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Sex differences in change of direction deficit and asymmetries in footballers with cerebral palsy.

Matías HenríquezIván Peña-GonzálezCarlos Albaladejo-GarcíaKabir P SadaranganiRaul Reina
Published in: Scandinavian journal of medicine & science in sports (2023)
The aims of this study were (1) to describe and examine differences in change of direction (COD) performance and the magnitude of asymmetries in para-footballers with cerebral palsy (CP) and controls and (2) to evaluate the association between COD outcomes and linear sprint performance. Twenty-eight international para-footballers with CP and thirty-nine non-impaired football players (control group) participated in this study. All participants completed a 10-m sprint and two attempts of the 505 COD test with the dominant and non-dominant leg. The COD deficit was calculated using the difference between the 505 test and the 10-m sprint time, while the asymmetry index was determined by comparing each leg's completion time and COD deficit. Players across groups showed interlimb asymmetries between the dominant and non-dominant legs in COD outcomes and deficit (p < 0.05, d g  = -0.40 to -1.46), although these asymmetries imbalance were not significantly different between the sexes with and without impairment. Males with CP exhibited a faster directional COD speed and a shorter COD deficit than their female counterparts (p < 0.01, d g  = -1.68 to -2.53). Similarly, the control group had faster scores than the CP groups of the same sex (p < 0.05, d g  = 0.53 to 3.78). Lastly, the female CP group and male control groups showed a significant association between sprint and the COD deficit in the dominant leg (p < 0.05, r = -0.58 to 0.65). Therefore, the use of directional dominance, the COD deficit, and asymmetry outcomes could be helpful for classification purposes to assess the impact of the impairment on sport-specific activity testing according to sex.
Keyphrases
  • cerebral palsy
  • high intensity
  • type diabetes
  • machine learning
  • insulin resistance
  • deep learning
  • adipose tissue
  • metabolic syndrome
  • skeletal muscle
  • body composition