Evidence map›Paper›PMID 26056498›Full record

ReviewOpen access journal of sports medicine2015

What predicts performance in ultra-triathlon races? - a comparison between Ironman distance triathlon and ultra-triathlon.

Beat Knechtle, Matthias Alexander Zingg, Thomas Rosemann, Michael Stiefel, Christoph Alexander Rüst

Abstract readReview
In one paragraph

Review in Open access journal of sports medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in IronmanInternational journal of environmental research and public health · 2023
    Article
  10. Article
  11. Article
  12. Self-Selected Pacing During a World Record Attempt in 40 Ironman-Distance Triathlons in 40 Days.International journal of environmental research and public health · 2020
    Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Beat KnechtleInstitute of Primary Care, University of Zurich, Zurich, Switzerland ; Gesundheitszentrum St Gallen, St Gallen, Switzerland.
Matthias Alexander ZinggInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Thomas RosemannInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Michael StiefelInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Christoph Alexander RüstInstitute of Primary Care, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis 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.

methodsA 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.

resultsAthletes 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.

conclusionAny 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.

Indexed as

agecyclingexperiencerunningswimmingultra-endurance

Identifiers

PMID26056498
PMCPMC4445872

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.