Skip to main content
Sports Medicine - Open
Download PDF
  • Original Research Article
  • Open access
  • Published:

Biopsychosocial and Environmental Correlates of Children’s Motor Competence: An Exploratory Study

Sports Medicine - Open volume 10, Article number: 90 (2024) Cite this article

Abstract

Background

Given the significance of motor competence (MC) for healthy development and as a cornerstone for lifelong physical activity (PA), it is crucial to understand the manifold factors that are associated with MC. Thus, the aim of the present study was to investigate correlates of children’s MC and their fundamental movement skills (FMS) within their daily life from a comprehensive biopsychosocial-ecological perspective.

Methods

This is a cross-sectional sub-study of the ‘Physical Literacy for Communities (PL4C)’ WAVES cohort study conducted in the West Vancouver School District, Canada. Motor competence was assessed using the PLAYfun tool including overall MC score and five FMS category scores, namely, running, locomotor skills, upper and lower body control and balance skills. By means of structural equation modeling (SEM), direct associationswith MC and with the specific FMS categories addressing physical activity behavior, self-perceived physical literacy, parenting, and school ground design were investigated.

Results

A total of 355 children with a mean age of 7.5 years and 111.1 min of MVPA per day participated. The group comprised 51% boys and 47% girls from 14 elementary schools. Most children were at an emerging MC-level (71%), while those at a competent MC-level exhibited significantly more daily minutes of MVPA (123 versus 109, p = 0.001). Additionally, they played outdoors more frequently and engaged in more instructor-led PA. The results revealed that logistical support from parents had not only a direct positive association with overall MC, both for girls and boys, but also with most of the FMS categories. However, the correlates of MC varied between genders and showed different patterns across the five FMS categories. While time spent in sports or coach-/instructor-led physical activities had a significant SEM generated direct effect only for boys’ MC and for locomotor, upper body object control and balance, the aesthetic design of the school grounds was only associated with girls’ MC and those same three FMS categories. Multivariate SEM could explain 26% of variance for girls’ MC and 30% for boys’.

Conclusions

This exploratory baseline assessment revealed parental logistical support as an important correlate of MC, irrespective of gender. There were distinct gender patterns across biopsychosocial-ecological correlates influencing MC and FMS. Despite the heterogeneity of the results, our findings indicate a potential role of school ground design in supporting the development of children’s MC, especially for girls.

Key Points

• The influence of biopsychosocial-ecological correlates on motor competence (MC) varied based on gender and the specific fundamental movement skill (FMS) categories.

• While parental logistical support demonstrated a positive direct association with both genders’ MC and most of the FMS categories, coach-led physical activities appeared to enhance only boys’.

• The positive direct association of outdoor play on boys’ MC and of school ground aesthetics on girls’ MC might suggest the role of the physical environment in MC development.

Background

Positive effects of physical activity (PA) on health and well-being are undoubted [1,2,3] and there has long been recognition that motor competence (MC) is a critical element in the pathway to PA and overall health [4, 5]. Since Stodden et al. (2008) [4] provided a foundational model to explain the developmental relationships and pathways between MC, PA, self-perceived motor competence and health related fitness, research about these relationships has grown. Within pediatrics, there is a longstanding tradition of monitoring motor development as a crucial part of a child’s healthy development [6,7,8]. Motor competence describes goal-directed human movement and the mastery of physical skills and movement patterns for enabling participation in physical activity [9]. Motor competence is influenced and dependent on different movement skills, often described as fundamental movement skills (FMS), typically organized into three main domains; (a) locomotor, (b) object control and (c) stability) [10, 11]. Some measures of MC assess various categories of FMS within the three main domains, e.g., distinguishing locomotor into running and hopping, object control into lower- (e.g. kicking) and upper- (e.g. throwing and catching) body control, and stability including balance [7, 12,13,14]. During the last decade, a discourse has been growing to include further categories of FMS like cycling and swimming and to broaden the term from fundamental to foundational skills [15]. A recent systematic review revealed that not all the pathways proposed by Stodden et al.’s model have been consistently confirmed by research and identified a need for more longitudinal studies [16]. Strong evidence was found for the positive pathway from PA to MC and for MC to PA with health-related fitness as a mediator [16]. Further, there was strong positive evidence for the pathway from MC to health-related fitness but not the reverse, with locomotor skills as the specific domain most evidently associated positively with fitness and MC. The hypothesised positive relationship between MC and PA was more recently supported by a systematic review and meta-analysis of 61 studies including 22 256 adolescents (r = 0.20 to 0.26) [17].

Meta-analysis of movement skills intervention studies with children aged 5–11 years has also provided evidence of a positive association between MC and PA [18]. Specifically, movement skills interventions resulted in an increase in MVPA of between 13.3 and 15.7 min/day. But the results should be interpreted with caution as only five out of 19 studies included explicitly measures of FMS and 47% of studies were assessed to have a moderate to serious risk of bias [18]. In studies targeting the early years of childhood (e.g. ages 3–6 years), a significant relationship between MC and PA was likewise demonstrated [8]. While the authors found some evidence that PA was a predictor for MC, they identified the need for more longitudinal studies throughout mid to late childhood in order to gain an in-depth understanding of the reciprocal relationship and mediation effects [8].

Beside the positive impact of PA and MC on physical health, a growing body of evidence confirms its positive relationship with mental health, cognitive, and social-emotional outcomes [17, 19,20,21]. In a recent review, Hill et al. proposed a conceptual model outlining pathways from MC to PA and to domains of cognition and social-emotional health, reflecting a more comprehensive biopsychosocial-ecological perspective [22]. While MC may mediate participation in PA through self-perception and physical fitness, it also mediates through its impact on behavioral, neurobiological, cognitive and social-emotional health outcomes [22, 23]. These pathways are influenced by moderators, including individual characteristics such as age, sex, maturation (i.e. biological); environmental characteristics such as home, school, socio-economic status, culture (i.e. socio-ecological); and task characteristics that can be both quantitative and qualitative [22, 23]. In their analysis of 49 studies (15 observational and 34 experimental) focusing on children primarily aged 3–9 years, Hill et al. concluded that both experimental and observational studies provided some support for the effect of MC on cognitive functions [22]. However, evidence for domain-specific relationships is still not clear. Commonly across the pathway studies, authors identified the need for more robust longitudinal studies, more adequate assessment of MC and FMS, studies that account for biological maturation and more rigorous methodology that considers the complexity of the processes and the necessity to encompass moderating and mediating influences [16, 17, 22].

Considering the importance of MC for healthy development and as a foundation for lifelong PA, understanding the correlates of MC is crucial. In line with Hill et al.’s call to action [22], the aim of the present study was to increase knowledge about correlates of children’s MC from a comprehensive biopsychosocial-ecological perspective. More specifically, this study of elementary school aged children sought to: (1) identify differences between children with emerging versus competent MC-level regarding sociodemographic, movement behaviors (i.e. playing outside, training, transport mode to and from school, device-measured PA, sport participation), self-perceived physical literacy, parenting and physical environment (i.e. neighborhood and school grounds) and (2) to assess gender-specific associations and SEM generated direct effects of these correlates on overall MC, and on five specific FMS categories (i.e. running, locomotor, upper and lower body object control, balance/stability). While the international motor development research consortium highlighted the necessity for more goal-directed and effective interventions to enhance MC [24], the focus of this study is to deepen our understanding of a broader array of correlates that influence MC and FMS within a child’s daily life.

