A Growth Mixture Modeling Study of Learning Trajectories in an Extended Computerized Working Memory Training Programme Developed for Young Children Diagnosed With Attention-Deficit/Hyperactivity Disorder

Abstract

This study explored (1) whether growth mixture modeling (GMM) could identify different trajectories of learning efficiency during a working memory (WM) training programme for young children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD), compared with a typically developing (TD) control group, and (2) if learning trajectories and outcomes were different for simple and complex training tasks. Children completed simple visuospatial short-term memory (VSSTM) and complex visuospatial WM (VSWM) tasks for 15 min a day, 5 days a week, and for 8 weeks. Parent-reported executive functioning, and children's WM and attention control, educational achievement, and IQ were measured prior to (T1), immediately following (T2) and 3 months after training (T3). GMM analysis showed that WM training was represented as one learning curve, and there was no difference for the trajectories of the ADHD and TD groups. The learning trajectory for the VSSTM tasks across groups was represented as one learning curve and for the VSWM tasks there were three learning curves. Learning for the VSSTM tasks and for most children in the VSWM tasks was characterized by an inverted-U shape, indicating that training was effective for up to 15 sessions, was stable and declined thereafter, highlighting an optimal training timeframe. For the VSWM tasks, the two remaining groups showed either a U-shaped or a high inverted U-shaped trajectory, with the latter group achieving the highest T1T2 change score (i.e., children showed a lower starting point and the most gain in terms of learning and post-training performance). There were no broader benefits of training at post-test or follow-up. Further research should explore who would benefit most from intensive cognitive training, as well as the potential benefits for mental health and well-being.

Effect of Cognitive Training Programs Based on Computer Systems on Executive Functions in Children With ADHD: A Systematic Review

“…Sixty-seven percent had a two-group design, while 19% and 14% included three and four groups, respectively. Regarding the measures, 76% reported results of the initial and final evaluation, 30% presented findings of post-training follow-up evaluation (Barzegar et al, 2020;Bigorra et al, 2016;Bikic et al, 2018;Dovis et al, 2019;Kofler et al, 2020;Orylska et al, 2019) (See Table 1).…”

“…(Kofler et al, 2020) 2 and 4 months The follow-up evaluation was exploratory, the authors measured the variables of decrease in ADHD symptoms but not the variables of executive functions. (Orylska et al, 2019) 3 months…”

Assessing the feasibility of a classroom-based visual attention training program targeting academics for students with extremely low IQ

“…With regard to inclusion criteria, our study addressed a gap in the current cognitive training literature, which typically excludes participants with intellectual functioning below the average range (see [14][15][16] for examples). Our results are consistent with those of the few previous studies which show that it is feasible to assess the effects of cognitive training in students with similar cognitive profiles [20][21][22].…”

“…The exclusion of participants with intellectual challenges in most studies (e.g., [14][15][16]) presents an area of opportunity to investigate the appropriateness of cognitive training for this population. To our knowledge, cognitive-based intervention protocols for adolescents with extremely low intellectual functioning, an underserved portion of the student population, are scarce.…”

Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention?

“…A similar approach has been applied in other areas of social science to explore unobserved groups. For instance in alcohol consumption among adolescents (Wang & Bodner, 2007), alcohol consumption and marijuana consumption (Hix-Small et al, 2004) and the effectiveness of learning during working memory for nursery and primary school children (Orylska et al, 2019). A mixture model is a flexible technique for identifying latent components by approximating the distribution of the observed data by a mixture of distributions (Geoffrey & Peel, 2000).…”