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Monitoring Athlete Load: Data Collection Methods and Practical Recommendations

Wing, Chris MSc

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Strength and Conditioning Journal: August 2018 - Volume 40 - Issue 4 - p 26-39
doi: 10.1519/SSC.0000000000000384
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Measuring and monitoring athlete load has become an increasingly important topic within sports performance. Through accurately monitoring load, athlete performance can be optimized, whereas also reducing the risk of injury and excessive levels of fatigue (24,30). Athlete load is any physiological work performed by an athlete during both competition and training, and the subsequent impact/stress of this load on the athlete (35,57). This load includes both the various movement demands of competition and match play, the full range of training interventions performed by the athlete, and their associated effect on athlete fatigue and mood status (35,57). Athlete load is better understood through subdividing load into 2 groups—internal and external (35). External load is simply the amount of “work” completed by an athlete during any phase of training and competition (69). This load is often multifaceted and may include work completed on the field/court, during resistance training programs, while performing therapeutic/recovery sessions, during competitive matches, and for the nonelite athlete during their working day (30,37). For example; during a typical training week, a cricket fast bowler may be subjected to running, bowling, throwing, resistance training, plyometric, and therapeutic loads (37). Internal training load can be seen as the physiological or psychological stress experienced by the athlete in response to the external training load (8,35). This internal load is commonly measured in a subjective way through self-reporting, but can also include measures surrounding heart rate data, biochemical analysis (such as creatine kinase) and salivary analysis (concentrations of testosterone and cortisol) (7,57). It may be valuable to use a combination of load-monitoring tools, for example, one external and one internal measure of load (34,35,71). This enables the measurement of both the dose and the athlete's response to the dose (34,35). Considering the physiological makeup of each athlete is often different, and therefore prescribing one exercise or drill may elicit a different internal response on an athlete-to-athlete basis, even though the external load would remain the same (10,30,35).

Collecting data surrounding athlete load is essential to aid the sport science and medical team to develop sound injury prevention and performance plans. There are several data collection methods available, ranging from simple session rating of perceived exertion (sRPE), wellness questionnaires, global positioning systems (GPS), and heart rate–based training impulse (TRIMP), to more complex methods such as biochemical and salivary analysis (7,57). These data sets are subsequently used to adapt and shape future training plans. This is highly important to the welfare of the athlete because subjecting them to inappropriately increased loads can lead to a decrease in performance (24) and also increase the risk of injury (30,33) and illness (70). If an excessive training load continues across an extended period, it may subject the athlete to overtraining syndrome (22). However, heightened levels of fitness, built through appropriate training loads, can increase the robustness of athletes and enable them to benefit from the protective element of training (9,30,38,45,46). Therefore, greatly reducing athlete load should also be met with caution because this may lead to reduced levels of fitness and result in inadequately prepared athletes for both competition and training (20,30). This has led to recent theories suggesting that increased injury risk may occur at both ends of the spectrum, during times when athlete load is both inappropriately low or high (30). Ascertaining athlete training zones (i.e., an upper and lower range), and ensuring that the athlete does not alternate too quickly between both ends of this spectrum appears to be the most beneficial method of loading (5,30,32,38). This zone may change during different phases of the macrocycle depending on the training focus.

The data collected can also be used to aid with team selection, program design, return from injury, and facilitate communication with both athletes and the wider coaching team (35). Which data measurement tool to use can often be decided by affordability but is also influenced by reliability, reproducibility, ease of use, and the data's ability to inform practice. This article explores the literature surrounding 4 common data collection methods and provides recommendations for how these may be used within a practical setting.


In order for a strength and conditioning (S&C) coach to implement a load management strategy, they must first adopt a data collection method that is most suitable for the athletic population they are working with. Four of the most commonly used methods are outlined in the following section: sRPE, GPS, TRIMP, and wellness questionnaires.


One of the most commonly used tools to measure athlete exertion, and ultimately load, is through the sRPE method devised by Foster (27). This involves athletes self-rating their physical exertion using a modified Borg scale by answering the question “How was your session today?” (65). Typically, a 10-point scale is used, with the closer the score to 10 the harder the session (6). It has been suggested that for consistency of data collection, sRPE should be recorded 30 minutes after exercise to allow for the athlete to reflect on the session as a whole (27,65). A period of education and learning may also be beneficial to improving the accuracy of responses (27). The sRPE score can then be multiplied by the session time to produce a training load score, measured in arbitrary units (AU) (27). For example, an athlete rating the session 7/10 after exercising for 65 minutes would have a training load of 455 AU.

The validity and reliability of the sRPE method has been extensively researched (28,36,39,51,54,58,59,62). The sRPE method seems to be valid as athletes reported a higher sRPE when exercising at a higher percentage of V̇o2max (62). Similarly, sRPE was significantly higher during competitive matches than training sessions (51). Furthermore, this method has been shown to correlate with several heart rate–based TRIMP methods (39,58,59) for both steady-state and intermittent-type exercise (28). Data from GPS devices, including total distance and high-speed running (58,59), as well as heart rate and various V̇o2 measures (36) have also been shown to correlate with the sRPE method. However, Scott et al. (59) report poor levels of reliability for sRPE when studying short intermittent running bouts, with weaker relationships between sRPE and high-intensity activities also being previously reported by Paulson et al. (54). With this evidence in mind, the sRPE method may be a better tool for recording overall session load as opposed to changes in short bouts of high-intensity exercise (59).


