabilities (Van der Molen et al., 2010). However, these training studies differ from the current trial in that they targeted working memory as a gateway to numeracy skills, rather than attention. Evidence indicates that attention abilities are strongly associated with numeracy skills in childhood (Steele et al., 2012) and adulthood (Ansari, Lyons, van Eitneren, & Xu, 2007). In particular, selective attention, the ability to discriminate between relevant and irrelevant information, has been shown to predict basic numeracy skills (Anobile, Stievano, & Burr, 2013; Steele et al., 2012). Recent investigations indicate that the current training program (TALI) is effective in promoting gains in selective attention in children with IDD (Kirk et al., 2016). It is, therefore, possible that these improvements in selective attention may have transferred to gains in related skills, such as numeracy abilities. Collectively, these findings might suggest that attention training rather than working memory training is more effective in promoting gains in numeracy skills.

In contrast, working memory training has been shown to encourage improvements in literacy skills (Dahlin, 2011; Karbach et al., 2015), whereas the present attention training program was unsuccessful in promoting gains in this domain. Research has consistently demonstrated a strong relationship between literacy performance and working memory capacity (Melby-Lervåg, Lyster, & Hulme, 2012; Silva, Faisca, Ingvar, Petersson, & Reis, 2012; Swanson, 2015; Swanson & Jerman, 2007; Swanson, Zheng, & Jerman, 2009), whereas the assocation between literacy and attention is less established (Anobile et al., 2013; Steele et al., 2012). The robust association between working memory and literacy may help to elucidate why previous working memory training studies have seen improvements in this academic domain, whereas the current attention training program did not. Future research should examine the impact of combined working memory and attention training programs to establish an optimal strategy for promoting gains in both literacy and numeracy outcomes. Evidence from multiple executive function training interventions in clinical populations, such as children with ADHD, have generally shown promising results, e.g., improvements in behavioral difficulties and increased neural activity (Halperin et al., 2012; Hoekzema et al., 2010; Johnstone et al., 2011). However, none of these studies have assessed the impact of multiple executive function training on academic-based skills such as literacy and numeracy.

The lack of training effects on parent- and teacher-rated measures of executive functions and behavioral/emotional problems are consistent with past working memory training studies, that have equally struggled to promote gains in these skills in both children with ADHD (Beck, Hanson, Puffenberger, Benninger, & Benninger, 2010; Dovis, Van der Oord, Wiers, & Prins, 2015; Gray et al., 2012; Tamm, Epstein, Peugh, Nakonezny, & Hughes, 2013), and IDD (e.g., Down syndrome; Bennett et al., 2013). Although children in the attention training group showed a reduction in parent-rated executive function difficulties and behavioral/emotional problems, these gains were no greater than those observed for children in the control group. The equal improvements in subjective outcomes might be the result of nonspecific training factors, such as the need to focus on the training tasks for an extended period of time. Additionally, parental support/engagement during training has been highlighted as a potential contributor to reported outcomes in children (de Vries, Prins, Schmand, & Geurts, 2015). Parents in both groups were asked to supervise their child's training over an intensive 5-week period, and as such invested an equally substantial amount of time/effort, which may have influenced the perceived benefits of their allocated program. Furthermore, parent's beliefs about treatment allocation have been shown to significantly influence reported outcomes (Guastella et al., 2015); namely, greater improvements are reported by parents who believe their child has received an intervention (even if they have not). Unfortunately, we did not record parent's beliefs about treatment allocation and were therefore unable to assess the potential impact of expectancy bias on the trial outcomes. These factors have the potential to undermine clinical trials, and highlight the importance of including objective outcome measures, and maintaining blinding of group allocation throughout the duration of the trial.

This study has some limitations. First, although a follow-up assessment was conducted, this occurred relatively soon after training (3 months). Past research has suggested that it may take some time for benefits in untrained domains to become apparent in standardized ability tests (Alloway et al., 2013; Titz & Karbach, 2014). This ascertion is supported by the increased effects at follow-up compared with immediate effects following training in the current study. Future research should endeavour to include longer follow-up assessments (e.g., 6 and 12 months), particularly where academic outcomes are being assessed. Second, the outcome measures of executive functioning and problem/emotional behaviors were indirect (e.g., rated by teachers or parents). Recent studies suggest that these indirect measures may be less sensitive in detecting changes in behavior than objective measures or direct observational measures of behavior (Green et al., 2012). In a recent placebo-controlled working memory training study in children with Down syndrome (Bennett et al., 2013), no specific intervention effects on parent-rated executive functions (assessed using the BRIEF) were found, but medium to large effects on objective measures of executive functions (e.g., working memory assessed using the AWMA) were observed. The primary implication of these findings is that utilizing parent/teacher ratings of behavior and executive functions alone may be insufficient in detecting changes. Although locating appropriate assessment measures for children with IDD poses significant challenges, future studies should aim to include multiple sources of behavioral assessment (e.g., observations of behavior such as fidgeting and vocalizing) alongside objective measures to accurately evaluate the impact of training on everyday activities.

