Associations Between Motor Competence and Executive Functions in Children and Adolescents : A Systematic Review and Meta-analysis

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Associations Between Motor Competence and Executive Functions in Children and Adolescents : A Systematic Review and Meta-analysis © The Author(s) 2024 Published version Bao, Ran; Wade, Levi; Leahy, Angus A.; Owen, Katherine B.; Hillman, Charles H.; Jaakkola, Timo; Lubans, David Revalds Bao, R., Wade, L., Leahy, A. A., Owen, K. B., Hillman, C. H., Jaakkola, T., & Lubans, D. R. (2024). Associations Between Motor Competence and Executive Functions in Children and Adolescents : A Systematic Review and Meta-analysis. Sports Medicine, Early online. https://doi.org/10.1007/s40279-024-02040-1 2024 Vol.:(0123456789) Sports Medicine https://doi.org/10.1007/s40279-024-02040-1 SYSTEMATIC REVIEW Associations Between Motor Competence andExecutive Functions inChildren andAdolescents: ASystematic Review andMeta‑analysis RanBao1,2,3· LeviWade1,2,3· AngusA.Leahy1,2,3· KatherineB.Owen4· CharlesH.Hillman5,6· TimoJaakkola7· DavidRevaldsLubans1,2,3,7 Accepted: 20 April 2024 © The Author(s) 2024 Abstract Background Motor competence and executive functions co-develop throughout childhood and adolescence, and there is emerging evidence that improvements in motor competence may have cognitive benefits in these populations. There is a need to provide a quantitative synthesis of the cross-sectional, longitudinal and experimental studies that have examined the association between motor competence and executive functions in school-aged youth. Objectives The primary aim of our systematic review was to synthesise evidence of the association between motor competence and executive functions in school-aged children and adolescents (5–18years). Our secondary aim was to examine key moderators of this association. Methods We searched the PubMed, PsycINFO, Scopus, Ovid MEDLINE, SPORTDiscus and EMBASE databases from inception up to 27 June 2023. We included cross-sectional, longitudinal and experimental studies that assessed the association between motor competence (e.g., general motor competence, locomotor skills, object control skills and stability skills) and executive functions (e.g., general executive functions, inhibition, working memory and cognitive flexibility) in children and adolescents aged 5–18years. Results In total, 12,117 records were screened for eligibility, and 44 studies were included. From the 44 included studies, we meta-analysed 37 studies with 251 effect sizes using a structural equation modelling approach in the statistical program R. We found a small positive association (r = 0.18, [95% confidence interval (CI) 0.13–0.22]) between motor competence and executive functions. The positive associations were observed in cross-sectional (r = 0.17, [95% CI 0.13–0.22]), longitudinal (r = 0.15, [95% CI 0.03–0.28]) and experimental studies (r = 0.25, [95% CI 0.01–0.45]). We also found that general motor competence (r = 0.25, [95% CI 0.18–0.33]), locomotor (r = 0.15, [95% CI 0.09–0.21]), object control (r = 0.14, [95% CI 0.08–0.20]) and stability (r = 0.14, [95% CI 0.08–0.20]) skills were associated with executive functions. We did not find any moderating effects for participants’ age on the associations between motor competence and executive functions. Conclusions Our findings suggest a small-to-moderate positive association between motor competence and executive functions in children and adolescents. The small number of experimental studies included in this review support the assertion that interventions targeting children’s motor competence may be a promising strategy to improve their executive functions; however, more research is needed to confirm these findings. Future studies should explore the underlying mechanisms linking motor competence and executive functions as their comprehension may be used to optimise future intervention design and delivery. PROSPERO Registration CRD42021285134. Extended author information available on the last page of the article R.Bao et al. Key Points Findings from our systematic review and meta-analyses suggest a small-to-moderate positive association between motor competence and executive functions in children and adolescents. Locomotor, object control and stability skills were all positively associated with executive functions (e.g., inhibition, working memory, cognitive flexibility). Most observational studies focused on children, while a limited number of studies involved adolescent participants. Due to the small number of experimental studies, we were unable to establish a causal link between motor competence and executive functions in young people. 