Editorial: Acute: Chronic Workload Ratio: Is There Scientific Evidence?

Abstract

Editorial on the Research Topic Acute: Chronic Workload Ratio: Is There Scientific Evidence? The scientific monitoring of athletes is fundamental to determine and understand the individual biological responses to training (Halson, 2014). In elite sports, it is crucial to regularly monitor training and performance to detect biopositive or negative responses that can be used to effectively program training according to the needs of each athlete (Bourdon et al., 2017). Moreover, workload monitoring can also help to assess fatigue and indicate the need for recovery in different physically demanding situations to ultimately avoid injuries (Halson, 2014). As there is evidence that lower injury rates are associated with higher team sport performances (Eirale et al., 2013), sport scientists and medical staff should regularly and accurately evaluate athletes' injury risk using workload measures (Halson, 2014). Based on Banister et al. (1975) fitness and fatigue model, Gabbett et al. (2016) introduced the concept of the acute:chronic workload ratio (ACWR) with acute workload hypothetically representing the fatigue component and chronic workload the fitness component of Banister's model (Figure 1). ACWR allows individualized performance development and injury prevention using the relation between acute to chronic workload data. For this purpose, internal (e.g., heart rate, session-rate of perceived exertion [sRPE] × duration) and external (e.g., performance measures, tracking variables such as running speed and/or acceleration using Global Positioning Systems [GPS]) load measures should be collected to compute ACWR during training and competition (Malone et al., 2017). It has previously been recommended to determine the ratio between acute (training load accumulated during the last 7 days) and chronic (mean training load over the previous 3 to 6 weeks) workloads (Gabbett et al., 2016; Gabbett and Whiteley, 2017). The underlying rationale is that athletes' physical fitness develops adequately if the chronic load progressively increases to high levels while the acute load remains below, similar to, or slightly above the chronic workload (i.e., ACWR range between 0.8 and 1.3). Conversely, the athlete is considered not well-prepared and likely at an increased risk of sustaining acute or overuse injuries if the acute workload exceeds the chronic load (i.e., ACWR ≥ 1.5) (Malone et al., 2017; Windt and Gabbett, 2017). Figure 1. Conceptual model for developing athlete monitoring systems according to the fitness-fatigue model using the acute: chronic workload (ACWR) approach and internal/external workload measures. While the fitness component is comparable to chronic workload (e.g., 28 days rolling average workload), fatigue is comparable to acute workload (e.g., 7 days rolling average workload) (adapted from Coutts et al., 2018). Over the past 15 years, the ACWR approach has received a lot of attention from researchers and practitioners who are active in different sports such as Australian football, cricket, rugby, and soccer (Gabbett, 2016, 2020; Griffin et al., 2020). Figure 2 illustrates the exponential growth in the number of PubMed-listed publications per year on ACWR research using the search syntax (“acute to chronic work load ratio” OR ACWR). In 2016, the International Olympic Committee (IOC) published a consensus statement (Soligard et al., 2016) that suggests the use of the ACWR approach for injury prevention. In addition, Myers et al. (2020) reported level A evidence in support of the sRPE ACWR as an effective tool to prevent non-contact injuries in elite athletes. Despite evidence in favor of the ACWR approach, different authors have raised substantial criticism (Impellizzeri et al., 2020a,b; Wang et al., 2020). For instance, the validity of ACWR has been recently questioned due to the large heterogeneity of the internal (e.g., session RPE, heart rate) and the external load (e.g., GPS data, training time) variables that are used for ACWR calculation (Impellizzeri et al., 2020a,b; Wang et al., 2020). In addition, opponents of the ACWR approach argue that there is no rationale as to the exact time span for acute and chronic workload monitoring (Impellizzeri et al., 2020a,b). In the absence of a rationale, other authors have selected multiple time windows (Delecroix et al., 2018), but again the selected time span appears arbitrary. Another major criticism that has been postulated is that ACWR is a measure of training workload, most often assessed through spatio-temporal metrics from GPS data, but not a mechanical load parameter (Impellizzeri et al., 2020a,b). In a strict biomechanical sense, repetitive mechanical load, but not training load, is associated with an increased risk of tissue damage (Brüggemann and Niehoff, 2011). In other words, training load is not a surrogate of mechanical loading. Overall, the literature on ACWR research is controversial, which is why more research is needed. Figure 2. Number of PubMed listed publications per year related to the topic Acute: Chronic Workload Ratio (ACWR) between 1980 and 2020. The following search was applied in the electronic database PubMed (“acute to chronic work load ratio” OR ACWR). Accordingly, it was timely to elucidate strengths and weaknesses of the ACWR approach in the form of a Frontiers Research Topic entitled “Acute: Chronic Workload Ratio: Is there Scientific Evidence?.” Thus, the aims of this Research Topic were to provide knowledge on the underlying physiological mechanisms of ACWR and if there is a scientific evidence to support the use of this ratio as an approach for injury risk prediction in different sports. Overall, seven articles were published in this Research Topic. The contributing 45 authors are from different countries across the globe including Australia, Brazil, China, Czech-Republic,...