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- Alex Hutchinson (@sweatscience)
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Here’s a graph, from a recent paper on nutrition during long (marathon and longer) endurance competitions, that’s worth a close look:
What do you see? A bunch of dots scattered randomly? Look a bit more closely. The data shows total carb intake (in grams per hour) by racers in Ironman Hawaii (top) and Ironman Germany (bottom), plotted against finishing time. It comes from a Medicine & Science in Sports & Exercise paper by Asker Jeukendrup’s group (with several collaborators, including Canadian Sport Centre physiologist Trent Stellingwerff) that looked at “in the field” nutritional intake and gastrointestinal problems in marathons, Ironman and half-Ironman triathlons, and long cycling races. The basic conclusion:
High CHO [carbohydrate] intake during exercise was related to increased scores for nausea and flatulence, but also to better performance during IM races.
So basically, taking lots of carbs may upset your stomach, but helps you perform better. It’s important to remember that gastrointestinal tolerance is trainable, so it’s worth putting up with some discomfort to gradually raise the threshold of what you’re able to tolerate.
Anyway, back to that graph: while it may look pretty random, statistical analysis shows a crystal-clear link between higher carb intake rates and faster race times, albeit with significant individual variation. Obviously there are some important caveats — it may be, for example, that faster athletes tend to be more knowledgeable about the benefits of carbs, and thus take more. Still, it’s real world data that tells us the people at the front of the race tend to have a higher carb intake rate.
One other point worth noting. The traditional thinking was that humans generally couldn’t process more than 60 grams of carb per hour. Over the last few years, thanks to multiple-carb blends, that threshold has been pushed up to 90 grams of carb per hour. In this data set, about 50% of the triathletes were taking 90 g/hr or more.
[UPDATE 10/26: Given all the comments below about the variability in the data, I think it’s worth emphasizing what should be a fairly obvious point. The only way this data would come out as a nice straight line is if Ironman finishing time depended ONLY on carb intake, and was totally independent of training, experience, talent, gender, body size, and innumerable other factors. This is obviously not the case, so we should expect the data to be very broadly scattered. What the statistical analysis shows is that, with p<0.001, faster finishers tended to have consumed carbs at a higher rate. There are many ways to interpret this data; one possibility is that, if your carb consumption is below average, you might wish to try a higher rate of consumption (e.g. 90 g/hr) to see if it helps.]
The study assumes that “carbs is carbs.” That’s common in research. In the field, though, it’s well known (okay, anecdotally) that some carbs cause more stomach problems than others. The Twinlab fuels or Gatorade always left me feeling, 18 miles into a marathon or ultra, as if I had swallowed a bowling ball, whereas Hammer Nutrition’s Sustained Energy cause no such problems and always took me safely through that dangerous “wall” phase. I always suspected it was the difference between simple sugars such as fructose, sucrose, and glucose that the former fuels used, versus the maltodextrin in SE. The difference was huge.
What do you think about the use of a product like Vitargo in performance model like this? Would be more effective?
Granted I didn’t read the original study, but I wonder if one caveat is that the study only included those athletes that finished the race. It would be interesting to know if there is a statistical trend in the carb g/h for those athletes that were forced to drop out due to gastrointestinal issues.
Alex
this is prime example of an “observational” study. You know as well as I do that there are several other confounding variables that could contribute to performance. What if those with a higher CHO intake were simply fitter ?? Did they control for anything ? what about caffeine intake, protein intake, electroylte intake, MCT intake, other supplement intake ? Then simply look at the individual data points. There are points which have a similar finish time, yet a high and a low CHO intake. So there were those that had a similar finish time whether they had 90g per hour or 30g per hour.
Juekendrup works for Gatorade remember, enough said 😉
@Barry
I would expect most confounders to even out except one. If I understand the abstract correctly, these are male and female times lumped together. My experience at the dinner table suggests that men tend to consume more and I would also expect them to spend more energy, and have better times in an Ironman. (Or should we say Ironperson.)
sorry pal, but r = -0.48 and r = -0.55 is not very convincing. even if you have it confirmed with a p < 0.001 (which is admittedly pretty damn good). also, the fact that it's an observational study makes me frown. better luck next time kids.
The point may be valid, but it also looks like there is a huge amount of variability in that data.
It’d be interesting to know if ultrarunners’ performances would be more affected by the increased nausea associated with the 90g/hr regimen than IM competitors.
@seydar: “sorry pal, but r = -0.48 and r = -0.55 is not very convincing.”
