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Novel Strength Test Battery to Permit Evidence-Based Paralympic Classification
Ordinal-scale strength assessment methods currently used in Paralympic athletics classification prevent the development of evidence-based classification systems. This study evaluated a battery of 7, ratio-scale, isometric tests with the aim of facilitating the development of evidence-based methods o...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4602423/ https://www.ncbi.nlm.nih.gov/pubmed/25068950 http://dx.doi.org/10.1097/MD.0000000000000031 |
Sumario: | Ordinal-scale strength assessment methods currently used in Paralympic athletics classification prevent the development of evidence-based classification systems. This study evaluated a battery of 7, ratio-scale, isometric tests with the aim of facilitating the development of evidence-based methods of classification. This study aimed to report sex-specific normal performance ranges, evaluate test–retest reliability, and evaluate the relationship between the measures and body mass. Body mass and strength measures were obtained from 118 participants—63 males and 55 females—ages 23.2 years ± 3.7 (mean ± SD). Seventeen participants completed the battery twice to evaluate test–retest reliability. The body mass–strength relationship was evaluated using Pearson correlations and allometric exponents. Conventional patterns of force production were observed. Reliability was acceptable (mean intraclass correlation = 0.85). Eight measures had moderate significant correlations with body size (r = 0.30–61). Allometric exponents were higher in males than in females (mean 0.99 vs 0.30). Results indicate that this comprehensive and parsimonious battery is an important methodological advance because it has psychometric properties critical for the development of evidence-based classification. Measures were interrelated with body size, indicating further research is required to determine whether raw measures require normalization in order to be validly applied in classification. |
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