I recently saw a good discussion online about what evidence-based methods exist for injury prevention. With recent insights demonstrating that things like Nordic hamstring curls(1) or other exercise programs may not be the magic bullets we once thought, the community’s attention quickly turned to ideas of “load management”. Load management, maybe most recently popularized (at least in Canada) by Kawhi Leonard’s time with the Toronto Raptors, is the idea that reducing a player’s game time gives the player more time to recover between games, therefore reducing injuries. In theory this sounds amazing, less gameplay = more recovery = less injuries. However, it fails to acknowledge that load management, at least in the sense of sitting out games, may actually work by the player having less exposure to injury opportunity rather than improving recovery. After all, it’s a lot harder to sprain your ankle in a game when you’re playing 50 games instead of 75. Also, since significantly more injuries in major sports(2) are acute versus overuse, maybe load management, at least how we think of it colloquially now, isn’t what we think it is. Maybe the popular discussion around load management actually misses a few key points that I want to cover here. Firstly, what exactly IS workload, and secondly, what does monitoring workload look like across and within various sports?
Part 1: Defining Workload
Workload, also referred to as training load, is defined as the product of volume and intensity of training (3,4). This definition primarily refers to external workload, the parameters that we assign to a given training session. For example, external workload in soccer exists as things like total distance covered, number of maximal sprints undergone, or total time of competition. In a sport like weightlifting, it’s much more simply measured – think weight, sets, reps, exercise selection etc. While external workload is most commonly what people refer to as workload, it fails to capture what is likely more important – internal workload. Impellizerri et al (2019) have an incredible paper (5) covering what workload looks like in the concept of training outcomes:
As we can see above, internal workload can be broken down into a wide variety of things. While each of these factors are important, I think the best way to look at it is through the lens of “not all squats are made equally”. What I mean is that on a given day, a 275-pound squat may be a maximal effort for an individual, while on a separate day, a 275-pound squat could act as an easy warm up. There are too many factors within internal workload for us to ONLY take an external workload approach with how we are monitoring athletes. This extends to team sports as well – a Tuesday practice that features 30 minutes of hard skating may be exponentially more taxing for a hockey team than it would typically be if we fail to monitor the overall internal workload and preparedness for activity of the athletes. So, how DO we measure both internal and external workload?
Part 2: Measuring workload
As mentioned earlier, measuring external workload can be a rather simple process in some sports (weightlifting/powerlifting), or it can require significant technology to adequately capture the appropriate data. It’s commonplace now in elite soccer for teams to regularly track distance covered via GPS, total accelerations using accelerometers, and heart rate (which is technically a measure of internal workload, but I digress). With this increase in available technology, we have begun to get a better understanding of the trends in workload seen in sport, as well as capturing objective data that can be monitored over time. Justifications for this hyper focused attention to external workload have existed largely under the premise that unprepared spikes in external workload (>10% increase in a given week compared to a 4-week average) may increase the likelihood of injury (6). However, this has been both contested and argued to be completely dismissed (7). It seems that while monitoring external workload is important for a wide variety of reasons, it does not accurately predict injury risk or fully capture the experience of athletes. So maybe monitoring external workload for injury risk, at least how we do it now, isn’t as relevant as we thought. Are we instead better off looking at internal workload?
So how then do we truly measure internal workload, and what does it mean. The simplest way to measure internal workload is accomplished by asking the athlete to evaluate their perceived effort using something like the rate of perceived exertion (RPE) scale. Below are two examples of common RPE scales, with the left being the modified BORG (more often seen in team sports), and the right (check out strongerbyscience.com) being RPE using reps in reserve as its anchor.
By asking athletes to rate how they perceive the difficulty of sessions, we can have a better understanding of their internal workload. We can even compare the perceived internal workload to the objective external workload of the training session and map out trends over time between the two. Using RPE in resistance training is a well established and well studied concept, with Dr. Michael Zourdos’ catalog of research demonstrating significant correlations between RPE, bar velocity, and reps in reserve (8). This shows that RPE is likely sufficient to help evaluate perceived difficulty of resistance training tasks without the need for additional technology (even if it is fun to look at).
In team sports, using RPE to effectively plan practices has already been studied in soccer, showing that is a valid way of evaluating the perceived intensity of a training session (8). It's also important to note that in team sports training, the intensity of the last activity in practice didn’t sway RPE rating (9). For the time being, monitoring athletes’ perceived session RPE (and pre-session preparedness) may be the simplest way to effectively plan and adjust training sessions as needed to avoid overtraining of team athletes.
Part 3: All RPE isn’t the same
This section deserves to be much longer, but for the sake of brevity, there is a key point I’d like to make here. When we think of monitoring athlete workload for performance and health, we need to consider both sport and position specific requirements. In soccer, highest intensity running requirements have been tracked and can be visualized below (10):
This data from the 2013-2014 La Liga season shows some pretty interesting findings. As we can see, position specific demands exist for the external load tasks of different positions on the pitch. If external loads and demands are different, what then is the likelihood that internal loads differ between positions within a team sport? Most importantly, this should signify that team training, especially as we approach elite levels, needs to be position targeted. This may even warrant further investigation to see if this can be used to improve athletic development at younger ages (but not too young here, we want our athletes to be actual athletes, not position robots).
When considering sport specific workload monitoring, I really like this slide from a presentation done by Dr. Martin Asker (11). In a talk about overhead athletes, Dr. Asker highlights in his work on handball athletes that simply monitoring athletes perceived workload isn’t sufficient to gather a clear idea of internal workload. Rather, because handball is an overhead sport, we should also consider what the shoulder specific internal training load is.
Clearly we need to map out and consider the variables we most care about when having a workload management approach. If we really believe that “load management” is the key to injury reduction, maybe we need to broaden even our simplest measurements.
Part 4: Conclusions on the current evidence on workload monitoring
As it currently stands, we don’t really have conclusive data that “load management” is the causal factor for significant reductions in injury. Rather, what we do know is that workload monitoring, both external and internal, should likely be guiding our training and program design. Workload monitoring helps us track if we are providing sufficient stimulus (e.g is the training difficult enough to elicit development), or if its repeatedly too difficult and taxing, leading to overtraining. This doesn’t mean that we won’t see future developments in the injury prevention side of things, but for now, we need to be careful about making claims around this concept. Instead, view workload management for what it is, a way to monitor and optimize appropriate training design for athletes, that can and should be implemented from youth athletes up to elite levels.
1. Impellizzeri FM, McCall A, van Smeden M. Why methods matter in a meta-analysis: A reappraisal showed inconclusive injury preventive effect of Nordic hamstring exercise. Journal of Clinical Epidemiology. 2021 Dec 1;140:111-24.
2. Yang J, Tibbetts AS, Covassin T, Cheng G, Nayar S, Heiden E. Epidemiology of overuse and acute injuries among competitive collegiate athletes. Journal of athletic training. 2012 Mar;47(2):198-204.
3. Impellizzeri FM, Rampinini E, Coutts AJ, Sassi AL, Marcora SM. Use of RPE-based training load in soccer. Medicine & Science in sports & exercise. 2004 Jun 1;36(6):1042-7.
4. VIRU, A., and M. VIRU. Nature of training effects. In: Exercise and Sport Science, W. Garrett and D. Kirkendall (Eds.). Philadelphia: Lippincott Williams & Williams, 2000, pp. 67–95.
5. Impellizzeri FM, Marcora SM, Coutts AJ. Internal and external training load: 15 years on. International journal of sports physiology and performance. 2019 Feb 1;14(2):270-3.
6. 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. British journal of sports medicine. 2016 Feb 1;50(4):231-6.
7. Impellizzeri FM, Woodcock S, Coutts AJ, Fanchini M, McCall A, Vigotsky AD. What role do chronic workloads play in the acute to chronic workload ratio? Time to dismiss ACWR and its underlying theory. Sports Medicine. 2021 Mar;51(3):581-92.
8. Zourdos MC, Klemp A, Dolan C, Quiles JM, Schau KA, Jo E, Helms E, Esgro B, Duncan S, Merino SG, Blanco R. Novel resistance training–specific rating of perceived exertion scale measuring repetitions in reserve. The Journal of Strength & Conditioning Research. 2016 Jan 1;30(1):267-75.
9. Fanchini M, Ghielmetti R, Coutts AJ, Schena F, Impellizzeri FM. Effect of training-session intensity distribution on session rating of perceived exertion in soccer players. International journal of sports physiology and performance. 2015 May 1;10(4):426-30.
10. Rivilla-García J, Calvo LC, Jiménez-Rubio S, Paredes-Hernández V, Muñoz A, Van den Tillaar R, Navandar A. Characteristics of very high intensity runs of soccer players in relation to their playing position and playing half in the 2013-14 Spanish La Liga season. Journal of human kinetics. 2019 Mar;66:213.
11. Martin Asker - Return to play in handball after shoulder injuries. 2019. https://www.youtube.com/watch?v=k-CgCIqibXg