Um, what were you expecting, “pal”? Did you really think that performance in a real-world Ironman would be a linear function of carb intake? In other words, you expect that someone who takes 65 g/hr of carb will finish faster than someone taking 60 g/hr of carb, totally independent of training, experience, talent, gender, and body size?!
There is no possible way you’d expect to see anything other than a very broadly distributed scatter plot for this data, because performance doesn’t just depend on carb intake! The point of this data, however, is that statistical analysis suggests that there’s a significant relationship between carb intake and finishing time.
This doesn’t mean that if you take one extra gel in your next triathlon, you’ll automatically finish faster!!! It just means that, on a population level, people who finish faster tend to have consumed carbs at a higher rate. There are many possible ways to interpret this data; one of those possibilities is that, if your intake tends to be on the low side, you might wish to consider trying a higher intake to see if it helps.
@Barry: So if I can sum up, you’re saying “Instead of this study, I wish they had done another study.” Fair enough. 🙂
Oh, and this study was completed long before Jeukendrup started working for Gatorade.
Alex
here’s how I would summarise something like the data you posted:
“As you can see, there is huge variability in the date. In addition, its only an observational study without any controls. It does appear to suggest that SOME people can tolerate a high carb intake. It also shows that SOME people do not need a high carb intake but can still achieve similar performances. In other words, no clear cut finding can be concluded from this study. Find out what works for you, the individual, and don’t follow any blanket statements or recommendations from the likes of people like me or sweatscience !”
Oh and Liza – I can tell you, from experience and research, intaking 90g/hr of CHO during a 50 or 100mile ultra would surely lead to a DNF. For the record, I did a trail ultra recently, took in about 30g/hr, and it took me just over 8hrs. And I won the race. 😉
From my point of view those correlations are alot higher than I would have guessed. If the relationship was purely causal the R2 would indicate about 25% of the variation in race performance being due to carb intake.
That seems intuitive unrealistic (to high). The correlation nevertheless is interesting. It does seem they could have controled for gender as previously mentioned which would probably drop those r’s a bit.
Another confounder could be simply based on the time itself. 10 hrs of 90g/hr carb consumption may be more plausible than 15/hrs at 90g/hr. So the slower athletes may be taking less carb per hr simply because it is harder to maintain that rate for the longer time period.
@Barry:
“…don’t follow any blanket statements or recommendations from the likes of people like me or sweatscience.”
“I can tell you, from experience and research, intaking 90g/hr of CHO during a 50 or 100mile ultra would surely lead to a DNF.”
Notice any contradictions between those two statements? 🙂
@Seth: Definitely agree that the total time is likely an important contributor to the observed relationship. The longer you’re out there, the harder it would be to maintain these super-high levels of intake.
Alex
hehe.. walked myself into that one ! I guess this is a prime example of what I mean. To re-phrase “intaking 90g/hr of CHO during a 50 or 100mile ultra would surely lead to a DNF, but you can give it a try and see!”
Sometimes, making an example of yourself is the best way to learn 😉
90 gm of carb is about 3 gels per hour – that is just about right!! – muscle might not need sugar but brains certainly do (or die without)
Could it simply be that the rate of fat metabolism cannot keep up with the higher energy rate requirements of the faster athletes, so the proportion of carbohydrate intake must increase in order to compensate?
Absolutely – that seems very likely to be a contributor to the observed relationship.
This is a correlation. Why have are so many comments, and the original blog entry, assuming causality? One could just as easily interpret the study as faster endurance athletes can consume more carbohydrates during a race than slower athletes. If you have the ability to perform at the edge of aerobic capacity across long distance, you may also require more carbohydrates to fuel that performance. If someone is slower, his demands might be lower. Alternatively, you could interpret the carbohydrates are more quickly processed (whether this leads/drives performance is not clear), so that more can be ingested when one is able to perform very quickly at endurance.
Also, your statement about a near straight line, with all data points lined up tightly, showing exceptional goodness of fit of a regression line, would not lead to the conclusion that carbohydrate intake is the only driver of performance. Why do you think that? You could still see perfect correlation and it would be just that – a perfect correlation. Another variable(s) could be responsible for causality. Again, it could be that speed of performance in endurance events, which is driven by fitness/skill of the athlete, induces more need for carbohydrates (or, again, greater ability to process, metabolize, clear, desire to eat more, etc.
Let’s be clear about something: there is nothing about this study that, even with purported statistical robustness (which is only a statement about the statistic(s) reported, rather than a statement about “underlying” causality or anything else, for that matter,), that suggest eating more carbohydrates will improve an athletes performance at endurance sports like distance running and tris.