Measuring acute to chronic workloads has become a ubiquitous practice within professional sports over the past few years. But, I really question the efficacy of these practices. Of course, anything that teams can use to manage training loads is better than nothing, but that’s less of an argument for the acute to chronic workload ratio (ACWR) concept and really just points to the fact that we should make logical decisions with how we handle training volume on a week to week basis.
For those unfamiliar with the ACWR concept, the idea is that coaches can predict injury risk by calculating the ratio between acute training loads (typically over a 5-7 day period) and chronic training loads (typically over a 3-4 week period). The theory is that if acute training loads are too high in relation to chronic workloads, the athlete is at an increased risk of injury. I have no qualms with either of these assertions, broadly speaking. Of course, if you double your training volume from one week to the next, it’s likely to open you up to an increased risk of injury, but the devil really is in the details. Most coaches measure workloads by multiplying session RPE and session duration, which creates a daily workload score in arbitrary units. This fails to acknowledge the influence of different types of training (say resistance training versus energy system training), and it also assumes that a given external workload will always create the same internal stress — having used technologies like NIRS for years now, I can comfortably say that is not the case.
Many people reading this will say, “Who cares if the methodology is flawed? If coaches are using acute to chronic workload ratios and it’s helping them make informed decisions about training loads, that’s a net positive.” I understand this argument, and it’s a good one. I’ve consulted with professional sports teams and military special operations training groups for years now and I’m comfortable saying that anything that increases dialogue around training loads and their impact is a win in and of itself. A few years back, it wasn’t uncommon for me to see MLB coaches that have no clue what their pitcher’s pitch counts were, volleyball teams that didn’t even think about jump counts, and CrossFit athletes that had no volume control whatsoever. To that effect, implementing ACWRs has been a game-changer in many cases. That being said, ‘better than nothing’ is far from ‘optimal’.
To make informed decisions about athlete's training loads, we not only need to have accurate measurements of their external training loads (which are harder to get than you'd think), but we also need to consider internal training loads — that is, the physiologic impact of training. Some strength and conditioning coaches in professional sports will acknowledge the limitations of ACWRs and use it as a tool to have discussions about training loads with the team's head coach or other support staff. I get it. However, the idea of measuring something for the sake of increasing the potential for conversation is a little silly. Additionally, anytime we measure something, we assign some inherent level of value to it. When we assign value to a measurement, it’s hard to dismiss it outright. Even if you know ACWR’s are not ideal, it’s hard not to get a little panicked when you see a player’s number jump from 1.6 (right in the sweet spot) to 2.2 (danger zone).
Challenging these load management ideas doesn’t mean we resort to nihilism and say they are meaningful or do nothing whatsoever. Instead, I propose a more nuanced approach to player load management where we account for both external and internal loads. While there are a number of load monitoring tools I see value in, like NIRS, SEMG, and infrared thermography the same principles govern how all of them inform an ‘athlete centric’ training system.
[A] Athlete does a workout, then the athletes performance in the session and response to the session inform both future training sessions as well as the ‘load monitoring algorithm’
[B] The load monitoring algorithm looks at the athletes response, total training volume, training intensity, and training distribution and tells us if the training load appears to be too much, too little, or ‘good’
[C] The load monitoring algorithm helps steer future training decisions. Should the coach increase volume? Is there a specific muscle group or region that needs less loading? Etc.
[D] The coach adjusts training to help steer the athletes response and progression, and then the cycle repeats itself
In this process load monitoring doesn’t always give comprehensive answers — instead, it allows coaches to ask better questions and helps them make informed decisions.
In terms of the types of load management we can do, I break them into offensive and defensive strategies. The offensive stratedgies include methods for auto-regulating intra-session volume and intensity, as well as training frequency. The defensive stratedgies include injury prevention stratedgies used to detect regions of interest (ROIs) and modify the training plan, treatment modification stratedgies, as well as return to play protocols. In a future article I will discuss the nuances of each of these approaches, as well as how i’ve approached load management with specific athletes, teams, and organizations.
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