A further limitation of the current study is the relatively small sample that prevented investigations of the influence of individual characteristics, such as type of IDD on training outcomes. Previous investigations have shown that cognitive training was beneficial in promoting improvements in working memory in children with Down syndrome (Bennett et al., 2013), but not children with a formal diagnosis of intellectual disability (Soderqvist et al., 2012). Although these discrepancies may be due to differences in trial design (e.g., Bennett et al., 2013, did not blind participants to group assignment), it is also possible that syndrome-specific deficits may dictate different outcomes. In addition, the current findings indicate demographic characteristics such as age and adaptive behavior were associated with training outcomes; however, greater investigations with larger samples are needed to clarify the predictive role of these factors on training progress. Evidence from the limited training studies conducted in children and adolescents with IDD, suggest that cognitive training offers greater benefits for younger children (6 to 12 years; Soderqvist et al., 2012) than older children (13 to 16 years; Van der Molen et al., 2010). Further, training interventions have been suggested to yield longer-term benefits in children who have more extreme problems in the targeted domains (Rabiner et al., 2010). However, evidence on the role of individual differences is still limited, especially within clinical populations such as IDD. It, therefore, remains unclear whether cognitive training may be of greater benefit to children with IDD who have fewer or greater impairments in the targeted domain. Future studies should assess whether children with IDD who present with subclinical attention deficits respond differentially to attention training than those with clinical attention deficits. Understanding who may benefit most from this type of intervention will be important in facilitating the development of tailored training programs that target the specific needs of children with varying degrees of executive functioning and learning difficulties.

Collectively, the current findings have several implications. Primarily, this study provides vital evidence for the feasibility of using cognitive training in children with IDD. Attrition rates for the trial were remarkably low, with all children in the training group attending every assessment session. In addition, compliance with the training program was high, with almost all children (90%) completing a minimum of 15 training sessions over the 5-week training period. These findings are particularly important given that children included in the current sample had significant cognitive difficulties alongside heightened attention problems. Further, unlike past training studies that have been delivered under tightly controlled experimental conditions (van der Donk et al., 2015), the current program was conducted within a home environment, representing a more realistic method of delivery. In addition, the training-based improvements in numeracy, although modest, are particularly informative given that very few studies have assessed the benefits of computer-based training on improvements in academic skills in either typically developing or clinical populations (Titz & Karbach, 2014). Although the small effect of the intervention on numeracy performance indicates that caution should be exercised when interpreting practical significance, the gains in numeracy (four point raw score change on the TEMA-3) are relatively sizeable given that the follow-up occurred within a short 3-month period. In contrast children in the control group only had a one point raw score change on the TEMA-3 over the same time period. In addition, children in the attention training group not only demonstrated greater gains in numeracy skills at follow-up than the control group, but also the frequency of children who improved was greater for the training group (74%) than the control group (57%). Collectively, these findings suggest that attention training may have the potential to overcome some of the academic difficulties faced by individuals with IDD and support children's educational development. Clearly, however, more research is needed to determine the optimal strategies for enriching a broader range of skills, including literacy in children with IDD. Emerging evidence indicates that increasing the intensity and duration of training in children with IDD may facilitate greater improvements on untrained tasks (Ottersen & Grill, 2015). Therefore, future studies should focus on the impact of training procedures on observed outcomes to establish the optimal training approach for children with IDD, as well as other clinical groups and typically developing children.

This investigation has demonstrated for the first time that certain untrained skills, such as numeracy, can be modestly improved in children with IDD as a result of intensive computerized attention training. However, the training program was unsuccessful in promoting gains in wider untrained skills, such as literacy, executive functions, and behavioral/emotional problems. These findings add to the limited number of studies that assess the benefits of computer-based training in children with IDD, and the fewer still that assess the impact of training on academic domains in this population. The overall restricted training effects indicate that further research is needed to determine optimal training strategies required to promote transfer to broader domains in children with and without IDD.

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Author notes

This research was funded with the support of an Australian Research Council (ARC) Linkage Grant (LP120200015) and a Monash University Scholarship.

The authors thank the children, parents, teachers, schools, and organizations involved in the study. We give thanks to Cathriona Clarke, Anna Atkinson, and Andrew Halim for assistance with data collection and to Mark Bellgrove for advice during the development of the training program. Finally, we give special thanks to Grey Innovation and Torus Games for their integral role in the development and production of the program.