It is well known that motor development and cognitive development are interrelated (11). From a neuropsychological perspective, the close association between motor and cognitive development is mediated by the coactivation of the cerebellum, important for complex and coordinated movements, and the prefrontal cortex, critical for higher-order cognitive functioning, that is, executive functioning (EF). EF is an umbrella term that includes several interrelated higher cognitive processes necessary for purposeful and goal-directed behavior (39). Typical EF processes are inhibition, cognitive flexibility, attention, working memory, planning, and problem solving (12,39). The neural link between the cerebellum and the prefrontal cortex occurs when tasks are complex and executed in novel and changing environments (11). In addition, motor skills and EF follow are similar developmental timescale with an accelerated development between 5 and 10 yr of age (1,4,33).
Both motor performance and EF are important in the overall development of children (8,24). Furthermore, EF plays a critical role in the development of academic abilities (23,5). Children who have major problems in academic abilities are children with learning disorders1 (LD). Research has shown that these children generally have poor motor skill performance (16,29,40,41) as well as poor performance in EF (17,36). Therefore, targeting motor skills and EF in children with LD may be crucial to boost their academic performance.
A recent meta-analysis reported positive effects of motor skill interventions on the improvement of fundamental motor skills in children (22). The studies included in the meta-analysis were conducted in different populations: typically developing children, children who are overweight or obese, children with developmental disorders or at risk of developmental disorders, and children with developmental language disorders. Logan et al. (22), found moderate effects of the interventions on locomotor skills (effect size Cohen’s d = 0.45) as well as ball skills (Cohen’s d = 0.41). The overall effect size for the control groups (i.e., free play) was very small (d = 0.006), indicating that fundamental motor skills do not develop naturally, but they need to be taught, practiced, and reinforced through developmentally appropriate movement programs.
In addition, several studies have shown positive effects of movement interventions on EF and academic skills in typically developing children (3,32) and in children who are overweight (10). However, these studies focused on the effects of physical activity on EF and/or academic skills rather than the effects of motor skills per se. To the best of our knowledge, research focusing on the effect of motor skill interventions on cognitive functioning is limited. Ericsson (14) examined the effect of extended physical education and motor training on motor skills (e.g., balance, hand–eye coordination, and bilateral coordination), academic skills, and attention. This study showed that extended physical education and motor training (five vs two times per week) improved children’s motor skills and demonstrated positive effects on reading, writing, and mathematics, but not on attention. Furthermore, Ericsson and Karsson (15) examined, in a 9-yr intervention study, the effect of extended physical education and motor skill training on motor skills and school performance (i.e., qualification for upper secondary school and school marks). The authors concluded that the intervention group had better motor skill performance in all follow-up assessments. Furthermore, in year 9, boys in the intervention group had higher school marks and were more often qualified for upper secondary school compared with boys in the control group. No differences in school performance were found between girls in the intervention and control group. The studies by Ericsson (14) and Ericsson and Karsson (15) extend current knowledge of this topic; however, the studies were only focused on academic achievement and not on EF.
