To predict your longevity, you have two main options. You can rely on the routine tests and measurements your doctor likes to order for you, such as blood pressure, cholesterol levels, weight, and so on. Or you can go down a biohacking rabbit hole the way tech millionaire turned did to live longer. Johnson’s obsessive self-measurement protocol involves tracking more than a hundred biomarkers, ranging from the telomere length in blood cells to the speed of his urine stream (which, at 25 milliliters per second, he reports, is in the 90th percentile of 40-year-olds).

Or perhaps there is a simpler option. The goal of self-measurement is to scrutinize which factors truly predict longevity, so that you can try to change them before it’s too late. A new study from biostatisticians at the University of Colorado, Johns Hopkins University, and several other institutions crunched data from the long-running National Health and Nutrition Examination Survey (NHANES), comparing the predictive power of 15 potential longevity markers. The winner—a better predictor than having diabetes or heart disease, receiving a cancer diagnosis, or even how old you are—was the amount of physical activity you perform in a typical day, as measured by a wrist tracker. Forget pee speed. The message to remember is: move or die.

How to Live Longer

It’s hardly revolutionary to suggest that exercise is good for you, of course. But the fact that people continue to latch on to ever more esoteric minutiae suggests that we continue to undersell its benefits. That might be a data problem, at least in part. It’s famously hard to quantify how much you move in a given day, and early epidemiological studies tended to rely on surveys in which people were asked to estimate how much they exercised. Later studies used cumbersome hip-mounted accelerometers that were seldom worn around the clock. The , published in Medicine and Science in Sports and Exercise, draws on NHANES data from subjects recruited between 2011 and 2014, the first wave of the study to employ convenient wrist-worn accelerometers that stay on all day and night.

Sure enough, it turns out that better data yields better predictions. The study zeroed in on 3,600 subjects between the ages of 50 and 80, and tracked them to see who died in the years following their baseline measurements. In addition to physical activity, the subjects were assessed for 14 of the best-known traditional risk factors for mortality: basic demographic information (age, gender, body mass index, race or ethnicity, educational level), lifestyle habits (alcohol consumption, smoking), preexisting medical conditions (diabetes, heart disease, congestive heart failure, stroke, cancer, mobility problems), and self-reported overall health. The best predictors for how to live longer? Physical activity, followed by age, mobility problems, self-assessed health, diabetes, and smoking. Take a moment to let that sink in: how much and how vigorously you move are more important than how old you are as a predictor of the years you’ve got left.

These results don’t arrive out of nowhere. Back in 2016, the American Heart Association issued a scientific statement calling for cardiorespiratory fitness, which is what VO2 max tests measure, to be considered a vital sign that doctors assess during routine checkups. The accumulated evidence, according to the AHA, indicates that low VO2 max is a potentially stronger predictor of mortality than usual suspects like smoking, cholesterol, and high blood pressure. But there’s a key difference between the two data points: VO2 max is about 50 percent determined by your genes, whereas how much you move is more or less up to you.

Fitness Trackers Are Key to New Longevity Findings

All this suggests that the hype about wearable fitness trackers over the past decade or so might be justified. Wrist-worn accelerometers like Apple Watches, Fitbits, and Whoop bands, according to the new data, are tracking the single most powerful predictor of your future health. There’s a caveat, though, according to Erjia Cui, a University of Minnesota biostatistics professor and the joint lead author of the study. Consumer wearables generally spit out some sort of proprietary activity score instead of providing raw data, so it isn’t clear whether those activity scores have the same predictive value as Cui’s analysis. Still, the results suggest that tracking your total movement throughout the day, rather than just formal workouts, might be a powerful health check.

The inevitable question, then, is how much movement, and of what type, we need in order to live longer. What’s the target we should be aiming for? Cui and his colleagues track the raw acceleration data in increments of a hundredth of a second, which doesn’t translate very well to the screen of your smartwatch. The challenge remains about how to translate that flood of data into simple advice regarding how many minutes of daily exercise you need, how hard that exercise needs to be, and how much you should move around when not exercising.

To be honest, though, I’m not sure the quest to determine an exact formula for how much we should move is all that different from the belief that measuring your urine speed will give you actionable insights about your rate of aging. Metrics do matter, and keeping tabs on biomarkers backed by actual science, like blood pressure, makes sense. But it’s worth remembering that the measurement is not the object; the map is not the road. What’s exciting about Cui’s data is how it reshuffles our priorities, shifting the focus from all the little things our wearable tech now tracks to the one big thing that really works—and which is also a worthwhile goal for its own sake. Want to live longer? Open the door, step outside, and get moving.

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The 2022 Tour de France Femmes was decided in the Vosges mountains, during a brutal seventh stage with three category-one climbs. Dutch rider Annemiek van Vleuten attacked on the second climb, then opened up a four-minute gap on the final push of the day, a grueling 3,163-foot ascent of the Grand Ballon. It was the hardest day of the Tour, and with another mountain stage coming the next day, recovery was crucial. But with their legs fried, their cortisol levels soaring, and their nervous systems cranked in fight-or-flight mode, would the riders actually be able to sleep properly?

Surprisingly, the answer was yes—or at least, mostly. Nine of the Women’s Tour riders were wearing Whoop bands on their wrists; their data, which was published earlier this year in Sports Medicine—Open, showed that the riders got an average of 7.6 hours of sleep that night, compared with an overall average of 7.7 hours both before and after the Tour. They did, however, spend a little more time than usual in light sleep and less in restorative REM sleep. Whether that matters in any practical sense is the fundamental question confronting athletes, coaches, and sports scientists as they enter a new era of sleep tracking. The technology is better than ever; we just have to figure out what to do with it.

Tracking Sleep Stages Is Still a Challenge

Sleep is hardly a new biohack, but it has been a hot topic in performance circles ever since neuroscientist Matthew Walker’s 2017 book Why We Sleep. The problem with first-generation sleep trackers, though, was that they relied on accelerometers and basically assumed that if you weren’t moving, you were asleep. The latest generation of devices is more sophisticated, adding heart-rate measurements and other physiological cues like breathing rate and skin temperature to refine their algorithms, and able to tell the differences between distinct sleep stages. As a result, says Charli Sargent, a sleep scientist at Central Queensland University in Australia and lead author of the Tour de France study, “The whole world is becoming a sleep laboratory.”

