What does the evidence show?
Why Caffeine Might Not Make You Faster
It's the most reliable performance booster in the world—unless you have the wrong genes
Alex Hutchinson
Study after study shows that caffeine enhances performance, particularly in endurance events, by a few percent—on average. (Dimitrije Tanaskovic/Stocksy)
Sometimes I feel bad that I’m such a supplement skeptic. “Do you think Pill X will make me a better athlete with healthier skin and more regular bowel movements?” someone will ask, pointing me to a small unblinded study with four subjects, all of whom are employed by the company selling Pill X. “Sorry, but no,” I’ll say. Then I’ll try to cushion the blow with some good news: “Caffeine works, though. For almost everything.”
It’s true that caffeine’s power as a performance booster is uniquely well-supported by a big pile of well-designed scientific studies. There’s even a whole book about its athletic applications, written by some of the most respected sports nutrition researchers in the world. Study after study shows that caffeine enhances performance, particularly in endurance events, by a few percent—on average.
But a few years ago, researchers started to look more closely at the individual variability in response to caffeine. Every study has some more or less random scatter in its results: If the average improvement is 3 percent, some people might actually get 6 percent better, while others don’t improve at all. But not all scatter is random. A few years ago, evidence started to emerge that some people were consistently more likely to benefit from caffeine than others, and some might actually get slower after taking caffeine, based on their genetic profile. The biggest study to date on this topic has just been published in Medicine and Science in Sports and Exercise, and the results are striking.
The study was conducted by Nanci Guest, a sports dietitian and doctoral candidate at the University of Toronto, and her supervisor Ahmed El-Sohemy, a professor of nutritional science. They put 101 well-trained male (more on that below) athletes from a variety of sports (everything from marathon running to boxing to soccer) through a series of three 10K cycling time trials. Before each one, the athletes received either a placebo, a low dose of caffeine (2 milligrams per kilogram of body weight), or a higher dose (4 mg/kg). If you weigh 150 pounds, that corresponds to either 136 or 272 mg of caffeine. (A typical cup of coffee has 100 to 160 mg of caffeine, and a single NoDoz tablet has 200 mg.)
As expected, caffeine boosted performance. The athletes cycled 3 percent faster with the high dose of caffeine compared to the placebo. The improvement with the lower dose wasn’t statistically significant. Here’s what those results looked like, showing the cycling time in minutes on the vertical axis:
(Medicine & Science in Sports & Exercise)
That’s where these studies usually end: Caffeine works, so go forth and get caffeinated. But in this case, the researchers also took a spit test to determine which version of a gene called CYP1A2 each subject had. More than 95 percent of the caffeine you drink is metabolized by an enzyme encoded by this gene. People with the AA version of the gene are considered “fast” metabolizers, breaking down the caffeine rapidly. People with the AC or CC version are “slow” metabolizers, with the latter group especially slow, meaning that the caffeine they drink lingers in their bodies for much longer.
Take a look at what happens when you break down the results by genotype:
In the AA group (the fast metabolizers), caffeine is helpful, and the more the better. They get 4.8 percent faster at the low caffeine dose and 6.8 percent faster at the high caffeine dose. In the CC group (the very slow metabolizers), the pattern is the opposite: The more caffeine they get, the slower they go. At the higher dose, they’re 13.7 percent slower! In the middle AC group, it seems to be pretty much a wash, with no significant difference either way.
In the study group, 49 percent of people were in the AA group. They’re the lucky ones in terms of getting a caffeine boost. Of the rest of the group, 43 percent were AC (slow) and just 8 percent were CC (very slow). This distribution is similar to what’s seen in other studies, suggesting that it’s relatively rare to get negative effects from caffeine but quite common to have no significant effect. Interestingly, there’s apparently no link between this gene and habitual caffeine consumption levels: Your rate of caffeine metabolism isn’t what determines whether you like caffeine.
Another interesting wrinkle is that the effects of caffeine metabolism aren’t limited to athletic performance. In previous work, El-Sohemy and his colleagues showed that fast metabolizers have a lower risk of heart attack if they drink one to three cups of coffee a day, perhaps thanks to its antioxidants and other health-promoting elements. In contrast, slow metabolizers have a 36 percent increase in heart attack risk if they drink two to three cups of coffee a day and a 64 percent increase if they drink four cups or more.
It’s also important to note that the subjects in this study were all male. In theory, El-Sohemy says, they would expect to see similar results in female athletes, and the researchers hope to extend the findings to women in a separate study. There’s some evidence that hormonal contraceptives can affect the activity of the caffeine metabolism enzyme, which raises the possibility that there might be some subtle effects related to the female hormonal cycle. The current study required all subjects to abstain from caffeine for four weeks during their four weekly visits to the lab. To study the question properly, Guest notes, women would have to come into the lab at the same time each month to ensure similar hormone levels, which would require four months of complete caffeine abstention. That practical barrier is why they started with the male study.
The big question, of course, is what you should do with this information. And at this point, it’s important to note that El-Sohemy is the founder of a company called Nutrigenomix, which offers genetic testing for personalized nutrition, including the caffeine metabolism gene. Guest serves on the company’s scientific advisory board. That’s exactly the sort of conflict I was making fun of at the top of this piece, and it should give us pause. I do think the study design here is really strong—the sample size is huge by the standards of this area of research, and supplement allocation was double-blinded so the researchers didn’t know who was getting the placebo when. (Remarkably, only three of the 101 subjects correctly guessed which pill they had received each time when they were debriefed after the study.) And there has been research from other groups that reached similar conclusions.
Still, I remain on the fence about the merits of getting tested to figure out how caffeine is likely to affect your performance and health. The hype about genetic predictions often outpaces the results—for example, a major attempt to use genotype to predict who would benefit from a low-fat versus a low-carb diet fell flat in real-world testing last month. Still, these results are intriguing, and if I drank coffee (which, as it happens, I don’t), I’d certainly be curious about which genotype group I fell into.
In the end, it’s also important not to fall into the same trap we originally fell into, assuming that average results reflect everyone’s individual reality. When I initially posted the abstract of this study on Twitter, I got a response from John Trautmann, a running legend who once held the U.S. high school record for the 3,000 meters, competed at the 1992 Olympics, and more recently set a 45–59 age group world record of 4:12.33 for the indoor mile. “Strange,” he wrote. “I’m CC, and I find it hard to work out without a caffeine boost. Placebo effect?”
I don’t know why Trautmann, who supposedly should get slower after taking caffeine, finds it to be an essential workout aid. It’s possible that his perceptions are wrong—that he feels better working out with caffeine, but he actually runs slower. Or it’s possible that other factors in that delicate balance between positive and negative effects are dominant. Maybe he’s ultrasensitive to the positive effects or insensitive to the negative effects. But the bottom line is that for a runner who has accomplished as much as Trautmann has, I would be very wary of telling him he’s been doing it all wrong on the basis of a DNA test. And the rest of us, even at less rarefied levels, should be similarly cautious.
Kilian Jornet’s double ascent of Mount Everest in 2017 was (as Outside contributing editor Nick Heil recounted) a truly bizarre saga. The surprising paucity of video, photo, and GPS evidence generated some doubts about the details of his trip that have never fully dissipated. On the other hand, most observers are willing to take Jornet’s word for what happened, if only because he has such a long and well-documented history of doing things that seemed completely impossible until he did them.
But forget about the controversy for a moment—because even if you think he didn’t quite make it to the actual summit one or both times, his trip was still ridiculous. He climbed past 8,000 meters four times in just 20 days. The first of those climbs, to the summit plateau of Cho Oyu at roughly 8,200 meters, took place just 10 days after he arrived in the Himalayas. For a guy who lives on the western coast of Norway—at sea level, in other words—that’s a heck of a rapid acclimatization. So how did he do it?
