The purpose of this study was to investigate gross and fine motor skill in overweight and obese children compared with normal-weight peers. According to international cut-off points for Body Mass Index (BMI) from Cole et al. (2000), all 117 participants (5–10 year) were classified as being normal-weight, overweight, or obese. Level of motor skill was assessed using the Movement Assessment Battery for Children (MABC). Scores for balance (p < .01) and ball skills (p < .05) were significantly better in normal-weight and overweight children as compared with their obese counterparts. A similar trend was found for manual dexterity (p < .10). This study demonstrates that general motor skill level is lower in obese children than in normal-weight and overweight peers.

Purpose: To assess injury risk in elite-level youth football (soccer) players based on anthropometric, motor coordination and physical performance measures with a machine learning model. Methods: A total of 734 players in the U10 to U15 age categories (mean age, 11.7 ± 1.7 yr) from seven Belgian youth academies were prospectively followed during one season. Football exposure and occurring injuries were monitored continuously by the academies’ coaching and medical staff, respectively. Preseason anthropometric measurements (height, weight, and sitting height) were taken and test batteries to assess motor coordination and physical fitness (strength, flexibility, speed, agility, and endurance) were performed. Extreme gradient boosting algorithms (XGBoost) were used to predict injury based on the preseason test results. Subsequently, the same approach was used to classify injuries as either overuse or acute. Results: During the season, half of the players (n = 368) sustained at least one injury. Of the first occurring injuries, 173 were identified as overuse and 195 as acute injuries. The machine learning algorithm was able to identify the injured players in the hold-out test sample with 85% precision, 85% recall (sensitivity) and 85% accuracy (f1 score). Furthermore, injuries could be classified as overuse or acute with 78% precision, 78% recall, and 78% accuracy. Conclusions: Our machine learning algorithm was able to predict injury and to distinguish overuse from acute injuries with reasonably high accuracy based on preseason measures. Hence, it is a promising approach to assess injury risk among elite-level youth football players. This new knowledge could be applied in the development and improvement of injury risk management strategies to identify youth players with the highest injury risk.

Lack of physical activity is a global public health problem causing not only morbidity and premature mortality, but it is also a major economic burden worldwide. One of the cornerstones of a physically active lifestyle is Motor Competence (MC). MC is a complex biocultural attribute and therefore, its study requires a multi-sectoral, multi-, inter- and transdisciplinary approach. MC is a growing area of research, especially in children and adolescents due to its positive association with a plethora of health and developmental outcomes. Many questions, however, remain to be answered in this field of research, with regard to: (i) Health and Developmental-related Associations of MC; (ii) Assessment of MC; (iii) Prevalence and Trends of MC; (iv) Correlates and Determinants of MC; (v) MC Interventions, and (vi) Translating MC Research into Practice and Policy. This paper presents a narrative review of the literature, summarizing current knowledge, identifying key research gaps and presenting questions for future investigation on MC in children and adolescents. This is a collaborative effort from the International Motor Competence Network (IMCNetwork) a network of academics and researchers aiming to promote international collaborative research and knowledge translation in the expansive field of MC. The knowledge and deliverables generated by addressing and answering the aforementioned research questions on MC presented in this review have the potential to shape the ways in which researchers and practitioners promote MC and physical activity in children and adolescents across the world.

Objective. The purpose of this study was to investigate differences in gross motor coordination in healthy-weight, overweight, and obese children of different ages. Methods. Using a cross-sectional design, data were collected in 954 Flemish primary school children (500 girls, 454 boys) stratified in consecutive age groups (5–7 years, 8–9 years, 10–12 years). Weight status (healthy-weight, overweight, obese) was defined according to the International Obesity Task Force body mass index (BMI) cut-off points for children. Gross motor coordination was assessed by means of the Körperkoordinationstest für Kinder (KTK). Results. Childhood overweight and particularly obesity were found to result in poorer KTK performances (p < 0.001), with the most apparent effect of BMI on items requiring physical properties next to dynamic body coordination. Expressed as an age-related Motor Quotient (MQ), overall KTK performance was featured by a BMI × AGE interaction (p < 0.01). Healthy-weight children displayed similar MQs across age groups (p = 0.999). Overweight and obese children in the 10–12-year-old group showed significantly poorer motor coordination performance compared with the corresponding 5–7-year-old group (p < 0.01). Less than 20% of the healthy-weight participants was identified as being motor impaired, while that proportion increased to 43.3% and up to 70.8% in children with overweight and obesity, respectively. Conclusion. Results indicate that BMI-related differences in gross motor coordination were more pronounced as children belonged to an older age group. Although this outcome needs to be confirmed in future longitudinal research, it emphasizes the need of an early focus on motor skill improvement to encourage overweight and obese children to be physically active.