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A battery of strength tests for evidence-based classification in Para swimming.

Luke W HogarthVaughan NicholsonJemima G SpathisSean M TweedyEmma M BeckmanMark J ConnickPeter van de VlietCarl J PaytonBrendan J Burkett
Published in: Journal of sports sciences (2018)
This study examined the validity of isometric strength tests for evidence-based classification in Para swimming. Thirty non-disabled participants and forty-two Para swimmers with physical impairment completed an isometric strength test battery designed to explain activity limitation in the freestyle discipline. Measures pertaining to dominant and non-dominant limb strength and symmetry were derived from four strength tests that were found to be reliable in a cohort of non-disabled participants (ICC = 0.85-0.97; CV = 6.4-9.1%). Para swimmers had lower scores in strength tests compared with non-disabled participants (d = 0.14-1.00) and the strength test battery successfully classified 95% of Para swimmers with physical impairment using random forest algorithm. Most of the strength measures had low to moderate correlations (r = 0.32 to 0.53; p ≤ 0.05) with maximal freestyle swim speed in Para swimmers. Although, fewer correlations were found when Para swimmers with hypertonia or impaired muscle power were analysed independently, highlighting the impairment-specific nature of activity limitation in Para swimming. Collectively, the strength test battery has utility in Para swimming classification to infer loss of strength in Para swimmers, guide minimum eligibility criteria, and to define the impact that strength impairment has on Para swimming performance.
Keyphrases
  • machine learning
  • deep learning
  • body composition
  • solid state