ArticleMedicine and science in sports and exercise2020
Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners.
Article in Medicine and science in sports and exercise, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Changes in Cardiorespiratory Fitness Following Exercise Training Prescribed Relative to Traditional Intensity Anchors and Physiological Thresholds: A Systematic Review with Meta-analysis of Individual Participant Data.Sports medicine (Auckland, N.Z.) · 2025Pooled it
- Field-based tests for determining critical speed among runners and its practical application: a systematic review.Frontiers in sports and active living · 2025Pooled it
- Breaking Barriers: What is the Probability of Achieving Mile World Records in the Near Future?Sports medicine (Auckland, N.Z.) · 2026Article
- RACLET: the Ramp Above Critical Level Endurance Test to evaluate critical velocity in running.European journal of applied physiology · 2026Article
- 35 Years of Joyner's Endurance Performance Model: Assessing the Contribution of Physiological Determinants of Performance Proxies in 888 Individuals from Recreational to World Class.Sports medicine (Auckland, N.Z.) · 2026Article
- The Measurement and Application of Critical Speed and D' in Running: A Scoping Review.Sports medicine (Auckland, N.Z.) · 2026Article
- Emerging sports footwear technologies and their effects on running economy, biomechanics, and performance: a systematic review.BMC sports science, medicine & rehabilitation · 2026Article
- Durability as an index of endurance exercise performance: Methodological considerations.Experimental physiology · 2025Review
- Prolonged running reduces speed at the moderate-to-heavy intensity transition without additional reductions due to increased eccentric load.European journal of applied physiology · 2025Article
- Influence of circadian preference, sleep inertia and their interaction on marathon completion time: A retrospective, cross-sectional investigation of a large mass-participation city marathon.Journal of sleep research · 2025Article
- The Training Intensity Distribution of Marathon Runners Across Performance Levels.Sports medicine (Auckland, N.Z.) · 2025Article
- Performance prediction equation for the Valencia Marathon based on time and pacing in the half marathon.Frontiers in physiology · 2025Article
- Pacing strategies in marathons: A systematic review.Heliyon · 2024Article
- A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon.Entropy (Basel, Switzerland) · 2023Article
- Variability in exercise tolerance and physiological responses to exercise prescribed relative to physiological thresholds and to maximum oxygen uptake.Experimental physiology · 2023Article
- Interaction of Factors Determining Critical Power.Sports medicine (Auckland, N.Z.) · 2023Review
- Decoupling of Internal and External Workload During a Marathon: An Analysis of Durability in 82,303 Recreational Runners.Sports medicine (Auckland, N.Z.) · 2022Article
- Connected model to optimize performance.Frontiers in sports and active living · 2022Article
- Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners.User modeling and user-adapted interaction · 2022Article
- Influence of advanced shoe technology on the top 100 annual performances in men's marathon from 2015 to 2019.Scientific reports · 2021Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCritical 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.
methodsWe 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.
resultsThe 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.
conclusionThis study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.
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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.