Quantification of training load

A number of variables such as intensity, duration, and frequency can be used to manipulate the dynamics of training (Smith, 2003). Training load is a function of these variables, which can be quantified through internal or external parameters (Halson, 2014; Smith, 2003). External load is the physical stimulus and amount of work completed by the athlete (Impellizzeri, Rampinini, & Marcora, 2005). The total distance covered, distance covered at various speed bands, and the total power output are examples of external training load (Aughey, 2011e; Ebert et al., 2005). Internal training load is the physiological and psychological impact of the completed work on the athlete, or in other words the internal response of the athlete to the external load (Impellizzeri et al., 2005). Heart rate, oxygen consumption, and ratings of perceived exertion are examples of internal training load (Borresen & Lambert, 2008; Foster, 1998; Jeukendrup & Diemen, 1998). External load and internal load are two different constructs and should both be monitored in order to allow coaches to determine whether the target training stimulus has been completed (external load) and how the athletes are responding to it (internal load) (Scott, Lockie, Knight, Clark, & Janse de Jonge, 2013).

Training load monitoring is now common practice in professional sports (Akenhead & Nassis, 2016; Taylor et al., 2012). A survey of 41 professional soccer clubs from three continents showed that all clubs monitor internal and external training load of individual athletes for the two main purposes of injury prevention and performance enhancement (Akenhead & Nassis, 2016). Another

18

survey of 55 coaches and sports scientists involved in various individual and team based high performance sports revealed that 90% of respondents implement some form of training load quantification (Taylor et al., 2012). The most important reasons for the training load monitoring practices were injury prevention (29%), monitoring the effectiveness of the training program (27%), maintaining performance (22%), and preventing overtraining (22%) (Taylor et al., 2012). Time-motion analysis has been a popular method of quantifying external load in field sport athletes over the past few decades, and was initially performed using manual video analysis. Variables such as distance covered and speed could be derived by tracking players’ movement on the field (Reilly & Thomas, 1976). The advancement of microtechnology and introduction of global positioning system (GPS) allowed for a more accurate and less labour-intensive quantification of external load (Aughey, 2011a). Global positioning systems are now commonly used in field sports to monitor the external load of individual athletes (Aughey, 2011a). Athlete movements are recorded as total distance covered during a match or training session, as well as the distance covered at different speed bands generally labelled as walking, jogging, high intensity running, and sprinting (Bradley et al., 2009; Rampinini, Coutts, Castagna, Sassi, & Impellizzeri, 2007; Varley, Fairweather, & Aughey1, 2012). Developments in GPS technology including increases in sampling rate of the hardware and advancement of the GPS chipsets have resulted in substantial improvements in the validity and reliability of GPS units (Aughey, 2011a; Scott, Scott, & Kelly, 2016; Varley et al., 2012). For example, the high error rates of the 1 Hz and 5 Hz MinimaxX GPS units particularly in quantification of high speed and short distance activities were

19

substantially improved by introduction of the 10 Hz units (Jennings, Cormack, Coutts, Boyd, & Aughey, 2010; Varley et al., 2012). High speed and short distance activities are usually of minimal duration, and higher sampling rates (more location data points per unit of time) allow GPS units to capture these efforts more accurately which in turn results in improved validity and reliability (Coutts & Duffield, 2010; Scott et al., 2016; Varley et al., 2012). Advancements in GPS chipsets have also contributed to improvements in validity and reliability through enhanced algorithms that process the positional information (Coutts & Duffield, 2010; Varley et al., 2012). In general, GPS technology is considered a valid and reliable tool for quantification of external load in football code athletes (Aughey, 2011a; Varley et al., 2012). For example, coefficients of variation of typically less than 10% for validity and 6% for reliability have been reported for 10 Hz GPS units in evaluation of instantaneous velocity (Varley et al., 2012). Activities such as changes of direction, tackling, bumping, and taking part in contested situations commonly occur in team sports and exert substantial load on athletes; however, these activities involve minimal displacement and are under-represented in measures derived from time-motion analysis (Boyd, Ball, & Aughey, 2013; Dawson, Hopkinson, Appleby, Stewart, & Roberts, 2004). Accelerometers are highly responsive motion sensors housed in the same unit as the GPS, which quantify the magnitude and frequency of movement in three dimensions (Boyd et al., 2013).

Accelerometers are widely used in quantification and evaluation of external load in various football codes. Accelerometers have been used to evaluate the sensitivity of neuromuscular and hormonal measures of recovery to external load

20

of soccer matches (Rowell, Aughey, Hopkins, Stewart, & Cormack, 2017). The influence of neuromuscular fatigue on external load of elite Australian footballers has been investigated using accelerometer units (Cormack, Mooney, Morgan, & McGuigan, 2013). Accelerometers have outperformed GPS in quantifying important differences in athlete movement during rugby union matches (Howe, Aughey, Hopkins, Stewart, & Cavanagh, 2017). Accelerometer-derived- measures have also been used to evaluate the relationship between training load and risk of injury (see section 2.2.5). Player Load is the most commonly used accelerometer derived measure of external load and has been validated for use in Australian football (Boyd et al., 2013; Gastin, McLean, Spittle, & Breed, 2013). Internal training load can be quantified using a number of objective and subjective measures. Objective measures are mostly based on heart rate and the assumption of a linear relationship between heart rate and oxygen consumption during steady state exercise (Hopkins, 1991). While objective measures are widely used in endurance sports to monitor internal training load, the non-steady- state and intermittent nature of team sports have limited the validity and application of heart rate based measures for monitoring internal training load in team sport athletes (Borresen & Lambert, 2009; Impellizzeri et al., 2005).

The session rating of perceived exertion (sRPE) is the only subjective measure of internal training load that has been widely adopted in team sports owing to being non-invasive, simple and cost-effective (Scott, Lockie, et al., 2013). The subjective rating of the overall session difficulty on a modified 0-10 Borg scale is multiplied by the session duration (in minutes) to obtain a single value

21

representing the internal training load of that session in arbitrary units (Foster, 1998; Foster et al., 2001; Foster et al., 1995).

The sRPE method has been used to evaluate the relationship between training load and match outcome in Australian football (Aughey, Elias, Esmaeili, Lazarus, & Stewart, 2016). The sensitivity of hormonal and subjective measures of recovery to training load has been determined using the sRPE method (Buchheit et al., 2013; Thorpe et al., 2016). The sRPE method is considered to be a valuable tool for coaches, as it can be used to guide the prescription and periodization of training in team sports (Kelly & Coutts, 2007). This method of training load quantification has also been widely used in evaluation of the relationship between training load and injury risk (see section 2.2.5).The sRPE method has been validated for quantifying training load in team sports, with moderate to very large correlations reported between sRPE load and other measures of internal and external load (Casamichana, Castellano, Calleja-Gonzalez, San Román, & Castagna, 2013; Impellizzeri, Rampinini, Coutts, Sassi, & Marcora, 2004; Scott, Black, Quinn, & Coutts, 2013).