Methods

Study Design and Participants

This is a cross-sectional sub-study embedded within a two-year longitudinal cohort study ‘Physical Literacy for communities (PL4C)’ referred to as the ‘WAVES study’ described elsewhere [25]. The WAVES study aimed to evaluate a school district initiative to increase children’s physical literacy (PL). Physical literacy has gained increasing importance in the discourse about how to create lifelong physically active people [26,27,28,29,30]. Indeed, PL is a multi-dimensional concept that embraces an integrative perspective and interconnection between body and mind including affective, physical and knowledge aspects [31,32,33,34].

In the present study, children in Grade 2 or Grade 2/3 split classrooms and their parents (n = 473) from all 14 elementary schools in the West Vancouver school district were invited to participate. West Vancouver is a an area with a high socio-economic status (e.g., higher average household income, higher education level compared to the British Columbia province [35]). A total of 355 children (183 boys, 166 girls, 6 non-binary, response-rate 75%) with an average age of 7.5 years (SD = 0.5) from all schools participated. Approximately 70% of parents (n = 253) were aged between 30 and 44 years, most worked full-time (63%), and almost all had at least university-level education (48.5% university-, 45% graduate-level). Most parents self-identified as Asian (46%) followed by European (29%).

Data Collection

Table 1 provides an overview of the included variables, organized according to their respective levels of a biopsychosocial-ecological framework.

Table 1 Overview of the included variables according to their respective perspective
Full size table

Physical-Behavioral Correlates

Motor Competence and Fundamental Movement Skills

Children’s motor competence was assessed using the PLAYfun tool [40]. Children were asked to perform 18 movement skill tasks across five FMS categories, i.e., (a) running (3 tasks), (b) locomotor (5 tasks), (c) object control - upper body (4 tasks), (d) object control – lower body (2 tasks), and (e) balance, stability, and body control (4 tasks) [36]. Assessment of the performance quality is done on a continuous criterion-referenced visual analogue scale (1-100) dividing motor competence level into four stages: initial (< 25), emerging (25–49), competent (50–74), and proficient (75–100). A score can be calculated for each of the five FMS categories, as well as for overall MC across all five FMS categories. In the present sample, the minimum and the maximum of the PlayFun overall score were 29.67 and 60.38 with a mean of 45.79, i.e. children were either on emerging or on competent level. Thus, for the upcoming analysis, the overall MC level of the study population was dichotomized into (1) emerging (< 50) and (2) competent ( > = 50).

The PLAYfun tool has been shown to be a valid and reliable measurement tool for measuring overall MC and specific categories of FMS within the concept of physical literacy (PL) in children and youth [37]. Factor analysis confirmed the five factor model with fit indices for RMSEA of 0.055; 90% confidence interval, 0.03–0.075; CFI, 0.95; TLI, 0.94 [13]. Convergent validity of the PLAYfun for MC and the five FMS categories was confirmed by moderate-to-large correlations (r = 0.40 to 0.57) with the Canadian Agility and Movement Skill Assessment (CAMSA) instrument (r = 0.40 to 0.57), and generally moderate correlations with the self-reported Physical Activity Questionnaire for Children (PAQ-C) (r = 0.30 to 0.40), with the exception of ‘balance’ which had a small correlation with both instruments (r = 0.24 for CAMSA and r = 0.18 for PAQ-C) [37]. Several studies have shown good to excellent inter-rater reliability of the PLAYfun tool, in particular for the overall MC score (ICC = 0.87–0.90) [37,38,39], while the internal consistency with two raters for average measures within each FMS category varied and showed moderate to good (e.g. ICC = 0.55 for balance) [37, 39]. In the present study, the inter-rater reliability was acceptable to very good for overall MC and for average measures across the five FMS categories (ICC = 0.72–0.87 between the two research assistants and ICC = 0.81–0.89 between the two PL specialists) [25]. For more information about the assessment in the present study, see Tang et al. [25].

Physical Activity

Travel mode to and from school was reported by parents asking about the usual transport mode (walk alone, walk with a friend/adult, cycling, school bus, public transport, and driven in vehicle). Parents also reported how many hours their child played outside on weekends and weekdays with the following response categories: ‘none at all’, ‘less than 1 hour’, ‘1 to less than 2 hours’, ‘2 to less than 3 hours’, ‘3 to less than 4 hours’, ‘4 to less than 5 hours’, ‘5 hours or more’. Additionally, parents reported the amount of time that their child engaged in sports or coach-/instructor-led physical activities during the last 7 days from ‘did not participate’, ‘less than 1 hour’, ‘1 hour to less than 3 hours’, ‘3 hours to less than 5 hours’, ‘5 hours to less than 7 hours’, ‘7 hours to less than 10 hours’, to ‘10 hours or more’. Children’s device-based physical activity was measured using Actigraph GT3X + BT (ActiGraph, Pensacola, FL) as described by Tang et al. [25]. Children were instructed to wear the triaxial accelerometer on their non-dominant wrist for seven consecutive days (except for water-based activities) with raw accelerations recorded at a 30 Hz sampling rate. To classify different PA intensities, the Chandler’s cut points [40] based on vector magnitude with an epoch lengths of 5-s were used, with the following categories: ≤305 (sedentary), 306–817 (LPA; light physical activity), 818–1968 (MPA; moderate physical activity), ≥ 1969 (VPA; vigorous physical activity).

Psychosocial Correlates

Self-Perceived Physical Literacy

To reflect psychosocial factors, data collected with the PLAYself tool were used. The PLAYself tool consists of three sub-scores, (1) children’s self-confidence to do sports and activities in different physical environments (environment score), (2) statements about doing sports and activities aimed to measure child’s self-efficacy for participation in PA and referred to as PL self-description score, and (3) children’s ranking of importance of 3 literacies (i.e., a) reading and writing, b) mathematics and numbers, and c) movement, activities and sport) in school, at home with family, and with friends (ranking score). The PL self-description score was used to assess the child’s self-efficacy and its connection to their engagement in PA. Self-efficacy is the individual’s belief in their capacity to succeed in various situations [41]. The total PLAYself score is the average across the sub-scores. The higher the score (0–100), the higher the self-perceived PL. The PLAYself tool has shown excellent test-retest reliability (ICC = 0.81–0.84) and good-excellent internal consistency (PSI = 0.70–0.82) [42]. To obtain a nuanced picture of possible correlates, the three sub-scores Environment, PL Self-description and Rank of Literacy were used for analysis with structural equation modeling (SEM).