1 Introduction Motor competence is defined as the mastery of physical skills and movement patterns that enable participation in a range of physical activities [1]. It includes locomotor (e.g., running and jumping), object control (e.g., catching and throwing) and stability (e.g., balancing and twisting) skills [1]. The umbrella term “motor competence” is often used interchangeably with “motor proficiency”, “motor performance”, “motor ability” and “motor coordination” [2]. The development of motor competence is associated with a range of psychological (e.g., perceived competence [3]), physiological (e.g., physical fitness [4, 5], healthy weight status [2, 6]), and behavioural (e.g., physical activity [6, 7]) benefits [8]. Further, there is accumulating evidence that developing motor competence may also have benefits for young people’s executive functions [9–11]. Executive functions (EFs) are complex and can be understood from multiple perspectives. For example, prior research has explored EFs from evolutionary (e.g., ability to make decisions and behave in a purposeful, goal-directed, future-oriented manner), syndrome-based (i.e., impairments in individuals’ cognitive functioning), neurobiological (i.e., changes in brain structure and function) and statistical (e.g., psychometric properties of tests that assess inhibitory control, updating/working memory and shifting/cognitive flexibility) perspectives [12, 13]. For the purposes of our review, we define EFs as higher-order cognitive processes underlying the selection, scheduling, coordination and monitoring of complex, goal-directed processes [14]. In this definition, EFs typically include three core components: inhibitory control (e.g., the ability to maintain focus and suppress prepotent or automated responses), updating/working memory (e.g., retaining and updating of information), and shifting/ cognitive flexibility (e.g., the ability to shift attention to changing task demands) [14, 15]. Previous studies have found positive associations between executive functions and academic performance (e.g., mathematics achievement) [16–18], and between executive functions and quality of life in children and adolescents [19]. Alternatively, poor executive functions are related to mental health disorders (e.g., depression, conduct disorder, attention deficit hyperactivity) [20, 21], poor physical health (e.g., obesity) [22], as well as social problems (e.g., violence, crime, reckless behaviour) [23, 24] in children and adolescents [25]. Engagement in physical activity has been shown to benefit executive functions [26], and it has been hypothesised that this may occur via a range of neurobiological, psychosocial and behavioural mechanisms [27, 28]. There is growing interest in the idea that the development of motor competence might provide some explanation of the effects of physical activity on executive functions. Indeed, there is evidence that motor competence and executive functions may develop via similar pathways [29–31]. The same cortical and subcortical regions of the brain (comprising functional neural networks) are partially responsible for the development of motor competence and executive functions in childhood, including the prefrontal cortex, cerebellum and basal ganglia [30]. Consequently, just as there is a neurobiological basis for asserting that regular physical activity in children contributes to the development of executive functions, there is also a neurobiological justification for the belief that involvement in motor learning activities can similarly enhance executive functions [15]. As highlighted by Hill (2023) in their review on the relationship of motor competence to broader cognitive and social outcomes, physical activity may improve executive functions via its quantitative characteristics (e.g., intensity, frequency, etc.,) or via qualitative characteristics (e.g., motor skill complexity). Learning motor skills requires the input of executive processes of inhibition, working memory and cognitive flexibility [10], meaning that greater executive functioning could plausibly translate into the performance of complex or novel skills. In sum, there is likely a bidirectional relationship between executive functions and motor skills acquisition and performance. A recent systematic review including a meta-analysis found a positive association between motor skills (except for object control skills) and executive functions in children [11]. However, this review only included children aged 3–12years and did not examine the role of key moderators (e.g., study