Companies like Apple, Garmin, Oura, Polar, and Whoop have gotten very good at detecting sleep. Compared with sleep-lab studies, where subjects are wired up to record brain and muscle activity, the latest consumer wearables were typically 86 to 89 percent accurate at determining whether a wearer was asleep or awake, Sargent and her colleagues found. Detecting individual sleep stages, on the other hand, is still a work in progress: the wearables only got it right 50 to 61 percent of the time.

The picture for athletes is more complex. Many of the new sleep-stage algorithms rely on heart-rate variability, or HRV, the subtle fluctuations in timing from one beat to the next. HRV changes with sleep stage, but it’s also influenced by vigorous exercise. Indeed, Sargent found that HRV was systematically lower after mountain stages in male Tour de France riders. Another new study, led by Marc Poulin of the University of Calgary, had a group of healthy volunteers do a hard interval workout in the early evening, then tracked their sleep with an HRV-based Polar watch as well as collecting gold-standard sleep-lab data. The good news: the accuracy of the sleep tracker was undiminished by the workout.

What Can Athletes Do with the Data?

Overall, then, wearable sleep trackers are already pretty good, and they will likely continue to improve. The next question—the really hard one—is what we should do with the data. If cyclists are getting less REM sleep after mountain stages, what should they do differently? “Ride easier” isn’t useful advice; and it hardly seems like we need a fancy algorithm to give us the usual sleep-hygiene advice about bedtimes, alcohol, and electronics before bed.

For some people, simply having objective data about when to hit the hay and when to wake up might function as a useful reminder to cover these bases, in the same way a step tracker spurs you to get your 10,000 steps. Athletes might also be interested in seeing how their sleep changes at altitude, as an indicator of whether they’ve acclimatized and are ready for hard workouts. And there may eventually be subtler insights: for example, preliminary data from Poulin’s lab in older adults suggests that those who don’t get enough deep sleep are more likely to develop cognitive problems years later. For now, the best approach is to establish a baseline and then look for changes, Sargent says. If you usually get 15 to 20 percent deep sleep and that changes to 10 to 15 percent, you should probably figure out why.

Against these putative benefits, you have to weigh the risks. Poor sleep is not always a problem that can be solved by trying harder and worrying more about it—or by collecting sleep-tracking data. “Anxiety related to sleep can be both a symptom and a cause of some types of sleep problems,” Sargent acknowledges. The study that sticks in my mind, from Oxford University in 2018, involved giving subjects bogus feedback about whether they’d slept well or poorly. Those who were told that they’d slept poorly the night before reported feeling scattered, fatigued, and cranky. A little bit of data can be a dangerous thing, especially if its accuracy is questionable.

As for the mystery behind the surprising finding that Tour cyclists sleep just fine, thank you very much, even after the physiological disruption of brutal mountain stages, Sargent and her colleagues propose a disarmingly simple explanation. The cyclists prioritized sleep: they went to bed early and consistently, and gave themselves plenty of time there; ergo, they slept well. Earlier studies found that super-intense endurance exercise, especially when repeated day after day, led to diminished sleep—but the new generation of athletes are on top of it. There will be plenty to learn in years to come from the new sleep-measurement techniques, combined with robust analytical approaches like machine learning and AI. “I consider sleep to be the next frontier in physiology,” Poulin says. But none of it matters if you’re not putting in your time in the sack.

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Recently, while sifting through some of the excruciatingly detailed performance data he’d collected over decades as a Colorado-based triathlon coach, Alan Couzens . All else being equal, the amount of aerobic fitness his athletes lost by getting a year older was almost identical to the amount they gained by adding an hour per month of training time. Want to freeze the biological clock from one birthday to the next? Find a spare 15 minutes per week and fill it with running.

The long-haul practicality of this approach is debatable: after a decade, that additional training time would total 2.5 hours a week. But the underlying premise of what we might call the Couzens Immortality Quotient taps into a fertile area of debate. How much of the aging process is an inevitable slide into decrepitude, and how much is a result of not getting enough exercise?

That’s the question Johannes Burtscher of the University of Lausanne, along with colleagues in Switzerland and Austria, in the International Journal of Environmental Research and Public Health. By pooling the results of more than a dozen studies, the group reached an encouraging, quantifiable conclusion: only about half of the fitness losses suffered by endurance athletes as they get older are attributable to the passage of time. The other half can be chalked up to reduced training.

The standard gauge of aerobic fitness is VO2Ìęmax, which measures how quickly you’re able to breathe oxygen into your lungs, pump it through your arteries, and use it to help fuel muscle contraction. It’s expressed in milliliters of oxygen per kilogram of body weight per minute, and in young adults it typically hovers somewhere in the forties. After age 25, it declines by about 10 percent each decade, dropping more quickly in your sixties and seventies. Among endurance athletes, the numbers aren’t so predictable. Some studies find losses of 5 percent per decade; others as much as 46 percent. What accounts for the difference? The extent to which you continue training as you get older. After all, the fitter you are, the more you stand to lose.

The effects of ceasing training entirely seem to be similar in athletes of all ages. Your VO2Ìęmax begins to plummet within a few days of stopping exercise, and you might lose as much as 20 percent after 12 weeks. These losses are explained mostly by changes in how much blood your heart can pump with each beat; the good news is that the trend can be reversed fairly quickly when training is resumed. Other age-related changes, such as stiffening arteries, occur more slowly and are harder to undo.

When Burtscher and his colleagues ran the numbers, they found that 54 percent of the variation in fitness loss by male endurance athletes was explained by differences in how much they trained. That number in women was 39 percent, but the scarcity of data for female subjects makes it impossible to tell whether there’s a real physiological difference between the sexes. Overall, the data fits with the observation that athletes who keep training at a fairly constant level over the years lose about 5 percent per decade—half as much as the typical nonathlete.

There are a couple of key questions raised by these findings. The first one: If you miss a year, or a decade, can you get back to where you were? Or is some of that fitness lost forever? There’s no research to suggest a solid answer, according to GrĂ©goire Millet, one of Burtscher’s colleagues at the University of Lausanne. It probably depends to some degree on how much you trained prior to stopping, and for how long. The risk upon resuming would be that your bones and connective tissue are no longer prepared to handle a heavy load, making you more susceptible to injury.

