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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. / Smyth, Barry; Muniz, Daniel.

In: Medicine and Science in Sports and Exercise, Vol. 52, No. 12, 2412, 01.12.2020, p. 2637-2645.

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@article{c2cc44927a3342ff842dcff5c85de0d5,
title = "Calculation of critical speed from raw training data in recreational marathon runners",
abstract = "INTRODUCTION: 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 recreational athletes before 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, and 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 ± SD: R = 0.9999 ± 0.0001). The estimated values of CS from all models were not different (3.74 ± 0.08 m·s), and all models correlated with marathon performance (R = 0.672 ± 0.036, error = 8.01% ± 0.51%). CS from the model including 400, 800, and 5000 m best predicted performance (R = 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 to 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.",
keywords = "EXERCISE, PERFORMANCE, PREDICTION, RUNNING",
author = "Barry Smyth and Daniel Muniz",
note = "{\textcopyright} 2020 the Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CC BY-NC-ND - https://creativecommons.org/licenses/by-nc-nd/4.0/), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.",
year = "2020",
month = dec,
day = "1",
doi = "10.1249/MSS.0000000000002412",
language = "English",
volume = "52",
pages = " 2637--2645",
journal = "Medicine and Science in Sports and Exercise",
issn = "0195-9131",
publisher = "Lippincott Williams and Wilkins",
number = "12",

}

RIS

TY - JOUR

T1 - Calculation of critical speed from raw training data in recreational marathon runners

AU - Smyth, Barry

AU - Muniz, Daniel

N1 - © 2020 the Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CC BY-NC-ND - https://creativecommons.org/licenses/by-nc-nd/4.0/), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - INTRODUCTION: 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 recreational athletes before 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, and 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 ± SD: R = 0.9999 ± 0.0001). The estimated values of CS from all models were not different (3.74 ± 0.08 m·s), and all models correlated with marathon performance (R = 0.672 ± 0.036, error = 8.01% ± 0.51%). CS from the model including 400, 800, and 5000 m best predicted performance (R = 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 to 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.

AB - INTRODUCTION: 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 recreational athletes before 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, and 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 ± SD: R = 0.9999 ± 0.0001). The estimated values of CS from all models were not different (3.74 ± 0.08 m·s), and all models correlated with marathon performance (R = 0.672 ± 0.036, error = 8.01% ± 0.51%). CS from the model including 400, 800, and 5000 m best predicted performance (R = 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 to 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.

KW - EXERCISE

KW - PERFORMANCE

KW - PREDICTION

KW - RUNNING

UR - http://www.scopus.com/inward/record.url?scp=85096203042&partnerID=8YFLogxK

U2 - 10.1249/MSS.0000000000002412

DO - 10.1249/MSS.0000000000002412

M3 - Article

C2 - 32472926

VL - 52

SP - 2637

EP - 2645

JO - Medicine and Science in Sports and Exercise

JF - Medicine and Science in Sports and Exercise

SN - 0195-9131

IS - 12

M1 - 2412

ER -