Exploring the Importance of Motor Competence for Behavioral and Health Outcomes in Youth

Motor Competence as a Protection Factor Against Pediatric Obesity: The Bidirectional Relationship Between Motor Competence and Weight Status

Purpose: Motor competence is negatively associated with body mass index (BMI) in young people. However, it is still unclear whether motor competence is linked to the risk of obesity in adolescents and longitudinal evidence is missing. The main purpose of this study was to assess the association between motor competence and risk of obesity across adolescence. Methods: A 2-year follow-up study was conducted with a total of 122 adolescents (59.8% girls) aged 12 to 13 years at baseline. Motor competence (KTK), weight status (BMI) and physical activity (questionnaire) were assessed at three time points. Generalized estimating equation and logistic regression models were conducted to examine the relationships between motor competence and weight status, adjusting for potential confounders (i.e. age, sex and physical activity). Results: Motor competence and BMI were inversely related over time. The chance to have overweight/obesity decreased by 6.5% and 8% with a 10-point increase in motor competence across time. In addition, adolescents had 6.4 to 8.2 greater chance to have low motor competence with a 1-point increase in BMI across time. Conclusion: Motor competence is inversely associated with the risk of overweight/obesity across adolescence. Therefore, the development of motor competence can be a protection factor against excessive weight gain. As such, we recommend the development of motor competence as a strategy to prevent pediatric obesity through diversified types of physical activities such as sports, active play and physical education classes.

Associations Between Motor Competence and Physical Activity, Physical Fitness and Psychosocial Characteristics in Adolescents: A Systematic Review and Meta-analysis

BACKGROUND

Motor competence is an integral component of the health and performance of youth. Numerous studies support the hypothesis that motor competence interacts with perceived motor competence and physical fitness during childhood to induce positive (e.g. healthy weight status) or negative (e.g. reduced physical activity engagement) trajectories. Yet, while adolescence is a key period of rapid growth and maturation, no systematic reviews and meta-analyses have examined the association between motor competence and physical activity, physical fitness and psychosocial characteristics solely within adolescents.

OBJECTIVES

This study aimed to (1) analyse the scientific literature evaluating associations between motor competence and physical activity, physical fitness and/or psychosocial characteristics amongst adolescents; (2) evaluate the associations between motor competence and physical activity, physical fitness characteristics and/or psychosocial characteristics amongst adolescents; and (3) investigate the impact of moderator variables (i.e., age, sex, type of motor competence assessment) on the associations.

METHODS

A systematic search of electronic databases was conducted, followed by a qualitative synthesis of study methods. Random-effects meta-analyses were performed to establish the magnitude and orientation of pooled correlation coefficients between motor competence and physical activity, physical fitness and psychosocial characteristics of adolescents, whilst considering potential moderators (i.e., age, sex, type of motor competence assessment).

RESULTS

Sixty-one studies were included, totalling 22,256 adolescents. Twenty-seven different assessments of motor competence were used, with 31 studies utilising product-orientated (i.e. outcome) motor competence assessments. Meta-analyses of 43 studies showed that motor competence was positively associated with physical activity (r = 0.20 to 0.26), some physical fitness characteristics (e.g. muscular strength, cardiovascular endurance; r = 0.03 to 0.60) and psychosocial characteristics (r = 0.07 to 0.34), and inversely associated with weight status (r =  - 0.36 to - 0.10), speed (r =  - 0.31) and agility (r =  - 0.37 to 0.41). Associations with flexibility were unclear.

CONCLUSIONS

The results of this systematic review and meta-analysis support the hypothesised interactions of motor competence with physical activity (positive), physical fitness (positive except for weight status, speed and agility) and psychosocial characteristics (positive) in adolescence. However, methodological approaches vary considerably (e.g. variety of motor competence assessments utilised), with limitations of the current literature including an inadequate assessment of motor competence, a lack of longitudinal observations and a failure to account for biological maturation. Future research assessing associations between motor competence and physical activity, physical fitness and psychosocial characteristics of adolescents should include longitudinal observations of a combined motor competence assessment (i.e. process and product) and account for biological maturation. Improved evaluation using these recommendations could provide more accurate data, leading to more targeted interventions to improve adolescents' physical and psychosocial outcomes.

CLINICAL TRIAL REGISTRATION

CRD42021233441 (PROSPERO ID).

Adolescents' Flexibility Can Affect Motor Competence: The Pathway from Health Related Physical Fitness to Motor Competence

Motor competence has been shown to predict health-related physical fitness (HRPF) components in youth, but there is indeterminate evidence for the reverse path. Even less is known about the specific relationship between motor competence and flexibility. Our aims in this study were to analyze the relationship between flexibility and motor competence among adolescents; and, secondarily, to examine the relationships between motor competence and other HRPF components (body composition and musculoskeletal fitness). We conducted a longitudinal study (14-months follow-up) with two measurement points of 128 participants (55.5% girls; 45% boys) aged 12-13 years at baseline. We assessed HRPF components at the first time point and determined motor competence at the second time point. We assessed HRPF in terms of body composition (body fat percentage), musculoskeletal fitness (sit-up test), and flexibility (sit and reach). We determined motor competence using the Körperkoordinationstest für Kinder (KTK). We employed multivariate analyses of variance (MANOVA) to examine the effect of HRPF on motor competence through three models, in which each HRPF component was considered an independent variable. Sex, age, physical activity, and body mass index (BMI) z-scores were considered confounders. In our crude models all HRPF components significantly affected motor competence. In adjusted models, flexibility (p = .002) and body composition (p < .001) significantly affected motor competence. Overall, these results supported the assumptions that HRPF components are related to motor competence in adolescence, and that flexibility can affect motor competence in young people.

Motor Competence Assessment (MCA) Scoring Method

The Motor Competence Assessment (MCA) is a quantitative test battery that assesses motor competence across the whole lifespan. It is composed of three sub-scales: locomotor, stability, and manipulative, each of them assessed by two different objectively measured tests. The MCA construct validity for children and adolescents, having normative values from 3 to 23 years of age, and the configural invariance between age groups, were recently established. The aim of this study is to expand the MCA’s development and validation by defining the best and leanest method to score and classify MCA sub-scales and total score. One thousand participants from 3 to 22 years of age, randomly selected from the Portuguese database on MC, participated in the study. Three different procedures to calculate the sub-scales and total MCA values were tested according to alternative models. Results were compared to the reference method, and Intraclass Correlation Coefficient, Cronbach’s Alpha, and Bland–Altman statistics were used to describe agreement between the three methods. The analysis showed no substantial differences between the three methods. Reliability values were perfect (0.999 to 1.000) for all models, implying that all the methods were able to classify everyone in the same way. We recommend implementing the most economic and efficient algorithm, i.e., the configural model algorithm, averaging the percentile scores of the two tests to assess each MCA sub-scale and total scores.