The New Science of “Fatigue Resistance”
What separates the best endurance athletes from everyone else isn’t their amazing lab test data or power values—it’s how well they maintain those values after a few hours of exhausting exercise
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When the lab data from Nike’s Breaking2 marathon project was finally published last fall, the most interesting insights were of the “dog that didn’t bark in the night” variety. Among a group of some of the greatest distance runners in history, none of the standard physiological measurements—VO2 max, lactate threshold, running economy—produced any seriously eye-popping values. To understand why these runners were so good, the researchers suggested, we might need another variable: fatigue resistance, which they defined as “the extent of the deterioration of the three [other variables] over time.”
Interestingly, that same new variable pops up in a new analysis of power data from pro cyclists. An international research team led by Peter Leo, a doctoral student at the University of Innsbruck, and James Spragg, a British cycling coach, crunched the numbers from a group of elite and near-elite professional cyclists in a five-day race called the Tour of the Alps. The best predictor of race performance, competitive level, and event specialty wasn’t the raw power or heart-rate data—it was, once again, fatigue resistance.
The subjects in the new study, which was published in the International Journal of Sports Physiology and Performance, came from three European cycling teams: Tirol KTM, Bora Hansgrohe, and Androni Giocattoli-Sidermec. The 14 participants from Tirol KTM were all under-23 riders competing in the developmental Continental tier of cycling competition; the ten participants from the other two teams were pros. There are lots of ways of comparing the two groups of riders, from simple observations (the pros were shorter and lighter than the U23 riders) to complex analyses of their “power profile” (the highest power sustained for various durations ranging from five seconds to 30 minutes over the course of the five-day race).
The power profile can tell you lots of useful things about your strengths and weaknesses as a rider. If you’re really good at sustaining sky-high power output for five-second bursts, that bodes well for your ability to win sprint finishes and cover sudden mid-race moves. If your 30-minute power is unusually good, that suggests you might be a climber or a time trialist. Overall, the power profiles turned out to predict almost perfectly what order the riders finished in and how far behind the leaders they were.
There was a surprise in the power profile data, though, somewhat reminiscent of the VO2 max data from Breaking2. When they compared the U23 riders to pros, there were no significant differences in the power profiles of the two groups—with the minor exception of the five-second power, which was actually higher in the U23s. Similarly, when they compared different types of cyclists like climbers and all-rounders, there weren’t major differences in the power profiles.
The default power profile was constructed by searching through each rider’s data for the entire five-day race to find, say, the five-second window with the highest average power. Same thing for ten seconds, 15 seconds, and so on up to 1,800 seconds (i.e. 30 minutes). But you can do a similar analysis while limiting your search to the highest five-second power produced after you’ve already done, say, 1,000 kilojoules of cycling during that day’s stage. According to Leo, a typical pro cyclist might accumulate 800 to 900 kilojoules of work during an hour of training, and up to 1,500 kilojoules per hour during a race.
So the researchers repeated that process to construct separate power profiles for the riders after 1,000, 1,500, 2,000, 2,500, and 3,000 kilojoules of work. Here’s how the resulting power profiles looked for the professionals versus the under-23 riders:
As you’d expect, the max powers are highest for the short bursts (on the left side of each graph) and lowest for the longer durations (on the right side). For the pros, the lines are mostly bunched together on top of each other. That means that even if they’ve been riding fairly hard for a few hours, they can still surge for a minute or two almost as quickly as they could when fresh. It’s only at the highest level of fatigue, after 3,000 kilojoules of work, that their sprint performance starts to drop off noticeably.
In contrast, the power profiles for the U23 riders are much more spread out. Even after just 1,500 kilojoules of work, their ability to sustain high-intensity efforts is noticeably impaired. In other words, it’s fatigue resistance that differentiates pros from U23s.
You see something similar when you compare different styles of rider. The way they divided the riders up is a bit complicated. First they used height, weight, and body surface area to divide them into climbers (small, light cyclists ideally suited to pedaling up Alps) and all-rounders (bigger, more versatile cyclists who can sprint and time trial well in addition to climbing). Then they divided the climbers into GC (general classification) riders, who placed in the top ten of the overall race standings, and domestiques, who placed outside the top ten. Here’s what their power profiles looked like:
The difference here is even starker. The GC riders—the ones who hope to actually win multi-stage races—have virtually no difference in their power profile even after 3,000 kilojoules. The less accomplished domestiques show a much greater effect of fatigue. And the all-rounders have the most pronounced drop in performance, which is presumably why they’re not given the assignment of trying to win the overall race. You can’t win a multi-stage tour unless your fatigue resistance is exceptional.
There are a number of nuances to consider. One is that this data was collected during a real-world race, which means that the power data reflects the particular tactics used by each team and how each stage played out. In a stage with an early breakaway, maybe no one really needed to max out their five-second power. And each rider’s role affects the resulting power profiles: the differences between GC rider and all-rounder profiles may be partly a result of the jobs they’re assigned.
Also, quantifying fatigue by the number of kilojoules expended is a very blunt measure. Cruising along at a steady 250 watts for an hour burns up 900 kilojoules; but so does cruising along at 230 watts with a couple of one-minute surges at 600 watts. The latter is likely to trash your legs far more than the former, and professional stage racing is full of sudden shifts between low and high intensities.
That complexity makes it hard to zero in on why some riders have better fatigue resistance than others. Fatigue, after all, has many different components: metabolic disturbances in your muscles, altered signals from your brain and through your spinal cord, depleted motivation and cognitive resources. The precise mix of these components at any given point during a five-day race will vary widely, so it’s not clear exactly what superpower the GC riders possess that enables them to shrug off a few hours of hard riding.
Still, when I asked Leo how to develop fatigue resistance, he did have a few practical suggestions. One is that running low on carbohydrates seems to make fatigue resistance worse—an observation that dovetails with other data from the Breaking2 project, which found that taking in 60 grams of carbohydrate per hour improved fatigue resistance. In training, Leo and his colleagues hypothesize that the volume of training you do is more important than the intensity for developing fatigue resistance. And you might try including intervals or sprints toward the end of a longer ride, he suggested: four x 8:00 hard with 4:00 recovery after three to four hours of lower-intensity riding, for example.
For now, there are more questions than answers about fatigue resistance. But I suspect we’ll see a lot more research about it in the years to come. “In longer endurance events,” Leo points out, “it’s all about how you can perform in a fatigued state, rather than a fresh state.”
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