Gym-based training is arguably the easiest variable for the S&C coach to manage due to fewer outside influences and the ability to periodize training variables with more accuracy. It has been suggested that resistance training load can be monitored through the following equation: training load = (weight lifted × repetitions) × sets and summed for all exercises within the session (25). Similarly, ground/foot contacts can be recorded for all plyometric sessions. Despite the value within these methods, they are stand-alone data sets which cannot then be added to measurements of conditioning/technical training to provide an overall load for a training day (65). It also does not account for training variability, as 4 repetitions using 20 kg would be recorded the same as 1 repetition at 80 kg. Furthermore, it cannot distinguish between intensity for plyometric variations. For example; single leg and/or faster movements are more intense than double leg and/or slower movements (67). This is not highlighted within the counting contacts method, despite the likelihood that differing amounts of load and fatigue will be created. An example of this method can be seen in Table 1.

Table 1
Table 1:
Table depicting an example of total lifting volume (left) and ground contact (right) methods of monitoring

In more recent times, the sRPE method, as previously described, has been shown to be effective for monitoring gym-based training. McGuigan et al. (48) report that sRPE is significantly higher after performing high-intensity versus low-intensity resistance exercise. This is supported in further studies which state that the closer an athlete is working to their 1 repetition maximum, the higher their sRPE, despite fewer repetitions being performed (19,62). The evidence is suggestive that sRPE can be accurately used within gym-based sessions and is also reported to be reliable, with intraclass correlation coefficients being reported at 0.95 (48). Once the sRPE for gym-based training has been collected, it can be added to “field-based” training to provide an overall training load score for the day (65). For example: Gym-based training load 360 AU (RPE 8, 45 minutes), field-based training load 570 AU (RPE 6, 95 minutes). Total daily training load = 930 AU.


GPS has enabled S&C coaches and sport scientists to gather highly valuable and reliable data concerning external athlete load across a range of metrics. Despite their common use within elite sporting environments, there is still conflicting evidence surrounding their accuracy. Coutts and Duffield (16) describe how they may be accurate when assessing total distance (Coefficient of variation: 3.6–7.1%) and peak speed (Coefficient of variation: 2.3–5.8%) but reported poor levels of accuracy when assessing high-speed and very high-speed running. This is somewhat reflected by Petersen et al. (55) who studied different GPS brands and found standard errors of measurement (SEM) to be between 3 and 24%, depending on the manufacturer and the measurement variable, with shorter distances showing less accuracy. Castelleno et al. (12) also found that accuracy improved with increased distance, with SEM scores being halved at 30 m when compared with 15 m. It also seems that sampling frequencies/rates (the speed at which data are gathered) are a contributing factor to the accuracy of GPS devices (40,68). It has been reported that both reliability and validity can be enhanced through using increased sampling rates (40,68). The research states that both validity and reliability increase when sampling at 5 Hz in comparison with 1 Hz (40), and further increases when sampling at 10 Hz (68).

Despite these limitations, GPS devices are common practice and are still the best available method for recording data of this kind. Although in some cases it may not be time-efficient, where possible the S&C coach can improve the accuracy of the collected data by removing artifacts from the data source preanalysis. This can be achieved through identifying periods of motion that are not in keeping with the athlete or the session performed (e.g., a tracing spike indicating a running speed of 50 km/h), and subsequently marking this outside the data to include within the final analysis. These artifacts may be present within the data for several reasons and include: failure to stop the device on completion of training, athlete leaving the field for treatment (including measuring motion while receiving treatment), time spent on the interchange bench, and the changing of the shirt in which the GPS is housed during training or match play.

As previously stated, GPS units provide coaches with several data metrics which can include: total distance, maximum velocity, distance in various speed zones (e.g., walking, jogging, high-speed running, and sprinting) as well as accelerations and decelerations (17). Although total distance is one method of describing the total load or volume performed by an athlete, it is sometimes better viewed relative to playing or training time (2,17) This can give a better indication of an athlete's intensity, and is often reported in meters per minute (m·min−1) (2,17). For example, 2 athletes train for 20 and 45 minutes, respectively, and both perform 2,500 m total distance. However, one will record a work rate of 125 m·min−1 and the other 55.55 m·min−1, indicating that the first athlete has worked at a substantially higher intensity than the other. The total distance may also be expressed across different speed zones, as identified previously, of which the speed boundaries often vary in their definition between sports (17). The total accelerations and decelerations may also be recorded, which provides information pertaining to the amount of high-intensity actions performed by an athlete (17). It has been proposed that accelerations and decelerations can contribute significantly to player load in intermittent-type sports, such as soccer, and therefore their value within athlete monitoring seems to be gaining increased importance (18).

The metrics gathered through GPS data can enable the S&C coach to assess the movement demands of athletes within their particular sport (2,17). The data can then be used to devise training drills that best mimic the demands of match play, or specific periods within it (42). This enables S&C coaches to ensure that their athletes are exposed to relevant intensities and movement patterns that may be required of them during competition (42). For example; a standard match may require an athlete to perform at an average intensity of 110 m·min−1. However, during the match, the athlete may be required to perform periods of exercise at an intensity of 150 m·min−1. To best prepare an athlete for this, the S&C coach should ensure that some drills within weekly training sessions are performed at, or above, an intensity of 150 m·min−1 (42). Exposing the athlete to this level of intensity will not only prepare the athlete for performance, but also act as an injury-prevention measure (20,46). The data that are collected from these drills during training sessions may also be calculated over time to provide drill averages, which can ultimately be used to inform training plans and aid with accurate athlete load monitoring (42). An example of this is depicted within the “Practical management of the A:C ratio” section of this article.


