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  5. Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners
 
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Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners

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Author(s)
Smyth, Barry 
Muniz-Pumares, Daniel 
Uri
http://hdl.handle.net/10197/12215
Date Issued
December 2020
Date Available
27T10:44:24Z May 2021
Abstract
ntroduction. Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons. Methods. We investigated running activities logged into an online fitness platform by >25,000 runners prior to big-city marathons. Each activity contained time, distance, and elevation every 100 m. We computed grade-adjusted pacing and the fastest pace recorded for a set of target distances (400, 800, 1000, 1500, 3000, 5000 m). CS was determined as the slope of the distance-time relationship using all combinations of, at least, three target distances. Results. The relationship between distance and time was linear, irrespective of the target distances used (pooled mean ± standard deviation: R2 = 0.9999±0.0001). T he estimated values of CS from all models were not different (3.74±0.08 m∙s-1), and all models correlated with marathon performance (R2 = 0.672±0.036, error = 8.01±0.51%). CS from the model including 400, 800 and 5000 m best predicted performance (R2 = 0.695, error = 7.67%), and was used in further analysis. Runners completed the marathon at 84.8±13.6% CS, with faster runners competing at speeds closer to CS (93.0 % CS for 150 min marathon times vs. 78.9% CS for 360 min marathon times). Runners who completed the first half of the marathon at >94% of their CS, and particularly faster than CS, were more likely slowdown by more than 25% in the second half of race. Conclusion. This study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Journal Article
Publisher
American College of Sports Medicine
Journal
Medicine & Science in Sports & Exercise
Volume
52
Issue
12
Start Page
2637
End Page
2645
Copyright (Published Version)
2020 the Authors
Keywords
  • Recommender systems

  • Exercise

  • Performance

  • Running

DOI
10.1249/MSS.0000000000002412
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
Scopus© citations
17
Acquisition Date
Feb 5, 2023
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