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The first years of life are an optimal time for developing motor competence. However, the evidence regarding motor competence in early childhood is fragmented and needs to be clearly synthesized and presented. To establish effective evidence-based decision making in research, practice, and policy for the early years, this expert statement, on behalf of the International Motor Development Research Consortium, draws together what is currently known about 3- to 5-year-old children on (a) how skilled are children around the world, (b) the link between motor competence and healthy developmental outcomes, and (c) the capacity to improve children’s motor competence through intervention. This expert statement presents a summary of recent evidence for each of these specific points, followed by recommendations for researchers, practitioners, and policymakers.

Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them—(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using “sensorized objects” that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders.

The worldwide increase in childhood overweight and obesity underscores the need to study variables like fundamental movement skill (FMS) levels from early childhood. This study investigated the bidirectional longitudinal relationship between body mass index (BMI) and process-oriented FMSs, including locomotor skills and object control skills in 675 Finnish children, aged 3–8 years at baseline (50.5% female, mean age 5.5 years) over 3 years. Standardized BMI-for-age SD scores (BMI SDS z-scores) followed Finnish national standards. The FMS assessment comprised four subtests from the Test of Gross Motor Development, third edition. Age-adjusted standardized residuals of FMS or skill domains and BMI SDS z-scores were used in a two-level, cross-classified, cross-lagged regression analysis, accounting for gender, and baseline value of the dependent variables. The results showed no statistically significant longitudinal relationship between BMI and FMS or its skill domains for either gender in either direction. This suggests that BMI and process-oriented FMS, encompassing locomotor skill and object control skill, develop independently, possibly influenced by unexplored variables. These findings contradict earlier results based on product-oriented measurements, which may include a physical capacity component. The outcomes further underscore the importance of monitoring weight status from early childhood, given its significant association with later-life weight conditions.

The dynamics of mastering the degrees of freedom in motor learning are still far from being understood. The present work explored coordination dynamics in a redundant task, relating it to performance and adaptation in a serial stimulus tracking task. One hundred and sixty-three children (10–14 years of age) continuously responded to sequential stimuli (containing five stimuli) by pressing the respective sensors before the next stimulus presentation. Participants performed 120 trials with a fixed sequence (4–2–5–3–1) and a fixed interstimuli interval (800 ms) to learn the first pattern (practice phase). Then, a changed sequence (4–2–5–1–3) with a shorter interval (700 ms) was presented for 40 trials (adaptation phase). To measure coordination and its change, we calculated the correlation matrix of the stimulus–touch interval between the five sensors in blocks of 20 trials of the practice phase and classified individuals in terms of clusters. We found associations between coordination dynamics, performance curves, and adaptation in both coordination and performance. Furthermore, using network analyses, we found a tendency for all groups to increase the clustering coefficient. We discuss the possibility of this result representing a process of progressive segregation.