Still, there are some encouraging hints in the literature. In 2020, researchers published lab data on Tommy Hughes, an Irish man who’d recently run an eye-popping 2:27 marathon at age 59. Hughes’s VO2Ìęmax was 65.4, more than twice what you’d expect from a largely sedentary man his age. Not surprisingly, Hughes was a former elite marathoner; he competed at the Barcelona Olympics in 1992. But he’d taken a 16-year break from running, starting again at the age of 48. We can’t know for sure if that pause hurt him—if it did, it couldn’t have been by much, given that he currently holds the world marathon record for the 60-to-64 age group, at 2:30:02.

The other question is how to maintain your training level as the years pass. We all have good intentions, but real life rarely resembles the smooth aging curve that results from graphing the average data from large groups of people. Instead, there are plateaus and gentle declines punctuated by steep drops—you break a leg, your first kid is born, you become addicted to social media, and so on. Avoiding periods of rapid decline goes a long way toward slowing the overall slide.

Another superstar case study, this one published in 2022 by a team led by Bas Van Hooren of Maastricht University in the Netherlands, illustrates the benefits of consistency. A 75-year-old middle-distance runner named Hans Smeets, who holds multiple European and world age-group records, had clocked a VO2Ìęmax of 50.5, equal to the highest known measurement for his age. Smeets only began running at 50, further evidence that it’s never too late to start (or start again). And once he’d begun, he kept going. Over the next 25 years, he never missed more than a week of training. Initially, he ran more than 85 miles per week, and at 75 he was still logging as many as 50. He attributed his ability to handle all that mileage without injury to doing most of his runs at, in his words, “an easy pace.”

That, as it turns out, aligns perfectly with Couzens’s view about what’s required for long-term athletic success: lots of low-intensity exercise. To be sure, the idea of adding an hour of training per month every year to ward off the effects of aging sounds suspiciously like an endurance version of the legend of Milo of Croton, the ancient Olympian wrestler said to have lifted a calf over his head every day until it was a full-grown bull. In both cases, each step in the process seems simple, but the result is nonetheless
 improbable, let’s call it.

Yet, as an aspiration, or simply as a formulation of what’s possible, the Couzens Immortality Quotient tells us the same thing as the examples of Tommy Hughes and Hans Smeets, and as Johannes Burtscher’s meta-analysis. You don’t train less because you’re getting old; you get old, to a surprising extent, because skipping that long Sunday run with your pals becomes a habit instead of a rare exception. Don’t do it.

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There are two schools of thought about the differences between trail running and road running. One is that running is running: terrain and environment are minor details, and the best athletes in one discipline will probably also succeed in the other one. The other is that they’re two separate sports whose demands favor different characteristics. Drop Kilian Jornet into a road race or Emily Sisson into a trail race, and they’d be suddenly mortal.

A new study from French researchers at the University of Lyon, , probes this question by bringing elite road and trail runners into the lab for testing. Actually, that’s not quite right—the researchers brought the lab to national-team training camps hosted by the French Athletics Federation for their trail and road-running teams. That meant using a treadmill that only went up to around 7:00 miles and 10 percent incline—modest challenges for athletes of that caliber. But the results still offer some interesting insights about the similarities and differences between top road and trail runners.

One longstanding question for both athletes and researchers is whether trail runners need to be more powerful. Bounding up steep hills, after all, requires powerful bursts of force. Simple tests of quadriceps and hamstring strength showed no differences between the groups. But strength isn’t the same as power, which also depends on how quickly you can produce force. To assess power, they used 8-second all-out sprints on a stationary bike and calculated the resulting force-velocity curve. Sure enough, the trail runners could generate 23 percent more torque and 16 percent more power than the road runners.

Another key comparison is running economy, a measure of how much energy it takes to sustain a given pace. Road running rewards a smooth, metronomic stride with no wasted motion. But previous research (which I wrote about here) has suggested that how efficiently you run on a flat road or treadmill doesn’t necessarily tell you how efficient you’ll be on uneven, hilly trails.

Sure enough, the road runners in the new study burned about 6 percent less energy than the trail runners at just under 7:00 mile pace on the flat treadmill. At 10 percent uphill incline, the two groups averaged roughly the same running economy. Given that the trail runners were less efficient on flat ground, this does indeed suggest that they’re better—relatively speaking, at least—on hills. And 10 percent is a modest incline for serious trail runners, so you’d imagine the relative advantage would widen on steeper slopes.

They also did some biomechanical analysis: ground contact time, flight time, cadence, leg stiffness, and so on. The results are easy to summarize: no differences between the two groups.

Overall, the differences in power and running economy support that idea that trail and road runners are both adapted to their particular specialties. However, the authors are also willing to consider an alternate hypothesis: maybe trail running, as a relatively young sport with considerably less prize money, simply hasn’t yet attracted as high a level of competition. It’s worth noting that the senior author of the study, Guillaume Millet, has an impressive history as a mountain-ultra-trail runner, including a podium finish at perhaps the toughest one of all, the Tor des GĂ©ants. He’s not trying to diss trail runners.

But the data does have at least one hint in this direction. The road runners reported training for a total of 79.0 hours per month, on average, including running, cross-training, and resistance training. The trail runners averaged just 43.6 hours. The seven road runners in the study (all of whom were male, as were the trail runners, for reasons that aren’t explained) are described as national-level, with an average 10K best of 29:17. The ten trail runners, on the other hand, are all ranked among the best in the world, having just won the team title at the 2019 World Trail Championship and placed fourth at the 2019 World Mountain Running Championships. The trail runners also race over longer distances of up to 14 hours, while the road runners max out at the marathon, so it’s surprising that the trail runners train less. Perhaps, Millet and his colleagues note delicately, they’d be even faster if they trained more.

This difference in training also raises questions about other conclusions. Are the trail runners really more powerful because they’ve honed their leg strength from hill training, or because stronger athletes are drawn to trail running? Or, instead, are the road runners comparatively weaker because they’re logging so much more mileage, which interferes with muscle growth? It’s interesting to note that the road runners actually spend more time resistance training than the trail runners, so it’s not that they don’t want to be strong.

Ultimately, there are limits to what we can conclude from an observational study. I suspect the very top road runners in the world, at least for now, pass through a narrower sieve than the top trail runners. They probably have rarer genetics and train more. But the demands of the two disciplines are undoubtedly different, and even the best road runners would likely struggle against the best trail runners on a sufficiently rugged trail course. Eliud Kipchoge that he wants to eventually move to ultras; let’s start by seeing how he does on the hills of the Boston Marathon next spring.