Monitoring training load using heart rate is often implemented through the use of a training TRIMP. Bannister (4) first developed the use of the TRIMP method through the following equation:Where t = duration (minutes), ΔHR = HRexercise − HRrest/HRmax − HRrest (fractional elevation in HR), y = weighting factor (to represent changes in exercise intensity). (where HR = heart rate)

Because of the use of average heart rates, Bannister's TRIMP may not be truly reflective of the work performed during intermittent sports (soccer, rugby, etc.) where fluctuations in heart rate are present (61). However, it may be more useful during long, steady-state exercise (such as a cycle road race) (53). Edwards further developed the TRIMP method, which attempted to address this issue through the use of 5 predefined training zones (see Table 2) (23). The time spent in each of these zones is multiplied by the corresponding coefficient and summed for total training load (23). Despite these zones giving rise to higher heart rates equaling higher workload, they are still arbitrary and may not be truly reflective of the work performed by the athlete (47,61). This is because blood lactate concentrations are said to rise exponentially with heart rate, which cannot be accounted for with the linear scaling provided within the Edwards' TRIMP method (47,61).

Table 2
Table 2:
Example of Edwards TRIMP method of load calculation

Lucia's TRIMP (43) is similar to Edwards' TRIMP in providing training zones with a fixed multiplier for total time spent in each. This method has 3 zones, based on ventilatory thresholds ascertained from laboratory testing (43). These are described below:

  • Zone 1: Below the ventilatory threshold (Multiplier: 1)
  • Zone 2: Between the ventilatory threshold and the respiratory compensation point (Multiplier: 2)
  • Zone 3: Above the respiratory compensation point (Multiplier: 3)

Despite these zones being ascertained by an athlete's physiology, the boundary lines are seen to be too broad, allowing for differing exercise intensities to be classified within the same zone (13). Manzi et al. (47) describe the further development of heart rate measures through an individualized TRIMP (TRIMP¡), which classified the TRIMP based on each individual's heart rate-blood lactate concentration profile. This is achieved through individualizing the weighting factor (y), previously used by Bannister (4), based on an individual's exponential rise in blood lactate concentration (47). This weighting factor is ascertained through plotting the rise in blood lactate concentration against the fractional rise in heart rate during a standardized treadmill test protocol (47). This allows the weighting factor to exponentially increase as exercise intensity, and ultimately heart rate response, rises (47). Thus, this method allows for the individualization to each athlete's physiological makeup, as well as giving greater significance to shorter bouts of high-intensity exercise (47). The TRIMPi (also referred to as the iTRIMP) method has been reported to be accurate when monitoring fitness and performance in distance runners (47) and team sports such as hurling (44). However, this method may be difficult to implement due to the blood lactate concentration testing methods being expensive, time-consuming, and often lacking in availability.

Although there are obvious limitations to the TRIMP methods, research supports their use within athletic settings. During wheelchair rugby performance, Banister's, Edwards', and Lucia's TRIMPs all showed similarly strong levels of correlation with total distance as well as time and distance of low-, moderate-, and very high-speed zones and the number of high-speed actions, measured using radio-frequency tracking systems (54). Furthermore, Bannister and Edwards TRIMPs have been reported to show a positive relationship to total distance within soccer players (58). The TRIMP method is also said to be sensitive when measuring fluctuations in exercise intensity and total distance, and therefore allowing for changes in athlete load to be compared over a given time frame (54,58). These articles suggest that the TRIMP method is an acceptable approach to athlete load monitoring. Furthermore, this method may also be a valuable tool with sports where locomotion cannot be measured using GPS (i.e., boxing, mixed martial arts (MMA)) to provide an alternative method of recording athlete load.


Internal athlete load may be monitored through the use of a self-reported wellness questionnaire, where athletes score their perceived level of wellbeing across several parameters (57). These may include: sleep (duration and quality), feelings of fatigue, muscle soreness, energy levels, and amount of psychological stress (57). These scores are often added together to give a total wellness score for the athlete (57).

There must be careful consideration when designing wellness questionnaires, with several factors affecting their success (56). Most notably, questions should be designed in a manner which enables them to be relevant to the athlete and their sport, while also being specific enough to allow for a full understanding across an entire squad (56). The design must also limit the time burden for both the athletes to complete and for the S&C coach to input and interpret the data (56). This may be best achieved through limiting responses to a number format, which has been reported as a preferred method among athletes (56). There are several standardized questionnaires that currently exist, with both the Profile of Mood State (POMS) (49) and the Recovery-Stress Questionnaire for Athletes (RESTQ-Sports) (41) being frequently used within athletic settings (57).

The usefulness of questionnaires as a monitoring tool has been widely reported (7,11,24,57,64). In fact, authors have previously stated that wellness questionnaires are a more valid approach to athlete monitoring than more complex objective measures, including those derived from heart rate measures, blood markers, and measurements made during exercise performance (57,64). Previous research supports this, with questionnaires being reported to be sensitive to changes in training load, with a decrease in wellness scores correlating with increases in training load (7,11,24). Similarly, during a period of tapering, where training load is reduced, an improvement in wellness scores could be seen (7). Furthermore, Thorpe et al. (64) found a 35–40% reduction in self-reported levels of sleep, fatigue, and muscle soreness within elite-level soccer players pre-versus post-match. They also reported an improvement in these levels of 17–26% 3 days post-match due to a reduction in training load (64). This evidence supports the use of the questionnaire as a tool for monitoring changes within athlete load on both a mesocycle and microcycle level.



