The purpose of this study was to investigate the optimal challenge point for learning motor skills in children with and without attention deficit/hyperactivity disorder (ADHD). Ninety-six 9- to 10-year-old children, including 48 children with ADHD and 48 neurotypical children, were randomly assigned to one of four practice groups with varying levels of nominal and functional task difficulty. They performed 63 trials of a dart throwing task in the acquisition phase and 18 trials in the retention and transfer tests a day later. The results showed that neurotypical children outperformed children with ADHD in all phases of the study. Both groups improved in the acquisition phase and performed better in the retention and transfer tests. Interestingly, low nominal task difficulty was associated with better learning for both groups, despite lower average performance for children with ADHD. Thus, despite their performance differences, we did not find a difference in the effective challenge point between children with ADHD and their neurotypical peers.
Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurobehavioral disorders in school-age children, affecting a large number of children worldwide (Veloso, Vicente, & Filipe, 2020). Also, since ADHD affects about 8 to 12 % of children worldwide (Faraone & Biederman, 1998) and studies conducted in Iran also show a prevalence of 6.7 to 12.6 % of this disorder among Iranian children (Bahrami, Yousefi, Bahrami, Farazi, & Bahrami, 2016). ADHD includes symptoms such as inattention, hyperactivity and impulsivity (Wolraich et al., 2014). Children with ADHD exhibit wide variation in executive functions, notably inhibitory control and reward processing, leading to diverse behavioral symptoms. These symptoms manifest across different contexts, affecting attention and activity levels to differing degrees across individuals (Brown, 2013; Roebers, 2017). According to the criteria in the Diagnostic and Statistical Manual of the American Psychiatric Association, the symptoms of the disorder must appear from the age of seven and the behaviors indicative of the disorder must be determined and identified in at least 6 to 12 behavioral signs in the individual (American Psychiatric Association, 2023). In total, based on this, three main types of attention-deficit hyperactivity disorder are diagnosed, which include: inattentive, hyperactive-impulsive and combined, which is the most common type of ADHD, which has both symptoms of hyperactivity and attention and concentration disorder (Veloso et al., 2020).
Motor deficiencies are common in children with ADHD, with prevalence rates between 30 % and 52 % depending on the measurement method (Barkley, 1990; Egeland, Ueland, & Johansen, 2012). Studies, including those by Pitcher et al. (Pitcher, Piek, & Hay, 2003), indicate that most children with ADHD experience motor problems. These deficiencies encompass a range of issues such as reduced handwriting skills, motor control, coordination, and movement accuracy (Demers, McNevin, & Azar, 2013; Eliasson, Rösblad, & Forssberg, 2004; Fliers et al., 2008; Kaiser, Schoemaker, Albaret, & Geuze, 2015; Pan, Tsai, & Chu, 2009; Piek, Pitcher, & Hay, 1999; Yan & Thomas, 2002). Movements are often described as jerky or less fluent (Dahan, Ryder, & Reiner, 2018; Eliasson et al., 2004), and there are impairments in movement speed, temporal organization (Eliasson et al., 2004; Rosa et al., 2015; Sweeney et al., 2018; Yan & Thomas, 2002), balance, body schema, and spatial organization (Goulardins, Marques, Casella, Nascimento, & Oliveira, 2013; Rosa et al., 2015). Both gross and fine motor skills are affected (Dahan et al., 2018; Fliers et al., 2008; Kaiser et al., 2015; Langmaid, Papadopoulos, Johnson, Phillips, & Rinehart, 2014; Piek et al., 1999; Tseng, Henderson, Chow, & Yao, 2004). In general, motor development seems to be anomalous in children with ADHD with a delay of nearly two years compared to neurotypical peers (Goulardins et al., 2013; Pan et al., 2009; Rosa et al., 2015; Sweeney et al., 2018).
Existing research has confirmed impaired performance on executive function tests in children with ADHD (Coghill, Seth, & Matthews, 2014; Williams et al., 2010). Researchers have also examined the effectiveness of perceptual-motor exercises on motor proficiency (Jasper, Solomon, & Bradley, 1938), visual and auditory attention. The results showed that movement exercises have reduced the lack of visual and auditory attention in children with ADHD compared to the control group. Therefore, the attention performance of children with ADHD is improved with the help of motor exercises (Sarli, Shahbazi, & Bagherzadeh, 2014). Also, constant and variable practice is one of the interventions that can be effective in improving the performance of children with ADHD and among them, variable practice is more effective (Lotfi, Salehi, & Karami, 2022).
Therefore, among the many factors affecting motor skills, practice is one of the most important factors for relatively permanent improvement of ability to perform a motor skill (i.e., motor learning). But other factors such as task difficulty, learner skill level and practice conditions can significantly affect motor skill learning. In this regard, Guadagnoli and Lee proposed the challenge point framework (CPF) as a tool for determining the optimal challenge point for motor learning (Guadagnoli & Lee, 2004). Using the CPF, the interaction between nominal task difficulty, practice conditions, and learner skill level shapes the functional task difficulty. Nominal task difficulty represents a fixed level of challenge intrinsic to the task itself, independent of the performer or the conditions of performance. This encompasses elements such as the sensory and motor demands required for task completion. Conversely, functional task difficulty of a task is indicative of the extent to which the task poses a challenge, taking into account the performer's skill level and the specific conditions under which the task is carried out (Guadagnoli & Lee, 2004). Broadly speaking, learners are expected to benefit from a moderate level of functional task difficulty. When functional task difficulty is too low, the CPF posits that there is no new information to be learned, but if functional task difficulty is too high, it is difficult for the learner to either process all of the available information or focus on the correct information in order to improve performance.
