Weekly Variations of Well-Being and Interactions with Training and Match Intensities: A Descriptive Case Study in Youth Male Soccer Players

Abstract

The aim of this study was two-fold: (i) analyze the weekly variations of well-being and training/match intensity measures in youth soccer players, and (ii) test relations between well-being and training intensity outcomes. The study followed a descriptive case study design. Twenty-seven under-17 male soccer players were monitored for well-being and training intensity parameters over seventeen consecutive weeks. An adjusted version of the Hooper questionnaire was used to monitor the perceptive sleep quality, readiness, fatigue, and delayed onset muscle soreness (DOMS) early in the morning. The CR-10 Borg’s scale was also used for monitoring the rate of perceived exertion (RPE) of players after training sessions. Repeated-measures analysis of variance was executed to test the between-week variations of both well-being and training intensity outcomes. Moreover, Pearson product moment correlation was used to test the relations between well-being and training intensity outcomes. Repeated measures ANOVA revealed significant differences between weeks in the sleep quality (F = 0.422; p < 0.001; ηp2 = 0.140), readiness (F = 0.8.734; p < 0.001; ηp2 = 0.251), fatigue (F = 4.484; p < 0.001; ηp2 = 0.147), DOMS (F = 3.775; p = 0.001; ηp2 = 0.127), RPE (F = 7.301; p < 0.001; ηp2 = 0.219), and session-RPE (F = 17.708; p < 0.001; ηp2 = 0.405). Correlations between well-being and training intensity outcomes in the same week revealed moderate correlations between fatigue and session-RPE (r = 0.325). As conclusions, it was found that well-being and training intensity fluctuates over the season, while well-being outcomes seems to be related with training intensity, although with a small magnitude.

Relationships between Internal Training Intensity and Well-Being Changes in Youth Football Players

“…Indeed, form our findings, it seems that with lower mTI (mean of the intensity of all sessions and match), higher values of sleep quality (bad sleep quality) are perceived by players. This finding is in concordance with the findings of a very recent study that suggested that higher TI levels are associated with greater levels of readiness and sleep quality [ 20 ]. As sleep quality has an enormous and determinant impact on athletes’ performance [ 30 ], ensuring a cautious planning of the imposed weekly TI measures, namely, TM, and mTI measures, can potentially improve the players’ sleep quality and readiness to train [ 20 ].…”

“…The magnitude of the between-week differences found for well-being measures at the early- and in-season-periods were similar to those reported in a recent study conducted on 27 under-17 football players [ 20 ]. Specifically, that study [ 20 ] revealed similar magnitudes for fatigue ( p < 0.001; η2 = 0.147) and for DOMS ( p = 0.001; η2 = 0.127) but not for sleep quality ( p < 0.001; η2p = 0.140), which had a small magnitude compared to the trivial magnitude found in the present study. However, our study followed a different design, i.e., 38 weeks were analyzed, while the aforementioned study [ 20 ] analyzed only 17 weeks, corresponding to the pre- and early-season.…”

“…Specifically, that study [ 20 ] revealed similar magnitudes for fatigue ( p < 0.001; η2 = 0.147) and for DOMS ( p = 0.001; η2 = 0.127) but not for sleep quality ( p < 0.001; η2p = 0.140), which had a small magnitude compared to the trivial magnitude found in the present study. However, our study followed a different design, i.e., 38 weeks were analyzed, while the aforementioned study [ 20 ] analyzed only 17 weeks, corresponding to the pre- and early-season. Still, another study conducted on 21 under-16 football players showed higher weekly variations for fatigue, stress, sleep quality, DOMS and hooper index (coefficient of variation [CV] ranging from −32.7% to 174% CV) during the in-season period [ 22 ].…”

“…A recent systematic review showed small-to-moderate associations between well-being measures and TI (based on s-RPE values) [ 19 ]. Indeed, a more recent study conducted on 27 under-17 football players [ 20 ], revealed that only small magnitude correlations were found between well-being and TI (based on RPE and s-RPE values) variations over 17 weeks. However, when considering some of the s-RPE related indices (TM and TS), moderate-to-large associations were found between well-being measures and TM and TS on youth football players [ 21 ].…”

Quantifying Athlete Wellness: Investigating the Predictive Potential of Subjective Wellness Reports Through a Player Monitoring System

“…Despite the widespread use of player monitoring systems, and the promising prospects of using machine learning to predict player wellness, there is currently a gap in research linking perceived wellness factors to individual player performance. While studies have investigated connections between wellness factors and various measures such as injuries (Sioud et al, 2023), training load (Moalla et al, 2016), and rate of perceived exertion (Silva et al, 2022), there is limited evidence supporting a causality between wellness and individual player performance. Despite the consensus that maintaining balance in wellness and psychological well-being can enhance elite athlete performance (e.g., Giles et al, 2020;McGuigan et al, 2020), more research is needed to evaluate any causal relationship between perceived wellness and objective match performance at the individual level.…”