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A few years ago, Swedish researchers published of dog ownership in 35,000 twin pairs. By comparing identical and fraternal twins, they were able to estimate how much of the decision to have a dog is environmental—you grew up in a house with a dog, say—compared to genetic. Overall, genes seemed to explain about half the variance in dog ownership, with increasing importance as you get older. If you have a dog when you’re 50, that has almost nothing to do with whether you had one as a kid.

Researchers were interested in this question because some (but not all) has suggested that dog owners live longer and have a lower risk of heart problems than non-dog-owners. Maybe that’s because dogs provide social support; maybe it’s because they need to be taken for walks every day. The genetic data suggests a third possibility: maybe there are “pleiotropic” effects, meaning that the same genes that predispose some people to dog ownership also predispose them to better health.

Is Your Exercise Habit Genetic?

Those dog findings caught my attention because they mirror some of the open questions about exercise and health. There’s overwhelming evidence that people who exercise more tend to be healthier and live longer. But how much of that reflects underlying predispositions to exercise and to be healthy? And to what extent is exercising regularly a “decision” versus a reflection of our inborn preferences?

As it happens, another Nordic twin study has some insights on these questions. This one, published in the Scandinavian Journal of Medicine and Science in Sports by a team in Finland led by Urho Kujala of the University of JyvĂ€skylĂ€, looks at 17 pairs of identical twins with a highly unusual characteristic: despite their shared genetics, they don’t have similar exercise habits.

The first thing to note is just how unusual such twin pairs are. The twins in the study were drawn from two previous Finnish twin studies that included thousands of pairs of identical twins. The vast majority of them had similar levels of physical activity. The High Runner mouse line that’s often used in lab studies took mice that loved to run, bred them with each other, and produced mice that love to run even more. I’d like to think that human behavior (and mating patterns) are a little more complex than that, but the twin data certainly suggests that our genes influence our predilection for movement.

Still, they found these 17 pairs whose paths had diverged. There were two different subgroups: young twins in their thirties whose exercise habits had diverged for at least three years, and older twins in their fifties to seventies whose habits had diverged for at least 30 years. On average, the exercising twins got about three times as much physical activity, including active commuting, as the non-exercising ones: 6.1 MET-hours per day compared to 2.0 MET-hours per day. For context, running at a ten-minute-mile pace for half an hour consumes .

All the twin pairs came in for physical examinations, and the results were pretty much what you’d expect. The exercising twins had higher VO2 max (38.6 vs. 33.0 ml/kg/min), smaller waist circumference (34.8 vs. 36.3 inches), lower body fat (19.7 vs. 22.6 percent), significantly less abdominal fat and liver fat, and so on. The study is if you want to dig further into the details, but the results aren’t surprising. Exercise clearly improves a bunch of health parameters, and genes clearly matter too—after all, the differences aren’t that big.

How big could the differences get? A 2018 from researchers at California State University Fullerton looked at a single identical twin pair, then aged 52. One was a marathoner and triathlete who had logged almost 40,000 miles of running between 1993 and 2015. The other was a truck driver who didn’t exercise. In this case, the exercising twin weighed 22 pounds less, and his resting heart rate was 30 percent lower. Most fascinatingly, muscle biopsies showed that the marathoner had 94 percent slow-twitch fibers while the truck-driver had just 40 percent slow-twitch. No one before or since (as far as I know) has shown such a dramatic change in muscle properties.

Genes Impact Your Health and Behavior

The burning question, especially for those of us who’d like to defy our apparent genetic fates, is what set these twin pairs on divergent paths. In the Fullerton study, the sedentary twin suffered a minor ankle injury that derailed his participation in high school sports, and he never ended up resuming exercise.

In the Finnish study, there was no overwhelming pattern for why one twin quit exercising and the other didn’t. In questions about their motivations for exercise, the active twins reported more interest in mastery, gaining physical fitness, and improving psychological well-being—but those differences may well be a result of differing exercise habits rather than a cause. One key barrier for inactive twins was the pressure of family and work commitments when they were young. Interestingly, those barriers eventually equalized between twin pairs once their kids were older and their careers farther along—but by then, the exercise patterns were set and the inactive twins never got back in the habit. The lesson: that maelstrom of early-career and young-kid craziness is the hardest time to maintain an exercise habit—but it’s also the most crucial.

Like all nature-nurture discussions, this one has to end somewhere in the middle. Clearly genes matter, not just for health outcomes but also for behaviors that we usually think of as purely voluntary. Equally clearly, our paths aren’t set in stone. In the Swedish dog data, the influence of your childhood environment drops close to zero by the time you’re 50, but half the variance in dog ownership is still attributed to “unique non-shared environmental effects”—in other words, to the vicissitudes of your path through life. If you want to be exercising regularly when you’re 50, choose your path with care, and try not to roll your ankle.

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A few years ago, I wrote an article about that crunched Strava data from 25,000 runners. They extracted each runner’s fastest training segments over distances ranging from 400 meters to 5K, plotted the data as a hyperbolic speed-versus-duration curve, used that curve to calculate the runner’s critical speed, and used the critical speed to predict their marathon time.

If none of that made sense to you, or if you don’t have a GPS watch, or if you simply can’t be bothered to upload all your training data into an all-seeing algorithm, then I’ve got a different kind of marathon prediction study for you. , Japanese researchers led by Akihiko Yamaguchi look at simpler variables like how much and how often you run, and come up with some big-picture insights that are worth bearing in mind next time you tackle 26.2 miles.

1. Monthly Mileage

The researchers surveyed about 500 runners about their training habits leading up to the Hokkaido Marathon, focusing on monthly training volume, number of running days per week, average run distance, and longest run distance. (According to the paper, Japanese runners and running media generally track their training volume by month, rather than the weekly totals more common in North America.)

Astute readers will notice that these variables are interconnected: if you know running frequency and average run distance, then you’ve already specified monthly training volume. That’s what makes this sort of analysis tricky. Lots of previous studies have tried to figure out which training variables are the best predictor of marathon time. But if, say, total training volume is a good predictor, it’s hard to know whether that’s because running every day is the most important thing, or whether having some really long runs is the key, or whether total mileage is what matters, regardless of how you accrue it.

