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Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics

The aim of this study was to assess the sensitivity of accelerometer-derived metrics for monitoring fatigue during an intermittent exercise protocol. Fifteen university students were enrolled in the study (age 20 ± 1 years). A submaximal intermitted recovery test (Sub-IRT) with a duration of 6 min a...

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Autores principales: Beato, Marco, De Keijzer, Kevin L., Carty, Benjamin, Connor, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606691/
https://www.ncbi.nlm.nih.gov/pubmed/31293447
http://dx.doi.org/10.3389/fphys.2019.00780
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author Beato, Marco
De Keijzer, Kevin L.
Carty, Benjamin
Connor, Mark
author_facet Beato, Marco
De Keijzer, Kevin L.
Carty, Benjamin
Connor, Mark
author_sort Beato, Marco
collection PubMed
description The aim of this study was to assess the sensitivity of accelerometer-derived metrics for monitoring fatigue during an intermittent exercise protocol. Fifteen university students were enrolled in the study (age 20 ± 1 years). A submaximal intermitted recovery test (Sub-IRT) with a duration of 6 min and 30 s (drill 1) was performed. In order to increase the participants’ fatigue, after that, a repeated sprint protocol (1×6 maximal 20 m sprints) was performed. Following that, participants repeated the Sub-IRT (drill 2) to evaluate the external and internal training load (TL) variations related to fatigue. Apex 10 Hz global navigation satellite system (GNSS) units were used to collect the variables total distance (TD), high metabolic distance (HMD), relative velocity (RV), average metabolic power (MP), heart rate maximal (HRmax) and mean (HRmean), muscular (RPEmus) and respiratory rating of perceived exertion (RPEres), dynamic stress load (DSL), and fatigue index (FI). A Bayesian statistical approach was used. A likelihood difference (between drill 1 and drill 2) was found for the following parameters: TD (BF(10) = 0.33, moderate per H(0)), HMD (BF(10) = 1.3, anecdotal), RV (BF(10) = 0.29, moderate per H(0)), MP (BF(10) = 1.3, anecdotal), accelerations (BF(10) = 1.6, anecdotal ), FI (BF(10) = 4.7, moderate), HRmax (BF(10) = 2.2, anecdotal), HRmean (BF(10) = 4.3, moderate), RPEmus (BF(10) = 11.6, strong), RPEres (BF(10) = 3.1, moderate), DSL (BF(10) = 5.7, moderate), and DSL•m(−1) (BF(10) = 4.3, moderate). In conclusion, this study reports that DSL, DSL•m(−1), and FI can be valid metrics to monitor fatigue related to movement strategy during a standardized submaximal intermittent exercise protocol.
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spelling pubmed-66066912019-07-10 Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics Beato, Marco De Keijzer, Kevin L. Carty, Benjamin Connor, Mark Front Physiol Physiology The aim of this study was to assess the sensitivity of accelerometer-derived metrics for monitoring fatigue during an intermittent exercise protocol. Fifteen university students were enrolled in the study (age 20 ± 1 years). A submaximal intermitted recovery test (Sub-IRT) with a duration of 6 min and 30 s (drill 1) was performed. In order to increase the participants’ fatigue, after that, a repeated sprint protocol (1×6 maximal 20 m sprints) was performed. Following that, participants repeated the Sub-IRT (drill 2) to evaluate the external and internal training load (TL) variations related to fatigue. Apex 10 Hz global navigation satellite system (GNSS) units were used to collect the variables total distance (TD), high metabolic distance (HMD), relative velocity (RV), average metabolic power (MP), heart rate maximal (HRmax) and mean (HRmean), muscular (RPEmus) and respiratory rating of perceived exertion (RPEres), dynamic stress load (DSL), and fatigue index (FI). A Bayesian statistical approach was used. A likelihood difference (between drill 1 and drill 2) was found for the following parameters: TD (BF(10) = 0.33, moderate per H(0)), HMD (BF(10) = 1.3, anecdotal), RV (BF(10) = 0.29, moderate per H(0)), MP (BF(10) = 1.3, anecdotal), accelerations (BF(10) = 1.6, anecdotal ), FI (BF(10) = 4.7, moderate), HRmax (BF(10) = 2.2, anecdotal), HRmean (BF(10) = 4.3, moderate), RPEmus (BF(10) = 11.6, strong), RPEres (BF(10) = 3.1, moderate), DSL (BF(10) = 5.7, moderate), and DSL•m(−1) (BF(10) = 4.3, moderate). In conclusion, this study reports that DSL, DSL•m(−1), and FI can be valid metrics to monitor fatigue related to movement strategy during a standardized submaximal intermittent exercise protocol. Frontiers Media S.A. 2019-06-26 /pmc/articles/PMC6606691/ /pubmed/31293447 http://dx.doi.org/10.3389/fphys.2019.00780 Text en Copyright © 2019 Beato, De Keijzer, Carty and Connor. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Beato, Marco
De Keijzer, Kevin L.
Carty, Benjamin
Connor, Mark
Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics
title Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics
title_full Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics
title_fullStr Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics
title_full_unstemmed Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics
title_short Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics
title_sort monitoring fatigue during intermittent exercise with accelerometer-derived metrics
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606691/
https://www.ncbi.nlm.nih.gov/pubmed/31293447
http://dx.doi.org/10.3389/fphys.2019.00780
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