A new paper in the International Journal of Sports Physiology and Performance, co-authored by University of Lausanne physiologist Grégoire Millet and Jornet himself, lays out the details of his acclimatization strategy. Following it doesn’t guarantee you a trip to the top of Everest (let alone two trips), but there are some interesting insights.
The first stage of Jornet’s preparation involved using simulated altitude at his home in Norway. Two months before his departure for the Himalayas, he started sleeping in an altitude tent two or three times a week to get used to it. One month before his departure is when the serious preparation began. He started sleeping in the tent every night, at equivalent altitudes between about 13,000 and 16,500 feet. Overall during that period, he spent 46 nights sleeping in the tent.
One month before departure he also started running most days on a treadmill using an altitude mask to simulate altitudes starting at around 13,000 feet and progressing to around 20,000 feet. (The mask actually delivers air with lower-than-normal oxygen content, using the same generator as an altitude tent. It’s not one of those masks that simply restricts airflow to make it harder to breathe.) The runs were initially hourlong easy sessions, but he later added some higher intensity training. A typical example of a high-intensity session done at just under 20,000 feet:
10-minute warm-up at 9:40/mile
4 sets of (5:00 at 6:26/mile; 5:00 at 9:40/mile; 5:00 at 9:40/mile at 15 percent incline)
5-minute cooldown
Jornet’s subjective estimate of how hard he ran was 65 to 70 percent of max for the easy runs, and up to 85 percent for the intense workouts. Millet cites some evidence suggesting you get better adaptations to altitude by exercising in thin air rather than just sleeping there—that’s why, instead of the usual “live high, train low” approach, they opted for this “live high, train low and high,” or LHTLH, protocol. We’ll see whether the acronym catches on.
Jornet also used a simple device called a finger pulse oximeter that measures the oxygen saturation of your blood. Under normal circumstances at sea level, most people have saturation levels of between 95 and 100 percent, meaning that virtually all of the hemoglobin in the blood heading to your muscles is carrying oxygen. At altitude, particularly if you’re exercising, that number starts to drop. During Jornet’s easy runs with the altitude mask, he saturation often dropped as low as 80 percent, then returned to 98 percent within an hour after the workout. During the hard workouts, it initially dropped as low as 70 percent, but as the month progressed it eventually was only dropping to 85 percent—a good sign that his body was adapting to thin air.
As meticulous as this altitude tent/mask protocol was, it couldn’t fully prepare Jornet for Everest. That’s because altitude tents provide air at normal pressure but with less oxygen than usual. On actual mountains, the air has lower pressure as well as less oxygen. Whether the body responds differently to “real” altitude compared to simulated altitude has been a topic of longstanding debate among physiologists, but Millet and others are convinced that there are important differences.
As a result, a week before heading to the Himalayas, Jornet flew to the Alps where he racked up 100 hours of “real” altitude exposure both while asleep and while awake ski mountaineering. On April 24, he flew to the Himalayas; by April 27, he was at Cho Oyu base camp at 16,700 feet, continuing his acclimatization. Five days later, he climbed to 24,600 feet; four days after that to nearly 27,000 feet. A few weeks later, carrying only a 13- to 15-pound pack with two liters of water and five to ten energy gels, he summited Everest. Probably.
There is, of course, a massive logical flaw in looking at the details of what Kilian Jornet and other super-athletes do and concluding I should do that too. Heck, Shaquille O’Neal thought his plastic PowerBalance bracelet was giving him magic powers. (Seriously, anytime you’re about to swallow an athlete testimonial, re-watch O’Neal’s PowerBalance video just to refresh your skepticism.)
But it would be equally foolish not to be curious about what the best are doing, and what advice they’re getting from their scientific team. In this case, I think the idea of training high as well as low is intriguing, as is the use of a finger pulse oximeter to monitor acclimation. And they clearly believe that an altitude tent is useful, but not sufficient to fully prepare you for a high-altitude trip. Which, in a way, is good—because in the end, who really wants to find that you can get away with spending even more time in a sterile altitude pod and less time in the mountains?
To counteract caffeine tolerance, new research suggests you need to swear it off temporarily
Using caffeine to your advantage might mean taking a break from it once in a while. (Nikita Sursin/Stocksy)
Photo: Nikita Sursin/Stocksy
Abstention is a very controversial topic—and yes, I’m talking about sports science here. You’re gearing up for an important race, and hoping to get the biggest performance-boosting jolt possible from your race-day caffeine. Should you deny yourself coffee for a week or so leading up to the race, so that you’re in a state of heightened caffeine sensitivity?
That’s a fairly common routine among elite endurance athletes, but definitely not a well-loved one. I once attended a sports nutrition panel where Des Linden talked about this part of her pre-race routine. This is a woman who (along with fellow elite runner Ben True) owns her own coffee company, linden & true. She doesn’t swear off coffee lightly, and you could hear the angst in her voice as she described how that coffee-free week felt to her. But hey, anything for an edge, right?
The trouble is that the scientific research into the supposed benefits of abstention has been murky and inconclusive at best. A number of studies have looked for an effect, but none have been particularly convincing. The most recent one that I wrote about, two years ago, involved 40 well-trained cyclists, and found no difference in the size of the boost the riders got from caffeine whether they were low, medium, or high caffeine consumers normally. This argues against any big habituation effect that dulls caffeine’s benefits with regular use.
Still, it’s very hard to pick up subtle differences in this sort of study, given that variations in individual performance are so large. To get a better handle on the question, you’d need to do some sort of crazy study that involved testing the same people over and over and over and over as they gradually habituated to caffeine. And that, as it happens, is exactly what a new study in PLoS ONE does.
A team of researchers from Camilo José Cela University in Spain, led by Juan Del Coso, put a group of 11 volunteers through a pair of grueling 20-day protocols. All the volunteers were normally light caffeine users, consuming less than 50 mg per day, much less than the 100 to 300 mg in a typical cup of coffee. For one of the 20-day protocols, they took a daily pill containing 3 mg of caffeine per kilogram of body weight, a relatively standard dose used by athletes for performance enhancement that works out to about 200 mg per day. For the other 20-day protocol, they were given a daily placebo. The two protocols were separated by seven days, with the order determined randomly and concealed from the subjects.
Before and after the study, and three times a week during the study, they completed a VO2max test to exhaustion and a 15-second all-out sprint on an exercise bike. This allowed the researchers to track exactly how the performance edge from caffeine changed as the subjects gradually got used to taking caffeine each day, and compare that to exactly the same progression when they got placebos instead.
Here’s what that data looks like for the peak power (Wmax) the volunteers reached in their VO2max tests before reaching exhaustion (caffeine is the black circles, placebo is the white circles):
(Courtesy PLoS ONE)
The bottom graph shows the actual power data. You can see that as soon as they start taking caffeine for the first time, on day 1, they immediately get a roughly 5 percent boost in peak power compared to the placebo. The top graph shows the effect size, which is a measure of how big the difference between caffeine and placebo is relative to the error bars. The boost for the first few days is considered a large effect size.
If you look at the other results from the study, you see similar patterns for VO2max and for the 15-second sprint power. The subjects get a big boost from caffeine in the first few days, then it starts to taper off, but it never gets to zero. In fact, day 20 generally looks pretty similar to day 6. Based on these results, you might figure that caffeine tolerance (at least at the doses tested here) never completely takes away the benefits of a caffeine boost. But it does, perhaps, rob you of a little bit of the edge you’d otherwise get. That’s probably a mixed blessing for Linden and co., because it suggests that a pre-race caffeine detox, no matter how painful, might be worthwhile after all if you're truly chasing every second.
