Τρίτη 6 Φεβρουαρίου 2018

The influence of lower limb impairments on RaceRunning performance in athletes with hypertonia, ataxia or athetosis

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Publication date: Available online 5 February 2018
Source:Gait & Posture
Author(s): Marietta L. van der Linden, Sadaf Jahed, Nicola Tennant, Martine H.G. Verheul
ObjectivesRaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled running bike that has a saddle and a chest plate for support but no pedals. For RaceRunning to be included as a para-athletics event, an evidence-based classification system is required. Therefore, the aim of this study was to assess the association between a range of impairment measures and RaceRunning performance.MethodsThe following impairment measures were recorded: lower limb muscle strength assessed using Manual Muscle Testing (MMT), selective voluntary motor control assessed using the Selective Control Assessment of the Lower Extremity (SCALE), spasticity recorded using both the Australian Spasticity Assessment Score (ASAS) and Modified Ashworth Scale (MAS), passive range of motion (ROM) of the lower extremities and the maximum static step length achieved on a stationary bike (MSSL). Associations between impairment measures and 100-meter race speed were assessed using Spearman’s correlation coefficients.ResultsSixteen male and fifteen female athletes (27 with cerebral palsy), aged 23 (SD = 7) years, Gross Motor Function Classification System ranging from II to V, participated. The MSSL averaged over both legs and the ASAS, MAS, SCALE, and MMT summed over all joints and both legs, significantly correlated with 100 m race performance (rho: 0.40-0.54). Passive knee extension was the only ROM measure that was significantly associated with race speed (rho = 0.48).ConclusionThese results suggest that lower limb spasticity, isometric leg strength, selective voluntary motor control and passive knee extension impact performance in RaceRunning athletes. This supports the potential use of these measures in a future evidence-based classification system.



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