Measurements of training monotony and training strain can be derived from the training load data. Training monotony can be calculated by taking the average load across a 7-day training week (including any days off) and dividing it by its SD (27,28). Training monotony provides an indicator of training variability, with a score closer to one showing higher levels of variability (65). The importance of training variability is to ensure that training does not become stagnant and that variety is included within the program, both in terms of mode and intensity (65). This can be achieved by alternating between “light” and “heavy” training days, which in turn will afford the athlete time to recover, and therefore reduce the likelihood of overtraining (27,65). The training monotony may then be multiplied by the total training load for the week to produce the training strain, a reflection of the overall stress of training (24). Measurements of training strain have been shown to be sensitive to changes in training load (24) and predictive of athlete illness (27,63). However, caution must be used when using training strain as a method to report the stress of training. If a measure of training monotony is recorded as “0,” then training strain will also be “0” due to the nature of the equation. This, therefore, would not be reflective of the overall stress of training.


When planning training sessions, an optimal level of training load with appropriate ceilings and floors should first be ascertained (10,14,30). The optimal load is dependent on several factors, most notably the demands of the specific sport and the current time within the macrocycle (14). When considering the sport's demands, it may be prudent to ascertain the average and worst case (maximum possible load) scenarios in terms of workload performed during competition (14,30). These demands may include a range of metrics depending on the sport and include: possible number of games/weeks of competition, running distances and intensities, and balls bowled/pitched (14). Training session load must then adequately prepare the athlete for these demands, through providing appropriate volume and intensities, as well as sufficient time for the practice of technical and tactical skills (14,20,30). The training floor ensures that a minimum level of training is afforded, allowing the athlete to maintain or improve levels of performance (depending on the phase of training), as well as maintaining adequate levels of tissue conditioning required to prevent injury (14,20,30). The ceiling ensures that the athletes do not perform a load that they have not been prepared for, as well as a load which may cause undue fatigue (e.g., in-season), which may lead to both a detriment in performance and an increase in injury risk (14,24,30). Athlete age and training history should also be taken into consideration. It has been reported that younger players take longer to reach higher chronic training loads (26), with the possibility that older athletes may be subjected to degeneration of muscular tissue and therefore have a reduced ability to tolerate load (15). Turner et al. (65) also recommend modifying athlete ceilings through identifying correlations between individual training load and injury/illness. For example, if an athlete has a history of becoming injured when training at loads above 800 AU (using the sRPE method), then it would seem to be a good practice to establish an athlete ceiling just below this level (65). It is important to remember that these ceilings may be raised over time as the athletes increase their levels of both fitness and robustness (14). A practical example of how to devise these ceilings and floors for a semi-professional Australian Football League (AFL) athlete is outlined in Table 3.

Table 3
Table 3:
An example of how the athlete ceiling and floor may be calculated for a semi-professional AFL player, during the in-season phase, using 3 different methods


The acute: chronic (A:C) ratio enables the S&C coach to measure athlete preparedness, while also being used as a tool to appropriately calculate increases or decreases in athlete loading (30,32). The acute workload is typically defined as the total work performed across a training week, measured using any single data point (i.e., total distance, high-speed running, cricket balls bowled, baseballs pitched, sRPE, TRIMP, etc.) (30,32). The acute load is therefore a representation of fatigue (30,32). The chronic workload is typically represented by the rolling 4-week average of acute workload, and is therefore a measurement of fitness (30,32). The A:C ratio is then calculated by dividing the acute workload by the chronic workload (30,32). Therefore, an athlete who has completed half the workload in the current week, compared with the previous 4, would display a ratio of 0.5, and an athlete completing twice the workload would display a ratio of 2.0 (5). An example of how this can be calculated is provided in Table 4.

Table 4
Table 4:
An example of how to calculate the A:C ratio for a cricket bowler using various metrics using the methods outlined by Gabbett et al. (32)

This has led to the identification of a “sweet spot” in the training load ratio of 0.8–1.3, with a ratio of >1.5 indicating a significant spike in training load (5,30). Through prescribing load within these working ranges (0.8–1.3), and thus avoiding training spikes, S&C coaches can ensure that their athletes receive a sufficient training stimulus to promote adaptation and readiness, without being subjected to inappropriately increased or decreased loads (30).

However, Buchheit (10) describes how there are some issues to consider when practically applying the A:C ratio. He states that due to the vast amount of data points collected regarding athlete load, contradictions in data may occur; for example, an optimal total distance ratio may reside at the same time as a high ratio for high-speed running (10). Also, as previously described, due to the multifaceted demands placed on an athlete, it is not always possible to take one overall score for athlete load, again leading to the possibility of several A:C ratio calculations (10). Therefore, it may be prudent for the S&C coach to only make A:C ratio calculations on the data that are most relevant to their particular sport.


To help effectively manage the A:C ratio using GPS, the data must first be accurately labeled, individualizing each training drill within GPS readouts (Figure 1 and Table 5). The data can then be used to devise drill averages over extended periods for both the team and individuals (10,42). Through sharing these data with the technical coaches, each session can be planned through selecting drills that allow for the desired acute training load and therefore ensuring that no spikes are present (42).

