Literature DB >> 27686573

Knowledge is power: Issues of measuring training and performance in cycling.

L Passfield1, J G Hopker1, S Jobson2, D Friel3, M Zabala4,5.   

Abstract

Mobile power meters provide a valid means of measuring cyclists' power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist's training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session's variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists' modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.

Keywords:  Modelling; cycling; endurance; performance; power output; training

Mesh:

Year:  2016        PMID: 27686573     DOI: 10.1080/02640414.2016.1215504

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  13 in total

1.  Influence of the Shod Condition on Running Power Output: An Analysis in Recreationally Active Endurance Runners.

Authors:  Diego Jaén-Carrillo; Luis E Roche-Seruendo; Alejandro Molina-Molina; Silvia Cardiel-Sánchez; Antonio Cartón-Llorente; Felipe García-Pinillos
Journal:  Sensors (Basel)       Date:  2022-06-26       Impact factor: 3.847

2.  Power Analysis of Field-Based Bicycle Motor Cross (BMX).

Authors:  Amin Daneshfar; Carl Petersen; Daniel Gahreman; Beat Knechtle
Journal:  Open Access J Sports Med       Date:  2020-07-10

Review 3.  The Critical Power Model as a Potential Tool for Anti-doping.

Authors:  Michael J Puchowicz; Eliran Mizelman; Assaf Yogev; Michael S Koehle; Nathan E Townsend; David C Clarke
Journal:  Front Physiol       Date:  2018-06-06       Impact factor: 4.566

4.  Considerations on the Assessment and Use of Cycling Performance Metrics and their Integration in the Athlete's Biological Passport.

Authors:  Paolo Menaspà; Chris R Abbiss
Journal:  Front Physiol       Date:  2017-11-09       Impact factor: 4.566

5.  Sprinting After Having Sprinted: Prior High-Intensity Stochastic Cycling Impairs the Winning Strike for Gold.

Authors:  Naroa Etxebarria; Steve A Ingham; Richard A Ferguson; David J Bentley; David B Pyne
Journal:  Front Physiol       Date:  2019-02-14       Impact factor: 4.566

6.  Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study.

Authors:  Andrea Zignoli; Alessandro Fornasiero; Matteo Ragni; Barbara Pellegrini; Federico Schena; Francesco Biral; Paul B Laursen
Journal:  PLoS One       Date:  2020-03-12       Impact factor: 3.240

7.  The Use of Online Training Tools in Competition Cyclists During COVID-19 Confinement in Spain.

Authors:  Antonio Moreno-Tenas; Eva León-Zarceño; Miguel Angel Serrano-Rosa
Journal:  Front Psychol       Date:  2021-03-18

8.  Anaerobic Power Assessment in Athletes: Are Cycling and Vertical Jump Tests Interchangeable?

Authors:  Micah Gross; Fabian Lüthy
Journal:  Sports (Basel)       Date:  2020-05-09

Review 9.  Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running.

Authors:  Diego Jaén-Carrillo; Luis E Roche-Seruendo; Antonio Cartón-Llorente; Rodrigo Ramírez-Campillo; Felipe García-Pinillos
Journal:  Sensors (Basel)       Date:  2020-11-13       Impact factor: 3.576

10.  New Procedure for the Kinematic and Power Analysis of Cyclists in Indoor Training.

Authors:  José Antonio Calvo; Carolina Álvarez-Caldas; José Luis San Román; Ramón Gutiérrez-Moizant
Journal:  Sensors (Basel)       Date:  2020-10-28       Impact factor: 3.576

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