For example, when spatial accuracy is controlled, movements with farther distances (distances of 1.70, 1.90 and 2.10 m from target) from the target compared to movement with closer distances (distances of 0.90, 1.10 and 1.30 m from target) from the target, has a higher nominal task difficulty (Naseri, Bahram, Salehei, & Daneshfar, 2021; Sanli and Lee, 2014, Sanli and Lee, 2015). Thus nominal task difficulty depends on the conditions of the task, whereas functional task difficulty reflects the interaction of the task and the learner. For instance, a long shot might have low functional task difficulty for an expert, but high functional task difficulty for a novice. As the skill level of the learner changes, so too does the functional difficulty of the task (even though nominal task difficulty remains constant). For instance, the set of distances [0.90, 1.10, 1.30] has lower nominal Task difficulty than the set of distances [1.70, 1.90, 2.10]. However, within the set of [0.90, 1.10, 1.30] a distance of 1.30 m will have a higher functional difficulty for a novice if it is practiced first, compared to if it is practice after 0.90 and 1.10 m.
Hodges and Lohse (2022) integrated the challenge-point framework with predictive coding theories, emphasizing that unexpected information enhances learning. They proposed adding learner motivation and practice characteristics as key factors in practice design. Adjusting these factors helps coaches tailor practices to various learning objectives (Hodges & Lohse, 2022). According to CPF, learner characteristics affect how they respond in specific practice conditions (Onla-Or & Winstein, 2008). Therefore, a useful practice situation for motor learning of typically developing children may not be useful for motor learning in neurodiverse children, such as attention-deficit-hyperactivity disorder (ADHD).
Numerous studies have shown that motor learning is impaired in different groups of people with neurological disorders (Onla-Or & Winstein, 2008; Shin & Ivry, 2003; Siegert, Taylor, Weatherall, & Abernethy, 2006). Findings related to adults with neurological impairments such as Parkinson's disease (PD), patients with multiple sclerosis (MS), and studies in neurodiverse children confirm this prediction (Onla-Or & Winstein, 2008; Zahiri & Tahmasebi Boroujeni, 2020). Based on this, the differences of observed among the studies related to the mentioned neurological impairments, are mainly due to the variations between studies in task demand and practice conditions (i.e., functional task difficulty of task in CPF) and also severity of impairment (i.e., affecting the skill level of learner in CPF). However, there is no research about task difficulty in CPF for children with ADHD disorder. Therefor the aim of this article is to examine CPF for motor learning in children with ADHD and neurotypical controls by manipulating nominal and functional difficulties of the task. Nominal Task difficulty is manipulated by the set of distances used; functional Task difficulty is manipulated through the schedule of practice and the comparison of the two groups.
Given that farther distances are assumed to have higher nominal Task difficulty, we predicted that learning would be greater for the short distances compared to the far distances on average. Additionally, at any distance, we assumed that children with ADHD would face greater functional Task difficulty compared to their neuro-typical peers, and thus we predicted that low nominal difficulty would be more beneficial for children with ADHD. Finally, although it is difficult to judge where the “optimal” challenge points should be for these two groups, we hypothesized that a schedule with lower functional difficult (progressing from short distances to far distances) would be more beneficial for children with ADHD compared to neuro-typical controls. The practical aim of this research is to identify effective motor learning methods for children with ADHD. Aside from learning (in the long term), training methods that improve motor performance (in the short term) can help enhance self-confidence and motivation towards physical activity.
The statistical population of the research included 9- and 10-year boy and girl students from Yazd city in 2023. As we have no prior data on which to base reasonable estimates for an a priori effect, we used the software G*Power to conduct sensitivity power analyses (i.e., given a fixed sample, what effects could be detected with 80 % power?) for main-effects of Group, and cross-level interactions of Group x Block and Task Difficulty x Block during acquisition, or Group x Test and task
Table 1 Descriptive information and comparison of mean data related to age of participating children in four samples of groups of children with and without ADHD disorder that has been performed with independent groups t-test is shown.
In the acquisition phase, there was a statistically significant main-effect of block, F(6,528) = 13.5, p < 0.001, hp2 = 0.13, such that participants generally improved from Block 1 to Block 7. Post-hoc Tukey's HSD tests confirm that each block was different from
The challenge point framework led us to hypothesize that some of the differences in previous studies on motor learning in individuals with neurological differences are due to the difference in the nominal task difficulty of the task, the functional task difficulty of the task and the severity of the disorder, which is well known to affect information processing ability (Doyon et al., 1997; Onla-Or & Winstein, 2008; Van der Molen, Van Luit, Jongmans, & Van der Molen, 2007). Therefore, we