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RESEARCH PRODUCT
Different sitting positions influence cross country sit skiers performance : Sitting position influence on force generation and cycle characteristics
Rosso ValeriaLindinger StefanGastaldi LauraLinnamo VesaKarczewska-lindinger MagdalenaRapp WalterVanlandewijck Yvessubject
musculoskeletal diseasesTrunk controlForce generationmedicine.medical_specialtySittingsitting positionbiomechanics03 medical and health sciences0302 clinical medicinePhysical medicine and rehabilitationmedicineta315paralympialaisetCross countrybiologyAthletesBiomechanicscross country sit skiing030229 sport sciencesmusculoskeletal systemSitting Positionsbiology.organism_classificationvammaisurheiluParalympic sport; Sitting position; Performance; Cross country sit skiing; BiomechanicsPosition (obstetrics)biomekaniikkaparalympic sportPsychologyhuman activities030217 neurology & neurosurgeryperformancedescription
Cross country sit skiing is a Paralympic discipline in which athletes due to physical impairment ski sitting on a sit-ski. The impairment influences performance directly and also through sitting position. Athletes with a better trunk control usually adopt a sitting position called “kneeing” in which the hip joints are higher than the knee joints. In contrast, athletes with high impact of impairment prefer a sitting position called “knee high” in which the hip joints are lower than the knee joints. Able bodied athletes skiing on the ergometer in these two sitting positions showed different performance. However, to the best of authors’ knowledge, no studies have examined performance, force production, and cycle characteristics of athletes with physical impairment who ski using different sitting positions. Therefore, the aim of the present study is to assess if athletes with physical impairment sitting in “kneeing” and “knee high” position perform differently while skiing. To obtain this purpose, a k-means cluster analysis was used to group ten male athletes according to their skiing performance in terms of maximal speed, generated force, and cycle characteristics. Cluster analysis results showed athletes aggregation in two clusters and a very high agreement between clusters analysis outcome and real athletes sitting position. Maximal speed and generated force were the most relevant variables for grouping athletes. Despite some limitations, these results suggest that the cluster analysis can be used to discriminate athletes according to their performance.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-06-01 |