Parental Support

The Activity Support Scale for Multiple Groups (ACTS-MG) [43] includes four parenting factors of logistic support (i.e., enrolling child in sports, taking child to places to be physically active, watching the child playing sports or any PA), modeling (i.e., encouraging child through leading by example, regularly and enjoying exercising by themselves), use of community resources to promote physical activity (i.e. encouraging use of resources in the neighbourhood to be active, enrolling child in community based programs, finding ways for the child to be active when after school), and restriction of sedentary behaviors related to screen-time (i.e. limiting time playing video games, watching TV, using the computer for others than homework). The instrument has adequate internal reliability and good construct validity, showing a model fit for the four factors of CFI = 0.94, RMSEA (90%CI = 0.05) < 0.001–0.086 for African American and 0.033–0.097 for white American parents [44, 45]. It has been repeatedly used in Canadian samples to capture parental support for PA [46, 47].

Ecological Correlates

Neighborhood

Neighborhood characteristics that may influence children’s physical activity participation [48] were assessed through two subscales of the Neighbourhood Environment Walkability Scale for Youth (NEWS-Y) [49]. Parents were asked to answer seven questions regarding pedestrian/automobile traffic safety, and six questions regarding crime safety on a 4-point Likert scale (strongly disagree to strongly agree). Prior to calculating subscale-scores (i.e. average across included questions), some questions needed reverse coding; higher scores represent a more favorable environment [50, 51]. The two subscales have demonstrated good test–retest reliability and internal consistency for parents of children (intraclass correlation coefficients (ICC), and Cronbach’s alphas of 0.87 for the crime safety subscale, and 0.74 for pedestrian/automobile traffic safety [49].

School Ground Assessment

The physical environment of the school was assessed with the Sport, Physical activity and Eating behavior: Environmental Determinants in Young People (SPEEDY) school grounds audit tool [52]. SPEEDY consists of 44 items, where six component scores and one overall school ground quality score can be derived; ‘cycling provision’ (e.g., cycle lanes, traffic calming, school warning signs for road users), ‘walking provision’ (e.g., marked pedestrian crossings, road safety signs), ‘sports and play facility provision’ (e.g., bright markings on play surfaces, playground equipment, courts), ‘other facility provision’ (e.g., benches, picnic tables), ‘design of the school grounds’ (e.g., suitability for informal games), ‘aesthetics’ (e.g., presence of litter, murals/outdoor art, graffiti). The total ‘school physical activity suitability score’ sums the items from ‘cycling and walking provision’, ‘sport and play facilities’ and ‘design of school grounds’ but counting for the overlapping individual items only once (n = 29) [52]. Two researchers completed the assessment independently. For elementary schools, the SPEEDY audit instrument was found to have acceptable reliability (kappa score above 0.41) and good construct validity [52].

Statistical Analysis

To investigate differences between children with emerging and competent MC, ‘active travel’ was dichotomized into (1) ‘active travel’ (bike/walk)/public transport and (2) ‘driven in vehicle’. The variables regarding time ‘playing outside’ on weekday or weekend were trichotomized into (1) not at all, (2) 30 min to less than 2 h, and (3) 2 h or more. Variables were tested for normality with Shapiro-Wilk tests. Differences between children with emerging and competent MC levels were analysed with t-test for continuous normal-distribution, Mann-Whitney U test and Chi-square test for proportions.

To assess the associations of the various biopsychosocial-ecological correlates on MC and on specific FMS categories (i.e. running, locomotor, upper and lower body object control, balance), structural equation modeling (SEM) for overall MC and each FMS category stratified by gender were processed (in AMOS ver. 29, IBM SPSS, Chicago, IL). The significant associations with overall MC and the five FMS categories identified through Pearson’s product-moment correlation for continuous normal-distributed and Spearman’s rho correlation for continuous not normal-distributed variables were the exogenous variables in the SEM. Age was included as a control variable (see Fig. 1). When describing SEM results and the calculated coefficients, the term ‘direct effect’ was used as per the SEM program.

Fig. 1
figure 1

Model to explain overall motor competency, respectively the specific 5 motor competencies. Intercorrelations between the explaining variables were modelled as identified in prior univariate correlation-analysis. PSQ_Environment, PSQ_SelfDescription, PSQ_RankofLiteracy are the 3 sub-scores from the PLAYself Questionnaire. SGA_Design, SGA_Aesthetics, SGA_Cycling, SGA_Walking, SGA_SportPlay, SGA_OtherFacilities are the 6 sub-scores from the SPEEDY Questionnaire. Logistic_support, Modeling, Community_use, Restricting are the 4 sub-scores from ACTS-MG Questionnaire. SportTime = hours child participated in instructor-led activities, OutdoorWD = hours child playing outside during weekday, Outdoor WE = hours child playing outside during weekend, all three parent-reported

Full size image

Device-measured PA was excluded from the SEM because PA showed the highest significant correlations with MC and FMS categories as the multiple tested endogenous variables, but also with biopsychosocial ecological correlates as the exogenous variables. Together with evidence from the literature [8], we assumed a mediating effect of PA in our sample. All statistical analyses were performed in SPSS version 29 with an alpha-level of 0.05 (IBM SPSS, Chicago, IL).

Prior to SEM analysis, cases with missing data were excluded resulting in n = 304 complete cases. To assess model fit, Bollen-Stine bootstrapping was conducted based on 5000 bootstrapping samples [53], as univariate non-normality was observed for many included variables. A p-value larger than 0.05 was used to indicate good model fit. Additionally, several other model fit indices were consulted, including the chi-squared (χ2) test, χ2/df, comparative fit index (CFI), incremental fit index (IFI), Tucker-Lewis index (TLI), standardized root mean squared residual (SRMR), and root-mean squared error of approximation (RMSEA). The model was deemed to have excellent fit based on a non-significant χ2 value (p > 0.05) and a χ2/df value between 1 and 3 [54], as well as high values for CFI, IFI, and TLI (all greater than 0.95), an SRMR close to 0.08, and an RMSEA value below 0.06 with an upper 90% confidence interval (HI90) below 0.08 and a PCLOSE value above 0.5 [55]. Multigroup path analyses were conducted to assess the established model separately for boys and girls. Standardized and unstandardized coefficients were presented for all pathways, and a p-value of less than 0.05 was considered significant.

Results

Characteristics of Study Population

Table 1 gives an overview of study participants’ movement behaviors, social and physical environment (i.e., parenting, neighborhood and school ground quality) and PLAYself scores.

Complete assessments of MC and FMS categories (PLAYfun scores) were gathered for 319 participants (90%; 166 boys, 147 girls, 6 non-binary) with the majority being at the emerging-level of MC (79%, n = 238) and 22% at the competent-level (n = 67). There were no significant gender differences for the PLAYfun overall MC score or for the running and balance category scores. While girls scored significantly higher in the locomotor domain than boys (43 versus 41, p = 0.01), they scored significantly lower in the upper and lower body object control domains (43 versus 48 and 47 versus 49, p < 0.001). Children’s PLAYself scores were between 68 and 72 (range 0-100) on average across all three sub-scores and no gender differences were detected.

