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Do non-elite older runners slow down more than younger runners in a 100 km ultra-marathon?

BACKGROUND: This study investigated changes in normalised running speed as a proxy for effort distribution over segments in male elite and age group 100 km ultra-marathoners with the assumption that older runners would slow down more than younger runners. METHODS: The annual ten fastest finishers (i...

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Detalles Bibliográficos
Autores principales: Rüst, Christoph A, Rosemann, Thomas, Zingg, Matthias A, Knechtle, Beat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430021/
https://www.ncbi.nlm.nih.gov/pubmed/25973205
http://dx.doi.org/10.1186/2052-1847-7-1
Descripción
Sumario:BACKGROUND: This study investigated changes in normalised running speed as a proxy for effort distribution over segments in male elite and age group 100 km ultra-marathoners with the assumption that older runners would slow down more than younger runners. METHODS: The annual ten fastest finishers (i.e. elite and age group runners) competing between 2000 and 2009 in the ‘100 km Lauf Biel’ were identified. Normalised average running speed (i.e. relative to segment 1 of the race corrected for gradient) was analysed as a proxy for pacing in elite and age group finishers. For each year, the ratio of the running speed from the final to the first segment for each age cohort was determined. These ratios were combined across years with the assumption that there were no ‘extreme’ wind events etc. which may have impacted the final relative to the first segment across years. The ratios between the age cohorts were compared using one-way ANOVA and Tukey’s post-hoc test. The ratios between elite and age group runners were investigated using one-way ANOVA with Dunnett’s multiple comparison post-hoc tests. The trend across age groups was investigated using simple regression analysis with age as the dependent variable. RESULTS: Normalised average running speed was different between age group 18–24 years and age groups 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59 and 65–69 years. Regression analysis showed no trend across age groups (r(2) = 0.003, p > 0.05). CONCLUSION: To summarize, (i) athletes in age group 18–24 years were slower than athletes in most other age groups and (ii) there was no trend of slowing down for older athletes.