Gender Differences in Fundamental Motor Skills Proficiency in Children Aged 3-6 Years: A Systematic Review and Meta-Analysis

The age range of 3−6 years is considered as a critical period in developing and learning fundamental motor skills (FMS). To make the formulation of future FMS guidance programs more targeted, we examined gender differences in children’s FMS proficiency using a meta-analysis. Structured electronic databases including PubMed, Scopus and Web of Science were systematically searched using key terms, and the Joanna Briggs Institute (JBI) was used to assess the quality of included literature. Finally, 38 articles (39 studies) met the pre-specified inclusion criteria. The results showed that boys had higher proficiency in total FMS and object control skills than girls (SMD = 0.17 (95% CI 0.03, 0.31), p = 0.02; SMD = 0.48 (95% CI 0.38, 0.58), p < 0.00001), and gender differences in locomotor skill proficiency approached significance, trending in favor of girls (SMD = −0.07 (95 % CI −0.15, 0.01), p = 0.09, I2 = 66%). Meta-regression shows that age is associated with gender differences in object control skills (p < 0.05). In addition, through subgroup analysis, we found that boys’ advantage in object control skills increased with age (3 years: SMD = 0.27 (95% CI 0.00, 0.54), p < 0.00001; 4 years: SMD = 0.58 (95% CI 0.38, 0.77), p < 0.00001; 5 years: SMD = 0.59 (95% CI 0.31, 0.88), p < 0.00001; 6 years: SMD = 0.81 (95% CI 0.61, 1.01), p < 0.00001). In this meta-analysis, we found gender differences in FMS levels in children aged 3−6 years. Notably, gender differences in skill proficiency in object control were influenced by age. We recommend focusing on and developing girls’ object control skills starting at age 3.

Primary School Pupils: Unequal GMC Developmental Pathways in a Single School Year

Gross motor coordination (GMC) is essential for the development of specific motor skills and long-term participation in physical activities and sports. Group analysis reveals that, on average, children develop these skills gradually; however, how individuals develop GMC is less clear. The main aim of this study is to increase the understanding of developmental patterns within one school year, and whether children’s grade, gender, or baseline GMC proficiency are associated with these developmental patterns. In total, 2594 Dutch children aged 6−12 years performed the modified Körper Koordinations Test für Kinder (KTK3+) twice in one school year (autumn and spring). The KTK3+ includes four subtests: walking backwards, moving sideways, jumping sideways, and eye-hand coordination (EHC) test. On average, children developed significantly on all subtests (p < 0.001). At baseline, children in higher grades scored significantly higher than children in lower grades, and children in grades 5 and 6 (age 9 and 10 years) showed most development (raw scores on average, p < 0.001). Boys outperformed girls on EHC across all grades, whereas girls outperformed boys on walking backwards. Nevertheless, both boys and girls developed similarly. Children with lower scores at baseline developed more quickly across all grades. Noteworthy is that 12.1−24% (depending on the test item) of the children scored lower in the spring than in the autumn tests. On average, children develop their GMC; however, we report large differences in their individual trajectories and note that a substantial number did not show a positive GMC development. Further research should examine GMC development with more possible influencing factors as well as over a longer time span to better understand differences in children’s GMC development. This may result in more individualized programs in PE lessons, enabling children to optimally develop their GMC, and better use of GMC assessment tools to monitor children’s development.

Modeling Longitudinal Outcomes: A Contrast of Two Methods

Background: Repeated measures analysis of variance (ANOVA) is frequently used to model longitudinal data but does not appropriately account for within-person correlations over time, does not explicitly model time, and cannot flexibly handle missing data. In contrast, mixed-effects regression addresses these limitations. In this commentary, we compare these two methods using openly available tools. Methods: We emulated a real developmental study of elite skiers, tracking national rankings from 2011 to 2018. We constructed unconditional models of time (establishing the “pattern” of change) and conditional models of time (identifying factors that affect change over time), and contrasted these models against comparable repeated measures ANOVAs. Results: Mixed-effects regression allowed for linear and non-linear modeling of the skiers’ longitudinal trajectories despite missing data. Missing data is still a concern in mixed-effects regression models, but in the present dataset missingness could be accounted for by skiers’ ages, satisfying the missing at random assumption. Discussion: Although ANOVA and mixed-effects regression are both suitable for time-series data, their applications differ. ANOVA will be most parsimonious when the research question focuses on group-level mean differences at arbitrary time points. However, mixed-effects regression is more suitable where time is inherently important to the outcome, and where individual differences are of interest.