Out of the 355 children, 258 (76.8%; 128 boys, 126 girls, 4 non-binary) provided valid PA data with Actigraph GT3X + BT (i.e., met the wear time criteria). Of those, participants accumulated approximately 111 min of MVPA per day (113 min/d for boys, 109 min/d for girls, t(252) = 0.89, p = 0.37). Almost all participants (97.3%) met the Canadian PA guideline criteria, i.e., accumulating on average 60 min of daily MVPA. The majority of children were driven in vehicles to (72%) and from school (70%), 5% of participants took a school bus or public transport, 15% walked or biked to, and 18% from, school. There were no significant gender differences in travel mode. All children except 5 played outside each day during the week, the majority between 30 min to less than 2 h (54%), whereas during the weekend most children played outside for more than 2 h (67%). 73% of children participated in sports or coach-/instructor-led physical activities each week. Children’s social and physical environment was supportive for both boys and girls. Scores on the four subscales of the ACTS-MG ranged between 3.3 and 3.6 (range 1–4). Across the 14 schools, the overall ‘school physical activity suitability’ score ranged between 13 and 22 with a mean of 20 (±2.29). On average, all six individual school ground quality component scores reached the upper one-third of the range.

Differences Between Participants with Emerging- and Competent Level of Motor Competency

Participants with competent-level MC were slightly but significantly older than those at emerging-level (7.6 years versus 7.4 years, p = 0.007). Children with competent-level MC had on average 15 min/day more MVPA compared to children with emerging-level MC (p = 0.001; moderate PA: t(243) = -2.724, p = 0.007; vigorous PA: t(243) = -4.129, p < 0.001). There were no significant differences in travel mode.

Children with competent-level MC spent significantly more time playing outside compared to children with emerging-level MC, both on weekdays and on the weekend (55% versus 40% weekdays, p = 0.036; 82% versus 65% weekends, p = 0.011). Children with competent-level MC were also more likely to spend at least 3 h in sports or PA with instructors than those at an emerging-level (63% versus 36%, p < 0.001). Regarding self-perception, there were no differences between competent- and emerging-level MC children for the Literacy ranking score, but children with competent-level MC scored significantly higher on the Environment and PL self-description score (78 versus 70, p < 0.001; 72 versus 66, p = 0.006).

There were no significant differences in the parenting of children with emerging-level MC and those at the competent-level, except for logistical support, which was slightly but significantly higher in children at competent level (3.7 versus 3.5, p < 0.001). There were no differences between children with competent- versus emerging-level MC in terms of neighborhood environment variables. Significant differences in school ground quality between those with competent- and emerging-level MC existed only for ‘Walking’ (4.8 versus 4.5, p = 0.022), ‘Other Facilities’ (4.4 versus 3.9, p = 0.014) and ‘Design’ (7.0 versus 6.8, p = 0.005)(see Table 2).

Table 2 Overview of study characteristics from a biopsychosocial-ecological perspective stratified by children with emerging and competent level of motor competency (measure with PLAYfun), N = 355
Full size table

Correlates and SEM Generated Direct Effects of Movement Behaviors, Parenting, School Grounds Design and Perceived Physical Literacy on Overall Motor Competence

Univariate analysis revealed significant correlations of MC with parenting (ACTS-MG scores), quality of school grounds (SPEEDY scores), self-perception (PLAYself sub-scores) and movement behaviors except for travel mode (see Tables 1, 2, 3 and 4 in supplemental file). Correlations differed between girls and boys, e.g., ‘playing outdoors during weekdays’ was significantly correlated with PLAYfun MC only for boys (r = 0.21, p < 0.001), while ‘playing outdoors during weekend’ was significantly correlated only for girls (r = 0.23, p < 0.001). The correlation between MVPA and MC was stronger for boys compared to girls (r = 0.35 versus 0.25, p < 0.01). Tables 3 and 4 provides an overview of the direct effects (unstandardized and standardized) as shown by the SEM for overall MC, differentiated for boys and girls. Significant positive direct effects on boys’ MC were shown for ‘Sport/instructor-led PA’ (γ = 0.182), ‘Playing outdoors on weekdays’ (γ = 0.187), ‘Walking’ (γ = 0.239) as well as ‘Logistics’ (γ = 0.269) but a negative direct effect by ‘Cycling provision’ (γ=-0.223), together explaining 30% of variance in MC. For girls’ MC, positive significant direct effects were identified for ‘Aesthetics’ (γ = 0.210), age (γ = 0.168) and ‘Logistics’ (γ = 0.243) and these factors explained 26% of the variance in MC.

Table 3 Direct effects of movement behavior, social and physical environment on motor competence, N = 304
Full size table
Table 4 Overview of the significant direct effects from school yards, parenting, physical literacy, age on motor competencies, left girls, right boys
Full size table

Correlates and SEM Generated Direct Effects of Movement Behaviors, Parenting, School Grounds Design and Perceived Physical Literacy on Fundamental Movement Skill Categories

All five FMS categories correlated significantly with MVPA, however, the strength of correlation differed between boys and girls, with stronger correlations for boys ranging from r = 0.21 to 0.40 for boys (no significance for Balance) versus to r = 0.14 to 0.25 for girls (no significance for Locomotor and Balance)(see Table 1 supplemental file).

Tables 5, 6, 7, 8 and 9 in the supplemental files provides an overview of the direct effects (unstandardized and standardized) as shown by the SEM for each FMS category, differentiated for boys and girls. While the greatest amount of variance in FMS categories could be explained for girls’ Locomotor (R2 = 0.31), and boys’ Upper Body Object Control and Balance (R2 = 0.30), the least could be explained by girls’ Running (R2 = 0.14). The number of significant correlates varied between the five FMS categories and across gender. For example, seven out of 15 correlates in the model turned significant for explaining boys’ Balance, whereas only four correlates for girls’ with ‘Other Facilities’ as the strongest direct effect for both genders (γ = 0.347, p < 0.001 respectively (γ = 0.320, p = 0.003). On the other hand, six significant correlates could explain 22% variance of girls’ Upper Body Object Control, though only four for boys’ explaining 30% variance with ‘Logistics’ as the strongest direct effect for both gender (γ = 0.255, p < 0.001 respective γ = 0.305, p < 0.001).

Table 4 provides an overview of the direct effects (positive or negative) of the significant correlates on overall MC and the five FMS categories, distinguished for boys and girls. ‘Sport/instructor-led PA’ and ‘Logistics’ had the most common positive effect on MC and across the five FMS categories for boys (n = 4), as did ‘Aesthetics’ for girls (n = 4).

