To the untrained observer, it doesn’t look like much: I am a skinny 31-year-old male in my��apartment��bedroom, sweating profusely in spandex bib shorts atop half a bicycle. I’ve swapped the bike’s rear wheel for a smart trainer that tracks my cadence, power output, and speed. It’s classic COVID-era indoor exercise in the same vein as a Peloton bike or Zwift. But instead of a��live feed of a cycling class��or a video��game racecourse, I’m staring at a series of blue��lumps��graphed��on��my desktop computer screen. The blue lumps represent the target power��measured in watts. As a lump grows, I have to work harder. When the lump shrinks, I get a rest. A thin yellow line shows my actual power output as I attempt to complete each interval. An on-screen timer shows me how long until the intensity changes again. Occasionally, white text pops up with some sage advice from a disembodied coach: “Quick legs, high power.” “Find your sit bones.”��It’s majorly nerdy, hardcore cycling training being foisted on one of Earth’s most��mediocre athletes who has absolutely no race aspirations.

But behind this��facade, a sophisticated artificial intelligence–powered��training program is adapting to my every pedal stroke. The app��I’m using is called , and in February, the company launched a��suite of new features on a closed beta app that��it��believes can��revolutionize how cyclists train.��The new technology is powered by machine learning: the idea that computers can be trained to hunt through massive troves of data and suss out esoteric patterns that are invisible to the human brain. The new TrainerRoad algorithm is watching me ride, evaluating my performance and progress, and comparing me to everyone else on the platform. (How many people, exactly?��The company��won’t say.)��This data is��then used to prescribe future workouts—ranging from slow and steady endurance work to high-intensity sprint intervals—that are tailored��just for me. “Our vision is that in ten��to 20 years everyone will have their workouts picked by an AI,” says Nate Pearson, CEO of TrainerRoad.��

The idea of using an algorithm to optimize training isn’t exactly new. Louis Passfield, an adjunct professor in kinesiology at the University of Calgary, has been dreaming of calculating his way to a yellow jersey since he was an undergraduate at the University of Brighton around 25 years ago.��“I thought that by studying physiology, I could calculate this perfect training program and then, in turn, win the Tour de France,” Passfield says. “This was back in 1987, before the concept of what they call ‘big data’ was even born.”��

What is��new is the proliferation of smart trainers. In the late 1980s, power meters were inordinately expensive and confined to Tour de France teams and sports science��laboratories. Now, more than 1��million people have registered for Zwift,��an app��where they can��obsess daily over their��watts per kilo, heart rate, and cadence. Finding a Wahoo Kickr��bike trainer��during the pandemic��has been about as easy as finding toilet paper or hand sanitizer last spring. All these cyclists��equipped with laboratory-grade trainers��are generating troves of high-quality data that makes researchers like Passfield swoon. “I’m infinitely curious,” he says. “I love what TrainerRoad is��trying to do and how they’re going about it. It’s an area I’m itching to get involved with.”

TrainerRoad was founded in 2010 by Pearson and Reid Weber, who now works as CTO at Wahoo’s��Sufferfest Training platform. It began as a way for Pearson��to replicate the experience of spin classes at home��and has evolved into a cutting-edge training app, especially since the smart trainer��boom.��

What TrainerRoad has done better than competitors is to standardize its data collection in a way that makes it scientifically powerful. There are��many��more rides recorded on Strava��than on TrainerRoad, but they��don’t contain enough information to make them useful: We can see that Rider A rode halfway up a hill at 300��watts, but is that an all-out effort for her or an easy spin?��Did she stop because she was exhausted or because there was a red light? More than maybe any other smart trainer software, TrainerRoad has built a data collection tool that can begin to answer these questions. There’s no racing. There’s no dance music (thank god). There are no KOMs (regrettably). There’s nothing to do on the platform except workouts. It’s also not for everyone: You log in and ride to a prescribed power for a prescribed time. It is often brutal. You either succeed or you fail. But ����’s the simplicity of the format that has allowed TrainerRoad to be the first cycling trainer software to offer this sort of workout.��

This pass/fail duality also underlies TrainerRoad’s nascent foray into machine learning. The technology behind the new adaptive training program is essentially an AI��classifier that analyzes a completed workout and marks it as fail, pass, or “super pass” based on the athlete’s performance. “At first, we actually tried to just do simple ‘target power versus��actual power’ for intervals, but we weren’t successful,” Pearson says. “Small variations in trainers, power meters, and how long the intervals were made it inaccurate.” Instead, TrainerRoad asked athletes to classify their workouts manually��until the company��had a data set big enough to train the AI.��

Humans are��quite adept at making��this type of categorization in certain situations. Like looking for pictures of a stop sign to complete a CAPTCHA, ����’s not hard to look at a prescribed power curve versus��your actual power curve and tell if ����’s a pass or fail. We can easily discount��obvious anomalies like��dropouts, pauses, or weird spikes in power that trip up the AI but��don’t actually indicate��that someone is struggling. When we see the power curve consistently lagging or trailing off, that’s a clear sign��that we’re failing. Now, with more than 10,000 workouts to learn from, Pearson says the AI��is outperforming humans in��deciding pass versus fail.