design). More recently, Hill and colleagues published a conceptual model and systematic review that included a qualitative synthesis of the association between motor competence and cognitive outcomes in young people [32]. Consistent with the model proposed Motor Competence and Executive Functions in Children and Adolescents by Lubans and colleagues [33], the authors acknowledged that motor competence may support cognitive development via a range of neurobiological, psychosocial and/or behavioural mechanisms, and pointed out that clear patterns of domain-specific relationships are lacking [32]. As noted by the authors, young people typically perform motor skills in environments (e.g., physical education and sport practice) that challenge both their motor and cognitive abilities, and thus likely contribute to improvements in both motor and cognitive development simultaneously. However, due to the small number of experimental studies, the authors were unable to draw conclusions regarding the causal relationship between motor competence and cognitive outcomes [32]. In their narrative review of the literature, Willoughby and Hudson [15] discuss the contribution of motor skills and physical activity to the development of executive functions in early childhood. Based on their review of the literature, they note that it remains unclear whether increases in physical activity are enough to explain improvements in executive functioning, and suggest that the development of children’s motor skills is more strongly associated with executive function development in early childhood than the frequency, duration or intensity of their physical activity. Despite increasing interest in the link between motor competence and executive functioning, no previous systematic review has conducted a quantitative synthesis of this relationship in children and adolescents. Narrative syntheses of quantitative data have notable limitations, including lack of transparency and conclusions based on subjective interpretation [34, 35]. Alternatively, metaanalyses are considered a superior approach to data synthesis because they: (i) involve the calculation of effect sizes, (ii) can correct for small-scale studies that are not adequately powered, and (iii) allow for the examination of moderator effects. Therefore, the primary aim of our systematic review and meta-analysis was to assess the strength of the association between motor competence and executive functions in school-aged children and adolescents (5–18years). Our secondary aim was to examine key moderators of this association. 2 Methods 2.1 Protocol andRegistration Our systematic review was conducted according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [36]. The current review was prospectively registered with PROSPERO (CRD42021285134). 2.2 Identification ofStudies andSearch Strategy We conducted a comprehensive literature search to identify studies on the associations between motor competence and executive functions in children and adolescents. We searched six electronic databases (PubMed, PsycINFO, Scopus, Ovid MEDLINE, SPORTDiscus and EMBASE) from the year of their inception up to 27 June 2023. After automatically and manually removing duplicates, the remaining studies were subjected to a screening process. Our search strategy was modified in accordance with each of the databases (e.g., using MeSH terms where possible), and included the following keywords: motor competence (e.g., locomotor, object control and stability skills), executive functions (e.g., inhibition, working memory, cognitive flexibility), and population (e.g., children, adolescents). The search terms are detailed in the Online Supplementary Material (OSM; TableS1). Where possible, search results were limited by language (English), species (human) and type (journal). The reference lists of included studies were checked for additional relevant studies. 2.3 Inclusion/Exclusion Criteria Two authors independently screened titles, abstracts and fulltexts (RB and LW) against inclusion and exclusion criteria. Any disagreements between the two authors were resolved via discussion, and a consensus was reached with two other authors (AAL and DRL). The following inclusion criteria were applied: (1) experimental or quasi-experimental studies designed to promote motor competency and executive functions (i.e., inhibition, working memory, and cognitive flexibility) in children and adolescents; (2) observational studies focused on the associations between motor competence and executive functions; (3) data on school-aged children and adolescents (5–18years of age); (4) data on participants’ motor competency (i.e., motor proficiency, motor performance, fundamental movement/motor skills, motor ability and motor coordination) and executive function (i.e., inhibition, working memory, cognitive flexibility); (5) objective assessment such as process-based (e.g., Test of Gross Motor Development-3rd Edition (TGMD-3)) and/or product-based measures (e.g., Movement Assessment Battery for Children2nd Edition (MABC-2)) of motor competence and performance-based measures of executive functions (e.g., flanker task, trail-making task, digit span task) were eligible for this review. The following exclusion criteria were applied: (1) only reported fine motor skills (e.g., writing); (2) utilised fitness as a proxy for motor competence (e.g., strength); (3) individuals living with disability (e.g., intellectual and/or physical disability); (4) measured perceived motor competence or used rating scales to measure executive functions; R.Bao et al. (5) reported general cognition, but did not provide outcome data specific to executive functions (either as a composite score or domain specific). 2.4 Data Extraction Information regarding study location, first author’s name, year of publication, design (i.e., experimental, quasi-experimental, longitudinal, or cross-sectional study design), participant age, sample size, measures of motor competence and executive functions, and correlations between motor competency and executive function outcomes, were independently extracted by two authors (RB and LW). Differences in the extraction and coding of information were discussed and resolved with two other authors (AAL and DRL). In accordance with previous studies [1, 3, 8], motor competence components were classified into three categories, including locomotor skills, object control skills, and stability skills. Additionally, those studies that only reported the association between the composite scores of motor competence (e.g., locomotor, object control, and stability skills) and executive functions were labelled as “general motor competence”. The term “motor competence” encompassed all motor competence components, including general motor competence, locomotor skills, object control skills, and stability skills. Similarly, executive function domains were also classified into three categories [25], including inhibition, working memory, and cognitive flexibility. Studies that only reported the association between motor competence and composite scores of multiple executive function domains (e.g., inhibition, working memory, and cognitive flexibility), were coded as “general executive functions”. The term “executive functions” comprised general executive functions, inhibition, working memory and cognitive flexibility. For tests of executive functions, where reported, we used information on the accuracy and reaction time (i.e., the reaction time of correct responses) for calculation of effect sizes. Where this was not reported, we used accuracy data, unless the primary outcome of the test was time to completion (e.g., in the trail-making test). 2.5 Criteria forRisk ofBias Assessment Risk of bias was evaluated independently by two authors (RB and LW). Experimental studies were assessed using the Cochrane Risk of Bias Tool (RoB 2.0) for randomised controlled trials (RCTs) and the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) for non-randomised controlled trials [37]. Disagreements between assessors were resolved by discussion. The criteria were: (1) randomisation process; (2) deviations from intended interventions; (3) missing outcome data; (4) measurement of the outcomes; and (5) selection of the reported results. Observational studies were assessed using the following items developed from the Joanna Briggs Institute critical appraisal checklists [37]: (1) study design allowed for causal inference (only for longitudinal study design); (2) random selection of study participants and/or study sites; (3) detailed description of sample characteristics; (4) valid assessment of motor competence; (5) valid assessment of executive functions; and (6) adjustment for relevant confounders in theanalysis. Based on the assessment criteria, the risk bias for each included study was categorised based on the percentage of items rated as “yes” > 70% (low), 50–69% (moderate), or < 50% (high) [38, 39]. 