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Association between Training Load and Well-Being Measures in Young Soccer Players during a Season

This study aimed to analyze the correlations among weekly (w) acute workload (wAW), chronic workload (wCW), acute/chronic workload ratio (wACWR), training monotony (wTM), training strain (wTS), sleep quality (wSleep), delayed onset muscle soreness (wDOMS), fatigue (wFatigue), stress (wStress), and H...

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Autores principales: Nobari, Hadi, Alves, Ana Ruivo, Haghighi, Hamed, Clemente, Filipe Manuel, Carlos-Vivas, Jorge, Pérez-Gómez, Jorge, Ardigò, Luca Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122726/
https://www.ncbi.nlm.nih.gov/pubmed/33922250
http://dx.doi.org/10.3390/ijerph18094451
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author Nobari, Hadi
Alves, Ana Ruivo
Haghighi, Hamed
Clemente, Filipe Manuel
Carlos-Vivas, Jorge
Pérez-Gómez, Jorge
Ardigò, Luca Paolo
author_facet Nobari, Hadi
Alves, Ana Ruivo
Haghighi, Hamed
Clemente, Filipe Manuel
Carlos-Vivas, Jorge
Pérez-Gómez, Jorge
Ardigò, Luca Paolo
author_sort Nobari, Hadi
collection PubMed
description This study aimed to analyze the correlations among weekly (w) acute workload (wAW), chronic workload (wCW), acute/chronic workload ratio (wACWR), training monotony (wTM), training strain (wTS), sleep quality (wSleep), delayed onset muscle soreness (wDOMS), fatigue (wFatigue), stress (wStress), and Hooper index (wHI) in pre-, early, mid-, and end-of-season. Twenty-one elite soccer players (age: 16.1 ± 0.2 years) were monitored weekly on training load and well-being for 36 weeks. Higher variability in wAW (39.2%), wFatigue (84.4%), wStress (174.3%), and wHI (76.3%) at the end-of-season were reported. At mid-season, higher variations in wSleep (59.8%), TM (57.6%), and TS (111.1%) were observed. Moderate to very large correlations wAW with wDOMS (r = 0.617, p = 0.007), wFatigue, wStress, and wHI were presented. Similarly, wCW reported a meaningful large association with wDOMS (r = 0.526, p < 0.001); moderate to very large associations with wFatigue (r = 0.649, p = 0.005), wStress, and wHI. Moreover, wTM presented a large correlation with wSleep (r = 0.515, p < 0.001); and a negatively small association with wStress (r = −0.426, p = 0.003). wTS showed a small to large correlation with wSleep (r = 0.400, p = 0.005) and wHI; also, a large correlation with wDOMS (r = 0.556, p = 0.028) and a moderate correlation with wFatigue (r = 0.343, p = 0.017). Wellness status may be considered a useful tool to provide determinant elite players’ information to coaches and to identify important variations in training responses.
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spelling pubmed-81227262021-05-16 Association between Training Load and Well-Being Measures in Young Soccer Players during a Season Nobari, Hadi Alves, Ana Ruivo Haghighi, Hamed Clemente, Filipe Manuel Carlos-Vivas, Jorge Pérez-Gómez, Jorge Ardigò, Luca Paolo Int J Environ Res Public Health Article This study aimed to analyze the correlations among weekly (w) acute workload (wAW), chronic workload (wCW), acute/chronic workload ratio (wACWR), training monotony (wTM), training strain (wTS), sleep quality (wSleep), delayed onset muscle soreness (wDOMS), fatigue (wFatigue), stress (wStress), and Hooper index (wHI) in pre-, early, mid-, and end-of-season. Twenty-one elite soccer players (age: 16.1 ± 0.2 years) were monitored weekly on training load and well-being for 36 weeks. Higher variability in wAW (39.2%), wFatigue (84.4%), wStress (174.3%), and wHI (76.3%) at the end-of-season were reported. At mid-season, higher variations in wSleep (59.8%), TM (57.6%), and TS (111.1%) were observed. Moderate to very large correlations wAW with wDOMS (r = 0.617, p = 0.007), wFatigue, wStress, and wHI were presented. Similarly, wCW reported a meaningful large association with wDOMS (r = 0.526, p < 0.001); moderate to very large associations with wFatigue (r = 0.649, p = 0.005), wStress, and wHI. Moreover, wTM presented a large correlation with wSleep (r = 0.515, p < 0.001); and a negatively small association with wStress (r = −0.426, p = 0.003). wTS showed a small to large correlation with wSleep (r = 0.400, p = 0.005) and wHI; also, a large correlation with wDOMS (r = 0.556, p = 0.028) and a moderate correlation with wFatigue (r = 0.343, p = 0.017). Wellness status may be considered a useful tool to provide determinant elite players’ information to coaches and to identify important variations in training responses. MDPI 2021-04-22 /pmc/articles/PMC8122726/ /pubmed/33922250 http://dx.doi.org/10.3390/ijerph18094451 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nobari, Hadi
Alves, Ana Ruivo
Haghighi, Hamed
Clemente, Filipe Manuel
Carlos-Vivas, Jorge
Pérez-Gómez, Jorge
Ardigò, Luca Paolo
Association between Training Load and Well-Being Measures in Young Soccer Players during a Season
title Association between Training Load and Well-Being Measures in Young Soccer Players during a Season
title_full Association between Training Load and Well-Being Measures in Young Soccer Players during a Season
title_fullStr Association between Training Load and Well-Being Measures in Young Soccer Players during a Season
title_full_unstemmed Association between Training Load and Well-Being Measures in Young Soccer Players during a Season
title_short Association between Training Load and Well-Being Measures in Young Soccer Players during a Season
title_sort association between training load and well-being measures in young soccer players during a season
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122726/
https://www.ncbi.nlm.nih.gov/pubmed/33922250
http://dx.doi.org/10.3390/ijerph18094451
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