“Some cases were obvious, but as we got our accuracy up, we found the human athletes weren’t classifying all workouts the same,” he explains. In��borderline cases, sometimes a minority of athletes would rate a workout as a pass while the majority and the AI��would rate it as a struggle. When presented with the AI’s verdict, the riders in the minority would usually change their opinion.��

Armed with an algorithm that can tell how you’re doing on workouts, the next step—and probably the one users will find most exciting—was to break down a rider’s performance into more granular categories, like endurance, tempo, sweet spot, threshold, VO2��max, and anaerobic.��These power zones are��common training tools, but in case you need a refresher, functional threshold power (FTP) represents the maximum number of watts a rider can sustain for an hour. Then, the zones are as follows:

• Active recovery: <55 percent FTP
• Endurance: 55 percent to 75 percent��FTP
• Tempo: 76 percent to��87 percent��FTP
• Sweet spot: 88 percent to��94 percent��FTP
• Threshold: 95 percent to��105 percent��FTP
• VO2 max: 106 percent to��120 percent��FTP
• Anaerobic capacity: >120 percent��FTP

As you complete workouts across these��zones, your overall score in a progression chart improves in the corresponding areas. Spend an hour doing sweet spot intervals—five-to-eight-minute efforts at 88 percent to 94��percent��of FTP, for instance—and your��sweet spot number��might increase by a point or two on the ten-point scale. Critically, your scores for endurance, tempo, and threshold are also likely to move up a bit. Exactly how much a given workout raises or lowers your scores in each category is a function of how hard that workout is, how much training you’ve already done in that zone, and some additional machine learning running in the background that analyzes how other riders have responded and how their fitness has changed as a result.

Here’s what my progression chart��looked like after I had used the new adaptive training program for a few days. The plan I’m on now is focused on��base training, so, according to the software, I’m leveling up in those lower endurance zones. If I were training for a crit, I’d probably be doing a lot more work in the VO2 max and anaerobic zones—which is��why I’ll never race crits.

In the future, TrainerRoad plans to expand the role of machine learning and build more features into the app, including one designed to help athletes who menstruate understand how their cycle affects their training��and another to help you forecast how a certain plan will improve your fitness over time.��The company is investigating how much age and gender affect the rest an athlete needs and is even planning to use the system to compare different training methodologies. For instance, one common criticism of some TrainerRoad plans is that they spend too much time in the��challenging��sweet spot and threshold zones, which��could lead to burnout. Meanwhile, there’s a large body of science that suggests a polarized approach—a training plan that spends at least 80 percent��of training time in Zone 1 and the other 20 percent��in Zone 5 or higher—yields better results and less overall fatigue, especially in elite��athletes who have lots of time to train. This debate has been ongoing in sports science for years, with no real end in sight. Now that TrainerRoad��has added polarized plans,��the company may be able to do some��A/B testing to see which plan ultimately leads to greater fitness gains. Tantalizingly, we might even learn which types of athletes respond better to which types of training. “The studies that exist are pretty small sample size,” says Jonathan Lee, communications director at TrainerRoad. “We have thousands upon thousands of people.”��

The potential for experimentation is impressive, but one of the limitations of machine learning is that it can’t explain why improvements are happening. The inner workings of the algorithm are opaque. The patterns that the AI��finds in the training data are so multifaceted��and abstract that they cannot be disentangled. This is where the system’s power comes from, but ����’s��also an obvious restriction.��“PhDs usually want to figure out what are the mechanisms that make��somebody faster, but we don’t necessarily know,”��Pearson says. “What we care about is just the outcome performance.” ��

But does this actually work? Does adaptive training make people faster than traditional static training programs, like something you’d find on TrainingPeaks, Sufferfest, or even the old version of TrainerRoad? For now, Pearson says ����’s too soon to tell. The closed beta program began on February 25��of this year, with only around 50 users, and has been expanding slowly, with new riders being added every week.��That��isn’t a large enough sample size to detect statistically significant differences yet. “It sounds like a great idea,” Passfield says. “What it needs is to be��objectively evaluated against a standard program��and, ideally, against a random program. From a scientific point of view, that’s kind of the ultimate baseline: we give you these sessions in a random order, we give you these sessions in a structured order, and then we give them to you in our AI-informed order.”