Figure 1.
Figure 1.:
Example global positioning system readouts showing how each drill can be individually labeled. Each color box represents a different drill or exercise. The readouts from this can be seen in Table 5.
Table 5
Table 5:
GPS readouts (from Figure 1) showing how each drill can be individually labeled, providing a range of metrics

Although planning the dose is beneficial, and certainly recommended, due to a multitude of external factors, planned dose does not always equate to actual dose. Therefore, Table 6 illustrates how a simple dashboard can be created, using a method outlined by Williams and Weaving (73), to ensure that the correct dose is received each week.

Table 6
Table 6:
An example of an athlete dashboard that may be used to help practically manage the A:C ratio.

The dashboard demonstrates a typical training week and the corresponding total distance for each day. The dashboard updates automatically when load is entered at the end of each training session. The dashboard not only provides a “live” A:C ratio for the week, but also calculates the remaining load that would represent an A:C ratio of 0.8, 1.3, and 1.5, respectively (columns U–W). Therefore, this dashboard provides S&C coaches with a practical tool which can be used to ensure that athletes remain within the “sweet spot,” as described by Gabbett (30). It must be noted that Table 6 represents total distance only; however, this process may be used for any variable that is deemed important to each particular sport. Implementing the A:C ratio becomes more complex during periods of heavy competition, for example, where there may be 2 matches per week. This in itself could contribute to a sudden spike in acute load and would require careful load management (60).

The A:C ratio also seems to be particularly attractive with return-to-play athletes, aiding to ensure that the athlete is best prepared for return to full training within a progressive fashion (5). This is described by Blanch and Gabbett (5), who provide guidelines concerning the A:C ratio regarding injury risk when returning to play. For example, they state that an athlete achieving 100% of acute workload but only 30% of chronic workload is at a 61.4% risk of re-injury (5). This again highlights the importance of moderate increases in athlete loading. Through the use of drill averages, as described previously, greater accuracy of planning for the return-to-play athlete can be afforded. This may include a combination of modified sessions and individual training (See Tables 7 and 8 for an example).

Table 7
Table 7:
Hypothetical session plan for a group session, using historical data for load
Table 8
Table 8:
Hypothetical modified session plan for a return-to-play athlete, using historical data for load

For this return-to-play athlete, total distance target for the session is 5,500–6,000 m with high-speed running between 800 and 900 m. As highlighted in the group session total (Table 7), these athletes would have received an inappropriate training load had they been allowed to return to full training. Using the longitudinal data collected, the athlete's session can be premodified, allowing them to complete part of team training with the inclusion of a supplementary conditioning drill to ensure that they still receive an adequate training stimulus. This may be further monitored/modified during training with the aid of live GPS.


The athlete training load can show vast levels of variation across a macrocycle, as they move through different phases of training. This variation may include periods such as pre-season, active recovery, functional overreaching, tapering, and competition. Although the load during these times is likely to be markedly different (e.g., functional overreaching versus active recovery), it is still important to move through these phases in a gradual manner (5,30,32,38). These gradual changes in training load include both progressively loading the athlete and also gradually reducing the load during periods of recovery or restoration (60). Therefore, during times of mesocycle change, it is still important to maintain an appropriate A:C ratio to avoid increasing the risk of injury (5,30).


Oftentimes, 2 methods (one internal and one external) of data collection are used (34,35,57,71). This allows the S&C coach to measure a dose-response relationship (8). If an athlete consistently reports lower than normal internal load to a given external stimulus, then an increase in fitness and tolerance may be indicated (8,34). Therefore, increasing athlete loading, alongside an increase in the training ceiling, may be required in order for the athlete to receive a training stimulus (14,34). See Figure 2 for an example of this. Conversely, the opposite may take effect, where higher internal loads than normal may be reported to a consistent external stimulus. This may indicate athlete fatigue and subsequently require a reduction in training load to allow the athlete to recover (8,34). However, caution must be used when making changes to athlete loading in this manner. The S&C coach should ensure that any changes to internal load remain consistent over time before a change (increase or decrease) in external load is initiated (66). Also, the training cycle of the athlete must be considered. For example, during a pre-season period or training camp, where the aim is to increase fitness, it is highly likely that athletes will report a high internal training load. However, the aim of this phase of training is to build levels of fitness, which cannot be achieved without creating a level of athlete stress (66). Therefore, reducing training load during this phase would be seen to be counterproductive (66).

Figure 2.
Figure 2.:
Hypothetical external versus internal training load. Highlighting errors in load and how external load may be adjusted in response to internal load. A:C = acute: chronic; AU = arbitrary units; M = meters; sRPE = session rating of perceived exertion.


Accurate load management enables players to be physically prepared for the demands of training and competition (30,45,46), as well as reducing the occurrence of both injury and illness (30,33,70). Early theories on training load attempted to establish a linear relationship between high training loads and the occurrence of injury and illness (3,27,29,31,33,63,70). In fact, Gabbett (29) explains that reducing training load within rugby league players showed a significant reduction in injuries. However, a loading strategy centered around reducing athlete loading and intensity is likely to produce underprepared athletes, who are not physically capable of meeting the demands of competition (20). Not only are they likely to underperform, but their lack of exposure to training may decrease the levels of robustness required to remain uninjured throughout the competitive season (20,30,38).