As it seems that complex motor skills are strongly associated with EF (3), it may be expected that an intervention focusing on complex motor skills may facilitate children’s EF. Budde et al. (6) compared the effect of coordinative exercises on cognition with that of noncoordinative, simpler exercise. It was found that coordinative exercises had more effect on the performance of concentration and attention tasks compared with noncoordinative, simpler exercises. The authors concluded that the complex coordinative exercises required frontal-dependent cognitive processes, which enhanced prefrontal neural functioning, whereas the noncoordinative, simpler exercises did not rely on the prefrontal neural circuit (6). In a motor skill intervention aimed at improving EF, it is important that complex motor skills are performed. Ball skills are assumed to be complex motor skills because they are generally practiced in complex play and sport settings (18). The current intervention, therefore, focused on ball skills, practiced under different conditions varying from simple, static settings to dynamic complex play settings like team games. The dynamic and novel character of team games make them ideal situations to effectively train EF because EF is especially important in novel and demanding situations (12). In dynamic sport settings (e.g., team games), cognitive skills such as action planning, problem solving, and cognitive flexibility are important factors for successful performance (3,38). Cognitive flexibility is a core component of EF. It refers to the ability to switch rapidly between simultaneous goals (1) and is of critical importance in environments in which attention demands are constantly changing (3,38). Action planning and problem solving (further summarized as problem solving) are complex EF tasks and are critical parts of goal-oriented behavior. They embody the ability to formulate actions in advance and to approach a task in an organized, strategic, and efficient manner (1). In the current intervention, ball skills were often practiced in dynamic sport settings; therefore, it was assumed that especially these EF tasks (i.e., problem solving and cognitive flexibility) would be facilitated. Both problem solving and cognitive flexibility are frequently linked to the development of academic abilities in children, especially in mathematics and reading (5). It is thought that improvements in EF facilitate improvements in academic abilities (4), or that adequate EF develops before behaviors affecting academic abilities (26). Cognitive flexibility has been found to be involved in mathematics (7), and reading performance (36) and problem solving seem to be fundamental for mathematic skills (28). Therefore, we expected that through enhancing these EF tasks, children’s academic skills may improve after the intervention.
The aim of the present study was to examine whether a ball skill intervention has an effect on the performance of 1) ball skills, 2) EF in terms of problem solving and cognitive flexibility, and 3) in how far improved EF leads to improved reading and mathematics in children with LD.
Ninety-one children with LD, between the ages of 7 and 11 yr old, were recruited from a special-needs primary school located in the northern Netherlands. Informed consent was obtained for all children, and all procedures were approved by the institutional Ethics Committee of Human Movement Sciences, University Medical Center Groningen, University of Groningen, as being in accordance with the ethical guidelines of the American Psychological Association.
The 91 children were from six different classes. These six classes were assigned randomly either to the intervention group or to the control group. Three classes with a total of 45 children participated in the intervention group, and three classes with a total of 46 children participated in the control group. Children only participated in the study if they had no physical disability (n = 0). Children who attended less than 80% of the sessions were excluded from the study sample (intervention group n = 2, control group n = 2). The definitive study sample consisted of 87 children: 43 children in the intervention group (31 boys, 12 girls) and 44 children in the control group (28 boys, 16 girls).
For each child, the individual school files containing child characteristics (e.g., age, sex, and IQ), a short medical history, and the child academic system were screened. The child academic system provides an overview of a child’s progress in academic skills by evaluating these skills twice a year. The progress in academic skills is expressed in a learning lag per academic domain. For example, a child with a learning lag of 0.35 on reading has not mastered 35% of the reading level that would have been expected given the months of formal education.
Ball skill assessment
The Test of Gross Motor Development-2 (TGMD-2) (33) was used to measure ball skill performance. The TGMD-2 is a qualitative, process-orientated measure (i.e., evaluates movement based on the demonstration of performance criteria, which provided information of how the movement was performed) to assess 12 gross motor skills divided into locomotor skills (run, gallop, hop, leap, jump, and slide) and ball skills (two-hand strike, stationary bounce, catch, kick, overhand throw, and underhand roll). Each skill was executed twice and evaluated based on the presence (success; score 1) or absence (failure; score 0) of three to five qualitative performance criteria. The highest total raw score for both subtests is 48. The higher the score, the better the performance. The reliability and the validity of the TGMD-2 to assess gross motor skills have been reported for typically developing children (33) and for children with LD (29). Because the present study evaluated a ball skill intervention, only the ball skill subtest of the TGMD-2 was reported.
Assessment of EF
Two tests were used. The Tower of London (TOL) (27), which specifically measures problem solving and planning ability (summarized as problem solving), and the Trail Making Test (TMT) (25) for the assessment of cognitive flexibility.
