Document Detail


Distribution of power output during cycling: impact and mechanisms.
MedLine Citation:
PMID:  17645369     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We aim to summarise the impact and mechanisms of work-rate pacing during individual cycling time trials (TTs). Unlike time-to-exhaustion tests, a TT provides an externally valid model for examining how an initial work rate is chosen and maintained by an athlete during self-selected exercise. The selection and distribution of work rate is one of many factors that influence cycling speed. Mathematical models are available to predict the impact of factors such as gradient and wind velocity on cycling speed, but only a few researchers have examined the inter-relationships between these factors and work-rate distribution within a TT. When environmental conditions are relatively stable (e.g. in a velodrome) and the TT is >10 minutes, then an even distribution of work rate is optimal. For a shorter TT (< or = 10 minutes), work rate should be increased during the starting effort because this proportion of total race time is significant. For a very short TT (< or = 2 minutes), the starting effort should be maximal, since the time saved during the starting phase is predicted to outweigh any time lost during the final metres because of fatigue. A similar 'time saving' rationale underpins the advice that work rate should vary in parallel with any changes in gradient or wind speed during a road TT. Increasing work rate in headwind and uphill sections, and vice versa, decreases the variability in speed and, therefore, the total race time. It seems that even experienced cyclists naturally select a supraoptimal work rate at the start of a longer TT. Whether such a start can be 'blunted' through coaching or the monitoring of psychophysiological variables is unknown. Similarly, the extent to which cyclists can vary and monitor work rate during a TT is unclear. There is evidence that sub-elite cyclists can vary work rate by +/-5% the average for a TT lasting 25-60 minutes, but such variability might be difficult with high-performance cyclists whose average work rate during a TT is already extremely high (>350 watts). During a TT, pacing strategy is regulated in a complex anticipatory system that monitors afferent feedback from various physiological systems, and then regulates the work rate so that potentially limiting changes do not occur before the endpoint of exercise is reached. It is critical that the endpoint of exercise is known by the cyclist so that adjustments to exercise work rate can be made within the context of an estimated finish time. Pacing strategies are thus the consequence of complex regulation and serve a dual role: they are both the result of homeostatic regulation by the brain, as well as being the means by which such regulation is achieved. The pacing strategy 'algorithm' is sited in the brain and would need afferent input from interoceptors, such as heart rate and respiratory rate, as well as exteroceptors providing information on local environmental conditions. Such inputs have been shown to induce activity in the thalamus, hypothalamus and the parietal somatosensory cortex. Knowledge of time, modulated by the cerebellum, basal ganglia and primary somatosensory cortex, would also input to the pacing algorithm as would information stored in memory about previous similar exercise bouts. How all this information is assimilated by the different regions of the brain is not known at present.
Authors:
Greg Atkinson; Oliver Peacock; Alan St Clair Gibson; Ross Tucker
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Publication Detail:
Type:  Journal Article; Review    
Journal Detail:
Title:  Sports medicine (Auckland, N.Z.)     Volume:  37     ISSN:  0112-1642     ISO Abbreviation:  Sports Med     Publication Date:  2007  
Date Detail:
Created Date:  2007-07-24     Completed Date:  2007-11-06     Revised Date:  2008-11-21    
Medline Journal Info:
Nlm Unique ID:  8412297     Medline TA:  Sports Med     Country:  New Zealand    
Other Details:
Languages:  eng     Pagination:  647-67     Citation Subset:  IM    
Affiliation:
School of Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, England. G.Atkinson@ljmu.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Bicycling / physiology*
Brain / physiology
Energy Metabolism*
Fatigue
Humans
Nutritional Physiological Phenomena
Oxygen Consumption / physiology
Physical Endurance*
Physical Exertion / physiology*
Task Performance and Analysis
Time Factors
Work / physiology

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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