The Future of Classification in Wheelchair Sports: Can Data Science and Technological Advancement Offer an Alternative Point of View?

Abstract

Classification is a defining factor for competition in wheelchair sports, but it is a delicate and time-consuming process with often questionable validity. New inertial sensor-based measurement methods applied in match play and field tests allow for more precise and objective estimates of the impairment effect on wheelchair-mobility performance. The aim of the present research was to evaluate whether these measures could offer an alternative point of view for classification. Six standard wheelchair-mobility performance outcomes of different classification groups were measured in match play (n = 29), as well as best possible performance in a field test (n = 47). In match results, a clear relationship between classification and performance level is shown, with increased performance outcomes in each adjacent higher-classification group. Three outcomes differed significantly between the low- and mid-classified groups, and 1, between the mid- and high-classified groups. In best performance (field test), there was a split between the low- and mid-classified groups (5 out of 6 outcomes differed significantly) but hardly any difference between the mid- and high-classified groups. This observed split was confirmed by cluster analysis, revealing the existence of only 2 performance-based clusters. The use of inertial sensor technology to obtain objective measures of wheelchair-mobility performance, combined with a standardized field test, produced alternative views for evidence-based classification. The results of this approach provide arguments for a reduced number of classes in wheelchair basketball. Future use of inertial sensors in match play and field testing could enhance evaluation of classification guidelines, as well as individual athlete performance.