Tower of London
The TOL consists of a board with three pegs of different lengths and three colored balls (red, blue, and yellow) with holes that can be manipulated on the pegs. Children have to solve 12 problems by transforming a fixed start state into a depicted goal state by applying three important rules: 1) only one ball can be moved at a time, 2) a ball cannot be moved while another ball is lying on top of it, and 3) the longest peg can carry three balls, the middle peg two and the shortest peg only one ball. Children were instructed to solve each problem within a minimum number of moves as given by the researcher, and a maximum of three trials was allowed to solve each problem. A problem was solved correctly, when the goal state was achieved within the minimum number of moves allowed for that problem. The test was preceded by a practice problem and children were encouraged to strive for accuracy as well as speed. The score on the TOL was determined by assigning three, two, or one point(s) per problem based on the number of trials required to solve the problem, with three points reflecting one trial, two points reflecting two trials, and one point reflecting three trials. The TOL total score was the sum of the points for all 12 problems with a maximum of 36. The TOL has been tested and validated for use with children (2).
Trail Making Test
The TMT is a paper-and-pencil task that consists of two parts, TMT A and TMT B. In TMT A, children were asked to draw a line to connect encircled digits (1–25) randomly arranged on a sheet of paper in ascending order (i.e., 1-2-3-4-5, etc.). This provides an estimate of attention and psychomotor speed (i.e., movement or motor activity associated with mental processes) (31). In TMT B, a series of encircled digits (1–13) and letters (A to L) should be connected in ascending order by alternating between digits and letters (i.e., 1-A-2-B-3-C-4-D-5-F, etc.), providing an estimate of cognitive flexibility (31). The children were instructed to execute both parts as quickly as possible. Both parts were preceded by an example. The time taken to complete each part was used as the test score. To give a more accurate measure of cognitive flexibility and to control for the effect of psychomotor speed, the TMT Delta was calculated by subtracting the total time of TMT A from the total time TMT B (13). The TMT has been used and validated in children from age 7 yr (31).
Assessment of academic skills
The progress in reading and mathematics was obtained from the child academic system of the school. The school used the Dutch Analyse van Individualiserings Vormen (Analysis of Individual Word Forms; AVI) (37) to assess children’s reading abilities. During the test, the child was required to read out several short stories, each displayed on a card, whose sentence structures and word complexity gradually increase in difficulty per card. The amount of mistakes (i.e., reading a word wrong, skipping, adding, or replacing a word) and the time that was needed to read the text were scored. The total score depends on the amount of mistakes, the reading speed, and the difficulty of the text. The reliability (r varied from 0.86 to 0.93 per AVI card), the content validity, and the construct validity of the AVI test are good (20). The school assessed the progress in mathematical skills using the Wereld in Getallen (World in Numbers) (19). During this test, children were asked to solve mathematical problems taken from everyday life. The test contains tasks aimed at math fluency (i.e., rapid calculation of single-digit addition, subtraction, and multiplication facts) and more complex mathematical problems, which rely on more cognitive skills like planning and problem solving. The Wereld in Getallen is a common method for teaching and assessing the progress in mathematics in the Netherlands.
A pilot intervention (9 wk, two times per week) was conducted at the same school to examine whether the exercises were appropriate for the children and their performance level. Information was gathered about the structure and the content of separate sessions, for example, how much time it takes to give adequate instructions and to execute the exercises. The information obtained was used for the development of the final intervention sessions.
