ReviewSports medicine - open2021
Using Field Based Data to Model Sprint Track Cycling Performance.
Review in Sports medicine - open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed.
- Machine Learning-Based Prediction of Track Sprint Cycling Performance in Elite-Level Male Cyclists: A Multinational Cross-Sectional Analysis.Sports medicine - open · 2026Article
- Effects of inspiratory muscle Pre-activation on 100 m sprint performance in physically active university students.Frontiers in sports and active living · 2026Article
- Integrative Physiological Strategies for Monitoring Demands in Functional Fitness.Sports (Basel, Switzerland) · 2025Review
- Performance analysis and mechanical determinants of the opening lap of the team sprint in elite-level track cycling.European journal of sport science · 2024Article
- AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets.Sensors (Basel, Switzerland) · 2023Article
- Article
- Article
- Power profiling and the power-duration relationship in cycling: a narrative review.European journal of applied physiology · 2022Review
- Reply to: Comment on: "Using Field Based Data to Model Sprint Track Cycling Performance".Sports medicine - open · 2021Article
- Comment on: "Using Field Based Data to Model Sprint Track Cycling Performance".Sports medicine - open · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cycling performance models are used to study rider and sport characteristics to better understand performance determinants and optimise competition outcomes. Performance requirements cover the demands of competition a cyclist may encounter, whilst rider attributes are physical, technical and psychological characteristics contributing to performance. Several current models of endurance-cycling enhance understanding of performance in road cycling and track endurance, relying on a supply and demand perspective. However, they have yet to be developed for sprint-cycling, with current athlete preparation, instead relying on measures of peak-power, speed and strength to assess performance and guide training. Peak-power models do not adequately explain the demands of actual competition in events over 15-60 s, let alone, in World-Championship sprint cycling events comprising several rounds to medal finals. Whilst there are no descriptive studies of track-sprint cycling events, we present data from physiological interventions using track cycling and repeated sprint exercise research in multiple sports, to elucidate the demands of performance requiring several maximal sprints over a competition. This review will show physiological and power meter data, illustrating the role of all energy pathways in sprint performance. This understanding highlights the need to focus on the capacity required for a given race and over an event, and therefore the recovery needed for each subsequent race, within and between races, and how optimal pacing can be used to enhance performance. We propose a shift in sprint-cyclist preparation away from training just for peak power, to a more comprehensive model of the actual event demands.
Identifiers
What OpenQuestion holds
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.