Discussion

The present study contributes to the scarce body of literature that has investigated correlates of MC, employing a SEM derived direct effect analysis to examine the biopsychosocial-ecological factors within a child’s daily life that collectively influence children’s overall MC and specific FMS categories. The present study sample, which includes physically active Canadian children, demonstrated a level of MC comparable to that of same age children in previous studies [25, 56,57,58]. Like recent studies [36], children who exhibited competent levels of MC were typically older, engaged in higher levels of MVPA, and were predominantly male. Our findings align with those presented in Barnett et al.’s review and meta-analysis on correlates of MC [59]. In their study, a positive association between age and MC was observed, although variations were noted when examining specific FMS categories. In the present study, children at a competent level of MC were significantly older, with no direct association with age among boys when controlling for other correlates. For girls, age was significantly associated with overall MC, locomotor and balance skills while controlling for those correlates. Given that the older children have more opportunities to engage in activities that foster skill development and that natural maturation occurs, it could be expected that MC and FMS would be greater in older children [59, 60]. Further, the amount of time playing outside, both during weekdays and at weekend, was also associated with a higher MC level. However, a direct association of playing outside weekdays was evident only for boys in relation to overall MC, lower body object control and running when investigated by SEM. While outdoor play has been consistently associated with higher physical activity levels and thereby connected to health-benefits [61,62,63], Lynch (2017) emphasized that outdoor play offers substantial and meaningful opportunities for motor development as well [64], which appeared to be the case for boys in our study.

In addition to playing outside, time participating in instructor-led PA/sports also had a positive impact on MC (as well on PA-level), and our results are consistent with previous research findings [59]. Our results revealed that two-thirds of children with competent MC levels spent three or more hours per week with coaches, contrasting with one third of those at the emerging MC level. The amount of time spent with coaches was associated with MC level for both genders, but no direct association persisted for girls when controlling for other correlates. The direct association remained for boys’ overall MC, and for locomotor, upper body object control and balance skills. Barnett et al.’s previous meta-analysis confirmed gender differences in the enhancement of FMS through sports activities, suggesting both gender preferences and gender normative parental support as contributing factors [59].

Our results revealed that children at a competent level of MC had higher self-confidence to be physically active in different environments and higher self-efficacy for participation in PA. However, evidence for the role of self-perception in the pathways from MC to PA and the reverse is still inconclusive [16]. In our analysis, we were able to identify a more nuanced result, indicating that feeling confident in participating in sports and activities across various environments (such as water, gym, playground, ice, snow, etc.) had a direct positive association on running for boys and on object control skills for girls. A relationship between perceived and actual MC, specifically regarding object control, was also supported by findings from a longitudinal study conducted by Strotmeyer et al. [65].

In terms of the social environment, parents often serve as the primary source of social support in early childhood. Interestingly, screen-time restrictions, and encouragement of community use for PA did not have a significant direct association on MC and the FMS categories in our study; however, the provision of logistical support was found to be significantly associated with higher levels of MC, regardless of gender or specific FMS categories, and remained when accounting for various correlates. Previous research on the impact of parental support has primarily focused on PA among children and less on children’s MCs [66,67,68,69]. In the latest meta-analysis conducted by Yao and Rhodes, encompassing 112 studies, a medium effect size was found for the relationship between overall parental support and child’s PA [66]. The analysis revealed significant heterogeneity in this relationship. Notably, parental encouragement emerged as the only social behavior with a moderate effect size. A recent study from He et al. confirmed both parental encouragement and engagement as positively associated with locomotor, ball and overall motor skills [70]. Our results align with this trend, as questions about encouragement were included in the scores for both, logistical support and community use. The impact of modeling remains uncertain; our findings showed a positive significant correlation with overall MC and locomotor exclusively in girls. However, modeling did not have a significant direct positive association when controlling for all factors, instead revealing a negative direct association with boys’ locomotor and running. Yao and Rhodes highlighted that the influence of parental support behavior on a child’s PA may vary depending on the gender of the parents [66]. In a more recent meta-analysis conducted by Su et al., which examined the effects of both positive and negative parental support (e.g., restrictions and punishment) on a child’s PA, a medium effect size for the relationship of positive parental support was also confirmed [67].

Looking closer into the associations between MC and the physical environment, specifically the design of school grounds, the results unveiled that the presence of amenities like benches, wildlife gardens, and available equipment (i.e. ‘other facilities’) had a significant direct positive association on balance for both – girls and boys. Further, school ground aesthetics (e.g. planted beds, trees) directly influenced positive girls’ overall MC and three specific FMS categories, namely locomotor, upper body control and balance; the latter as well among boys. It is worth mentioning that sport play facilities did not appear to have a direct positive association on either children’s overall MC or specific FMS categories in the present study. Possible explanations for these findings might be that all 14 schools scored consistently highly on sports play facility with a low variability between schools, and the children in the study were very physically active, with 90% meeting the Canadian guidelines for PA and around three-quarters participating in organized sports. While Jones et al. found a significant difference of nearly two minutes in boys’ MVPA-level between schools with lowest and highest scores regarding the provision of sports play facilities, they only identified a significant difference of about one minute in girls’ MVPA-level between schools with lowest and highest scores on school ground design [52]. The positive influence of school grounds and the physical environment on PA-levels, thereby potentially enhancing overall MC and specific FMS, has been confirmed by previous studies [71,72,73]. Our findings that characteristics of school ground design may have differential effects for movement behaviors and thereby motor development for boys and girls warrant closer attention in future research.

Overall, the results of our present study unveiled diverse patterns, contingent on gender, overall MC and specific FMS categories. Consistent with prior research, these findings support the assumption that the development of MC and FMS is related to multifaceted factors and their interplay. By collectively considering the biopsychosocial-ecological correlates in a child’s daily life, we were able to explain between 26 and 30% variance in overall MC and between 14 and 31% in FMS categories.

Limitations

When interpreting the findings of the present study, it is important to consider some limitations (see also Tang et al. [25]). Firstly, the study population consisted of individuals from a high socioeconomic status, attending schools with favorable environments. Additionally, most of the children met the PA-guidelines. As a result, the sample may not be generalizable to other Canadian and International samples. Secondly, the design was cross-sectional which only allows identification of associations between various correlates and MC and FMS respectively with direct effects determined theoretically through SEM. To draw more robust conclusions, a longitudinal analysis of the cohort study will be conducted in the future. Thirdly, to measure overall MC and specific FMS categories we used the PLAYfun tool. The tool was originally developed for practitioners and more focused on feasibility in practice than in research. The reliability of the raters using PLAYfun in the present study was acceptable to very good. A small number of validation studies show mixed but mainly satisfactory results for its validity and reliability, especially for overall MC [37]. Further studies might be necessary to confirm its psychometric properties and our findings should be interpreted in light of this.

Conclusions

This exploratory baseline assessment embedded within a longitudinal PL4C study provides insight into a broader range of correlates of MC and FMS, revealing distinct gender patterns across biopsychosocial-ecological factors. Parental logistical support consistently proved to be an important correlate of MC in children, regardless of gender. Furthermore, while the aesthetics of the school grounds and age were related to girls’ MC and several FMS categories, spending time engaged in instructor-led PA and engaging in free-play during weekdays were two correlates common positively associated with boy’s MC and some FMS categories. Our findings highlight the potential role of school ground design in supporting the development of children’s MC, in particular for girls. However, the study is an explorative investigation. The longitudinal nature of the WAVES study will allow for ongoing assessment of the inter-relationships between these correlates over time and will contribute to a more robust understanding of factors influencing MC and FMS development.

Data Availability

The datasets used and/or analyzed during the current study are available from author GF on reasonable request.