Of course, performance is a complicated equation. For some people, swearing off caffeine would make race week so miserable that they’d probably lose more than they gain. And there’s also some emerging genetic research suggesting that some people don’t actually get a performance boost from caffeine anyway. But perhaps the biggest remaining question is the opposite of the one that this study tackled: how many days of caffeine abstention does it take for caffeine addicts to get their full boost back? It’s a pressing practical question. But good luck recruiting subjects to answer it.
A study of the cognitive and psychological effects of outdoor cycling finds equal results for normal bikes and e-bikes
Outside of racing, electric bikes, a new study reminds us, also have a much more positive side. (GIC/Stocksy)
Photo: GIC/Stocksy
One of my favorite cycling controversies was the rumor—complete with Zapruder-style video analysis—that Fabian Cancellara used a hidden electric motor in his bike to surge away to victory in the 2010 Tour of Flanders. These days, cycling authorities deploy mobile X-rays and thermal imaging to screen bikes at major races, and last year a French amateur rider was sentenced to 60 hours of community service after being caught for “mechanical doping.” But outside of racing, electric bikes, a new study reminds us, also have a much more positive side.
The study, from researchers at Reading and Oxford Brookes universities in Britain, looks at cognitive function and psychological well-being in adults over the age of 50. The grim truth is that cognitive function tends to decline as we get older, and that’s often associated with declines in well-being. But the encouraging caveat is that these declines are far less inevitable than we used to think, and there are two simple tactics that can fight against them: being active, and getting outside.
In PLoS ONE, the researchers report on a study that deployed both these tactics together. Instead of studying exercise in the stultifying environment of the lab, they sent their volunteers out in the world on bikes. Of 100 subjects between the ages of 50 and 83, 36 received normal bikes, 38 received Raleigh Motus e-bikes, and 26 were in the non-cycling control group. None were in the habit of cycling when the study started, and they were asked to do at least 30 minutes of biking three times a week, on their own, for eight weeks. Before and after the study, they completed a series of cognitive tests and psychological questionnaires.
The main hypothesis was that this relatively modest amount of biking would be sufficient to boost cognitive function, particularly a subset known as executive function that enables you to plan, organize, and complete tasks. The results generally supported this hypothesis, though the effect wasn’t as strong and uniform as expected. In several tests of executive function, both the cycling groups saw significant improvement relative to the control group. This isn’t a big shocker given previous research, but it’s nice to see that even something as mild as noodling around on a bike at your own pace for half an hour a few times a week produces a measurable effect.
The interesting twist came in the comparison between the regular and e-bike groups. The researchers hypothesized that the regular bike group would get the biggest effects, because they would have to work harder. The e-bikes had five motor settings, ranging from turbo to off. The subjects ended up spending 26 percent of their time in turbo, 7 percent in sport, 24 percent in tour, 28 percent in eco, and 15 percent with the motor off. So they were getting plenty of help during their rides. Perhaps in consequence, the e-bikers spent slightly more time in the saddle: 2.39 hours per week on average, compared to 2.07 for the normal bikers.
Contrary to expectations, though, the results showed little or no difference between the two bike groups. Here, for example, is one of the outcome measures for a cognitive test called the Stroop Task, which measures your ability to inhibit unwanted reactions (a subdomain of executive function):
The control stayed roughly constant, while both bike groups showed improvement (in this case a lower number after the intervention). Overall, the e-bike subjects scored just as well as the regular bike subjects, and in some cases (such as one of the well-being measures) even better. Heck, maybe Cancellara just wanted to feel good about himself.
The apparent conclusion from these results could be that the actual physical exercise you get from riding a bike is less important for your cognitive health than getting out the door into the rich outdoor environment. I’d be cautious about drawing that conclusion, though. For one thing, the exercise dose here was so low that it may not have had much impact at all. Based on previous literature, it’s reasonable to assume that if they’d done more or quicker cycling, you might see more exercise-specific benefits. There are also some more subtle possibilities, like the fact that learning to ride an e-bike for the first time represented a cognitive challenge that forced the riders to use their brains more than those who were pedaling a more familiar machine.
Still, the unexpectedly good performance of the e-bikes is food for thought. The study was part of a larger project called Cycle Boom that sought to “understand cycling among the older population and how this affected independence, health, and wellbeing.” Some of the comments the researchers got after the study noted that having an e-bike gave subjects confidence to explore a little more widely without worrying about whether they would be able to get home.
These are not minor details—because overcoming the barriers that may keep older people from cycling (or being active in other ways) is far more significant than marginal differences in the effectiveness of one type of exercise over another. A few years ago, while my dad was waiting for a knee replacement, I tried to convince him to consider an e-bike since he was finding it painful to cycle around his hilly neighborhood. He wasn’t taken with the idea, and to be honest I understood his perspective. It felt like cheating, somehow. But looking at these results now makes me wish I’d pushed a little harder.
Why Even a Little Nature Is Good for Your Brain
In a new study, mountain hiking had similar psychological and stress-reducing benefits whether or not there were signs of human habitation
You don't have to make it totally off the grid. (Christopher Simpson/Gallery Stock)
Photo: Christopher Simpson/Gallery Stock
It’s summer trip-planning time, and I’ve been fretting over the tiny black squares on my canoe route maps. Cabins! The last thing I want in the middle of a backcountry adventure is evidence that I’m not actually the first person (other than the mysterious fairies who clear portage trails and campsites) to pass through this particular slice of wilderness. Nature, I’ve always figured, should look as natural as possible.
But would the glimpse of a building or road really harsh my vibe? A new study in the International Journal of Environmental Research and Public Health, from scientists at two Austrian universities and with funding from the Austrian Alpine Association, explores precisely this question.
The researchers sent 52 volunteers to spend three October days in “a renowned summer and winter sports area” in the Austrian Alps. On back-to-back days in random order, they did two hikes that were as identical as possible—a bit more than 4 miles, climbing and then descending about 2,500 feet, taking about three hours, walking at similar speeds—with one crucial difference. One of the hikes took place in an area with virtually no signs of human habitation, while the other was continually in sight of features like a highway, ski lift, snow cannons, construction sites, and a parking lot. The purpose of the study wasn’t disclosed, so the subjects weren’t alerted to the differences between the two hikes.
The key outcome measures were a series of questionnaires to assess feelings like anxiety, elation, anger, calmness and so on, plus a series of spit tests to measure levels of the stress hormone cortisol. There’s plenty of previous research backing the idea that spending some time being active in a natural environment can alter these variables for the better, and (as I’ve written about previously) a few hints that wilder nature might pack a bigger punch. That’s certainly what my canoe-trip-planning intuition tells me.
The unanswered question, though, is whether we respond mostly to the positive attributes of nature, or to the negative effects of man-made environments. Do we love trees, hate skyscrapers, or some mix of the two?
The results of the Austrian study suggest that getting into nature is great regardless of the presence or absence of manmade features. The cortisol data, for example, showed a nice drop from pre-hike to mid-hike, and a further drop by the end of the hike—but no differences between the two hikes:
(Courtesy International Journal of Environmental Research and Public Health)
It’s worth noting that other factors like circadian rhythms affect cortisol levels, so some of the decrease may simply be due to time of day. But the absence of any difference between the two groups is the key point.
The volunteers’ moods also changed more or less as expected, with an increase in positive feelings like elation and a decrease in negative feelings like anxiety after the hikes. But once again, there was no statistically significant difference between the two hikes: seeing a highway or a ski lift didn’t make the experience any less beneficial.