To get around this, the researchers divided their runners into subgroups. For example, they created four subgroups of monthly mileage: those who ran less than 100K (62 miles) per month; 101 to 150K; 151 to 200K; and over 200K. Within each of those groups, monthly mileage had no power to predict who would run the fastest marathon, because everyone was doing similar mileage. Then you can ask what variables do predict marathon time. Is it running frequency? Average run distance? Longest run distance? The answer, interestingly, is that none of them have any significant predictive power. For people running similar overall mileage, the other training variables tell you nothing useful.

2. Run Frequency

They followed a similar procedure for training frequency, dividing the subjects into homogeneous groups running one to two times a week, three to four times, and five to seven times, then analyzing the effect of the other variables. In this case, the strongest predictor was monthly mileage: for a given running frequency, the more you run, the better. Average run distance was also a predictor, but that’s not adding anything new: if you’re running the same number of days per week, then those with higher average run distance will also have higher monthly mileage.

Subgrouping the other two variables (average run distance and longest run distance) produced similar results: in each case, total monthly mileage was the best predictor of marathon time within each subgroup. But that relationship only held for people whose average run was at least six miles and whose longest run was at least 12 miles. Below a certain minimum level of training, all predictions are off.

3. Long Runs

So far, this might seem painfully obvious: those who run more mileage race faster marathons. But the subgroup analysis allows us to draw some stronger conclusions. Most notably, it doesn’t seem to matter how you accumulate that mileage: a bunch of short runs or a few long runs produce similar results. That parallels about the health benefits of being a so-called weekend warrior: long-term mortality depends on how much exercise you get, but it doesn’t matter whether you spread your exercise throughout the week or pack it in on the weekend.

If you dig further into the subgroup analyses, you also find that the longest run was a better predictor than the average run. As a result, the researchers conclude that at a given level of mileage, it’s better to do one long run and several short ones rather than doing all your runs at a similar distance. This, too, lines up with marathon orthodoxy that says there’s no substitute for long runs.

Compared to the 25,000-runner Strava study, this one has a lot of shortcomings. It’s very small, the training data is self-reported and (as a result) doesn’t include any measure of speed, the subjects are very lightly trained (averaging 93 miles per month, or roughly 23 miles per week, with an average finishing time of 4:20). If you’re looking to qualify for the next Olympics, or even for Boston, don’t look for any secrets here: you should be accumulating volume and frequency and long runs, not trying to figure out which variables you can neglect.

But there are times in the life of every runner when training slips down a few notches on your priority list. In those situations, the rule of thumb from this study seems more useful than the formula for how to calculate critical speed from your Strava data. The rule is: accumulate as much mileage as you can, whenever you can, in whatever dose you can get it. Sometimes the runs may be shorter or less frequent than you like, but come race day it all counts.

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You’ve just put in a great block of training. Now your knee hurts. Does that mean you’re injured? Well
 it’s complicated, according to in the British Journal of Sports Medicine. Athletes are constantly dealing with pains and niggles, some that disappear and others that persist. Judging which ones to ignore and which ones to take seriously is a delicate art—and how we choose to label those pains, it turns out, can affect the outcome.

The new article is by Morten Hþgh, a physiotherapist and pain scientist at Aalborg University in Denmark, along with colleagues from Denmark, Australia, and the United States. It argues that, in the context of sports medicine, pain and injury are two distinct entities and shouldn’t be lumped together. When pain is inappropriately labeled as an injury, Hþgh and his colleagues argue, it creates fear and anxiety and may even change how you move the affected part of the body, which can create further problems.

To start, some definitions: A sports-related injury refers to damage to some part of the body. It’s usually indicated by physical impairment, an identifiable mechanism of injury, and perhaps signs of inflammation. If you tear your ACL, there’s no doubt that you’re injured. One important caveat: If you look hard enough, you’ll often find something that looks like an injury. Take X-rays of a middle-aged athlete with knee pain, and you may see signs of cartilage degeneration in the bad knee—but you might also see the same thing in the good knee, too. That’s a common consequence of aging, and it doesn’t explain why the bad knee is hurting.

Pain, on the other hand, is defined in the paper as “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage.” The italics are mine. It certainly feels like something is damaged. But pain is fundamentally a subjective, patient-reported phenomenon, and it can exist even without an identifiable injury. One of the examples in the paper is patellofemoral pain, which is a very common diagnosis in runners that basically means your knee hurts but they can’t figure out exactly why it’s hurting. In comparison, patella tendinopathy is knee pain with a clinically identifiable cause for the pain (a damaged or inflamed tendon).

The paper includes an infographic (viewable ) that outlines the differences between what they call “sports-related injuries” and “sports-related pain.” Here are some of the key points:

• Pain is influenced by “context, expectations, beliefs, and cognitions”; injuries aren’t. As it happens, the New York Times ran just last week on how words like “burning” and “stabbing” affect how you feel pain. My favorite nugget from that story: the patient in Australia who returned to her native Nepal for treatment because no one understood her description of “kat-kat,” an untranslatable expression of achiness that can feel deeply cold.
• Injuries are objectively observable; pain isn’t. That said, subjective assessments of pain, including a simple zero to ten rating, can be remarkably repeatable and informative. That’s how we know that effort, not pain, is what causes people to give up in tests of cycling endurance.
• The prognosis for an injury will depend on which body part is affected: injured muscles heal better than, say, spinal disks, and the healing will proceed in predictable stages. Pain, in contrast, often comes and goes unpredictably, and its severity doesn’t necessarily depend on the healing stage.
• The fundamental principle of rehab from injury is gradually increasing the load on the damaged tissue until healing is complete and it’s capable of handling the demands of training and competition. The focus for sports-related pain is improving the patient’s ability to manage the pain, for example by avoiding negative responses like pain catastrophizing that make it feel worse. This process isn’t as linear as rehabbing damaged tissue: you can’t just gradually increase training load and assume that pain will go away.

The themes in Hþgh’s paper overlap with , this one from Australian physician Daniel Friedman and his colleagues, on the dangers of diagnostic labels. Calling a knee injury a meniscal tear rather than a meniscal strain, for example, might toward opting for arthroscopic surgery, even though that’s not considered the best approach to that injury. More generally, Friedman writes, the words chosen to describe injuries “may catalyze a looping effect of catastrophization, anxiety, and fear of movement.”

