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Submaximal force-velocity relationships during mountain ultramarathon: Data from the field
Clément Delhaye1, Pablo Rozier-Delgado1, Mylène Vonderscher1
1Inter-University Laboratory of Human Movement Sciences, University Savoie Mont Blanc, Chambéry, EA, France.
Journal of Sports Sciences
|October 13, 2024
Summary
This study introduces a new way to analyze mountain ultramarathon performance using Strava data, revealing key velocity-force relationship parameters that indicate athlete ability and fatigue during extreme races.
Area of Science:
- Sports Science
- Biomechanics
- Ultra-endurance Physiology
Background:
- The velocity-force (V(F)) relationship is crucial for understanding athletic performance.
- Evaluating this relationship in mountain ultra-endurance races presents unique challenges due to terrain and duration.
- Crowdsourced data offers a potential solution for large-scale analysis.
Purpose of the Study:
- To develop and validate a novel method for assessing the submaximal velocity-force (V(F)) relationship in mountain ultramarathon running.
- To characterize the V(F) profile of elite mountain ultramarathon runners using crowdsourced data.
- To investigate the impact of fatigue on the V(F) relationship during an ultra-endurance event.
Main Methods:
- Utilized crowdsourced GPS data from 408 participants of the 171-km UTMB® 2023 race.
- Segmented the race into 100-m intervals to compute mean net propulsive force and velocity.
- Modeled the submaximal V(F) relationship using a rational function with parameters F₁, V₀, and C.
Main Results:
- Established normative V(F) parameters: F₁ = 1.80 ± 0.33 N·kg⁻¹, V₀ = 2.36 ± 0.42 m·s⁻¹, and C = 0.66 ± 1.81.
- Identified that top-performing athletes exhibit higher F₁, V₀, and C values.
- Observed significant decreases in V(F) parameters due to fatigue during the race (20.9% to 59.8% reduction).
Conclusions:
- The V(F) relationship provides an original and effective approach to studying performance in mountain ultra-endurance.
- The characterized V(F) parameters can serve as indicators of athlete capability and fatigability.
- Crowdsourced data analysis is a viable method for large-scale physiological research in extreme sports.
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