Evidence map›Paper›PMID 26346992›Full record

ReviewOpen access journal of sports medicine2015

Variables that influence Ironman triathlon performance - what changed in the last 35 years?

Beat Knechtle, Raphael Knechtle, Michael Stiefel, Matthias Alexander Zingg, Thomas Rosemann, 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 18 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 3 pooled it
–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

18 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Effects of Training on Cardiorespiratory Fitness in Triathletes: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2021
    Pooled it
  3. HRV-Based Training for Improving VOInternational journal of environmental research and public health · 2020
    Pooled it
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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
  12. Article
  13. Article
  14. Cut-Off Values in the Prediction of Success in Olympic Distance Triathlon.International journal of environmental research and public health · 2020
    Article
  15. The Age-Related Performance Decline in Ironman 70.3.International journal of environmental research and public health · 2020
    Article
  16. Article
  17. Article
  18. 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

6 authors.

Beat KnechtleGesundheitszentrum St Gallen, St Gallen, Switzerland ; Institute of Primary Care, University of Zurich, Zurich, Switzerland.
Raphael KnechtleInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Michael StiefelInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Matthias Alexander ZinggInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Thomas RosemannInstitute 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 findings for Ironman triathlon performance and intends to determine potential predictor variables for Ironman race performance in female and male triathletes.

methodsA literature search was performed in PubMed using the terms "Ironman", "triathlon", and "performance". All resulting articles were searched for related citations.

resultsAge, previous experience, sex, training, origin, anthropometric and physiological characteristics, pacing, and performance in split disciplines were predictive. Differences exist between the sexes for anthropometric characteristics. The most important predictive variables for a fast Ironman race time were age of 30-35 years (women and men), a fast personal best time in Olympic distance triathlon (women and men), a fast personal best time in marathon (women and men), high volume and high speed in training where high volume was more important than high speed (women and men), low body fat, low skin-fold thicknesses and low circumference of upper arm (only men), and origin from the United States of America (women and men).

conclusionThese findings may help athletes and coaches to plan an Ironman triathlon career. Age and previous experience are important to find the right point in the life of a triathlete to switch from the shorter triathlon distances to the Ironman distance. Future studies need to correlate physiological characteristics such as maximum oxygen uptake with Ironman race time to investigate their potential predictive value and to investigate socio-economic aspects in Ironman triathlon.

Indexed as

agebody fatcyclingrunningsexswimming

Identifiers

PMID26346992
PMCPMC4556299

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.