2.6 Meta‑analyses We combined effect sizes using a structural equation modelling approach to multilevel meta-analysis. The main advantage of this approach is that it is not limited by the assumption of independence (i.e., effect sizes are nested within studies), and multiple effect sizes can be included from each study [40]. Unconditional mixed-effects models using maximum likelihood estimation were conducted to calculate the overall pooled effect size. For each pooled effect size, 95% likelihoodbased confidence intervals (Cis) were calculated. All analyses were conducted using the metaSEM package in R Version 4.2.2 (code provided in OSM 1) [41]. We extracted various summary measures, including standardised mean differences and correlation coefficients. Summary metrics that were not reported as r values were converted to correlation coefficients (r) for observational and experimental studies. For observational studies, correlation coefficients were extracted according to whether the effect sizes were cross-sectional effect (one point in time) or longitudinal (changes over time). Correlation coefficients that controlled for relevant covariates (e.g., age and sex) were extracted. Where these were not available, the zero-order correlation coefficients were extracted. The correlation coefficients were converted into Fisher’s z for all analyses using the following formula (Eq.1) [42]: where ln is the natural logarithm [43]. Then, the results were converted back to r-values using the following conversion formulas (Eq.2): where e refers to the base of the natural logarithm [44]. For experimental studies, correlation coefficients (r) of change scores for both motor competence and executive functions between pre and post measurements were extracted from one (1) z =0.5 ∗ln (1 + r 1−r), (2) r = ( e2∗z−1 ) ( e2∗z+1 ), Motor Competence and Executive Functions in Children and Adolescents study. For another study, we used the pre and post experimental means and standard deviations to calculate Cohen’s d using the formula (Eq.3): where X1 is the mean change in the first group, X2 is the mean change in the second group, sd is the pooled standard deviation of the two groups [42]. Then, Cohen’s d was converted into r values using the following formula (Eq.4): where d is Cohen’s d [42]. To aid with interpretation, the correlation coefficients are referred to as small (0.10–0.20), moderate (0.21–0.35) or large (> 0.35) [45]. For consistency, negative summary outcomes (i.e., where negative numbers indicate greater performance – such as outcomes including reaction time) were inverted to positive summary measures. In addition, 95% likelihood-based CIs were calculated for each effect size. Heterogeneity was assessed using the I2 statistic, with values from 0–40%, 30–60%, 50–90%, and 75–100% indicating low, moderate, substantial, and high heterogeneity, respectively [46]. Publication bias was analysed using funnel plots and Egger’s regression test, which provides a metric of the asymmetry present in the plot (with a p-value > 0.05 indicating no evidence of publication bias). Moderator analyses were conducted to determine whether the association between motor competence and executive functions differed according to the type of motor competence (e.g., locomotor, object control, andstabilityskills), executive function domain (e.g., inhibition, working memory, cognitive flexibility), age of participants (treated as a continuous variable), and study design (e.g., cross-sectional, longitudinal, experimental). Initially, we treated age as a binary moderator, but age did not influence the association between motor competence and executive functions. Given the small number of the included studies involving adolescents, we subsequently treated age as a continuous moderator, which allowed for a more precise examination of its effects on the association between motor competence and executive functions. The R2 values were computed to determine the proportion of explained variance for each potential moderator variable. 3 Results 3.1 Study Selection After conducting a comprehensive literature search, a total of 20,498 records were identified. After removing 8,381 duplicates, 12,117 records were screened via title and abstract (3) d = X1−X2 s d , (4) r = d √ d2+4 , for suitability. After the removal of irrelevant records, 220 records were assessed for eligibility via full-text screening. Finally, 44 studies were included in the qualitative synthesis and 37 studies [47–83] were included in the meta-analysis (Fig.1). 