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Towards an Evidence-Based Classification System for Para Dressage: Associations between Impairment and Performance Measures.

Sarah Jane HobbsJill AlexanderCeleste A WilkinsLindsay B St GeorgeKathryn NankervisJonathan K SinclairGemma PenhorwoodJane Michelle WilliamsHilary Mary Clayton
Published in: Animals : an open access journal from MDPI (2023)
This study follows a previously defined framework to investigate the impact of impairment on performance in Para dressage athletes. Twenty-one elite Para dressage athletes (grades I to V) and eleven non-disabled dressage athletes (competing at Prix St. Georges or Grand Prix) participated. Data were collected in two phases: performing a two minute custom dressage test on a riding simulator while kinematic data were synchronously collected using inertial measurement units (2000 Hz) and optical motion capture (100 Hz), and clinically assessed using a battery of impairment assessment tools administered by qualified therapists. Impairment and performance measures were compared between Para and non-disabled athletes. Significant differences between athlete groups were found for all impairment measures and two performance measures: simulator trunk harmonics ( p = 0.027) and athlete trunk dynamic symmetry ( p < 0.001). Impairment assessments of sitting function and muscle tone could predict 19 to 35% of the impact of impairment on performance in Para athletes but not in non-disabled athletes. These findings provide the basis for a robust, scientific evidence base, which can be used to aid in the refinement of the current classification system for Para dressage, to ensure that it is in line with the International Paralympic Committee's mandate for evidence-based systems of classification.
Keyphrases
  • machine learning
  • skeletal muscle
  • high resolution
  • mass spectrometry
  • virtual reality
  • lower limb