Ball Skill Intervention
The intervention is based on the constraints-led approach of motor skill learning (9). Essential in this approach are three types of constraints influencing motor skill learning: constraints related to the child (e.g., age and LD), the demands of the task (e.g., ball catching with one hand and game rules), and the constraints related to the physical or social environment (e.g., temperature, peers, and teacher). Those constraints limit behavior; however, they also give opportunities for motor skill learning because task and environmental constraints can be manipulated. For example, the teacher can purposefully manipulate the demands of the task through simplifying the task for children with the most severe motor problems and vice versa. The current intervention focused primarily on the manipulation of the task and the social environment because they are easy to manipulate by a physical education teacher. Besides manipulation of constraints, the roles of the teacher and the child are important in the development of motor skills. For children self-exploration, problem solving and an active involvement in their own learning process are critical factors in motor skill development (35). Targeted support and feedback by a teacher plays an important role in this process (9). Therefore, in the current intervention, the teacher served as a mediator, meaning the teacher monitored, guided, and facilitated the learning process of individual children (9) through the manipulation of task constraints. For example, the teacher increased the distance to a basket for children who were successful in scoring goals and vice versa.
The current intervention focused on learning ball skills in a structured way. This means that the ball skills were first practiced in more simple, static settings with simple exercises like throwing and catching with two children or bouncing and turning around cones. The simple exercises in static settings were aimed at an adequate development of basic ball skills (i.e., automatization of ball skills). The automatization of basic ball skills enables children to apply these skills to participate in ball games that require more advanced ball skills and cognitive skills (38). Later on, the tasks became more complex such as throwing, catching, and bouncing during a ball game, where children needed to pay attention to teammates, opponents, game rules, and time, which required more cognitive engagement than simple exercises. In the current intervention, the first 4 wk focused only on simple ball exercises followed by 12 wk wherein simple exercises were repeated and more complex ball exercises and ball games were added.
The intervention consisted of 32 sessions, twice a week for 16 wk, and focused on improving six ball skills (i.e., strike, bounce, catch, kick, throw, and roll). Each session lasted approximately 40 min and consisted of a warm-up (5 min), a 30-min ball skill training, and a cooldown (5 min). In a population with much variability in ball skill performance, like in children with LD, is it important that children are challenged based on their own skill level (34). To optimize the learning environment and challenge each individual child, each class in the intervention group (in Dutch special-needs primary schools a class consists of a maximum of 16 children) was divided into two groups of a maximum of eight children based on their ball skill level assessed by the physical education teachers. Two teachers conducted the intervention, so the teacher–child ratio was 2:16 in all intervention sessions. Within the two groups, the same exercises were performed, but under different task constraints to fit individual skill levels.
In this study, an experimental pre-post-retention design was used. The scores on the ball skill test, EF tasks, and academic tests of both groups at T0 (i.e., pretest) were used as the baseline data. The scores on T1 (i.e., posttest, directly after the intervention) and T2 (i.e., retention test, 6 months after the end of the intervention) were used to examine intervention effects.
The TGMD-2, the TOL, and the TMT were individually administered by specially trained test administrators. All test administrators were blind to which children attended the intervention group or the control group. Before the testing, they received training to become familiar with the test protocol and the test scoring.
One week after the pretest, the ball skill intervention started during the physical education lessons at school. Two physical education teachers from the school performed the intervention. In the same period, the control group received from the same teachers regular physical education of the same duration and frequency as the intervention program, but the teachers alternated teaching physical education to the control group. The teacher–child ratio was 1:16, which is the normal ratio in Dutch special-needs primary schools. The control group received a varied program consisting of gymnastics (30%), circuit training (12%), athletics (10%), ball games (44%), and other training (4%). Before the intervention, all sessions were discussed with the physical education teachers to ensure that they fully understood what was intended with the intervention.
During the intervention sessions, the researcher observed the first four sessions to verify whether the teachers conducted the program as intended and to give verbal feedback if needed. After this period, the researcher observed selectively and unannounced six sessions to obtain information about the way of practicing, the content of the sessions, and to score the exact session time of the intervention and the control group to compare both groups.
The descriptive statistics were performed using the Statistical Package for the Social Sciences (version 20.0; SPSS Inc., Chicago, IL), and the significance level was set at 0.05. Independent t-tests were conducted to examine differences in group characteristics between the intervention and the control group. Differences at baseline on the TGMD-2 ball skill scores, the TOL scores, the TMT scores, and the learning lags on reading and mathematics between the intervention and the control group were explored with ANCOVA controlled for IQ.