3.2 Characteristics ofIncluded Studies Study characteristics are presented in TableS2 (OSM). Most studies were conducted in Germany (n = 7) [49, 55, 61, 64, 70, 73, 74], followed by Switzerland (n = 6) [53, 54, 56, 62, 75, 76], the Netherlands (n = 6) [63, 69, 78, 84–86], Australia (n = 5) [47, 52, 57, 66, 87], Brazil (n = 3) [48, 50, 88], Italy (n = 3) [51, 65, 89], the USA (n = 2) [58, 72], Norway (n = 2) [71, 79], China (n = 2) [77, 81], Denmark (n = 1) [90], Finland (n = 1) [59], Greece (n = 1) [83], Ireland (n = 1) [82], Portugal (n = 1) [67], and Spain (n = 1) [80]. One study focused on children from Germany and Switzerland [68], and another targeted children from Germany and Oman [60]. Cross-sectional designs were used in 34 studies [48–50, 53–55, 57, 59, 60, 62–67, 69, 71, 73, 75–90], with the remainder consisting of longitudinal (n = 5) [47, 56, 61, 68, 74], randomised controlled trials (n = 3) [52, 58, 70] and non-randomised controlled trials (n = 2) [51, 72]. Most studies included children between the ages of 5 and 12years (n = 39), with the remaining five studies focusing on adolescents (between the ages of 13 and 18years) [53, 57, 59, 64, 87]. 3.3 Measurement ofMotor Competence andExecutive Functions Locomotor skills were examined in 26 studies [47, 48, 50–54, 56, 58, 59, 62, 63, 67, 69, 72, 74–78, 81–84, 86, 88], object control skills were reported in 28 studies [48, 49, 52–55, 57–61, 63, 64, 66, 69–71, 74, 77, 78, 80, 82–87, 89], and stability skills were assessed in 32 studies [49, 50, 53–55, 57, 61–63, 65–70, 72–75, 78–90]. The most widely used measurement tools for assessing motor competence were the Movement Assessment Battery for Children (MABC) [49, 55, 57, 61, 66, 70–72, 78, 80, 87, 89] and the Körperkoordinationstest Für Kinder (KTK) [48, 50, 62–64, 67, 69, 75, 81, 84, 86, 88], which were both used in 12 studies. Additionally, six studies used the Bruininks-Oseretsky Test of Motor Proficiency, second edition (BOT-2) [63, 69, 73, 82, 83, 86], three studies used the Basic Motor Competence in Fifth Grades (MOBAK-5) [53, 54, 74], and the Test of Gross Motor Development, second edition (TGMD-2) was used in four studies [48, 58, 77, 82]. Additionally, one study used the Peabody Developmental Motor Scale-Second Edition (PDMS-2) to assess motor competence [51]. Three core components of executive functions were measured, including inhibition (n = 21) [51, 52, 54, 56, 57, 59, 61–63, R.Bao et al. 66, 70–73, 75, 78, 80, 81, 84, 86, 89], working memory (n = 33) [47, 51–57, 59–64, 68, 69, 71, 73–75, 77–87, 89, 90], and cognitive flexibility (n = 17) [50–52, 54, 57, 62, 64, 70–73, 75, 76, 80, 81, 83, 88]. The most common tests used to assess inhibition were flanker tasks (n = 15) [48, 52, 54, 56, 59, 61–63, 65, 72, 73, 75, 76, 80, 81] and stop signal tasks [63, 66, 78, 84, 86]. Cognitive flexibility was most often assessed using the mixed flanker task [54, 56, 62, 72], the trail-making task [50, 51, 71, 88], and the dimensional change card sort test [52, 75, 80]. The digit span test [55, 60, 63, 64, 71, 78, 83, 84, 86, 91] and N-back tasks [47, 54, 56, 57, 81, 87] were the most common measures of working memory. A list of the executive function measures used in the included studies can be found in TableS3 (OSM). 3.4 Risk ofBias ofIncluded Studies Based on the information provided, 65.9% of the included studies were considered to have a low risk of bias, while the remaining studies had a moderate risk (all risk-of-bias information is in OSM 2). Among crosssectional studies, the majority (70.6%) were considered to have a low risk of bias, with 29.4% studies having a moderate risk of bias. Regarding the measurement tools of all studies, a high percentage of studies used valid and reliable instruments to assess motor competence (91.2%) and executive functions (76.5%). Additionally, 61.8% of included studies controlled for relevant covariates when examining the association between motor competence and executive functions. Among longitudinal studies, 60% were considered to have a low risk of bias and the remaining studies had a moderate risk of bias. All longitudinal studies used valid and reliable measurement tools to assess motor competence, and the majority (80%) used valid and reliable measurement tools to assess executive functions. All longitudinal studies controlled for relevant covariates when examining the association between motor competence and executive functions. All RCTs were deemed to have a moderate risk based on the following criteria: “bias due to deviations from intended intervention” and “bias in selection of the reported result”. Both quasi-experimental studies were considered to have a low risk of bias. Fig. 1 PRISMA flow diagram for searching and selection of the included studies Records identified from: Databases (n = 20 498) PubMed (n = 3 190) PsycINFO (n = 1 565) Scopus (n = 8 669) Ovid Medline (n = 2 431) SPORTDiscus (n = 806) EMBASE (n = 3 837) Recordsremoved before screening: Duplicate records removed (n = 8 381) Records marked as ineligible by automation tools (n = 0) Records removed for other reasons (n = 0) Records screened (n = 12 117) Irrelevant recordsexcluded (n = 11 895) Reportssought for retrieval (n = 222) Reportsnot retrieved (n = 2) Reportsassessed for eligibility (n = 220) Reportsexcluded with reasons (n = 176) Not focused on the association between Motor competence and executive functions (n = 45) Not children and adolescents (n = 47) General cognitive function (n = 33) Not journal articles (n = 14) Only fine motor skills (n = 13) Not typical children (n = 11) Other reasons (n = 4) Wrong measures (n = 4) Already included in initial searches (n = 3) Review studies (n = 2) Studies included in qualitative synthesis (n = 44) Studies included in metaanalysis (n = 37) Identification Screening Included Identification of studiesvia databases and registers Motor Competence and Executive Functions in Children and Adolescents 3.5 Synthesis ofResults 3.5.1 Overall Associations Table1 displays the results of the meta-analysis of the association between motor competence and executive functions. Overall, the findings of the meta-analysis, which included 37 studies and 251 effect sizes, indicate a small positive association between motor competence and executive functions in children and adolescents (r = 0.18, 95% CI 0.13–0.22). For this effect size, there was low heterogeneity within studies (I2 = 0.15) and moderate heterogeneity between studies (I2 = 0.63). The funnel plot (Fig.2) and Egger’s regression (z = 1.18, p = 0.24) suggest that there was no evidence of publication bias. General motor competence (r = 0.25, 95% CI 0.18–0.33), locomotor (r = 0.15, 95% CI 0.09–0.21), object control (r = 0.14, 95% CI 0.08–0.20) and stability (r = 0.14, 95% CI 0.08–0.20) skills were all positively associated with executive functions. Motor competence components explained the small variance of the heterogeneity within (Tau2 < 0.01) and between (Tau2 = 0.02) studies, while demonstrating negligible variation within (R2 = 0.15) and between studies (R2 = 0). In terms of motor competence, positive associations were observed for general executive functions (r = 0.29, 95% CI 0.21–0.36), inhibition (r = 0.18, 95% CI 0.13–0.23), working memory (r = 0.17, 95% CI 0.12–0.23), and cognitive flexibility (r = 0.13, 95% CI 0.08–0.19). Executive function domains explained little variance in the heterogeneity within (Tau2<0.01)and between (Tau2 = 0.02) studies, while demonstrating negligible variation within (R2 = 0.19) and between studies (R2 = 0). Regarding the role of study design, positive associations were found for cross-sectional (r = 0.17, 95% CI 0.13–0.22), longitudinal (r = 0.15, 95% CI 0.03–0.28) and experimental (one RCT and one quasiexperimental study) (r = 0.25, 95% CI 0.01–0.45) studies. Study design explained none of the heterogeneity within (R2 = 0) and little between (R2 = 0.03) studies. Age was not a moderator of the association between motor competence and executive functions (r < 0.01, 95% CI –0.03to0.04). 3.5.2 Motor Competence andExecutive Function Domains Motor competence and general executive functions: The results pertaining to general executive functions are outlined in Table2. A positive association was observed between motor competence and general executive functions (r = 0.29, 95% CI 0.22–0.35). Most of the heterogeneity was attributed to the differences observed within studies (I2 = 0.57) and no heterogeneity was observed between (I2 = 0) studies. General motor competence (r = 0.34, 95% CI 0.25–0.42), locomotor (r = 0.32, 95% CI 0.18–0.44), object control (r = 0.23, 95% CI 0.13–0.33), and stability (r = 0.19, 95% CI 0.02–0.36) skills were positively associated with general executive functions. Motor competence components explained a small proportion of the heterogeneity within studies (R2 = 0.32) and Table 1 Associations between motor competence and executive functions in children and adolescents EFs executive functions, Pooled ES pooled effect sizes, Heterogeneity at Level 2 ES from the same study, Heterogeneity at Level 3 ES from the different studies, I2 is only available for the overall ES. Fisher’s z has been converted to r values Variable No. of studies (No. of ES) Pooled ES 95% CI Q R2_2 R2_3 Tau2_2 Tau2_3 I2_2 I2_3 Summary effect 37 (251) 0.18 0.13–0.22 Q250 = 709.70 (p = 0) < 0.01 0.02 0.15 0.63 Moderation Design 37 (251) Q250 = 709.70 (p = 0) 0.00 0.03 < 0.01 0.02 Cross-sectional 34 (239) 0.17 0.13–0.22 Longitudinal 4 (8) 0.15 0.03–0.28 Experimental 2 (4) 0.25 0.01–0.45 Age (treated as continuous) 36 (247) < 0.01 – 0.03–0.04 Q246 = 697.65 (p = 0) 0.04 0.00 < 0.01 0.05 Motor competence types 37 (251) Q250 = 709.70 (p = 0) 0.15 0.00 < 0.01 0.02 General 13 (41) 0.25 0.18–0.33 Locomotor 12 (74) 0.15 0.09–0.21 Object control 15 (64) 0.14 0.08–0.20 Stability 17 (72) 0.14 0.08–0.20 Executive function domains 37 (251) Q250 = 709.70 (p = 0) 0.19 0.00 < 0.01 0.02 General 5 (14) 0.29 0.21–0.36 Inhibition 21 (74) 0.18 0.13–0.23 Working Memory 28 (103) 0.17 0.12–0.23 Cognitive Flexibility 17 (60) 0.13 0.08–0.19 R.Bao et al. negligible variation between studies (R2 = 0). No effect of age (r = –0.01, 95% CI –0.03 to 0.11) was observed in the association between motor competence and general executive functions. Motor competence and inhibition: For inhibition (Table3), there was a positive association with motor competence (r = 0.15, 95% CI 0.11–0.18). Low heterogeneity was attributed to the differences observed within (I2 = 0.07) and between (I2 = 0.32) studies. General motor competence (r = 0.12, 95% CI 0.03–0.20), locomotor (r = 0.14, 95% CI 0.09–0.18), object control (r = 0.16, 95% CI 0.11–0.21), and stability (r = 0.16, 95% CI 0.12–0.21) skills were positively associated with inhibition. Motor competence components explained a large proportion of the heterogeneity within studies (R2 = 0.65) and negligible variation between studies (R2 = 0). The positive association between motor competence and inhibition was observed in cross-sectional (r = 0.14, 95% CI 0.11–0.18) and experimental (r = 0.24, 95% CI 0.05–0.41) studies. However, no positive association was observed in only one longitudinal study (r = 0.15, 95% CI –0.03 to 0.31). Study design explained a small proportion of the heterogeneity within studies (R2 = 0.01) and negligible variation between studies (R2 = 0.03). No effect of age (r = 0.01, 95% CI –0.02 to 0.03) was observed in the association between motor competence and inhibition. Motor competence and working memory: We observed a positive association between motor competence (r = 0.16, 95% CI 0.12–0.20) and working memory (Table4). Low heterogeneity was attributed to differences observed within (I2 = 0.19) and moderate heterogeneity between (I2 = 0.41) studies. General motor competence (r = 0.20, 95% CI 0.13–0.28), locomotor (r = 0.14, 95% CI 0.09–0.20), object control (r = 0.14, 95% CI 0.08–0.19) and stability (r = 0.15, 95% CI 0.09–0.20) skills were positively associated with working memory. Motor competence components explained a small proportion of the heterogeneity within (R2 = 0.20) and between (R2 = 0.13) studies. Positive associations between motor competence and working memory were observed in cross-sectional (r = 0.16, 95% CI 0.12–0.20) and longitudinal (r = 0.14, 95% CI 0.03–0.26) studies. Study design explained a small proportion of the heterogeneity within (R2 = 0) and between (R2 = 0.01) studies. No effect of age (r < 0.01, 95% CI –0.03 to 0.04) was observed in the association between motor competence and working memory. Motor competence and cognitive flexibility: We found a positive association between motor competence (r = 0.14, 95% CI 0.03–0.25) and cognitive flexibility (Table5). Most of the heterogeneity was attributed to differences observed between studies (I2 = 0.86). Locomotor (r = 0.20, 95% CI 0.08–0.30), object control (r = 0.14, 95% CI 0.02–0.26), and stability (r = 0.15, 95% CI 0.03–0.26) skills were positively associated with cognitive flexibility. Motor competence components explained a large proportion of heterogeneity within (R2 = 0.59) studies. We observed a positive association between motor competence and cognitive flexibility in cross-sectional studies (r = 0.13, 95% CI 0.02–0.24). However, no positive association was observed in only one experimental (r = 0.26, 95% CI –0.22 to 0.64) study. No heterogeneity was found within (R2 = 0) and between (R2 = 0.01) studies. 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Authors and Affiliations RanBao1,2,3· LeviWade1,2,3· AngusA.Leahy1,2,3· KatherineB.Owen4· CharlesH.Hillman5,6· TimoJaakkola7· DavidRevaldsLubans1,2,3,7 * David Revalds Lubans [email protected] 1 Centre forActive Living andLearning, University ofNewcastle, Callaghan, NSW, Australia 2 College ofHuman andSocial Futures, School ofEducation, University ofNewcastle, Callaghan, NSW, Australia 3 Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia 4 SPRINTER, Prevention Research Collaboration, Sydney School ofPublic Health, The University ofSydney, Sydney, NSW, Australia 5 Center forCognitive andBrain Health,Department ofPsychology, Northeastern University, Boston, MA, USA 6 Department ofPhysical Therapy, Movement, andRehabilitation Sciences, Northeastern University, Boston, MA, USA 7 Faculty ofSport andHealth Sciences, University ofJyväskylä, Jyvaskyla, Finland