One additional wrinkle: the subjects were also asked how they thought the hikes had affected their mood, with the following question: “How did the environment of the mountain hiking tour influence your well-being?” (It probably reads a little more smoothly in German.) On a scale of 1 to 10, with 10 being the most positive, they rated the more natural hike 8.5 and the human-influenced one 6.3. Like me, in other words, they liked the idea of seemingly untrammeled wilderness, even if it didn’t have any clearly measurable physiological or psychological effects.
In the end, that conscious preference isn’t necessarily something to ignore. For whatever reason, I love getting as far as possible from signs of human habitation. But studies like this have helped me be a little less dogmatic about it. Even though I live in a city of 4 million, I bought a used kayak last fall, figuring that frequent short paddles on the semi-urban river a couple of blocks from my house are better than waiting all year for a single epic trip. And as I plot a relatively easy route to explore with my 3- and 5-year-old daughters this summer, I’m realizing that we’ll probably have to accept some black squares on the map. As long as there’s no cell service, we’ll be fine.
Your Psychological Skills Can Predict Injury Risk
A simple questionnaire assessing self-regulatory skills—planning, self-monitoring, evaluation, reflection—offers clues about who will push themselves to a sports injury
Knowing which pains you can train through and which require some time off is a skill that requires careful and honest self-appraisal. (Dave Kennedy/Gallery Stock)
Photo: Dave Kennedy/Gallery Stock
Pushing the limits of your athletic potential requires, as the author John L. Parker, Jr., once put it, “a certain amount of time spent precisely on the Red Line, where you can lean over the manicured putting green at the edge of the precipice and see exactly nothing.” Balancing on that edge is tricky, and it’s why top athletes get injured pretty frequently, having pushed just a little too far. Of course, some athletes get injured more than others. Is that just a matter of luck and biomechanics—or is there some degree of skill in managing the delicate balance between enough and too much?
That’s the question at the heart of a new study from researchers at the University Medical Centre Groningen in the Netherlands, published in the Scandinavian Journal of Medicine and Science in Sports. A group of 73 tennis players in the country’s national high performance program, all between the ages of 11 and 14, completed a series of psychological tests at the beginning of the season, and were then tracked weekly for the rest of the year to see who got injured and how severely.
The researchers hypothesized that self-regulatory skills would help distinguish who got injured and who stayed healthy. In particular, they focused on four “meta-cognitive”—which means thinking about thinking, basically—elements of self-regulation: planning (“I determine how to solve a problem before I begin”); self-monitoring (“I keep track of my progress”); evaluation (“I go back and check my work”); and reflection (“I often reappraise my experiences so I can learn from them”). In essence, the idea is that knowing which pains you can train through and which require some time off is a skill that requires careful and honest self-appraisal, and that you can get better at if you’re sufficiently reflective.
The results were pretty stark. During the season, the 73 players suffered a total of 88 overuse injuries, not including acute injuries and illnesses. For the most part, they were pretty good at self-regulation: 28 of them score highly on planning, 36 on self-monitoring, 48 on evaluation, and 43 on reflection. But the players with moderate or low overall self-monitoring scores were 4.6 times more likely to suffer an overuse injury that required missing training or competition compared to those with high self-monitoring skills. That’s a huge difference.
There was an additional wrinkle when they broke the results down by gender. Among the 45 boys in the study, self-regulatory skills didn’t actually have a significant relationship to injury risk. Among the 38 girls, on the other hand, those with moderate or low self-regulation were a whopping 10.8 times more likely to lose time to overuse injuries. It’s worth looking a little more closely at that finding.
It’s tempting to combine these results into some sort of pseudo-evolutionary narrative about how the psychological traits that made males good hunters long ago now make them prone to tennis elbow, and how women evolved to ignore discomfort because of childbirth or whatever. Let’s resist that temptation. In a small study like this, it’s hard to know whether the gender-based differences they observe are generalizable differences, or simply reflect the particular characteristics of the 45 boys and 38 girls in the study. For example, the average international ranking for the boys was 436, compared to 281 for the girls—so maybe competitive level, or some other factor, is the real source of these differences.
Still, the overall data suggests that psychological traits do affect how likely you are to get injured, joining previous studies like the one I wrote about last year that found runners with perfectionist tendencies were 17 times more likely to miss training time with injuries. While this area of research is still in its infancy, it’s hard not to wonder what you can actually do about it. There’s plenty of research about how to improve self-regulation in the context of eating habits, and even about teaching pre-schoolers better self-regulation through mindfulness-based training. I don’t know how much of this we can apply to sports injuries, but I’m sure there are people working on it.
The authors of the study suggest that coaches and trainers should be on the lookout for athletes with weak self-monitoring skills, and try to help hold them back when necessary. This seems reasonable. But maybe the bigger point is simply that overuse injuries are not acts of god. They flow, at least in part, from the choices we make. For an athlete who is training hard, those choices are difficult. You’re always gambling, trying to guess which aches you can safely ignore and how much fitness you can afford to lose. And you’ll inevitably guess wrong some of the time. But if you don’t gradually get better at that guessing game, perhaps you’re simply not paying close enough attention.
On the surface, that makes no sense: less air resistance means you burn less energy at fast treadmill speeds, so why should heart rate and effort be higher? The answer to this riddle may once again come down to comfort, the authors suggest. It can be a little scary to really hammer at fast speeds on a treadmill: despite lower energy consumption, “the lack of comfort with higher-speed treadmill running might cause greater increases in [heart rate] and perceived effort due to feelings of anxiety about falling,” they write. It’s also possible that the lack of air resistance becomes a liability when you’re really working hard, because you don’t have moving air to cool you down.
There’s a similar disconnect when you look at maximal and near-maximal treadmill running. In this case, the max values of energy consumption (i.e. VO2max) and heart rate are pretty much the same on a treadmill or overground. But actual performance—how fast you go in a time trial or how long you last in a time-to-exhaustion test—is worse on a treadmill. Again, it probably comes down to familiarity and comfort rather than subtle differences in physiological variables.
When you put all this together, the sense I’m left with is that the biggest differences between treadmill and outdoor running aren’t in how you run, but rather in how you respond to the experience. It’s probably true that people run slightly differently on treadmills: when you look in detail at the biomechanics, you find subtle differences in things like knee angles and peak ground forces, but the overall pattern (as one such study concluded) is that the two movements are close enough that you don’t have to worry about the differences—as long as the belt is properly calibrated and not too soft.
So if you want to run on the treadmill, be my guest. It’s not my cup of tea, but there are plenty of great runners who’ve sworn by it. My favorite example is probably Christine Clark, the treadmill-trained Alaskan who emerged from the boreal winter to win the 2000 Olympic Marathon Trials in South Carolina. If you do, don’t worry too much about whether a 7:00 treadmill mile equates to a 6:55 or a 7:05 outside. Just run, get suitably tired, and repeat. The pace, when you get back outside, will take care of itself.
One of the new studies, published in PLOS One, comes from a group of researchers in South Korea led by Joung-Kyue Han of Chung-Ang University. Ten volunteers did a time-to-exhaustion running test at 80 percent of VO2max, a pace they could sustain for roughly 20 minutes. Before the running test, they received 20 minutes of either real or sham brain stimulation with Halo headphones. Each volunteer performed the test twice a few days apart, once in either condition, in randomized order.
After the real brain stimulation, the runners lasted about 15 percent longer in the endurance test: 21.18 minutes compared to 18.44 minutes on average. Seven of the 10 volunteers had better results with brain stimulation. Because of the additional energy required for even subtle changes of pace, time-to-exhaustion tests tend to produce much bigger changes in performance than races or time trials: a conservative estimate is that a 15 percent increase in time-to-exhaustion is the equivalent of going about 1 percent faster in a race. Still, that’s a big deal.