In the present study, children were nested within classes. To take this into account, multilevel modeling (MLwiN 2.23) was used to investigate intervention effects. Multilevel modeling is considered to be the most appropriate data analysis technique for nested data (30). Multilevel models were created for the scores on the ball skills and EF tasks (i.e., TOL and TMT) separately, with individual child differences at level 1 and class differences at level 2. Group (intervention or control), sex, and the interaction between group and sex were entered separately in the initial models as possible predictors. Random intercepts for class (level 2) were considered, accounting for possible class differences (30). All models were adjusted for the baseline score on the outcome variable and IQ. Goodness of fit was evaluated by comparing the −2*log likelihood of the previous model with the most recent model. Variables with a nonsignificant contribution to the model (P < 0.05) were removed for further analysis. The scores on the TMT were not normally distributed; therefore, the TMT scores were transformed to z-scores.
As it was expected that enhancing EF children’s academic skills may improve after the intervention, Pearson’s correlations were conducted to determine whether changes in EF between baseline and posttest (T1–T0) and between baseline and retention test (T2–T0) were related to changes in scores on reading and mathematics in the intervention period (i.e., delta).
Groups Characteristics, Ball Skills, EF, and Academic Achievement at Baseline
The characteristics of both groups are shown in Table 1. There was no significant difference between the groups in terms of age (P = 0.071), sex (P = 0.404), and comorbidity (i.e., attention deficit hyperactivity disorder or autism spectrum disorder; P = 0.361). The control group had a significantly higher IQ compared with the intervention group (t = −2.884, P = 0.005). Table 2 lists the mean TGMD-2 ball skill scores, the TOL scores, the TMT scores, and the learning lags on reading and mathematics for the intervention and control group during pretest, posttest, and retention test. The pretest scores for the TGMD-2 ball skills (P = 0.165), the TOL (P = 0.716), the TMT (P = 0.989), the learning lag on reading (P = 0.492), and the learning lag on mathematics (P = 0.921) were not significantly different between the groups. This indicated that both groups had comparable performance on ball skills, problem solving, cognitive flexibility, reading, and mathematics at the start of the intervention.
Effect of the Intervention on Ball Skill Performance
Results of the multilevel modeling (Table 3) show a significant effect of group (intervention or control) on ball skill performance favoring the intervention group on the posttest (P < 0.0001) and on the retention test (P = 0.002), explaining 15.1% and 12.3% of the total variance, respectively. Sex and interaction between sex and group were not significant at posttest and at retention test and were therefore not included in the final model. The random intercept for class was significant at posttest (P = 0.003) but not on the retention test (P = 0.26), indicating different intercepts for each class on the ball skill score at posttest.
Effect of the Intervention on EF
Group (intervention or control), sex, and interaction between sex and group did not significantly influence the model at posttest as well as at retention test. The random intercept for class did not improve the model fit, indicating that there was no class effect of the intervention. As no intervention effects were found with multilevel modeling, Pearson’s correlations were conducted to determine whether changes in ball skills between pretest and posttest (T1–T0) were related to changes in TOL scores between pretest and posttest (T1–T0) and retention test (T2–T0) in both groups separately (i.e., delta). In the intervention group, no correlation was found between the change in ball skills from pretest to posttest and the change in TOL performance from pretest to posttest (r = 0.010, P = 0.947). A positive significant correlation was obtained between the change in ball skills from pretest to posttest and the change in TOL performance from pretest to retention test (r = 0.41, P = 0.007). This indicates that the larger the improvement in ball skills from pretest to posttest, the larger the improvement in TOL performance from pretest to retention test. In the control group, no significant relationships were found between the change in ball skills from pretest to posttest and the change in TOL performance from pretest to posttest (P = 0.992) or from pretest to retention test (P = 0.095).