Abbreviations

ACTS-MG:

Activity Support Scale for Multiple Groups

CFI:

Comparative fit index

FMS:

Fundamental movement skills

IFI:

Incremental fit index

MVPA:

Moderate to vigorous physical activity

MC:

Motor competence

NEWS-Y:

Neighbourhood Environment Walkability Scale for Youth

PA:

Physical activity

PL:

Physical Literacy

PL4C:

Physical Literacy for communities

RMSEA:

Root-mean squared error of approximation

SPEEDY:

Sport, Physical activity and Eating behavior: Environmental Determinants in young people school grounds audit tool

SRMR:

Standardized root mean squared residual

SEM:

Structural equation modeling

TLI:

Tucker-Lewis index

References

  1. Biddle SJH, Ciaccioni S, Thomas G, Vergeer I. Physical activity and mental health in children and adolescents: an updated review of reviews and an analysis of causality. Psychol Sport Exerc. 2019;42:146–55.

    Article  Google Scholar 

  2. Biddle SJH, Bengoechea EG, Wiesner G. Sedentary behaviour and adiposity in youth: a systematic review of reviews and analysis of causality. Int J Behav Nutr Phys Activity. 2017;14.

  3. Lobelo F, Young DR, Sallis R, Garber MD, Billinger SA, Duperly J, et al. Routine Assessment and Promotion of Physical Activity in Healthcare Settings: A Scientific Statement from the American Heart Association. Circulation. 2018;137(18):E495–522.

    Article  PubMed  Google Scholar 

  4. Stodden DF, Goodway JD, Langendorfer SJ, Roberton MA, Rudisill ME, Garcia C, et al. A developmental perspective on the role of Motor Skill competence in physical activity: an Emergent Relationship. Quest. 2008;60(2):290–306.

    Article  Google Scholar 

  5. McIntyre F, Parker H, Chivers P, Hands B. Actual competence, rather than perceived competence, is a better predictor of physical activity in children aged 6–9 years. J Sports Sci. 2018;36(13):1433–40.

    Article  PubMed  Google Scholar 

  6. Engel AC, Broderick CR, van Doorn N, Hardy LL, Parmenter BJ. Exploring the relationship between Fundamental Motor Skill interventions and physical activity levels in children: a systematic review and Meta-analysis. Sports Med. 2018;48(8):1845–57.

    Article  PubMed  Google Scholar 

  7. Hulteen RM, Terlizzi B, Abrams TC, Sacko RS, De Meester A, Pesce C, et al. Reinvest to assess: advancing approaches to motor competence measurement across the Lifespan. Sports Med. 2023;53(1):33–50.

    Article  PubMed  Google Scholar 

  8. Jones D, Innerd A, Giles EL, Azevedo LB. Association between fundamental motor skills and physical activity in the early years: a systematic review and meta-analysis. J Sport Health Sci. 2020;9(6):542–52.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Robinson LE, Stodden DF, Barnett LM, Lopes VP, Logan SW, Rodrigues LP, et al. Motor competence and its Effect on Positive Developmental Trajectories of Health. Sports Med. 2015;45(9):1273–84.

    Article  PubMed  Google Scholar 

  10. Gallahue DLOJ, Goodway JD. Understanding motor development: infants, children, adolescents, adults. 7th ed. ed: New York, NY: McGraw-Hill; 2012.

    Google Scholar 

  11. Goodway JD, Ozmun JC, Gallahue DL. Understanding Motor Development: infants, children. Adolescents, Adults: Jones & Bartlett Learning; 2019.

    Google Scholar 

  12. Longmuir PE, Boyer C, Lloyd M, Borghese MM, Knight E, Saunders TJ, et al. Canadian agility and Movement Skill Assessment (CAMSA): validity, objectivity, and reliability evidence for children 8–12 years of age. J Sport Health Sci. 2017;6(2):231–40.

    Article  PubMed  Google Scholar 

  13. Cairney J, Veldhuizen S, Graham JD, Rodriguez C, Bedard C, Bremer E et al. A construct validation study of PLAYfun. Med Sci Sports Exerc. 2018;50(4).

  14. Logan SW, Ross SM, Chee K, Stodden DF, Robinson LE. Fundamental motor skills: a systematic review of terminology. J Sports Sci. 2018;36(7):781–96.

    Article  PubMed  Google Scholar 

  15. Hulteen RM, Morgan PJ, Barnett LM, Stodden DF, Lubans DR. Development of Foundational Movement skills: a conceptual model for physical activity across the Lifespan. Sports Med. 2018;48(7):1533–40.

    Article  PubMed  Google Scholar 

  16. Barnett LM, Webster EK, Hulteen RM, De Meester A, Valentini NC, Lenoir M, et al. Through the looking glass: a systematic review of Longitudinal evidence, providing New Insight for Motor competence and health. Sports Med. 2022;52(4):875–920.

    Article  PubMed  Google Scholar 

  17. Burton AM, Cowburn I, Thompson F, Eisenmann JC, Nicholson B, Till K. Associations between Motor competence and physical activity, physical fitness and psychosocial characteristics in adolescents: a systematic review and Meta-analysis. Sports Med. 2023;53(11):2191–256.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Graham M, Azevedo L, Wright M, Innerd AL. The effectiveness of Fundamental Movement Skill interventions on moderate to vigorous physical activity levels in 5-to 11-Year-old children: a systematic review and Meta-analysis. Sports Med. 2022;52(5):1067–90.

    Article  PubMed  Google Scholar 

  19. Haapala EA, Lintu N, Väistö J, Tompuri T, Soininen S, Viitasalo A, et al. Longitudinal associations of Fitness, Motor competence, and adiposity with cognition. Med Sci Sports Exerc. 2019;51(3):465–71.

    Article  PubMed  Google Scholar 

  20. Leisman G, Moustafa AA, Shafir T, Thinking. Walking, talking: Integratory Motor and cognitive brain function. Front Public Health. 2016;4.

  21. van der Fels IMJ, te Wierike SCM, Hartman E, Elferink-Gemser MT, Smith J, Visscher C. The relationship between motor skills and cognitive skills in 4–16 year old typically developing children: a systematic review. J Sci Med Sport. 2015;18(6):697–703.

    Article  PubMed  Google Scholar 

  22. Hill PJ, McNarry MA, Mackintosh KA, Murray MA, Pesce C, Valentini NC, et al. The influence of Motor competence on broader aspects of Health: a systematic review of the Longitudinal associations between Motor competence and cognitive and social-emotional outcomes. Sports Med. 2024;54(2):375–427.

    Article  PubMed  Google Scholar 

  23. Hill PJ, McNarry MA, Mackintosh KA, Murray MA, Pesce C, Valentini NC et al. The influence of Motor competence on broader aspects of Health: a systematic review of the Longitudinal associations between Motor competence and cognitive and social-emotional outcomes. Sports Med. 2023.

  24. Duncan MJ, Foweather L, Bardid F, Barnett AL, Rudd J, O’Brien W, et al. Motor competence among children in the United Kingdom and Ireland: an Expert Statement on Behalf of the International Motor Development Research Consortium. J Motor Learn Dev. 2022;10(1):7–26.

