PURPOSE: To describe the objective and subjective match load (ML) of wheelchair basketball (WB) and determine the relationship between session heart-rate (HR) -based ML and rating-of-perceived-exertion (RPE) -based ML methods. METHODS: HR-based measurements of ML included Edwards ML and Stagno training impulses (TRIMPMOD), while RPE-based ML measurements included respiratory (sRPEres) and muscular (sRPEmus). Data were collected from 10 WB players during a whole competitive season. RESULTS: Edwards ML and TRIMPMOD averaged across 16 matches were 255.3 ± 66.3 and 167.9 ± 67.1 AU, respectively. In contrast, sRPEres ML and sRPEmus ML were found to be higher (521.9 ± 188.7 and 536.9 ± 185.8 AU, respectively). Moderate correlations (r = .629-.648, P < .001) between Edwards ML and RPE-based ML methods were found. Moreover, similar significant correlations were also shown between the TRIMPMOD and RPE-based ML methods (r = .627-.668, P < .001). That said, only ≥40% of variance in HR-based ML was explained by RPE-based ML, which could be explained by the heterogeneity of physical-impairment type. CONCLUSION: RPE-based ML methods could be used as an indicator of global internal ML in highly trained WB players.
PURPOSE: To describe the objective and subjective match load (ML) of wheelchair basketball (WB) and determine the relationship between session heart-rate (HR) -based ML and rating-of-perceived-exertion (RPE) -based ML methods. METHODS: HR-based measurements of ML included Edwards ML and Stagno training impulses (TRIMPMOD), while RPE-based ML measurements included respiratory (sRPEres) and muscular (sRPEmus). Data were collected from 10 WB players during a whole competitive season. RESULTS: Edwards ML and TRIMPMOD averaged across 16 matches were 255.3 ± 66.3 and 167.9 ± 67.1 AU, respectively. In contrast, sRPEres ML and sRPEmus ML were found to be higher (521.9 ± 188.7 and 536.9 ± 185.8 AU, respectively). Moderate correlations (r = .629-.648, P < .001) between Edwards ML and RPE-based ML methods were found. Moreover, similar significant correlations were also shown between the TRIMPMOD and RPE-based ML methods (r = .627-.668, P < .001). That said, only ≥40% of variance in HR-based ML was explained by RPE-based ML, which could be explained by the heterogeneity of physical-impairment type. CONCLUSION: RPE-based ML methods could be used as an indicator of global internal ML in highly trained WB players.