Article Text

Anthropometric and physiological factors predicting young adults' motor skills
  1. E I Iconaru,
  2. C Ciucurel,
  3. M M Ciucurel,
  4. L Georgescu,
  5. S Toma,
  6. M I Tudor
  1. University of Pitesti, Pitesti, Romania


Recent studies indicate that performance of motor skills is influenced by a diversity of physiological parameters, such as weight status, aerobic capacity, endurance and muscular strength, but the scientific determinants of physical performance are very specific. The aim was to identify the anthropometric and physiological determinants of motor skills in young adults. The authors studied anthropometric and physiological factors (as predictor variables) among 230 subjects (average age 22 years, sex ratio 1/1). The authors considered as criterion variable the results of the Fundamental Movement Skills Test, a Romanian semiquantitative test utilised for evaluation of candidates for entrance into Romanian Physical Education and Sport Faculties. Above the measured variables, the strongest predictors for the motor skills (for a statistical significance level p=0.002) were: the body mass index, the aerobic capacity (Astrand-Rhyming test), the anaerobic power (Sargent test), the statokinesigram and the vital capacity. These unified factors, as a battery test, account for 65% of the variance of the subjects' motor skill level (multiple R square=0.65, multiple R=0.805). The acquisition of motor skills is substantially associated with some anthropometric and physiological determinants. Thus, the understanding of these important factors is beneficial for large groups of individuals, by helping professionals in physical education and sport to develop a conceptual model of human performance. Our findings suggest that the most relevant five factors predicting the motor skills in young adults are the body mass index, the aerobic capacity, the anaerobic power, the statokinesigram and the vital capacity. Furthermore, more effective studies appear like innovative approaches, in condition of applying the multiple regression and mitigating the effect of multicollinearity.

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