    Article  Google Scholar 

  25. Tang Y, Algurén B, Pelletier C, Naylor P-J, Faulkner G. Physical literacy for communities (PL4C): physical literacy, physical activity and associations with wellbeing. BMC Public Health. 2023;23(1):1266.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mciennan N, Tompson J. Quality Physical Education (QPE): Guidelines for Policy Makers. Paris, France; 2015.

  27. Whitehead M. Physical literacy in the context of physical literacy in the secondary school. Phys Educ Matters. 2007;2(2):24.

    Google Scholar 

  28. Essiet IA, Lander NJ, Salmon J, Duncan MJ, Eyre ELJ, Ma JN et al. A systematic review of tools designed for teacher proxy-report of children’s physical literacy or constituting elements. Int J Behav Nutr Phys Activity. 2021;18(1).

  29. Dudley D, Cairney J, Wainwright N, Kriellaars D, Mitchell D. Critical considerations for Physical Literacy Policy in Public Health, Recreation, Sport, and Education agencies. Quest (00336297). 2017;69(4):436–52.

    Article  Google Scholar 

  30. WHO. More active people for a healthier world. Global action plan on physical activity 2018–2030. Geneva: World Health Organization; 2018.

    Google Scholar 

  31. International Physical Literacy Association. IPLA definition. Retrieved from https://www.physical-literacy.org.uk/. 2017.

  32. Whitehead M. Definition of physical literacy and clarification of related. ICSSPE Bull J Sport Sci Phys Educ. 2013;65:28–33.

    Google Scholar 

  33. Ryom K, Hargaard AS, Melby PS, Maindal HT, Bentsen P, Ntoumanis N, et al. Self-reported measurements of physical literacy in adults: a scoping review. BMJ Open. 2022;12(9):e058351.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Carl J, Barratt J, Topfer C, Cairney J, Pfeifer K. How are physical literacy interventions conceptualized?-A systematic review on intervention design and content. Psychol Sport Exerc. 2022;58.

  35. BC Centre for Disease Control. BC Community Health Data. 2016. http://communityhealth.phsa.ca/HealthProfiles/HealthReportFactorsThatAffectHealth/West%20Vancouver [Cited 2024 May10th].

  36. Kriellaars D, Kozera T, Robillard B. Physical Literacy Assessment for Youth. 2016.

  37. Stearns JA, Wohlers B, McHugh T-LF, Kuzik N, Spence JC. Reliability and validity of the PLAYfun Tool with Children and Youth in Northern Canada. Meas Phys Educ Exerc Sci. 2019;23(1):47–57.

    Article  Google Scholar 

  38. Cairney J, Clark H, Dudley D, Kriellaars D. Physical literacy in children and Youth—A construct validation study. J Teach Phys Educ. 2019;38(2):84–90.

    Article  Google Scholar 

  39. Caldwell HA, Di Cristofaro NA, Cairney J, Bray SR, Timmons BW. Measurement properties of the Physical Literacy Assessment for Youth (PLAY) Tools. Appl Physiol Nutr Metab. 2021;46(6):571–8.

    Article  PubMed  Google Scholar 

  40. Chandler J, Brazendale K, Beets M, Mealing B. Classification of physical activity intensities using a wrist-worn accelerometer in 8–12‐year‐old children. Pediatr Obes. 2016;11(2):120–7.

    Article  PubMed  CAS  Google Scholar 

  41. Caldwell HA, Di Cristofaro NA, Cairney J, Bray SR, Timmons BW. Measurement properties of the Physical Literacy Assessment for Youth (PLAY) Tools. Applied Physiology. Nutr Metabolism. 2021;46(6):571–8.

    Google Scholar 

  42. Jefferies P, Bremer E, Kozera T, Cairney J, Kriellaars D. Psychometric properties and construct validity of PLAYself: a self-reported measure of physical literacy for children and youth. Appl Physiol Nutr Metab. 2021;46(6):579–88.

    Article  PubMed  Google Scholar 

  43. Davison KK, Li K, Baskin ML, Cox T, Affuso O. Measuring parental support for children’s physical activity in white and African American parents: the activity support scale for multiple groups (ACTS-MG). Prev Med. 2011;52(1):39–43.

    Article  PubMed  Google Scholar 

  44. Davison KK, Li KG, Baskin ML, Cox T, Affuso O. Measuring parental support for children’s physical activity in white and African American parents: the activity support scale for multiple groups (ACTS-MG). Prev Med. 2011;52(1):39–43.

    Article  PubMed  Google Scholar 

  45. Trost SG, McDonald S, Cohen A. Measurement of General and specific approaches to physical activity parenting: a systematic review. Child Obes. 2013;9(s1):S–40.

    PubMed Central  Google Scholar 

  46. Nuss K, Coulter R, DeSilva B, Buenafe J, Sheikhi R, Naylor PJ, et al. Evaluating the effectiveness of a family-based virtual childhood obesity management program delivered during the COVID-19 pandemic in Canada: prospective study. JMIR Pediatr Parent. 2022;5(4):e40431.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Rhodes RE, Spence JC, Berry T, Deshpande S, Faulkner G, Latimer-Cheung AE, et al. Understanding action control of parental support behavior for child physical activity. Health Psychol. 2016;35(2):131–40.

    Article  PubMed  Google Scholar 

  48. Datar A, Nicosia N, Wong E, Shier V. Neighborhood environment and children’s physical activity and body mass index: evidence from military personnel installation assignments. Child Obes. 2015;11(2):130–8.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rosenberg D, Ding D, Sallis JF, Kerr J, Norman GJ, Durant N, et al. Neighborhood Environment Walkability Scale for Youth (NEWS-Y): reliability and relationship with physical activity. Prev Med. 2009;49(2):213–8.

    Article  PubMed  Google Scholar 

  50. Robinson T, Nathan A, Murray K, Christian H. Parents’ Perceptions of the Neighbourhood Built Environment Are Associated with the Social and Emotional Development of Young Children. Int J Environ Res Public Health. 2022;19(11).

  51. Saelens BE, Sallis JF, Black JB, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation. Am J Public Health. 2003;93(9):1552–8.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Jones NR, Jones A, van Sluijs EMF, Panter J, Harrison F, Griffin SJ. School environments and physical activity: the development and testing of an audit tool. Health Place. 2010;16(5):776–83.

    Article  PubMed  Google Scholar 

  53. Bollen KA, Stine RA. Bootstrapping goodness-of-fit measures in structural equation models. Sociol Methods Res. 1992;21(2):205–29.

    Article  Google Scholar 

  54. Carmines EG, McIver JP. Analyzing models with unobserved variables: analysis of Covariance structures. In: Bohrnstedt GW, Borgatta EF, editors. Social measurement: current issues: Beverly Hills., CA: Sage; 1981. pp. 65–115.