The predictors group (intervention or control), sex, and interaction between sex and group did not significantly improve the model fit indicating no intervention effects on cognitive flexibility. The random intercept for class was not significant. As no effect of the intervention was found with multilevel modeling, Pearson’s correlations were conducted to determine whether changes in ball skills between pretest and posttest (T1–T0) were related to changes in scores on the TMT between pretest and posttest (T1–T0) and retention test (T2–T0) in both groups separately. No significant correlations (P > 0.05) were observed between the change in ball skills and the change in cognitive flexibility in the intervention period and 6 months after the intervention in both groups.
Effect of the Intervention on Academic Achievement
As it was hypothesized that enhancing EF children’s academic abilities may improve after the intervention, Pearson’s correlations were only conducted for the changes on TOL performance and the changes on reading and mathematics in the intervention period. This is because only a relation between ball skill performance and TOL performance was found in the intervention group. No significant correlations were found between the changes in TOL performance and the changes in learning lags on reading and mathematics (all P values > 0.05) in the intervention period and the retention test 6 months after the intervention.
This study examined the effects of a 16-wk ball skill intervention on the performance of 1) ball skills, 2) EF tasks (i.e., problem solving and cognitive flexibility), and 3) how far improved EF leads to improved reading and mathematics in children age 7–11 yr old with LD.
Children who received the ball skill intervention for a period of 16 wk demonstrated a significant improvement in their ball skill performance, whereas children in the control group did not. To our knowledge, this is the first study that focused on the improvement of ball skills in children with LD. The findings of the present study were supported by the meta-analysis of Logan et al. (22). The meta-analysis included interventions with much variation in duration and frequency (duration of 6–15 wk and 480–1440 min); however, they found no significant relationship between the effect size of the intervention and the total intervention time. Further research is needed to examine the optimal intervention time (duration and frequency). However, based on the present study, it can be concluded that an intervention time of 16 wk and 960 min of specific ball skill training is sufficient for improving the ball skills of children with LD.
The results of the present study showed that children with LD benefited from participation in the current ball skill intervention. The control group showed no improvement in their ball skills in this period, although 44% of their gym lessons consisted of ball games. A possible explanation for the difference between the groups is that the intervention group practiced their ball skills in a structured way: in simple ball exercises, static practice settings; and in complex ball exercises, dynamic settings that were adapted to aid the child’s mastery of the motor skills. The control group practiced the ball skills only in ball games. It appears that children with LD have impaired ball skills (17,40). Practicing ball skills in a simple setting was therefore aimed at the development and mastery of a basic level of ball skills, which is needed in complex ball games (38). As the control group did not practice basic ball skills such as throwing a ball to the wall, bouncing without moving the feet, or kicking a ball to another child, it might be that these children have not developed a basic level of ball skills. Therefore, the complex ball games might have been too difficult for the children in the control group resulting in less ball skill development and may explain the relatively low ball skill scores of these children.
The children in the intervention group could practice the ball skills at their own skill level because the teacher modified the practice setting or task for individual children. This was not the case in the control group: the whole group performed the same exercise with the same difficulty level. Furthermore, due to the teacher–child ratio (2:16), children in the intervention group received more individual feedback compared with the control group. Finally, the amount of time devoted to ball skills was different for the intervention group and the control group, which may also explain the difference in the improvement in ball skill performance. To summarize, structured practice of ball skills (i.e., from simple to complex) and offering ball skill exercises matching individual skill levels may be critical factors in the development of ball skills in children with LD.