More recently, it has been stated that exposure to appropriately planned chronic load actually aids to protect against increases in acute load (7,32,37,38) and provides athletes with a protective effect of training against injury (37,38,45,46). This is supported by Malone et al. (46) who reported that athletes exposed to maximal velocity training and higher chronic training loads benefit from a protective effect of training. Another study involving elite-level soccer players demonstrated that those with superior aerobic capacity were at a reduced risk of injury and were able to better tolerate increases in training load (45). These heightened levels of fitness can only be developed through increased exposure to training (i.e., chronic load) (30,32,66). The benefits of exposing athletes to intense training are further supported by Drew and Purdam (20). They explain how significantly reducing athlete load can lead to a subsequent increase in injury rates due to the deconditioning of tissue (20). This was highlighted within a case study, which reported that athlete injury occurred more frequently during the periods of the lowest training load during a 365-day period (20).

Gabbett et al. (32) describes how high training loads alone are not the issue, how you get there is. This has seen current theories shift attention away from the total training load and toward spikes in training load (1,30,32,38). This has been highlighted within an elite rugby league population, where an A:C ratio ≥2.11 was reported to lead to a 16.7% risk of injury in that training week and an 11.8% in the following week (38). Furthermore, a 28.6% increase in injury risk could be seen with those displaying a high chronic workload alongside a ratio ≥1.54 (38). Other sports have reported similar findings, with cricket reporting spikes in acute workload (37) and AFL reporting a high A:C ratio (52) to show a relationship to the likelihood of injury. Further research within Australian rules football also supports the notion that injury does not have a linear relationship to load and that sudden increases (spikes) in workload are more significant (21). Here, the authors state that the total volume of high-speed running performed in a season did not correlate with the incidence of hamstring injury, whereas spikes in high-speed running volume did (21). The authors suggest that a reduction in high-speed running every 4 weeks may prove beneficial to reducing injury rates (21).

It is important that an athlete is physically prepared for the demands of training and sport through exposure to training loads, which both increase performance and reduce injury (20,30). This may be best achieved through moderate workloads, with an approximately moderate increase in load, which allows players to benefit from a protective element of training (30,45). The International Olympic Committee consensus statement supports this ideology, and recommends that gradual increases in load should be applied to best avoid training spikes and their associated risk of injury (60). This should be achieved through following correct loading patterns, individualization, and sound periodization principles (30,60).


Monitoring athlete load is an essential part of successful programming and periodization. Ensuring that the athlete receives an appropriate training dose, at the correct time, is essential to build fitness and robustness (45,46) as well as reducing the incidence of injury and illness (30,33,70). This has been highlighted by Gabbett's paradox (30), which describes a “sweet spot” for training load, which is both beneficial to performance and injury prevention. There are several data collection methods that can be used, each with their own strengths and weaknesses (these are highlighted within Table 9).

Table 9
Table 9:
Strengths and weakness of the 4 collection methods

Ultimately, the collection method used is often determined by availability and affordability. With this in mind, the sRPE and wellness questionnaire methods afford an inexpensive and accurate method of measuring athlete load (57). This is an important consideration for nonelite athletes and teams, as it provides an opportunity to monitor load without the need for expensive devices and software. Whichever approach is taken, data should only be collected if they will be used to inform training decisions and improve both athlete welfare and performance (72). The data collected must also be viewed within the context of the current phase of training, and to the individual need and physiology of the athlete.


