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Effect of gradient on cycling gross efficiency and technique
Marco Arkesteijn1, Simon A Jobson, James Hopker
1School of Sport and Exercise Sciences, University of Kent, Kent, England, UK. marco.arkesteijn@gmail.com
Medicine and Science in Sports and Exercise
|December 19, 2012
Summary
Uphill cycling significantly decreases cycling gross efficiency and alters pedaling technique. Increased gradients lead to earlier muscle activation and greater force effectiveness during the pedal stroke.
Area of Science:
- Exercise Physiology
- Biomechanics
- Sports Science
Background:
- Understanding the biomechanical and physiological responses to cycling on gradients is crucial for optimizing performance and training.
- Pedaling technique, including force application and muscle activation patterns, significantly influences cycling efficiency.
Purpose of the Study:
- To investigate the impact of varying gradients on cycling gross efficiency.
- To analyze the effects of gradient on pedaling technique, including force effectiveness and muscle activation timing.
Main Methods:
- Eighteen trained cyclists completed cycling trials on a treadmill at 0%, 4%, and 8% gradients across three cadences (60, 75, 90 rpm).
- Measurements included cycling gross efficiency, index of pedal force effectiveness (IFE), dead center size (DC), and electromyography of eight leg muscles.
Main Results:
- Cycling gross efficiency decreased significantly at an 8% gradient compared to 0% and 4% gradients.
- Index of pedal force effectiveness (IFE) and dead center size (DC) increased at an 8% gradient, indicating altered force production.
- Muscle activation onset occurred earlier with increasing gradient, particularly in the vastus lateralis, vastus medialis, gastrocnemius lateralis, and gastrocnemius medialis, with increased overall muscle activity.
Conclusions:
- Uphill cycling negatively affects cycling gross efficiency.
- Pedaling technique is modified by gradient, characterized by changes in force application and muscle activation patterns.
- These findings highlight the importance of considering gradient in cycling performance analysis and training strategies.
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