UZH-Logo

Personal best times in an olympic distance triathlon and a marathon predict an ironman race time for recreational female triathletes


Rüst, Christoph Alexander; Knechtle, Beat; Wirth, Andrea; Knechtle, Patrizia; Ellenrieder, Birte; Rosemann, Thomas; Lepers, Romuald (2012). Personal best times in an olympic distance triathlon and a marathon predict an ironman race time for recreational female triathletes. Chinese Journal of Physiology, 55(3):156-162.

Abstract

"The aim of this study was to investigate whether the characteristics of anthropometry, training or previous performance were related to an Ironman race time in recreational female Ironman triathletes. These characteristics were correlated to an Ironman race time for 53 recreational female triathletes in order to determine the predictor variables, and so be able to predict an Ironman race time for future novice triathletes. In the bi-variate analysis, no anthropometric characteristic was related to race time. The weekly cycling kilometers (r = -0.35) and hours (r = -0.32), as well as the personal best time in an Olympic distance triathlon (r = 0.49) and in a marathon (r = 0.74) were related to an Ironman race time (< 0.05). Stepwise multiple regressions showed that both the personal best time in an Olympic distance triathlon ( P = 0.0453) and in a marathon (P = 0.0030) were the best predictors for the Ironman race time (n = 28, r² = 0.53). The race time in an Ironman triathlon might be partially predicted by the following equation (r² = 0.53, n = 28): Race time (min) = 186.3 + 1.595 × (personal best time in an Olympic distance triathlon, min) + 1.318 × (personal best time in a marathon, min) for recreational female Ironman triathletes."

"The aim of this study was to investigate whether the characteristics of anthropometry, training or previous performance were related to an Ironman race time in recreational female Ironman triathletes. These characteristics were correlated to an Ironman race time for 53 recreational female triathletes in order to determine the predictor variables, and so be able to predict an Ironman race time for future novice triathletes. In the bi-variate analysis, no anthropometric characteristic was related to race time. The weekly cycling kilometers (r = -0.35) and hours (r = -0.32), as well as the personal best time in an Olympic distance triathlon (r = 0.49) and in a marathon (r = 0.74) were related to an Ironman race time (< 0.05). Stepwise multiple regressions showed that both the personal best time in an Olympic distance triathlon ( P = 0.0453) and in a marathon (P = 0.0030) were the best predictors for the Ironman race time (n = 28, r² = 0.53). The race time in an Ironman triathlon might be partially predicted by the following equation (r² = 0.53, n = 28): Race time (min) = 186.3 + 1.595 × (personal best time in an Olympic distance triathlon, min) + 1.318 × (personal best time in a marathon, min) for recreational female Ironman triathletes."

Citations

6 citations in Web of Science®
11 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

2 downloads since deposited on 17 Aug 2012
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of General Practice
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2012
Deposited On:17 Aug 2012 07:24
Last Modified:05 Apr 2016 15:56
Publisher:Chinese Physiological Society
ISSN:0304-4920
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:10.4077/CJP.2012.BAA014
PubMed ID:22784279
Permanent URL: http://doi.org/10.5167/uzh-64257

Download

[img]
Content: Published Version
Filetype: PDF - Registered users only
Size: 71kB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations