Seeking a Grand Theory of Injury Prevention
A new study of Western States ultrarunners illustrates the power of looking beyond simple risk factors to predict injury
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On the great philosophical question of why bad things (i.e. injuries) happen to good people (i.e. runners who obey the ten percent rule), the sages are divided. Some say it’s because we haven’t yet figured out precisely which form flaw, muscle imbalance, or training error triggers a given injury. Others say that the problem runs deeper—that we need a comprehensive causal framework that links together training and life stresses, the resulting biomechanical loads applied to different parts of the body, and the ever-changing capacity of each joint and tissue to absorb those loads.
In a new (and free to read) opinion piece in the International Journal of Sports Physical Therapy, running injury experts Chris Napier of the University of British Columbia and Rich Willy of the University of Montana advance that latter perspective. For example, they point out, simple rules about training load are doomed to failure because increasing the stress on a tissue like a tendon by just ten percent will cause it to fail 50 percent earlier. That’s one reason that the addition of speedwork so often triggers problems even if you’re not running any farther than usual. You can’t prevent an injury unless you understand the sequence of events that’s likely to cause it.
But drawing that web of causal arrows remains a tall order—a point illustrated nicely by another new study, this one on Western States ultramarathoners. A team led by Emily Kraus, a sports medicine doctor and researcher at Stanford University, studied 123 runners (83 men, 40 women) who ran the 100-mile race in 2018 and 2019, publishing the results in the Clinical Journal of Sports Medicine. Their goal: to explore the components of the female and male athlete triads, including the risk of stress fractures and other bone stress injuries, in ultramarathoners.
In women, the athlete triad refers to “the combination of low energy availability, menstrual irregularities, and low bone mineral density (BMD).” In men, low levels of sex hormones such as testosterone substitute for menstrual irregularities. It’s a subset of the broader condition known as relative energy deficiency in sport (RED-S).
In theory, the causal arrow here is pretty straightforward. If you don’t eat enough, either overall or during the periods of the day when you need it most to support your training, you’ll end up with lower levels of sex hormones. That’s what triggers the warning sign of irregular or absent periods. And over time, it can lead to lower bone mineral density, which in turn leaves you vulnerable to stress fractures, even at training loads you might previously have been able to handle. There’s good evidence for every step of this chain.
Based on a self-reported questionnaire (sample question: “Are you trying to change your body weight or body composition to improve your performance?”), lots of the Western States runners appeared to be at risk of disordered eating: 62.5 percent of the women and 44.5 percent of the men. Quite a few (16.7 and 30.1 percent, respectively) had low bone density, defined as a Z-score less than -1. Many (37.5 and 20.5 percent) had a history of stress fractures, which matches data from other studies of hardcore runners. Add in a few other variables like low body mass and irregular periods, and you can calculate a cumulative triad risk score, which identified 61.1 percent of women and 29.2 percent of men as being at moderate risk for bone stress injuries and 5.6 percent of each as high risk.
In a sense, this is a nice illustration of Napier and Willy’s point. If you zero in on a single risk factor like bone mineral density, you’re not going to get a very useful gauge of injury risk. Relatively few of the women had low BMD, but lots had stress fractures; for men, it was the opposite. Hormone levels, measured using InsideTracker’s blood testing battery, were similarly ambiguous. In women, there did seem to be a link between low levels of testosterone and estradiol and low bone mineral density. The same pattern didn’t show up in men, though.
If you broaden the causal diagram, as with the cumulative triad risk score, you get a more meaningful assessment of injury risk. In one of Kraus’s previous studies, for example, every one-point increase in the cumulative risk score for male athletes (which doesn’t even include any direct assessment of hormones, since there’s no simple proxy like menstrual dysfunction for men) produced a 57 percent increase in the risk of a subsequent stress fracture. For women, it’s even more pronounced: a diagnosis of moderate risk doubles your chances of a stress fracture, and high risk quadruples it.
That’s still just one part of Napier and Willy’s grand plan, though. Napier co-authored another recent paper (with Karrie Hamstra-Wright of the University of Illinois at Chicago and Kellie Huxel Bliven of A.T. Still University) that describes a “holistic approach” to bone stress injuries, suggesting that “athletes have their own cumulative risk profile that influences their capacity to withstand specific training loads.” Here they’re no longer talking only about triad risk factors: instead they’re summing up a vast web of non-modifiable (sex, race, age, genetics, alignment, prior injury…) and modifiable (strength, fatigue, flexibility, biomechanics, stress, recovery, nutrition…) intrinsic factors, as well as extrinsic factors like footwear, training surface, and training load.
There’s no equation for this hypothetical super-calculation of injury risk—yet. In a sense, this is simply an attempt to mathematically describe what already goes on inside the head of a good clinician when he or she is assessing an athlete. The big question is whether the equation—or, more likely, a future machine-learning algorithm—will ever be able to combine all those inputs and produce injury advice that’s meaningfully better than, say, the ten percent rule plus occasional reminders not to do anything stupid. Only time, and a lot of painstaking research, will tell.
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