    Google Scholar 

  55. Lt H, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Modeling: Multidisciplinary J. 1999;6(1):1–55.

    Article  Google Scholar 

  56. Bremer E, Graham JD, Bedard C, Rodriguez C, Kriellaars D, Cairney J. The Association between PLAYfun and physical activity: a convergent validation study. Res Q Exerc Sport. 2020;91(2):179–87.

    Article  PubMed  Google Scholar 

  57. Caldwell HAT, Di Cristofaro NA, Cairney J, Bray SR, MacDonald MJ, Timmons BW. Physical literacy, physical activity, and Health indicators in School-Age Children. Int J Environ Res Public Health. 2020;17(15).

  58. Caldwell HAT, Miller MB, Tweedie C, Zahavich JBL, Cockett E, Rehman L. The Impact of an After-School Physical Activity Program on Children’s Physical Activity and Well-Being during the COVID-19 Pandemic: A Mixed-Methods Evaluation Study. Int J Environ Res Public Health. 2022;19(9).

  59. Barnett LM, Lai SK, Veldman SLC, Hardy LL, Cliff DP, Morgan PJ, et al. Correlates of Gross Motor competence in children and adolescents: a systematic review and Meta-analysis. Sports Med. 2016;46(11):1663–88.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Behan S, Belton S, Peers C, O’Connor NE, Issartel J. Moving well-being well: investigating the maturation of fundamental movement skill proficiency across sex in Irish children aged five to twelve. J Sports Sci. 2019;37(22):2604–12.

    Article  PubMed  Google Scholar 

  61. Gray C, Gibbons R, Larouche R, Sandseter EB, Bienenstock A, Brussoni M, et al. What is the relationship between Outdoor Time and Physical Activity, sedentary Behaviour, and physical fitness in children? A systematic review. Int J Environ Res Public Health. 2015;12(6):6455–74.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Tremblay MS, Gray C, Babcock S, Barnes J, Bradstreet CC, Carr D, et al. Position Statement on active Outdoor Play. Int J Environ Res Public Health. 2015;12(6):6475–505.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Truelove S, Bruijns BA, Vanderloo LM, O’Brien KT, Johnson AM, Tucker P. Physical activity and sedentary time during childcare outdoor play sessions: a systematic review and meta-analysis. Prev Med. 2018;108:74–85.

    Article  PubMed  Google Scholar 

  64. Lynch T. Physically educated: developing children’s health and wellbeing through movement and motor skills. In: Garvis S, D. P, editors. Health and wellbeing in childhood. 2nd ed. ed. Cambridge: Cambridge University Press; 2017. pp. 77–94.

    Chapter  Google Scholar 

  65. Strotmeyer A, Herrmann C, Kehne M. A longitudinal analysis of reciprocal relationships between actual and perceived motor competencies and physical self-concept in primary-school age children. Psychol Sport Exerc. 2022;63.

  66. Yao CA, Rhodes RE. Parental correlates in child and adolescent physical activity: a meta-analysis. Int J Behav Nutr Phys Activity. 2015;12.

  67. Su DLY, Tang TCW, Chung JSK, Lee ASY, Capio CM, Chan DKC. Parental influence on child and adolescent physical activity level: a Meta-analysis. Int J Environ Res Public Health. 2022;19(24).

  68. Rhodes RE, Guerrero MD, Vanderloo LM, Barbeau K, Birken CS, Chaput JP et al. Development of a consensus statement on the role of the family in the physical activity, sedentary, and sleep behaviours of children and youth. Int J Behav Nutr Phys Activity. 2020;17(1).

  69. Liszewska N, Scholz U, Radtke T, Horodyska K, Liszewski M, Luszczynska A. Association between Children’s Physical Activity and Parental Practices Enhancing Children’s Physical Activity: The Moderating Effects of Children’s BMIz-Score. Front Psychol. 2018;8.

  70. He Y, Zhou L, Liang W, Liu Q, Liu W, Wang S. Individual, family, and environmental correlates of fundamental motor skills among school-aged children: a cross-sectional study in China. BMC Public Health. 2024;24(1):208.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Morton KL, Corder K, Suhrcke M, Harrison F, Jones AP, van Sluijs EMF et al. School polices, programmes and facilities, and objectively measured sedentary time, LPA and MVPA: associations in secondary school and over the transition from primary to secondary school. Int J Behav Nutr Phys Activity. 2016;13.

  72. Harrison F, van Sluijs EMF, Corder K, Jones A. School grounds and physical activity: associations at secondary schools, and over the transition from primary to secondary schools. Health Place. 2016;39:34–42.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Parrish A-M, Okely AD, Batterham M, Cliff D, Magee C. PACE: a group randomised controlled trial to increase children’s break-time playground physical activity. J Sci Med Sport. 2016;19(5):413–8.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the school district for participating in the study and the teaching staff for supporting the study. We are particularly grateful to all the children and parents participating in the project.

Funding

The Physical Literacy for communities (PL4C) was supported by the Physical Literacy for Communities project through the Healthy Canadians and Community Fund from the Public Health Agency of Canada and financial and in-kind contributions of Sport for Life and its partners. In addition, members of the research team were supported by a Canada Foundation for Innovation (CFI) award to GF and a research visit grant from the Faculty of Education, University of Gothenburg.

Le présent document a été produit grâce à la contribution financière de l’Agence de la santé publique du Canada. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.

Open access funding provided by University of Gothenburg.

Author information

Authors and Affiliations

  1. Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg, Gothenborg, Sweden

    Beatrix Algurén

  2. School of Kinesiology, Faculty of Education, University of British Columbia, Vancouver, BC, Canada

    Yiling Tang & Guy Faulkner

  3. School of Health Sciences, University of Northern British Columbia, Prince George, BC, Canada

    Chelsea Pelletier

  4. School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC, Canada

    Patti-Jean Naylor

Authors
  1. Beatrix Algurén
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiling Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chelsea Pelletier
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patti-Jean Naylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guy Faulkner
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GF and PJN were responsible for the overall funding proposal and study design, GF and YT contributed to the data collection. BA initiated and together with YT and GF developed research questions for this study. BA, YT, and GF conducted data analyses. BA visualized the results, drafted and edited the paper. YT, GF, CP, and PJN reviewed and edited the paper and BA finalized it. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Beatrix Algurén.

Ethics declarations

Ethics Approval and Consent to Participate

All methods were carried out in accordance with relevant guidelines and regulations. The study was approved by the University of British Columbia Institutional Behavioural Research Ethics Board (#H21-03586) and the School Board. Informed consent was obtained from parents and/or legal guardians, and children gave assent to participate in the study.

Consent for Publication

Not applicable.

Competing Interests

Beatrix Algurén, Yiling Tang, Chelsea Pelletier, Patti-Jean Naylor, Guy Faulkner declare no competing interest relevant to the content of this article. The funding agencies were not involved in the analysis or interpretation of the results.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Algurén, B., Tang, Y., Pelletier, C. et al. Biopsychosocial and Environmental Correlates of Children’s Motor Competence: An Exploratory Study. Sports Med - Open 10, 90 (2024). https://doi.org/10.1186/s40798-024-00763-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40798-024-00763-z

Keywords