None of the below-the-neck measurements could explain the change in performance: heart rate, oxygen consumption, and breathing were all the same at any given time point with or without brain stimulation. Unexpectedly, the subjective rating of perceived exertion was also the same in both conditions. That undercuts the idea that you last longer on the treadmill because it feels easier, and conflicts with the previous results I mentioned above. While there’s a lengthy discussion of this finding in the paper, the bottom line is that they’re not sure what to make of it.
The second paper, published in Frontiers in Physiology by a group led by Xinyan Zheng of Shanghai University of Sport in China, looks at sprint cycling and cognitive performance. The design is pretty similar, with nine volunteers who do a cycling task that involves five all-out sprints of six seconds each with 24 seconds of easy pedaling in between.
The added twist was the inclusion of two cognitive tests, one before the brain stimulation and another after the cycling test. They used something called the Stroop Test, which involves responding to color words (like “green”) as they flash on a screen in a font color that may or may not match the word. Trust me, it’s even more confusing than it sounds when you try to do it! It’s a test of executive function that’s sometimes used to assess how your reaction time and decision-making are affected by mental fatigue.
(Courtesy Frontiers in Physiology)
The cognitive results were a little harder to parse, but it does appear there was a benefit. In the “incongruent” Stroop test (the trickier situation where the word and font color are mismatched), the response accuracy rate decreased after exercise following sham brain stimulation, but increased after exercise following real stimulation. Some of the other outcomes like reaction time and “congruent” accuracy don’t show an effect, so I’m much less confident in these results, but the researchers point out that if the cognitive boost is real, it could be significant in sports where decision-making matters, like soccer, and technical sports like skiing and mountain-biking.
Interesting stuff, but at risk of sounding like a party-pooper, let me circle back to some caveats. The discussion sections of both papers float all sorts of “bio-plausible” ideas about how the brain stimulation might be working: brain area A might be talking to brain area B which might be talking brain area C, all of which play different roles, and somehow this results in you running faster even though your sense of effort hasn’t really changed. There are a lot of “mights” in there.
(Okay, I can’t resist. Here’s a brief sample from the cycling paper: “First, sensory input from the peripheral system to M1 reduces motor output (supraspinal fatigue), and a neural pathway that interconnects the spinal cord, thalamus, secondary somatosensory cortex, medial insular cortex, posterior cingulate cortex, anterior cingulate cortex, premotor area, supplementary motor area (SMA), and primary motor cortex constitutes the inhibition system…” The paragraph goes on like that for a while, then concludes: “This hypothesis needs to be evaluated in future studies.”)
Now, just because something is complicated doesn’t mean it’s wrong. But the failure of the studies to offer some clear insights on how performance is enhanced is a bit worrying. I’d be much more confident in the results if the running study had seen a change in perception of effort, as previous lab-based studies have.
The configuration of the Halo headphones in the studies involves three electrodes: an anode right at the top of the head, and two cathodes partway down toward the ear on either side (picture a typical set of headphones with the band running across the top of the head). In theory this sends excitability-enhancing current through the motor cortex on both sides of the top of the head. But how crucial is the exact position of the electrodes? Are there are negative effects from having the cathodes so close to the anode?
One answer to those questions would be: hey, the runners and cyclists improved their performance, so obviously the electrodes are fine the way they are. But that’s a risky conclusion. This is a pretty sexy new technology, and I’m guessing that there are dozens of labs around the world are running experiments with Halo. These are the first performance-related studies I’ve seen—but it’s impossible to know how many others are sitting in desk drawers because the results were deemed too boring to publish.
In other words, these studies by no means offer the final word on Halo’s performance. There are plenty of reasons to remain cautious. Still, when considered in conjunction with the previous lab-based studies, the findings are awfully suggestive. My best guess at this point is that carefully implemented brain stimulation is probably capable of enhancing endurance performance. Until now, I’ve been pretty skeptical that Halo’s setup meets that threshold. But I’m prepared to reconsider.
Researchers dig into the nitty-gritty of interval training, with insights of what, why, and how you should train for different goals
In training, one size does not fit all. (Steve Boyle/Gallery Stock)
Photo: Steve Boyle/Gallery Stock
My eye started twitching a little this morning when my training partner suggested we aim for nine repeats up the hill in front of us. I’ve long said that if you see a training log full of workouts with odd numbers like nine and 13, you know the athlete has a problem dialing in the right intensity and finishing what they start. Fortunately, I came up with a mental solution: it’s three sets of three, I told myself. That’s a perfectly rational and intentional workout.
I’ve been thinking a lot about the details of how we prescribe interval workouts because of a massive doorstop of a book called Science and Application of High-Intensity Interval Training that I got recently. (To say that it’s 664 pages long undersells its heft. Amazon lists its shipping weight at 4.6 pounds.) It’s edited by sports scientists Paul Laursen and Martin Buchheit, and includes contributions from more than 30 experts from Olympic teams, pro sports leagues, and universities around the world. It goes into a lot of detail.
I’m not going to engage in the hopeless task of trying to summarize a book that has separate chapters (numbers 28 and 29) on how to apply interval training for rugby union versus rugby sevens. For that, I suggest you pick up the book, check out Laursen and Buchheit’s website, and perhaps sign up for their online course. But there are three high-level principles in their framework that are interesting to think about as you plan your own training.
The first is to consider what you’re hoping to get out of your training session. Laursen and Buchheit identify three key physiological responses that interval training can trigger: aerobic (pushing your VO2max, say), anaerobic (high lactate levels), and neuromuscular (muscle recruitment patterns, soreness).
Before you can decide what the appropriate workout is, you have to decide which of those three elements you want to trigger. It’s not an all-or-nothing choice: you can do workouts that target any two or even all three elements. But the goal, as Laursen explained to me, is to target your workouts with “Navy Seal precision” rather than the usual WMD approach that wipes you out in every respect. If you’re a soccer player doing mid-season conditioning, you don’t want a workout that leaves you with a bunch of neuromuscular fatigue that will compromise your performance in a subsequent game, so (as we’ll see below) you should stick to short intervals.
With that in mind, Laursen and Buchheit subdivide interval workouts into five basic types:
Long intervals, typically lasting 2 to 5 minutes with 1 to 3 minutes recovery.
Short intervals, lasting 10 to 60 seconds with less than a minute recovery.
Short sprints (“repeated sprint training”), lasting 3 to 10 seconds with less than 45 seconds recovery.
Long sprints (“sprint interval training”), lasting 20 to 30 seconds all-out with 1 to 4 minutes passive rest.
Game-based high-intensity interval training, which in team sports typically involves small-sided games interspersed with rest.
Each of those workouts has different characteristics, which can be further tweaked depending on your goals. Long intervals, for example, will give you an aerobic and anaerobic stimulus, and can also trigger neuromuscular fatigue if you do enough intervals. Long sprints, on the other hand, will predominantly hit the anaerobic and neuromuscular systems.
So how do you hit those various systems? Laursen and Buchheit have a nice flowchart that takes you through the decision:
Okay, don’t worry if it doesn’t make sense immediately. Basically it’s a decision tree: you decide if you want aerobic, anaerobic, and/or neuromuscular strain, and it tells you what types of workouts are appropriate. Notice that it’s not a single unique answer: for example, short intervals (shown on the chart as HIIT Short) can give you aerobic, aerobic plus neuromuscular, aerobic plus anaerobic, or aerobic plus anaerobic plus neuromuscular stimuli.
That, as it turns out, leads to the third big principle: each workout type is fully customizable. The indefatigable Laursen and Buchheit identify 12 variables you can tailor to make a workout suit your needs. They have another flowchart illustrating these factors, but the gist is as follows:
For each given interval, you can choose how hard (1) and how long (2) it is.
