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

Hadi Nobari, Ana Ruivo Alves, Hamed Haghighi, Filipe Manuel Clemente, Jorge Carlos-Vivas, Jorge Pérez-Gómez and Luca Paolo Ardigò
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Hadi Nobari: Department of Physical Education and Sports, University of Granada, 18010 Granada, Spain
Ana Ruivo Alves: Department of Arts, Humanities and Sports, Polytechnic Institute of Beja, School of Education, 7800-295 Beja, Portugal
Hamed Haghighi: Department of Sport Injuries and Corrective Exercises, Faculty of Sport Sciences, University of Isfahan, Isfahan 81746-7344, Iran
Filipe Manuel Clemente: Polytechnic Institute of Viana do Castelo, School of Sport and Leisure, 4900-347 Viana do Castelo, Portugal
Jorge Carlos-Vivas: HEME Research Group, Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain
Jorge Pérez-Gómez: HEME Research Group, Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain
Luca Paolo Ardigò: Department of Neurosciences, Biomedicine and Movement Sciences, School of Exercise and Sport Science, University of Verona, 37134 Verona, Italy

IJERPH, 2021, vol. 18, issue 9, 1-14

Abstract: 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.

Keywords: athlete monitoring; fatigue; football; performance; psychological; soreness; sports training; team sports; young (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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