The Influence of Changes in Acute Training Load on Daily Sensitivity of Morning-Measured Fatigue Variables in Elite Soccer Players

Abstract

Purpose:To determine the sensitivity of a range of potential fatigue measures to daily training load accumulated over the previous 2, 3, and 4 d during a short in-season competitive period in elite senior soccer players (N = 10). Methods: Total highspeed-running distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), countermovement-jump height (CMJ), submaximal heart rate (HRex), postexercise heart-rate recovery (HRR), and heart-rate variability (HRV: Ln rMSSD) were analyzed during an in-season competitive period (17 d). General linear models were used to evaluate the influence of 2-, 3-, and 4-d total high-speed-running-distance accumulation on fatigue measures. Results: Fluctuations in perceived ratings of fatigue were correlated with fluctuations in total high-speed-running-distance accumulation covered on the previous 2 d (r = -.31; small), 3 d (r = -.42; moderate), and 4 d (r = -.28; small) (P < .05). Changes in HRex (r = .28; small; P = .02) were correlated with changes in 4-d total high-speed-running-distance accumulation only. Correlations between variability in muscle soreness, sleep quality, CMJ, HRR%, and HRV and total high-speed-running distance were negligible and not statistically significant for all accumulation training loads. Conclusions: Perceived ratings of fatigue and HRex were sensitive to fluctuations in acute total high-speed-running-distance accumulation, although sensitivity was not systematically influenced by the number of previous days over which the training load was accumulated. The present findings indicate that the sensitivity of morning-measured fatigue variables to changes in training load is generally not improved when compared with training loads beyond the previous day's training. Keywords: training, performance, wellness, recoveryThe locomotor demands of elite soccer have progressively increased in recent years. 1,2 Since leading teams are also required to compete in a high number of matches over the course of season, 3 implementation of effective recovery strategies are paramount to avoid the debilitating effects associated with overtraining and injury. 4 Increasing attention in the literature has therefore focused on evaluating the effectiveness of a range of monitoring tools that may serve as valid indicators of fatigue status of athletes. 5 For the purpose of this manuscript, fatigue will be defined as an inability to complete a task that was once achievable within a recent time frame. 6 Recent research has examined the sensitivity of potential measures of fatigue to daily fluctuations in training load in Australian Rules Football (AFL). 7,8 In AFL players, perceived ratings of wellness, 7,8 submaximal heart rate (HRex), 7 and an index (LnSD1) of vagal-related heart-rate variability (HRV) 7 were sensitive to the fluctuations in daily training load during a preseason training period. Similarly, in elite soccer players competing in the English Premier League (EPL), 9 both rating of perceived fatigue and vagal related HRV measure Ln rMSSD were mo...

Monitoring player fatigue status in the English Premier League

“…In the third study, perceived ratings of fatigue were significantly correlated to short-term load accumulation (1–4 days), respectively 5. These findings demonstrate that athlete self-report measures, particularly perceived fatigue was significantly sensitive to daily and short-term training load accumulation.…”

Influence of Perceptions of Sleep on Well-Being in Youth Athletes

“…Such research has shown that athletes are liable to suffer reduced sleep quantity and quality (16,22,32), which can lead to decrements in sporting performance (7,23), increased risk of illness (6) and deviations in wellbeing measures (19,27). These findings have resulted in practitioners commonly including measures of perceived sleep quality in daily wellbeing questionnaires aimed at monitoring their athletes (2,24,40).…”

“…Daily wellbeing questionnaires usually consist of items related to muscle soreness, appetite, sleep quality, mood, stress and fatigue, and are tailored to the needs of the practitioners in question (24,26,40). These subscales can be evaluated alone or grouped together to provide a total wellbeing score, which can be compared to the previous day's training load to assess whether changes are congruent with the training stress imposed on the athlete (25,36,37,40). However, given the influence of sleep quality on athlete wellbeing (19,27,29), it is pertinent to question whether perceptions of sleep should be an input, rather than an output measure of this athlete monitoring process.…”

“…Although the influence of training stress, measured by training load and exposure to match play, on muscle soreness and fatigue/recovery based measures is well established (2,37,40), its relationship with the overall wellbeing score has been questioned in the only study to consider a measure of recovery alongside the training stress imposed (37). In this study, the authors found self-reported sleep duration, as a measure of recovery, to have a small effect on a daily wellbeing scale (DWB; 23), its fatigue subscale and the Perceived Recovery Status scale (PRS; 20), and a moderate effect on the sleep quality DWB subscale in youth athletes (37).…”

External training loads and smartphone-derived heart rate variability indicate readiness to train in elite soccer

“…Practitioners need to accurately and efficiently monitor an individual athlete's fatigue (readiness to train) to potentially make informed and immediate adjustments to their TL. Various objective and subjective measures have been utilised to inform practitioner decision-making (Saw, Main, & Gastin, 2015); however, a "gold-standard" single or battery of measures is not adopted within general practice (Halson, 2014) or soccerspecific practice (Akenhead & Nassis, 2016;Thorpe et al, 2015Thorpe et al, , 2016aThorpe et al, , 2016b. Common methods employed are subjective questionnaires (Thorpe, Atkinson, Drust, & Gregson, 2017), visual analogue scales (Saw et al, 2015), blood and saliva indices (Ascensao et al, 2008;Morgans, Owen, Doran, Drust, & Morton, 2015;Thorpe & Sunderland, 2012), physical performance protocols and/or their physiological responses (Buchheit, 2014;Buchheit et al, 2013).…”