Seasonal variations of the relationships between measures of training monotony and strain in professional soccer players

• Published in: Scientific reports 2022-06, Vol.12 (1), p.10930-10930, Article 10930
• Main Authors: Nobari, Hadi, Martins, Alexandre Duarte, Oliveira, Rafael, Ardigò, Luca Paolo
• Format: Article
• Language: English
• Subjects: 639/166; 639/301; 692/308; Adult; Correlation analysis; Correlation of Data; Humanities and Social Sciences; Humans; Male; multidisciplinary; Physical Exertion; Physical training; Science; Science (multidisciplinary); Seasonal variations; Seasons; Soccer; Training; Upper Extremity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-15278-4

The purposes of this study were (a) to determine the variations in internal and external measures of training monotony (TM) and strain (TS) in professional soccer players according to periods of the season and playing positions, and (b) to analyze the relationships between internal and external measures of TM and TS. Twenty male professional players (age = 29.4 ± 4.4 years) were followed for 20 weeks through session rating of perceived exertion (s-RPE), total distance (TD), high-speed running distance (HSRD) and sprint distance (SpD). Regardless of measure, highest mean TM and TS scores were observed in mid-season and end-season. In general, wingers and strikers tended to have greater values in TM. Midfielders exhibited greater TS of TD and SpD. Correlation results for TM revealed that s-RPE was positively associated with SpD in early-season (r = 0.608) and negatively associated in mid-season (r = − 0.506). Regarding the TS, result demonstrated that s-RPE is negatively associated with HSRD in early-season (r = − 0.464) and positively associated in mid-season (r = 0.476). In general, there different meanings in correlations between internal and external measures across the season. On the one hand, our findings highlighted that TM and TS of professional soccer players is sensitive to period of the season and player’s position, but on other hand, correlation analyses proved that changes in one external/internal measure does not cause changes in another external/internal measure which support the constant monitoring of these values across the season.