For the recovery period between each interval, you can choose how hard (3) and how long (4).
How many intervals in each set (5), and how many sets in total (6).
How long (7) and how hard (8) the recovery between sets is.
The total training volume of the workout (9).
The type of workout, like run versus bike, road versus trail, etc (10).
Environmental factors like heat and altitude (11).
Your nutritional status, such as whether you’re fully fueled or fasted (12).
Astute readers may notice that the ninth variable, total volume, is actually dictated by the first eight variables. It’s basically an overall estimate of the duration and intensity of the workout. The reason they list it separately is that they argue that it’s an important number to keep track of for the big picture of your training progression. In simple terms, if you’re used to doing 12 x 400 meter reps, and decide that you want to try some 1,600 meter reps, being aware of your total volume suggests that you’ll be able to safely handle about three reps, or perhaps four since the intensity will likely be lower.
So how do you put all this information into practice? That’s the 664-page question, of course. My workout this morning, which was three sets of three hills that took me a little over a minute to climb and two minutes to jog down, doesn’t fit neatly into any of Laursen and Buchheit’s categories: it’s somewhere between long and short intervals. In their framework, I suspect I got good anaerobic and neuromuscular jolts, but a weaker aerobic stimulus. With an 8K race coming up soon, that will inform my choices in my next workout: I’ll do a more classic long interval session. And I’ll definitely do an even number of reps.
How Likely Is a Sub-Two Hour Marathon in 2020?
A new statistical approach brings sophisticated probability analysis to the future of marathoning
Eliud Kipchoge has come the closest to a sub-2 hour marathon at 2:01:39. (Paul Harding/AP)
Photo: Paul Harding/AP
There are two basic approaches to predicting the athletic future. One is to start with the human body, try to understand how it works, and use that physiological insight to estimate what some future human might be capable of. That’s the approach that Michael Joyner, a Mayo Clinic physiologist, used back in 1991 to forecast that an ultimate marathon time of 1:57:58 might someday be possible.
The other approach ignores physiology and simply extrapolates from past trends to predict the future. That, in a nutshell, is what an Australian economist named Simon Angus does in a new paper published in Medicine & Science in Sports & Exercise—and the result is a forecast that an ultimate marathon time of 1:58:05 might someday be possible. This apparent convergence is intriguing, and it’s just one of several interesting insights that make Angus’s analysis worth a closer look.
The simplest version of the statistical-historical approach is taking a plot of world records over the years, drawing a straight line through them, and seeing where it leads. That’s how you get predictions like the famous one, published in Nature in 1992, that women would surpass men in the marathon in 1998. But as Angus explains, you can take a much more sophisticated approach to this sort of analysis, by thinking more carefully about which historical data you use, what sort of line you draw through it, and what it actually means to predict a future occurrence.
That last point about the nature of prediction is key. When you draw a graph of world record trends and see where the line leads, what you’re implicitly doing is identifying when events will have a 50 percent probability of occurring. If your line crosses the two-hour threshold in 2036 (as one previous model did), that doesn’t mean there will be a sub-two in that year. It means that there’s a one-in-two chance it will happen—which isn’t necessarily what we intuitively mean when we ask the question.
Angus, a professor at Monash University (and trail runner) whose research focuses on data science and complexity, takes official IAAF record data since 1950 for both men and women, and fits a non-linear model to the data. But rather than simply using that model to predict future performances, he uses a statistical approach called “prediction intervals” to reflect the fact that the performance of individual runners in the future won’t conform exactly to some mathematical function. We can’t say with certainty when a future event will happen—we can only say how likely a given event will be at a given time.
Let’s make this more concrete: when will the two-hour barrier be broken? If you set the odds at 1 in 10, Angus’s model predicts May 2032. That means he figures there’s a 10 percent chance of it happening then, but it could easily happen earlier or later. He pegs the odds as 1 in 34 in 2020, 1 in 12 in 2030, and 1 in 6 in 2040. The chances of it ever happening are about 1 in 2.
Here’s what those predictions look like in graphical form. The thick line in the middle is the basic prediction of the model at a probability of 50 percent. The parallel lines below show the times at longer odds like 1 in 4, 1 in 10, and so on. (The smaller inset graph shows the odds of a sub-two marathon by year.)
An interesting side note: if you’d asked the model last fall how likely it was that Eliud Kipchoge would run a 2:01:39 marathon (which is what he did in Berlin), the answer would have been roughly 1 in 4. Unlikely, but not a farfetched possibility, in other words.
Another feature of the model is that its predictions approach an asymptote, which corresponds to a hypothetical ultimate record. This too is subject to given levels of probability: the model doesn’t tell you what record can never be broken, but it tells you what ultimate performance has only a 10 percent (or 1 percent, or whatever you choose) chance of being broken. At the 10 percent level, the ultimate record for men is 1:58:05, strikingly similar to Joyner’s physiological prediction. The corresponding women’s time is 2:05:31.
That women’s time, nearly 10 minutes faster than the current record, should perhaps be a warning. Angus uses the men’s and women’s models to suggest some ideas about the appropriate women’s equivalent of a men’s two-hour barrier. Since a two-hour marathon is 1.62 percent slower than the men’s ultimate 10-percent limit, the equivalent women’s time would be 2:07:33. Rounding up to the nearest convenient round number, he suggests that 2:10:00 should be the benchmark that women’s moonshot marathon goals target. According to the model, there’s a 10 percent chance that this threshold will be crossed in... January 1996.
How is it possible that the model produces such a seemingly nonsensical prediction? Angus stands by his model and argues that the reason top female marathoners are currently so far away from the model’s predictions (relative to the men) is that the talent pool of East African marathoners hasn’t yet been fully tapped to the degree it has been for men. Given the sociological constraints that have traditionally faced female athletes in countries like Kenya, that’s an entirely reasonable hypothesis, but I don’t think it explains anywhere near the whole gap.
Instead, I suspect the problem here is the same one that bedeviled the UCLA researchers who predicted female primacy by 1998. The key question facing any attempt to predict the future based on the past, as Angus himself points out, is whether the same factors that produced past trends are still the dominant factors in the future.
This question is a hot one in the two-hour debate. The men’s record has been dropping like a rock since the late 1990s, and if that rate of improvement—five minutes and 11 seconds since 1998—continues, we would expect to see a sub-two in the next six or seven years. But there have been some major changes in marathoning over that period, like far larger prize purses drawing the best runners away from the track at a younger age, and more recently, advanced shoe technologies making runners more efficient. We can’t assume that there will be comparable advances in the decade to come.
On the women’s side, the elephant in the room is that women mostly weren’t even allowed to run marathons until the 1970s. Angus’s model starts in 1950, and the first record in his dataset is Merry Lepper’s 3:37:07 in 1963. The factors that took women from 3:37 in 1963 to 2:38 in 1975 have very little in common with what will be required for women to go from 2:15 to 2:10, and a model that blindly assumes that the former has something to tell us about the latter is likely to make some false assumptions about how easy it is to get faster.
In the end, of course, the biggest mistake we could make is to assume that any one model can tell us everything we want to know about the future. The closer we get to our hypothetical limits, the more the nuances of individual physiology will matter. But as someone who gets asked pretty frequently when a sub-two will happen, I also really like Angus’s approach. Combining “when?” with “how likely?” gives you more robust and informative predictions. And as an added bonus, it’s easier to weasel out of predictions that go wrong.
