By Alex Hutchinson
Researchers debate whether sex differences are in the mind or the muscles.
Earlier this year, researchers published a paper analyzing 92,000 marathon performances to determine that women are “better” at pacing themselves—that is, women slowed down by 11.7 percent on average in the second half of their races, while men slowed down by 15.6 percent.
The question is: why? Two of the authors of that paper have recently presented differing views of the source of sex differences in fatigue, competitiveness, and athletic performance. First, evolutionary psychologist Robert Deaner, of Grand Valley State University, who was the first author of the marathon pacing paper, has written an interesting essay for The Conversation, giving an overview his research from the past several years, titled “Distance running is a perfect lab to investigate whether men are more competitive than women.” (Amby Burfoot also wrote about Deaner’s research for Runner’s World a few years ago.)
Deaner’s research follows several different lines of reasoning. For example, as the marathon study showed, women tend to slow less than men, suggesting that men are more likely to undertake a “competitive, risky pace.” Among competitive college runners, even at the highest levels, women report training less and focusing more of their studies. And participation, particularly in settings like masters track where the focus is on competition, skews heavily male.
What causes these differences? One possibility, Deaner notes, is that “the sex difference in competitiveness reflects, at least in part, innate predispositions that evolved in response to the different challenges men and women faced during our evolutionary history.”
But might there be other explanations? At the ACSM conference last week, one of the keynote presentations was on sex differences in fatigue, by Marquette University exercise scientist Sandra Hunter, who also happens to be one of the authors of the marathon pacing paper. Her talk focused on actual physiological differences between men and women. As she pointed out, every cell in your body has a sex, as encoded in your chromosomes, and those differences manifest throughout the body. Men, for example, have more muscle mass, larger hearts, more hemoglobin, and less body fat.
What was surprising to me is the large body of research suggesting that women are usually less fatigable than men, as Hunter documented in a review in Acta Physiologica last year. Ask a group of people to perform a muscle contraction at a given percentage of max, then ask them to repeat it over and over, and it’s men whose force will generally decline first. Of course, men start from a higher initial force, because they’re stronger, so that may have something to do with it.
Interestingly, the differences in fatigability vary depending of the specific demands of the task— for example, Hunter and her colleagues had subjects sustain an elbow-flexor contraction at 20 percent of max for as long as they could while performing a cognitive task. When the task was simple (counting backward in increments of one), men and women were the same; when it was more complicated (counting backward in increments of 13 from a four-digit number), women fatigued more quickly than men.
What’s the conclusion from all this? First, it’s that there are physiological differences in fatigue between men and women. Second, it’s that we’re a long way from understanding these differences. Hunter also points out that the apparent differences could be skewed by the fact there are so many more studies of men than women, just as our perceptions of male-female differences in racing results may be skewed by the low participation numbers in many female age-groups.
Which brings us back to Deaner’s arguments. Are participation rates a consequence of underlying male-female differences in competitiveness, or a confounding factor? At this point, we simply don’t know. I agree with Deaner that distance running offers an interesting lab to study male-female differences, but I think we need to be careful to understand all the different factors that could affect the data.
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