ArticleNature communications2020
Human running performance from real-world big data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed.
- Built environment determinants of residents' running activity in Guangzhou, China: what are the nonlinear relationship and pathways?BMC public health · 2025Article
- Special endurance coefficients enable the evaluation of running performance.Scientific reports · 2025Article
- Smart Textiles for Personalized Sports and Healthcare.Nano-micro letters · 2025Review
- Evolution of world running record performances for men and women: physiological characteristics.Frontiers in physiology · 2024Article
- DVT: a high-throughput analysis pipeline for locomotion and social behavior in adult Drosophila melanogaster.Cell & bioscience · 2023Article
- The molecular athlete: exercise physiology from mechanisms to medals.Physiological reviews · 2023Review
- Relationships between training dose and record power outputs in professional road cyclists: insights and threats to validity.Biology of sport · 2023Article
- Sensitiveness of Variables Extracted from a Fitness Smartwatch to Detect Changes in Vertical Impact Loading during Outdoors Running.Sensors (Basel, Switzerland) · 2023Article
- How do world class top 5 Giro d'Italia finishers train? A qualitative multiple case study.Scandinavian journal of medicine & science in sports · 2022Article
- Marathon Performance Depends on Pacing Oscillations between Non Symmetric Extreme Values.International journal of environmental research and public health · 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
- Estimating the cost of training disruptions on marathon performance.Frontiers in sports and active living · 2022Article
- The Effect of Exercise Training Intensity on VOTranslational sports medicine · 2022Review
- Contribution of Solid Food to Achieve Individual Nutritional Requirement during a Continuous 438 km Mountain Ultramarathon in Female Athlete.International journal of environmental research and public health · 2021Article
- Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete.Frontiers in sports and active living · 2021Article
- Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data.Frontiers in physiology · 2021Article
- How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon.PloS one · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identification of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantified, pointing to an optimal training. Our findings hint at new ways to quantify and predict athletic performance under real-world conditions.
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Registered trials
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.