When the tobacco hornworm has nearly completed its 18-day metamorphosis, it uses a set of powerful abdominal muscles to escape from the confines of its old exoskeleton. In its new life as a tobacco hawkmoth, it doesn’t need those oversized abs anymore—so they shrink. Over the course of just three days, the muscles get 40 percent smaller, a loss that’s equivalent in human terms to an 80-year-old after three decades of the muscle-wasting condition sarcopenia. But there’s something curious about this shrinkage: as the muscle cells die, all of the cell nuclei inside live on.
According to a new article in Frontiers in Physiology by University of Massachusetts, Amherst biologist Lawrence Schwartz, the hawkmoth’s metamorphosis tells us something important about the human phenomenon of muscle memory. If you achieve a certain level of fitness and then lose it, it’s easier to regain that fitness than it was to get there for the first time. This is conventional wisdom in both strength and endurance training, and there are undoubtedly many factors—psychological and practical as well as physiological—that contribute to it. But in recent years, there has been growing evidence that your muscle cell nuclei play a key role.
Muscle cells are very unusual in that they can have more than one nucleus per cell. In fact, they can have hundreds of nuclei in a single cell. That’s because they can be enormous: a single muscle fiber from the sartorius can be 23 inches long. In order to synthesize enough muscle proteins to keep this fiber intact, you need more than one nucleus. A theory called the “myonuclear domain hypothesis” suggests that each nucleus can only support a given cell volume, so as muscle cells get bigger—because you’re hitting the gym, say—your muscle cells add a proportionate number of additional nuclei.
The big question is what happens when you slack off from the gym. Scientists initially thought that the number of nuclei would decrease as your muscle cells shrink, and there was some evidence that seemed to suggest that nuclei were indeed succumbing to “programmed cell death” as muscles atrophied. But a series of careful mouse experiments a decade ago by Norwegian physiologist Kristian Gundersen contradicted this idea: when mice stopped exercising, their muscle shrank by as much as 50 percent, but the number of nuclei stayed exactly the same.
This observation suggested a mechanism for the anecdotal idea of muscle memory: if you get fit then unfit, you’ll still have all the extra nuclei hanging around in your muscles, making it easier for them to grow when you start exercising again. In one of Gundersen’s experiments, mice who had previously been strong (and thus had more dormant nuclei) gained 36 percent in muscle size during an exercise program, while other mice doing the same program for the first time only gained 6 percent.
One of the challenges in trying to figure out what happens to your muscles when you stop exercising is that various other types of cells are mixed in with your muscle fibers in any given muscle. That’s why scientists initially thought the nuclei were destroyed as muscles shrank: their tests showed indirect evidence of defunct nuclei that, it turns out, were probably from other cells. Schwartz’s moth data helps clear that confusion up, since the moth muscles are almost exclusively composed of muscle fibers.
Another research group, at Temple University, also published some intriguing data in rats last fall. While most of the muscle memory research focuses on muscle size, the Temple team looked at the mitochondria within the muscles—the “powerhouses” that generate the fuel for sustained exercise.
The most significant adaptation to endurance training is an increase in the amount of mitochondria in your cells. When you stop training, the amount of mitochondria declines again. But the extra nuclei, which stick around, contain the genetic information that controls the formation of new mitochondria. As a result, the Temple researchers demonstrated that when you start training again, your cells are already primed to ramp up mitochondria production more rapidly if you’ve been fit before.
One frequently discussed implication of this muscle memory research is that athletes who dope gain an advantage that lasts far longer than the typical four-year ban, and possibly for the rest of their careers—if not longer. When I wrote about this topic a few years ago, Gundersen told me that the existing evidence suggests that human muscle cell nuclei can persist for decades. His estimate: “It’s possible that they are more or less permanent once they are formed.”
There’s also a public health angle, Schwartz points out: “During adolescence, muscle growth is enhanced by hormones, nutrition, and a robust pool of stem cells, making it an ideal period for individuals to ‘bank’ [muscle nuclei] that could be drawn upon to remain active in old age.” That ship sailed long ago for me, but the implications are still relevant. Even if putting on muscle and gaining fitness isn’t as easy for me now as it was 20 years ago, it’s still possible, and it’s easier than it will be in another 20 years. If I don’t want to end up as the human version of that withered hawkmoth, now’s the time to hit the gym and bank some nuclei.
In my house, it’s well understood that missed runs and snippy exchanges are causally related. That may seem obvious—after all, there are big piles of evidence about exercise’s mood-altering power to reduce feelings like anxiety and depression. But, as a new study in Medicine & Science in Sports & Exercise points out, there has been very little research on the links between exercise and anger. And that makes me very, very angry. (I’ve been injured and unable to run for a few weeks.)
Researchers at the University of Georgia decided to address this gap by showing a series of “emotionally evocative” pictures—soldiers firing at children, a Ku Klux Klan rally—to volunteers before and after a 30-minute “moderate-to-vigorous” bike ride (or, in the control condition, 30 minutes of sitting quietly). They assessed their volunteers’ anger levels with questionnaires and by measuring brain activity with EEG electrodes. The results are more nuanced than I expected.
The first thing to note is that there’s a difference between moods and emotions. This may seem obvious, but it’s something I hadn’t really thought about in this context, and it turns out there’s a long and complicated academic literature arguing about the precise differences between the two. In broad strokes: moods tend to be longer-lasting, they are less strongly associated with an immediate trigger, and there’s nothing you can measure in the brain that reveals them. In contrast, emotions are shorter, are a response to a specific triggering event, and are linked with consistent and measurable patterns of brain activity. You can be in an angry mood, and you can also experience anger as an emotion.
The Georgia study, which was led by former graduate student Nathaniel Thom (now at Wheaton College), started out by screening 430 students in order to select 16 men with a high propensity for getting angry. (This approach makes it more likely that you’ll see significant changes, much like testing blood pressure meds on people who have high blood pressure.) During the picture-viewing, EEG brain activity and questionnaire responses measured the subjects’ anger as an emotion in response to each scene. Before and after the sessions, they also completed psychological questionnaires to check whether the overall experience had put them in an angry mood, and whether that was affected by the presence or absence of exercise.
The results, which were first reported at a conference several years ago, showed that the 30-minute bike ride did indeed have two positive effects on angry mood. The volunteers felt less angry immediately after the bike ride; and the post-exercise picture-viewing session was also less likely to put them in an angry mood. In other words, exercise both reduces your anger levels and helps immunize you against getting into an angry mood in the first place.
But when you consider anger as an emotion, the picture changes. Exercise had no effect whatsoever on the intensity of emotions reported during picture viewing—not just for the anger-inducing pictures, but also for pictures chosen to induce feelings of fear and pleasantness. And the EEG data also showed no difference in emotional intensity. Exercise may put you in a better mood, but—in this particular study, at least—it didn’t seem to dull or prevent passing emotions.
It will take a lot more research to tease out these subtleties and figure out how generalizable they are. For now, it’s interesting to see some apparent confirmation that exercise really can function as a sort of prophylactic against irritation—but not an all-powerful one. Even after a run, things that make you angry will still make you angry. Just remember that the transient emotion will pass, and your Zen-like post-exercise good mood will return.
(Courtesy JAMA Network Open)
Given the small number of cardiac events, the relationship isn’t perfect: those who did 31 to 40 push-ups actually fared a bit worse than those who did 21 to 30. But the trend is pretty clear, and it’s very obvious that those who managed less than 10 push-ups were at significantly higher risk. (These numbers, unfortunately, are specific to men in their 30s and 40s like the firefighters in the study. As is so often the case, we’d need a broader study to figure out useful benchmarks for the rest of the population.)
Still, when my rib settles down, I’m eager to see whether I can eke out 40 push-ups at that 40-per-minute rhythm. It could go either way, but I suspect there’s a pretty good chance I’ll drop off the required pace somewhere around 30. Here’s the crucial question, though: if I don’t make it, but then spend a month or two focusing on push-ups so that I can hit 40 in 60 seconds, will I really have changed my long-term cardiovascular prognosis? Or will I have simply cheated—and thus invalidated—the test by cramming for it?