In many cases, of course, these nuances aren’t a big deal. If you get a stress fracture, it will hurt. You’ll have to rest it until it heals, gradually increase the load on it, and then pain should no longer be an issue. The injury and its associated pain are tightly coupled. But other cases aren’t so straightforward. For people with chronic Achilles pain, there’s often no clear link between the physical state of the tendon and how it feels, so reducing and managing pain sufficiently to return to training is a more useful goal than waiting for the tendon to be “healed.” Figuring out where any given flare-up falls on that spectrum is tricky, but the first step, according to Hþgh, is simply recognizing that sometimes pain is just pain.

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I won’t pretend to be impartial here: I love the mile. It demands the legs of a sprinter, the lungs of a marathoner, and the tactical cunning of a chess grandmaster. Lasting roughly four minutes, it’s long enough for a narrative arc to unspool, and for the personalities of the various players to be revealed in their thrusts and counterthrusts, but too short for all but the very worst TV coverage to cut away for commercials or gauzy profiles. It’s .

But the very elements that make the mile so much fun to watch also make it tricky for physiologists to study. Long-distance running is a maximization challenge: almost anything you can do to boost your VO2 max, lactate threshold, or running economy will make you better. Sprinting is also a maximation challenge, focused instead on the ability to generate the most powerful forces and release large amounts of anaerobic energy as quickly as possible. It’s relatively straightforward to study how to maximize these parameters.

In contrast, middle-distance events—800 meters, 1,500 meters, and the mile—require a compromise between these two extremes. Increasing the force you transmit to the ground with each step, for example, might worsen your efficiency, and vice versa. Instead of a maximization challenge, middle-distance training is all about making the best trade-offs possible between the conflicting demands of speed and endurance. In other words, as argues, it’s an art.

The authors of the new paper are all sports scientists, hailing from four different universities in Norway, led by Thomas Haugen of Oslo’s Kristiania University College. But they admit that our knowledge about middle-distance training is mostly derived from “the practical experience and intuition of world-leading athletes and coaches.” Their goal is to lay out the current state-of-the-art in order to identify gaps that scientists can begin to fill—and the result is a handy (and free-to-read) guide to what it takes to run a great mile. Here are some of the highlights.

The Fuel Mix

There are two main ways your body can convert food into the energy you need to run. Aerobic energy relies on chemical reactions involving oxygen. You’ve got a nearly inexhaustible supply, but it can only be delivered in a trickle, so it’s ideal for long endurance races. Anaerobic energy, which relies on a different set of chemical reactions, can deliver big surges but is quickly exhausted, so it’s ideal for sprinting.

Whether you’re running a 5K or a marathon, you’re relying almost entirely on aerobic energy, so the training for these events is remarkably similar despite the fact that one is more than eight times farther than the other. The middle-distance events, on the other hand, require a fuel mix that depends very sensitively on the distance. In an 800-meter race, which lasts about two minutes, you get 60 to 75 percent of your energy from aerobic sources. In the 1,500 meters and the mile, it’s more like 75 to 85 percent aerobic. That means 800 runners and milers are more different, metabolically speaking, than 5K runners and marathoners.

How sharp is that knife’s edge? Elite female 800-meter runners are about 15 seconds slower than elite males. That tiny difference seems to be enough to change the optimal physiological requirements of the event: female 800 runners are more like milers than male 800 runners. Sure enough, if you look at the top 200 performers of all time, there are 55 women who appear on both the 800 and 1,500 lists, but only 38 men.

The Subspecialists

A few years ago, I wrote about British researcher Gareth Sandford’s work on a concept called speed reserve, which compares your maximum aerobic speed to your maximum sprint speed. Sandford used this ratio to distinguish between different kinds of 800-meter competitors, each with different characteristics and different training needs: 400/800 runners, pure 800 specialists, and 800/1,500 runners. Haugen and his colleagues extend this taxonomy to include pure milers and 1,500/5,000 runners.

What’s the difference between all these flavors of middle-distance runner? For one thing, they train differently. According to the various training logs, books, and interviews synthesized in the new paper, 800-meter runners tend to cover about 30 to 75 miles per week. Milers cover 75 to 105; 5K and 10K runners hit up to 125. (These ranges are mostly based on reports from male runners, so the authors hypothesize that female runners probably spend a similar amount of time training but rack up slightly less mileage on average, since their running speeds are typically about 11 percent slower.) Sandford’s point is that where you sit in these ranges isn’t just a function of maturity or competitive level; it’s a function of what physiological type of 800 runner or miler you are.

The same differences show up in other training variables. Of the roughly 500 to 600 training hours that milers rack up annually, 90 percent of them are running, with the rest focused on strength and power, drills, plyometrics, and stretching. For 800-meter runners, it can be as little as 400 hours, with just 70 to 80 percent of those hours spent running.

Stephen Seiler, one of the co-authors of the new paper, was one of the pioneers of analyzing the “intensity distribution” of how real-world athletes train. One of his key insights: across endurance sports, elite athletes tend to do about 80 percent of their training sessions at low intensity and just 20 percent at high intensity. Milers seem to follow that rule, but 800 runners do just 60 to 70 percent of their sessions at low intensity. (That said, their high-intensity sessions include lots of jogging, so if you look at the total time spent in different zones rather than the total number of workouts, even 800 runners spend 90 percent of their training time at low intensity.)

The Training Zones

The for endurance athletes don’t translate well for milers. Instead, they need the training equivalent of : zones that go above the usual max. Haugen and his colleagues propose two scales: a detailed nine-zone scale for when you need that extra push over the cliff, and a simplified five-zone scale. You can read the full details , but the basic five-zone structure is as follows:

• Low-intensity training: Long runs and recovery runs at marathon pace or slower
• Moderate-intensity training: Fartleks, threshold runs, progression runs around half-marathon pace
• High-intensity training: Intervals or hill reps lasting one to seven minutes, typically at 3K to 10K race pace
• Very-high-intensity training: Intervals or hill reps lasting 15 to 90 seconds at mile race pace or faster
• Short-sprint training: Accelerations or maximal sprints lasting less than 15 seconds

How you put these ingredients together in a coherent training plan is where things get really tricky. The paper has a nice table defining the you might use, to help clarify the characteristics and purpose of things like anaerobic threshold intervals and lactate tolerance training; another nice table outlining the of concepts like interval training, periodization, and polarized training; and some from champion athletes. There’s a lot to chew on.