1. Anderson L, Triplett-McBride T, Foster C, Doberstein S, Brice G. Impact of training patterns on incidence of illness and injury during a women's collegiate basketball season. J Strength Cond Res 17: 734–738, 2003.
2. Aughey RJ. Applications of GPS technologies to field sports. Int J Sports Physiol Perform 6: 295–310, 2011.
3. Bacon CS, Mauger AR. Prediction of overuse injuries in professional u-18-u-21 footballers using metrics of training distance and intensity. J Strength Cond Res 31: 3067–3076, 2017.
4. Banister EW. Modelling elite athletic performance. In: Physiological Testing of Elite Athletes. MacDougall JD, Wenger HA, Green HJ, eds. Champaign, IL: Human Kinetics, 1991.
5. Blanch P, Gabbett TJ. Has the athlete trained enough to return to play safely? The acute: Chronic workload ratio permits clinicians to quantify a players risk of subsequent injury. Br J Sports Med 50: 471–475, 2016.
6. Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med 2: 92–98, 1970.
7. Bouaziz T, Makni E, Passelergue P, Tabka Z, Lac G, Moalla W, Chamari K, Elloumi M. Multifactorial monitoring of training load in elite rugby sevens players: Cortisol/cortisone ratio as a valid tool of training and load monitoring. Biol Sport 33: 231–239, 2016.
8. Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, Gabbett TJ, Coutts AJ, Burgess DJ, Gregson W, Cable NT. Monitoring athlete training loads; consensus statement. Int J Sports Physiol Perform 12(Suppl 2): 161–170, 2017.
9. Bowen L, Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute: Chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med 51: 452–459, 2017.
10. Buchheit M. Applying the acute: Chronic workload ratio in elite football: Worth the effort? Br J Sports Med 51: 1325–1327, 2017.
11. Buchheit M, Racinais S, Bilsborough JC, Bourdon PC, Voss SC, Hocking J, Cordy J, Mendez-Villanueva A, Coutts AJ. Monitoring fitness, fatigue and running performance during pre-season training camp in elite football players. J Sci Med Sport 16: 550–555, 2013.
12. Castellano J, Casamichana D, Calleja-Gonzalez J, Roman JS, Ostojic SM. Reliability and accuracy of 10Hz GPS devices for short-distance exercise. J Sports Sci Med 10: 233–234, 2011.
13. Cejuela-Anta R, Esteve-Lanao J. Training load quantification in triathlon. J Hum Sport Exerc 6: i–XV, 2011.
14. Charlton P, Drew MK. Can We Think About Training Loads Differently? Canberra, Australia: Australian Institute of Sport, 2015.
15. Clarke A, Schwab L. Practical considerations in the management of older Australian rules football players. J Aust Strength Cond 25: 20–23, 2017.
16. Coutts AJ, Duffield R. Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport 13: 133–135, 2010.
17. Cummins C, Orr R, O'Connor H, West C. Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Med 43: 1025–1042, 2013.
18. Dalen T, Ingebrigtsen J, Ettema G, Hjelde GH, Wisloff U. Player load, acceleration, and deceleration during forty-five competitive matches of elite soccer. J Strength Cond Res 30: 351–359, 2016.
19. Day ML, McGuigan MR, Brice G, Foster C. Monitoring exercise intensity during resistance training using the session RPE scale. J Strength Cond Res 18: 353–358, 2004.
20. Drew MK, Purdam C. Time to bin the term “overuse” injury: Is “training load error” a more accurate term? Br J Sports Med 50: 1423–1424, 2016.
21. Duhig S, Shield AJ, Opar D, Gabbett TJ, Ferguson C, Williams M. Effect of high speed running on hamstring strain injury risk. Br J Sports Med 50: 1536–1540, 2016.
22. Edmonds RC, Sinclair WH, Leicht AS. Effect of a training week on heart rate variability in elite youth rugby league players. Int J Sports Med 34: 1087–1092, 2013.
23. Edwards S. High performance training and racing. In: The Heart Rate Monitor Book. Edwards S, ed. Sacramento, CA: Feet Fleet Press, pp. 113–123, 1993.
24. Elloumi M, Makni E, Moalla W, Bouaziz T, Tabka Z, Lac G, Chamari K. Monitoring training load and fatigue in Rugby Sevens players. Asian J Sports Med 3: 175–184, 2012.
25. Fleck SJ, Kraemer WJ. Designing Resistance Training Programs (4th ed). Champaign, IL: Human Kinetics. pp. 7, 2014.
26. Fortington LV, Berry J, Buttifant D, Ullah S, Diamantopoulou K, Finch CF. Shorter time to first injury in first year professional football players: A cross-club comparison in the Australian Football League. J Sci Med Sport 19: 18–23, 2016.
27. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sport Exerc 30: 1164–1168, 1998.
28. Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C. A new approach to monitoring exercise training. J Strength Cond Res 15: 109–115, 2001.
29. Gabbett TJ. Reductions in pre-season training loads reduce training injury rates in rugby league players. Br J Sports Med 38: 743–749, 2004.
30. Gabbett TJ. The training injury prevention paradox: Should athletes be training smarter and harder? Br J Sports Med 50: 273–280, 2016.
31. Gabbett TJ, Domrow N. Relationships between training load, injury, and fitness in sub-elite collision sport athletes. J Sports Sci 25: 1507–1519, 2007.
32. Gabbett TJ, Hulin BT, Blanch P, Whiteley R. High training workloads alone do not cause sports injuries: How you get there is the real issue. Br J Sports Med 50: 444–445, 2016.
33. Gabbett TJ, Jenkins DG. Relationship between training load and injury in professional rugby league players. J Sci Med Sport 14: 204–209, 2011.
34. Gabbett TJ, Nassis GP, Oetter E, Pretorius J, Johnston N, Medina D, Rodas G, Myslinski T, Howells D, Beard A, Ryan A. The athlete monitoring cycle: A practical guide to interpreting and applying training monitoring data. Br J Sports Med 51: 1451–1452, 2017.
35. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med 44(Suppl 2): s139–s147, 2014.
36. Herman L, Foster C, Maher MA, Mikat RP, Porcari JP. Validity and reliability of the session RPE method for monitoring exercise training intensity. South Afr J Sports Med 18: 14–17, 2006.
37. Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JW. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med 48: 708–712, 2014.
38. Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: High chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med 50: 231–236, 2016.
39. Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc 36: 1042–1047, 2004.