The reason the push-up test is interesting is because it’s simple, easily accessible, and free. There are plenty of other ways of assessing heart health, including treadmill tests, but they take more time, equipment, and money. As it happens, the firefighters in this study all completed a submaximal treadmill test that estimated their VO2max based on the speed they reached at 85 percent of their estimated max heart rate. This type of test isn’t as accurate as true maximal tests, where you go until you fall off the back of the treadmill, but it gives a decent estimate of cardiovascular fitness. Amazingly, in the firefighter study, the push-up score provided a slightly better predictor of cardiovascular risk than the submaximal treadmill test.
That doesn’t mean that upper body strength is the most important predictor of heart health, though. Instead, the push-up test has a lot in common with other tests of functional capacity. For example, there are dozens and dozens of studies showing that grip strength is a powerful predictor of future disability and death, including from cardiovascular disease. Same with self-chosen walking speed, and even, in older populations, the time it takes you to get up from a chair.
You can come up with arguments for why these tests make sense—maybe poor grip strength means you can’t get the peanut butter jar open, so you waste away. But overall, deliberately trying to improve any of those parameters probably isn’t the best way to go. In fact, there’s some evidence that typical strength training routines don’t improve your grip strength anyway. Instead, all of these tests likely reflect a larger constellation of healthy traits and behaviors: people who walk fast, have a crushing handshake, and can do a lot of push-ups probably tend on average to eat well, stay active, pay attention to their health, and so on.
So, by all means, check your push-up prowess. But remember that it’s just one among many indicators of your general robustness, along with others like, say, how easily you get injured while playing basketball. The best defense? Focus on the whole picture, rather than the details: do hard things, find new challenges, and don’t stop just because you’re getting older and feeling more fragile—because that’s a self-fulfilling prophecy.
The key feature to notice is the little spike on the left side of the curve. That’s your heel slamming into the ground, a split second before the full force of the rest of your body presses into the ground. The dominant view among biomechanists is that it’s not the overall size of the force (which tops out at about 2.4 body weights here) that determines how likely you are to get injured; instead, it’s how quickly the force is applied, otherwise known as the loading rate. Thanks to the mini-spike of the heel strike, the force in this picture climbs very steeply, which is presumably bad.
Now compare how the force looks when you land instead on your forefoot:
The little spike is gone! That means the loading rate is lower, and you’re less likely to get injured. At least, that’s the theory.
In truth, according to the new study, the little spike is still there; it’s just obscured by the bigger one. Back in 2014, Weyand, Clark, and Ryan proposed what they called a “two-mass model” to explain how these sorts of force curves vary under different conditions. In this model, the force curve always consists of two distinct components: a small spike that corresponds to your foot and lower leg smacking into the ground and almost immediately jarring to a halt; and a bigger, slower spike that corresponds to the rest of your body reaching the lowest point of its up-and-down motion. The overall force is simply the sum of those two spikes.
The curve shown here looks a lot like Lieberman’s heel-strike curve. But in a 2017 study, the SMU group showed that you get a very similar double peak when you look at world-class sprinters—even though they land on their forefoot. That was an important clue that the mini-spike isn’t a function of which part of the foot touches the ground first. Instead, it’s a function of how quickly you’re applying force to the ground, which depends not just on footstrike but also on speed, shoe characteristics, and other factors.
In the new study, the SMU team compared the force data from eight volunteers in four different shoe conditions: barefoot, minimalist (Vibram FiveFinger KSO), thin-soled racing flat (Nike Zoom Waffle Racer VII), and thick-soled running shoe (Asics Gel Cumulus-14). They wanted to explore a longstanding mystery called the “impact force anomaly,” which is that putting more cushioning in shoes doesn’t seem to reduce the loading rate that runners experience—a seemingly counterintuitive result.
Here’s what their data looks like, showing both the mini-spike associated with the lower leg (J1) as well as the overall force that’s the sum of both spikes, for two speeds (~6:40 per mile and ~3:50 per mile). The vertical axis, as before, is force in body weights, and the horizontal axis is time in seconds.
(Courtesy Journal of Applied Phys)
The first thing to notice is that for each speed the rising slope of the overall force curve—the loading rate—is pretty much the same in all conditions. But the lower leg component (J1) does change: the more cushioning they have, the steeper and higher that peak is. So what’s going on?
What the new results suggest is that we’re wired to automatically adjust our biomechanics to keep the overall loading rate roughly the same. In this study, the runners adjusted the angle of their foot strike to control how long that J1 impact took. When barefoot, they landed on their forefoot, which prolongs and softens the landing, with the calf muscles and Achilles acting as a shock absorber. In the thick-soled trainer, the presence of cushioning allowed them to slam down directly on their heel, which led to a sharper J1 curve without changing the overall loading rate.
What’s key here is the timing of that impact peak. If the initial peak is delayed long enough, it effectively disappears into the main peak, as in Lieberman’s 2010 data. But by delaying that peak, it ends up occurring at a point where the other, slower component of force from the rest of the body is much bigger. In other words, you reduce one of the forces but increase the other, and end up with a similar total force.
I realize we’re getting into the biomechanical weeds here, so let’s zoom out to consider what this means. The biggest practical insight: in the new SMU data set, changing your shoes, on its own, doesn’t significantly change your loading rate. Either your shoe cushioning softens the impact, or you adjust your landing to get cushioning from your calf and Achilles. Take your pick, because the end result—at least under these particular conditions—is the same.
That doesn’t mean this system is foolproof. If you head out for a barefoot run and land on your heels—either because that’s what you’re used to from a lifetime of running in shoes, or because, as in the Lieberman experiment, the guy in the lab told you to—then you’re going to generate really high loading rates, because neither protective mechanism (shoe cushioning or forefoot strike) is switched on.
Weyand is justifiably hesitant to generalize, though. This is a small study of a few volunteers running under very specific conditions at fast speeds. It doesn’t give us the final answers about what shoes or running styles are best for everyone, or exactly how these forces translate into injury risk—but he believes it does give us a better way to answer those questions.
Crucially, the simplicity of the two-mass model means that you no longer need a prohibitively expensive force-measuring treadmill to assess impact forces and loading rates. Instead, all you need to know is how fast your lower leg is moving when it hits the ground; how long your foot stays on the ground; and how long each step takes. You can get those parameters with a high-speed video camera, or these days you could do it with a small leg-mounted accelerometer. Plug them into the two-mass model equation, and it spits out the force curve. So with some fairly simple wearable tech, you should be able to head to a shoe store, try on five pairs of shoes, and know in real time what impact forces and loading rates you generate with each one.
For distance runners, the goal would presumably be to minimize impact forces. But it’s worth noting that the opposite is true for sprinting. The primary way people run faster is by striking the ground harder: the more force you can plow into the ground, the faster you’ll go. “In this regard,” Weyand points out, “minimizing impact and loading rate is directly in conflict with increasing speed.” That’s one reason training shoes and racing shoes are so different—and it’s something else you can test for with this model, trying different shoes and form tweaks to see what maximizes your force output.
If there’s one thing we’ve learned in the decade since Lieberman’s initial results, it’s that we should be cautious about looking at force curves in the lab and assuming we understand how they translate into real-world outcomes like injuries and race times. That applies to the new SMU data too. But the model has the potential to solve two very current problems: taking biomechanics out of the rarefied lab environment and into the real world; and extracting useful insights from the firehose of personalized data generated by emerging wearable tech. Hopefully there will soon be an app for that.