What none of the theory can tell you, though, is what it feels like to race a mile. Going from the 5K to the half-marathon is a different distance; going from 5K to the mile is, as the physiology suggests, almost a different sport. Because of the road-race scene, there are a lot of people out there who took up running as adults who were probably born to run middle-distance but have never tried it—like șÚÁÏłÔčÏÍű contributor Charles Bethea, whose quest to run a five-minute mile a few years ago revealed some hidden talent that had never emerged in his attempts at longer distances.ÌęI’m not saying it’s easy or fun; it’s exhilarating. But hey, don’t take my word for it.

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The fact that two experienced climbers died near the summit of Everest last week is sad but unsurprising. As Alan Arnette pointed out, expeditions on the Nepal side of the mountain alone have been averaging almost four deaths a year since the turn of the century. But the situation this year is a little more fraught, with a severe wave of coronavirus ripping through Nepal and a worsening outbreak at Everest Base Camp.

Authorities in Nepal were quick to dismiss any link between the deaths and the virus. “Reaching to that height is impossible if someone is infected with the COVID,” the director general of Nepal’s tourism department, Rudra Singh Tamang, . The head of Seven Summit Treks, which was guiding both of the deceased climbers, said the same thing, attributing the deaths instead to altitude illness. On the surface, that seems like a reasonable claim (and I have no specific information to either refute or support it), but it prompts a question: what is it, exactly, that does kill climbers on Everest?

There’s plenty of data on this question, thanks to the comprehensive started by the late Elizabeth Hawley. And there have been several attempts by researchers to analyze the patterns in this data. Sometimes the causes of death are clear. There’s no ambiguity about the 15 people who died at Everest Base Camp in the 2015 avalanche. But when someone collapses in the so-called Death Zone above about 26,000 feet (8,000 meters), it’s much harder to distinguish between the various forms of altitude illness, cold-related injuries, and straightforward exhaustion,Ìęall of which leave them stranded to die of exposure. Even if they fall off a cliff, you don’t know whether it was a consequence of impaired balance and cognitive function due to altitude illness, or perhaps a loss of coordination from frostbite.

With those caveats in mind, here are some stats. In 2008, a team led by anesthesiologist Paul Firth published in the British Medical Journal of 192 deaths among more than 14,000 Everest climbers and Sherpas between 1921 and 2006. Of that total, 59 percent of the deaths were attributable to trauma either from falls or hazards such as avalanches. In 14 percent of the cases, the bodies were never found so details are unknown. The remaining 27 percent are the most interesting ones, attributed to non-trauma causes like altitude illness and hypothermia.

When you restrict the data to the 94 people who died above 8,000 meters, some interesting details emerge. Even among those who fell to their deaths, many were described as showing signs of neurological dysfunction, such as confusion or loss of balance. This is significant, because altitude illness comes in several forms. The mild version is acute mountain sickness (AMS), which mostly just manifests as feeling like crap. The two more serious versions, either of which can be fatal, are high-altitude cerebral edema (HACE, meaning swelling in the brain) and high-altitude pulmonary edema (HAPE, or swelling in the lungs).

One dog-that-didn’t-bark detail, according to the study, is that “respiratory distress, nausea, vomiting, and headache” were rarely noted in those who died above 8,000 meters. That may be, in part, because those symptoms—characteristic of AMS or HAPE—might be unambiguous enough to prompt you to turn back before it’s too late. In contrast, if your thinking is a little cloudy thanks to incipient HACE, that may not seem like such a big problem—and your ability to recognize the problem is compromised by the cloudiness of your thinking.

I’ll admit that I’m skeptical of the assertion that no one with COVID can get to 8,000 meters. Depending on the timing and severity of your infection, you might be healthy enough to get to the highest camp, and just start showing very mild respiratory symptoms on the day of your summit push—not enough to realize that you’re in trouble, but just enough to put you in danger as the day wears on. But the data above suggests that, for the most part, it’s not lung problems that kill people near the summit. That doesn’t rule out the possibility that COVID was involved in this year’s deaths, but it certainly lowers my index of suspicion.

There’s a more recent analysis that’s also worth digging into, by a team co-led by biologist Raymond Huey of the University of Washington and statistician Cody Carroll of the University of California, Davis. Huey and his colleagues had published an earlier analysis of all 2,211 climbers making their first attempt to ascend Everest between 1990 and 2005, looking for patterns in who succeeded and who didn’t. The new paper updates that analysis with another 3,620 first-time climbers between 2006 and spring 2019, and there are some notable insights about the differences.

Of course, there have been plenty of changes on Everest since 2006. As the viral photographs and permit numbers reveal, it’s way more crowded. The standard critique is that guiding companies are hauling rich, inexperienced dilettantes up the mountain who create traffic jams and make bad decisions, putting everyone at greater risk. Interestingly, the death rate has decreased a bit, from 1.6 percent in the earlier period to 1.0 percent in the more recent period. That said, since the number of climbers has quadrupled, the actual number of deaths has increased. The more recent climbers were also twice as likely to reach the summit: “This supports (I think) the idea that better logistics, weather forecasting, fixed ropes, experience (of expedition leaders and high-altitude porters) have improved success rates and slightly lowered death rates,” Huey told me in an email. “But we have no direct data to evaluate these suspicions.”

The role of crowding is a little trickier. Nepal issued a record 408 climbing permits to foreigners this year, and more than 100 climbers summited on May 11 and 12 alone. Huey and his colleagues compared the summiting and death rates on crowded and uncrowded days, and didn’t see any differences. But that doesn’t mean crowding doesn’t matter. “Perhaps the ‘uncrowded days’ had relatively bad weather or poor snow conditions, and climbers waited for better conditions,” Huey says. “If that is the case, then the crowded days would be crowded because conditions were favorable, and favorable conditions compensated for any detrimental effects of crowding.”

Indeed, it’s hard to imagine that crowding doesn’t make a difference. It inevitably causes delays, and your risk of getting caught by an avalanche or rock fall is directly proportional to how long you’re out there—one of Reinhold Messner’s rationales for rapid alpine-style climbing, Huey notes. Perhaps even more importantly, the longer you’re at extreme altitude the more the effects of altitude illness may accumulate.