The second aim of the current study was to enhance EF and academic performance of children with LD, specifically their problem solving, cognitive flexibility, and performance in reading and mathematics. The results showed no significant interaction effects between group and time for any parameters, suggesting no intervention effects on the cognitive parameters. However, within the intervention group, a significant positive correlation was found between the changes in ball skills and the changes on the TOL performance from preretention to retention test. This indicates that children who improved more on their ball skills from pretest to posttest demonstrated more improvement in TOL performance from preretention to retention test compared with children in the intervention group who improved less on their ball skills. A possible explanation for this finding might be that the children who improved more on their ball skills were better able to apply the ball skills in the complex exercises in the intervention (i.e., the ball games) than the children with less improvement in ball skills. During exercises in the complex and changing settings, a greater demand on cognitive skills was required (3). Engagement in more cognitively demanding situations is difficult for children with relatively low ball skill proficiency. For adequate ball skill performance in these more cognitively demanding situations, a basic level of ball skills is required so that the ball skills are automatized or well developed and the children can pay attention to the cognitive elements (38). Therefore, we suppose that the children with greater ball skill improvement could more fully participate in the complex exercises in the intervention and thereby have better facilitated their problem solving, compared with the children with lower ball skill improvement. The improvement in TOL performance between pre to retention test might indicate a lagged effect. More research is needed to confirm this suggestion. In addition, future research should examine whether a longer intervention program may have more effect on EF in children with LD. In a longer intervention program, the children could practice the ball skills more frequently in complex and cognitive demanding situations, which may have more effect on children’s EF.
There are some study limitations. First, it was not possible to randomly assign individual children to the intervention group or the control group, but only classes of children. The reason for this is that the study set out to develop an intervention that is applicable in primary schools and could be conducted during the physical education lessons at these schools. Although the intervention group and the control group differed on IQ, they were comparable on age, sex, baseline scores on ball skills, EF, and learning lags on reading and mathematics. Therefore, the statistical analyses were controlled for IQ. Furthermore, in the present study, two EF tasks were assessed, namely, problem solving and cognitive flexibility; however, it is possible that other aspects of EF improved after the intervention, for example, response inhibition. In future intervention studies investigating EF, it is recommended that researchers examine intervention effects on the whole spectrum of EF. It is believed that despite these limitations, the present ball skill intervention is a valuable contribution to the physical education practice and extends the current literature on this topic.
This study has several practical implications. First, children with LD could benefit from participation in a structured ball skill intervention in relatively small groups. For this reason, children should regularly practice their ball skills during physical education at primary schools through a structured program, which will enable them to develop their skills at their own level. Indeed, it is recommended to begin such programs in early childhood education to promote ball skills development in young children (22). Future studies may wish to investigate whether the findings here are specific to ball skills or are also present when training less complex fundamental movement skills.
Second, this study demonstrated that larger improvement in ball skills led to larger improvement in children’s problem solving. This finding stresses the importance of well-developed motor skills for cognitive development, specifically well-developed ball skills for problem solving. Physical education in primary schools is an excellent environment to facilitate children’s motor skill development as well as their cognitive development through the implementation of structured motor skills interventions. Therefore, primary schools should invest in physical education and should not spare on this topic.
In conclusion, the current ball skill intervention is an effective instrument to improve the ball skills of children with LD. No intervention effects were found on the cognitive parameters. However, within the intervention group, children who showed more improvement in ball skills demonstrated more improvement in problem solving compared with children who improved less in ball skills. Therefore, although the current study did not demonstrate large effects on EF and academic achievement, evidence was found to suggest that practicing ball skills might have a positive influence on EF, specifically on problem solving.
The authors wish to thank all teachers and children at the participating school for their cooperation in this study.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
The authors have no financial disclosures or conflicts of interest. The result of the present study does not constitute endorsement by the American College of Sports Medicine.
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1Children with learning disorders are defined here as children with problems in academic skills, like reading and mathematics, who attend Dutch special-needs primary schools. Since 2001 in the Netherlands, children with an IQ between 50 and 79 without a physical disability and children with learning and/or pedagogic problems with an IQ of 80 and higher attend the same special-needs primary school (21). Cited Here...