Cluster analysis of impairment measures to inform an evidence-based classification structure in RaceRunning, a new World Para Athletic event for athletes with hypertonia, ataxia or athetosis

Abstract

RaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled frame that has a saddle, handle bars and a chest plate. For RaceRunning to be included as a para athletics event, an evidence-based classification system is required. This study assessed the impact of trunk control and lower limb impairment measures on RaceRunning performance and evaluated whether clusters analysis of these impairment measures produce a valid classification structure for RaceRunning. The Trunk Control Measurement Scale (TCMS), Selective Control Assessment of the Lower Extremity (SCALE), the Australian Spasticity Assessment Scale (ASAS), and knee extension were recorded for 26 RaceRunning athletes. Thirteen male and 13 female athletes aged 24 (SD=7) years participated. All impairment measures were significantly correlated with performance (rho=0.55-0.74). Using ASAS, SCALE, TCMS and knee extension as cluster variables in a two-step cluster analysis resulted in two clusters of athlete. Race speed and the impairment measures were significantly different between the clusters (p<0.001). The findings of this study provide evidence for the utility of the selected impairment measures in an evidence-based classification system for RaceRunning athletes.