Factors that Impact Self-reported Wellness Scores in Elite Australian Footballers

Relationship between Objective and Subjective Fatigue Monitoring Tests in Professional Soccer

Studying fatigue is challenging because it is influenced by physiological, psychological, and sociological states. Fatigue can be assessed objectively or subjectively, but the literature has difficulty understanding how an analytical test relates to a response via a questionnaire. Thus, the purpose of this study was to evaluate the relationships between objective fatigue variables (Squat Jump (SJ) and Countermovement Jump (CMJ)) measured on day-2 to the game and subjective fatigue (Rating Perceived Exertion (RPE) measured on day-3 to the game and Hooper Index (HI) measured on day-2). The sample comprised 32 professional football players from the First Portuguese League aged 25.86 ± 3.15 years. The Spearman correlations and regression analyses were used to study the relationships between the variables. The results showed statistically significant (p < 0.05) but small correlations (0.113−0.172) between several objective metrics and the subjective metrics evaluated. In addition, we found two weak models with statistical significance (p < 0.05) between the dependent objective variables (contact time, height, and elasticity index) and the HI (R2 = 3.7%) and RPE (R2 = 1.6%). Also, nine statistically significant (p < 0.05) but weak models were observed between the subjective dependent variables (HI and RPE) and contact time (R2 = 1.8−2.7%), flight time (R2 = 1.1−1.9%), height (R2 = 1.2−2.3%), power (R2 = 1.4%), pace (R2 = 1.2−2.1%), and elasticity index (R2 = 1.6%). In conclusion, objective and subjective fatigue-monitoring tests in professional soccer do not measure identical but rather complementary aspects of fatigue, and therefore, both need to be considered to gain a holistic perspective.

Analysing the predictive capacity and dose-response of wellness in load monitoring

This study aimed to identify the predictive capacity of wellness questionnaires on measures of training load using machine learning methods. The distributions of, and dose-response between, wellness and other load measures were also examined, offering insights into response patterns. Data (n= 14,109) were collated from an athlete management systems platform (Catapult Sports, Melbourne, Australia) and were split across three sports (cricket, rugby league and football) with data analysis conducted in R (Version 3.4.3). Wellness (sleep quality, readiness to train, general muscular soreness, fatigue, stress, mood, recovery rating and motivation) as the dependent variable, and sRPE, sRPE-TL and markers of external load (total distance and m.min^-1) as independent variables were included for analysis. Classification and regression tree models showed high cross-validated error rates across all sports (i.e., > 0.89) and low model accuracy (i.e., < 5% of variance explained by each model) with similar results demonstrated using random forest models. These results suggest wellness items have limited predictive capacity in relation to internal and external load measures. This result was consistent despite varying statistical approaches (regression, classification and random forest models) and transformation of wellness scores. These findings indicate practitioners should exercise caution when interpreting and applying wellness responses.