40. Jennings D, Cormack S, Coutts AJ, Boyd L, Aughey RJ. The validity and reliability of GPS units for measuring distance in team sport specific running patterns. Int J Sports Physiol Perform 5: 328–341, 2010.
41. Kellmann M, Kallus K. Recovery-Stress Questionnaire for Athletes: User Manual. Champaign, IL: Human Kinetics, 2001.
42. Loader J, Montgomery PG, Williams MD, Lorenzen C, Kemp JG. Classifying training drills based on movement demands in Australian football. Int J Sports Sci Coach 7: 57–67, 2012.
43. Lucia A, Hoyos J, Santalla A, Earnest C, Chicharro JL. Tour de France versus Vuelta a Espana: Which is harder? Med Sci Sports Exerc 35: 872–878, 2003.
44. Malone S, Collins K. Relationship between individualized training impulse and aerobic fitness measures in hurling players across a training period. J Strength Cond Res 30: 3140–3145, 2016.
45. Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ. The acute:chronic workload ratio in relation to injury risk in professional soccer. J Sci Med Sport 20: 561–565, 2016.
46. Malone S, Roe M, Doran DA, Gabbett TJ, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic Football. J Sci Med Sport 20: 250–254, 2017.
47. Manzi V, Iellamo F, Impellizzeri F, D'Ottavio S, Castagna C. Relation between individualized training impulses and performance in distance runners. Med Sci Sports Exerc 41: 2090–2096, 2009.
48. McGuigan MR, Egan AD, Foster C. Salivary cortisol responses and perceived exertion during high intensity and low intensity bouts of resistance exercise. J Sports Sci Med 3: 8–15, 2004.
49. McNair P, Lorr M, Droppleman L. POMS Manual (2nd ed). San Diego, CA: Education and Industrial Testing Service, 1981.
50. Microsoft office. Define and solve a problem by using Solver, 2017. Available at: Accessed December 20, 2017.
    51. Moreira A, Crewther B, Freitas CG, Arruda AFS, Costa EC, Aoki MS. Session RPE and salivary immune-endocrine responses to stimulated and official basketball matches in elite young male athletes. J Sports Med Phys Fitness 52: 682–687, 2012.
    52. Murray NB, Gabbett TJ, Townshend AD, Hulin BT, McLellan CP. Individual and combined effects of acute and chronic running loads on injury risk in elite Australian footballers. Scand J Med Sci Sports 27: 990–998, 2017.
    53. Padilla S, Mujika I, Orbananos J, Santisteban J, Angulo F, Jose Goiriena J. Exercise intensity and load during mass-start stage races in professional road cycling. Med Sci Sports Exerc 33: 796–802, 2001.
    54. Paulson TA, Mason B, Rhodes J, Goosey-Tolfrey VL. Individualized internal and external training load relationships in elite wheelchair rugby players. Front Physiol 6: 1–7, 2015.
    55. Petersen C, Pyne D, Portus M, Dawson B. Validity and reliability of GPS units to monitor cricket specific movement patterns. Int J Sports Physiol Perform 4: 381–393, 2009.
    56. Saw AE, Main LC, Gastin PB. Monitoring athletes through self-report: Factors influencing implementation. J Sport Sci Med 14: 137–146, 2015.
    57. Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: Subjective self-reported measures trump commonly used objective measures: A systematic review. Br J Sports Med 50, 281–291, 2016.
    58. Scott BR, Lockie RG, Knight TJ, Clark AC, Janse De Jong XA. A comparison of methods to quantify the in season training load of professional soccer players. Int J Sports Physiol Perform 8: 195–202, 2013.
    59. Scott TJ, Black CR, Quinn J, Coutts AJ. Validity and reliability of the session-RPE method for quantifying training in Australian football: A comparison of the CR10 and CR100 scales. J Strength Cond Res 27: 270–276, 2013.
    60. Soligard T, Schwellnus M, Alonso JM, Bahr R, Clarsen B, Dijkstra HP, Gabbett TJ, Gleeson M, Hägglund M, Hutchinson MR, Van Rensburg CJ, Khan KM, Meeusen R, Orchard JW, Pluim BM, Raftery M, Budgett R, Engebretsen L. How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. Br J Sports Med 50: 1030–1041, 2016.
    61. Stagno KM, Thatcher R, van Someren KA. A modified TRIMP to quantify the in-season training load of team sport players. J Sports Sci 25: 629–634, 2007.
    62. Sweet TW, Foster C, McGuigan MR, Brice G. Quantitation of resistance training using the session rating of perceived exertion method. J Strength Cond Res 18: 796–802, 2004.
    63. Thornton HR, Delaney JA, Duthie GM, Scott BR, Chivers WJ, Santuary CE, Dascombe BJ. Predicting self reported illness for professional team sport athletes. Int J Sports Physiol Perform 11: 543–550, 2016.
    64. Thorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B, Gregson W. Tracking morning fatigue status across in-season training weeks in elite soccer players. Int J Sports Physiol Perform 11: 947–952, 2016.
    65. Turner A, Bishop C, Marshall G, Read P. How to monitor training load and mode using sRPE. Prof Strength Cond 39: 15–20, 2015.
    66. Turner AN, Bishop C, Springham M, Stewart P. Identifying readiness to train: When to push and when to pull. Prof Strength Cond 42: 9–14, 2016.
    67. Turner AN, Jeffreys I. The stretch-shortening cycle: Proposed mechanisms and methods for enhancement. Strength Cond J 32: 87–99, 2010.
    68. Varley MC, Fairweather IH, Aughey RJ. Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. J Sports Sci 30: 121–127, 2012.
    69. Wallace LK, Slattery KM, Coutts AJ. The ecological validity and application of the session-RPE method for quantifying training loads in swimming. J Strength Cond Res 23: 33–38, 2009.
    70. Watson A, Brickson S, Brooks A, Dunn W. Subjective well-being and training load predict in-season injury and illness risk in female youth soccer players. Br J Sports Med 51: 194–199, 2017.
    71. Weaving D, Marshall P, Earle K, Nevill A, Abt G. Combining internal- and external-training-load measures in professional rugby league. Int J Sports Physiol Perform 9: 905–912, 2014.
    72. Williams S, Trewartha G, Cross MJ, Kemp SPT, Stokes KA. Monitoring what matters: A systematic process for selecting training-load measures. Int J Sports Physiol Perform 12: S2101–S2106, 2017.
    73. Williams S, Weaving D. Load monitoring workshop. World Rugby Science Network Conference. University of Bath, United Kingdom, September 12, 2017.

    global positioning systems; illness; injury; overtraining; session rating of perceived exertion; training impulse; training load

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