| Title |
What predicts performance in ultra-triathlon races? – a comparison between Ironman distance triathlon and ultra-triathlon
|
|---|---|
| Published in |
Open Access Journal of Sports Medicine, May 2015
|
| DOI | 10.2147/oajsm.s79273 |
| Pubmed ID | |
| Authors |
Beat Knechtle, Matthias Alexander Zingg, Thomas Rosemann, Michael Stiefel, Christoph Alexander Rüst |
| Abstract |
This narrative review summarizes recent intentions to find potential predictor variables for ultra-triathlon race performance (ie, triathlon races longer than the Ironman distance covering 3.8 km swimming, 180 km cycling, and 42.195 km running). Results from studies on ultra-triathletes were compared to results on studies on Ironman triathletes. A literature search was performed in PubMed using the terms "ultra", "triathlon", and "performance" for the aspects of "ultra-triathlon", and "Ironman", "triathlon", and "performance" for the aspects of "Ironman triathlon". All resulting papers were searched for related citations. Results for ultra-triathlons were compared to results for Ironman-distance triathlons to find potential differences. Athletes competing in Ironman and ultra-triathlon differed in anthropometric and training characteristics, where both Ironmen and ultra-triathletes profited from low body fat, but ultra-triathletes relied more on training volume, whereas speed during training was related to Ironman race time. The most important predictive variables for a fast race time in an ultra-triathlon from Double Iron (ie, 7.6 km swimming, 360 km cycling, and 84.4 km running) and longer were male sex, low body fat, age of 35-40 years, extensive previous experience, a fast time in cycling and running but not in swimming, and origins in Central Europe. Any athlete intending to compete in an ultra-triathlon should be aware that low body fat and high training volumes are highly predictive for overall race time. Little is known about the physiological characteristics of these athletes and about female ultra-triathletes. Future studies need to investigate anthropometric and training characteristics of female ultra-triathletes and what motivates women to compete in these races. Future studies need to correlate physiological characteristics such as maximum oxygen uptake (VO2max) with ultra-triathlon race performance in order to investigate whether these characteristics are also predictive for ultra-triathlon race performance. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 14% |
| Canada | 1 | 14% |
| United States | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 2 | 29% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Chile | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 102 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 26 | 25% |
| Student > Master | 19 | 18% |
| Student > Ph. D. Student | 10 | 10% |
| Other | 7 | 7% |
| Student > Postgraduate | 7 | 7% |
| Other | 14 | 13% |
| Unknown | 22 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Sports and Recreations | 38 | 36% |
| Medicine and Dentistry | 20 | 19% |
| Agricultural and Biological Sciences | 7 | 7% |
| Nursing and Health Professions | 4 | 4% |
| Biochemistry, Genetics and Molecular Biology | 4 | 4% |
| Other | 8 | 8% |
| Unknown | 24 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2017.
All research outputs
#6,206,206
of 22,805,349 outputs
Outputs from Open Access Journal of Sports Medicine
#109
of 250 outputs
Outputs of similar age
#73,569
of 264,354 outputs
Outputs of similar age from Open Access Journal of Sports Medicine
#4
of 9 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 250 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one has gotten more attention than average, scoring higher than 56% of its peers.
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We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.