The 2008 BMJ analysis notes that there are two main explanations for why climbers would develop balance and cognitive impairments. One is that you’re not getting enough oxygen to the brain, either because you run out of supplemental oxygen or because you’re exercising really hard. But there were no apparent differences in patterns of death for those with or without supplemental oxygen, and there were very few deaths while ascending just below the summit, when the physical demands of the ascent are greatest. So the more likely explanation is that these climbers are suffering from the brain-swelling effects of HACE.

Back in 2006, a British doctor named Andrew Sutherland wrote titled “Why are so many people dying on Everest?” He’d recently summited Everest, and had paused to help a climber with HAPE at 23,000 feet—and then, farther up the mountain, passed the bodies of four less fortunate climbers.

“I think it is likely that we all develop a certain degree of pulmonary and cerebral oedema [i.e. swelling] when going to the summit,” he wrote, “and that it is only a matter of time before we succumb to it.” The mild disorientation from HACE leads to bad decisions and a slower rate of climbing, which in turn (along with factors like crowding) lengthens the amount of time you’re exposed to extreme altitude, causing the symptoms to worsen. This root cause, he argued, likely contributes to many deaths whose final blow is dealt by a fall or hypothermia or exhaustion.

After his own climb, Sutherland had to visit to theÌęFrench consulate in Kathmandu to identify the body of a Frenchman who’d reached the summit but been too exhausted to descend, managing only about 150 feet in six hours before being abandoned by his expedition partners. The consul shook his head. “He didn’t reach the summit until 12:30; that is a 14-hour climb—it is too long. All the files we get of those that die on the mountain, c’est toujour la mĂȘme chose—they take too long to reach the summit.”

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This will go down as the Year of the Solo Time Trial: high school kids ; Michael Wardian running around the block for two and a half days in the Quarantine Backyard Ultra; every cyclist in the world sweating on Zwift. Going solo, as you’ve probably already discovered, is different from doing it with friends, in a pack, or in a giant mass-participation race. Some of the differences are obvious and quantifiable, like the lack of drafting, but some are more subtle.

As it happens, a from earlier this year in the International Journal of Sports Physiology and Performance offers some interesting insights into the psychology of the time trial. In particular, the study zeroes in on the role of affective feelings, which basically means how much pleasure or displeasure you’re experiencing. It’s a complex topic that’s hard to nail down in simple terms, but the data tells a compelling story about why it’s important.

The study comes from a Brazilian group led by Everton do Carmo of the University of SĂŁo Paulo, working also with of the University of Worcester in Britain. They recruited 14 male runners to complete a pair of 10K races: one alone on the track, and the other (at least a week before or after) competing against all the other runners in the study. Not surprisingly, the runners were faster in the group race, with an average time of 39:32 compared to 40:28.

This is not a novel result: plenty of previous studies have found that competition allows you to go faster, and we intuitively understand that the presence of competitors (and perhaps of a crowd) somehow allows us to push harder. But what does that really mean? Attempts to understand the psychology of endurance usually focus on the subjective sense of perceived exertion, which incorporates both physiological (breathing rate, lactate levels, etc.) and mental cues.

Take a look at the data on ratings of perceived exertion (RPE, on a scale of 6 to 20) during the two 10K races. For both the solo time trial (TT) and the head-to-head (HTH) race, RPE climbs in a more or less straight line approaching the maximum value at the finish:

This, again, is a textbook result. That’s how we pace ourselves, running at a perceived effort that steadily increases throughout the race, at a rate (based on prior experience) that will hit max right around the finish line. It’s like the classic John L. Parker, Jr. quote from Once a Runner, about how a runner rations energy during a race: “He wants to be broke at precisely the moment he no longer needs his coin.”

What’s notable is that the two RPE lines (for TT and HTH) are pretty much right on top of each other. Even though the runners are moving faster in the group race, it doesn’t feel as though they’re trying harder. Their pacing pattern—fast start, slower middle, accelerate at the end—was also the same in both races. So there has to be something else that distinguishes the subjective experience of solo efforts and group races.

The other psychological data collected by the researchers each lap was affective feelings, on a scale of -5 (displeasure/negative) to +5 (pleasure/positive). And here there’s a very distinct pattern: the solo trialists feel increasingly negative as the race progresses, while the racers stay at a relatively stable level.

There are numerous explanations we could offer for why life seems to suck more when you’re trying to push your limits all alone. And they might all be right: the researchers note that there was lots of variation in the individual affective responses, which makes it very hard to generalize. That’s an observation that dates back to some of the on affective responses in exercise in the 1980s: there’s a somewhat consistent relationship between perceived effort and how hard your body is working, but affective feelings at a given level of effort are all over the map.

Interestingly, three of the subjects in the study dropped out of the head-to-head race before the finish, while none dropped out of the time trial. At the point where these runners dropped out, their reported effort levels were no different than they were at the same stage of the solo trial, but their affective feelings were actually 3 to 5 points more negative (contrary to the usual pattern of more positive feelings in the group race). That illustrates how widely the affective responses vary, and it also suggests that the runners didn’t drop out because the pace or the effort felt too hard. Instead, they quit because they felt bad.

It’s tricky to put your finger on what “feeling bad” means. One study of affective feelings during exercise described it as “.” That means it’s possible for a workout to feel hard and good at the same time—or easy and unpleasant.

In this case, we don’t have any specific information about why these runners felt good or bad at any given moment. One point the Brazilian researchers make is that in a group context, your attention shifts from internal to external focus. That might give you a feeling of solidarity with the other participants, or a sense of accomplishment that you’re beating at least some of the others. Or, if you’re dropping off the back of the pack, it might make you feel worse. Perhaps that’s what happened to those who dropped out.

As a result, it’s much harder to formulate a general theory for how affective feelings contribute to endurance performance. There have been a few previous studies looking at affective feelings in different contexts, including one by , a former world-class miler from Spain, that compared group to solo running in interval workouts. The results were similar, but the dynamics are subtly different: in a group workout, the people around you are teammates working together towards a goal instead of competitors trying to beat you. (At least that’s how group workouts are supposed to work.)

For now, the key point is simply that these things make a difference. Don’t expect to replicate your best real-world performances alone in the basement. The good news, on the other hand, is that there’s also research showing that even —racing against a computerized avatar representing your own previous ride—boosts performance. Combine that result with the Brazilian study, and you can’t help wondering if all those enthusiastic Zwifters were right all along: doing it with others, even virtually, increases your pleasure.

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