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Reference Values of Physical Performance in Handball Players Aged 13-19 Years: Taking into Account Their Biological Maturity.

Chirine AouichaouiSamir KrichenMohamed TounsiAchraf AmmarOussama TabkaSalem ChattiMonia ZaoualiMohamed ZouchYassine Trabelsi
Published in: Clinics and practice (2024)
Biological maturity status significantly influences success in handball, impacting an athlete's performance and overall development. This study aimed to examine the anthropometric and physical performance variables concerning age and maturity status, establishing reference values for physical performance among Tunisian players. A total of 560 handball players (309 males and 251 females aged 13-19 years) were categorized based on maturity status: early ( n = 98), average ( n = 262), and late ( n = 200), determined through Mirwald and colleagues' equations. Anthropometric, physical fitness, and physiological data were collected for reference value creation. Our findings revealed significantly higher anthropometric parameters ( p = 0.003) in late-maturing athletes compared to their early-maturing counterparts. Post-pubertal athletes showed significantly superior ( p = 0.002) jumping ability, change of direction, and aerobic performance compared to their pre-pubertal peers. Additionally, male athletes outperformed females in both fitness ( p = 0.001) and aerobic ( p = 0.001) performance. A notable age-by-maturity interaction emerged for most performance outcomes (η 2 ranging from 0.011 to 0.084), highlighting increased sex-specific differences as athletes progressed in age. Percentile values are provided for males and females, offering valuable insights for coaches and sports scientists to design personalized training programs. Understanding a player's performance relative to these percentiles allows trainers to tailor workouts, addressing specific strengths and weaknesses for enhanced development and competitiveness.
Keyphrases
  • high school
  • body composition
  • physical activity
  • mental health
  • public health
  • high intensity
  • electronic health record
  • machine learning