Here’s what I can tell you, though. The adaptive training is definitely more likely to make me stick with a plan. Back in the fall, I spent a few weeks using TrainerRoad vanilla for the sake of comparison. I found it excruciatingly difficult, because I am not a highly motivated rider. I’m not training for a race or trying to get KOMs on local climbs. Without motivation, the intervals become pointless torture. With the static training plan, quitting put you behind. The next workout was going to feel even harder since you missed part of the previous one. If you fell behind the curve, you had almost no shot at digging out. Now, if I fail a workout, ����’s fine. The next one gets a bit easier. When you open up the dashboard, you’ll see a message like this:

In the old version, I had to show up well-rested, focused, fueled, and perfectly hydrated to complete workouts. But this does not always gel with my lifestyle, man.��Before COVID-19, I had friends who��liked to��drink beer and stay up late.��I play hockey twice a week.��I surf whenever there are waves.��I eat fast food frequently. With the adaptive training, all of this is fine. I can drink three beers after hockey and show up for my workout the next day with nothing but��McDonald’s in my body. The AI adjusts for the fact that I’m a deeply flawed, suboptimal human, and honestly, it feels so good to be seen.��

The post Is This App the Future of AI Workouts? appeared first on ���ϳԹ��� Online.

Sal Masekela steps off a helicopter onto the white sands of Tavarua Island Resort, a tiny speck in the Fiji archipelago, and walks into a gorgeous open-air restaurant that overlooks a world-famous reef break appropriately dubbed Restaurants. He greets the Fijian staff by name, hugging them, asking them about their lives since his last visit.

Masekela, you may recall, was the face and voice of , hosting both the summer and winter events for more than a decade. With his iconic dreadlocks and smooth baritone, he was a fixture at the center of the action-sports universe, narrating nearly every history-making moment at the games, from on a motorcycle in 2006 to in 2012.

Today, six years since a breakup with ESPN, Masekela remains deeply entrenched in action sports. He is here, on the surf mecca of Tavarua, for a vacation with a group of friends comprised of athletes, movie stars, entrepreneurs, Instagram influencers, and their families. As he makes the rounds, a guest compares him to Ricardo Montalbán, the suave Mexican actor best known for playing Mr. Roarke on Fantasy Island. Somehow, despite the fact that Masekela is a stocky black man, and recently bald, ����’s a rather apt observation. It can be challenging to walk anywhere with Masekela, because everyone who sees him wants to stop and talk with him and he wants to talk to everybody. He is Larry David’s worst nightmare.

This is Masekela’s 16th trip to Tavarua but nonetheless a special one, because ����’s his first visit since his father died from prostate cancer six months ago. Hugh Masekela was a trumpeter and is often credited as the father of South African jazz. He played and toured with everyone from Paul Simon to Dave Matthews and was nominated for three Grammys. During apartheid, Hugh left South Africa to study music in the United States, but he remained outspoken against the brutality of South African racial segregation. In 1986, he recorded “,” a song demanding the release of Nelson Mandela that would eventually become a rallying cry for the anti-apartheid movement.

Tavarua is Masekela’s favorite place on earth, and he’d implored his father to travel there with him. They made plans for the fall of 2016 and even purchased tickets, but at the last minute, Hugh postponed. A year and a half later, he passed away. This trip, these waves, Masekela says, are for his dad.

The vacation also comes at a significant moment in Masekela’s career—a moment when he hopes to find a path back into the limelight. Since walking away from the X Games, he has continued to work in television, hosting a series for Red Bull Media House, reporting stories for NBC at the 2014 Winter Olympics in Sochi, and hosting a sports documentary series on Viceland, among other gigs. He’s had bit parts in movies. His band, , which blends jazz, soul, and R&B, has been featured on HBO and Showtime and released its second album last summer. Still, Masekela has grander ambitions, though he struggles to define them.

Like many major figures from the heyday of action sports, Masekela is still coming to grips with the fact that his world has lost much of its cultural and commercial cachet. As recently as 2011, an average of more than a million viewers tuned in to watch the four-day-long Summer X Games on television. By 2017, that number dropped to 385,000. (ESPN says viewership is actually up when you account for streaming and social viewers, but declined to share year-over-year numbers.) The formerly rebel sports of snowboarding, BMX, and skateboarding have been adopted by the Olympics. The bad-boy stars of yesterday are now middle-aged dads.

Masekela has ridden the action-sports wave as far and well as he could’ve hoped, but no ride lasts forever.

That Masekela became the face of the X Games in the first place was wildly improbable. He was born in 1971 in Los Angeles, the first child of Hugh and Haitian immigrant Jessie Lapierre. By the time he turned four, his parents had moved to New York City and split up, and his mother was remarried to a Jehovah’s Witness, who raised Masekela in the church. But despite his stepdad’s best efforts, Hugh’s influence endured. Masekela split time between marijuana-clouded jazz clubs and going door to door spreading the Truth. “Growing up between those worlds gave me a strange set of skills,” he says. “For a long time they felt like a burden, like I was always working to fit in.”

His mom and stepdad moved around a lot, ultimately abandoning the East Coast for Carlsbad, California, at the start of Masekela’s senior year of high school. Relocating across the country was difficult for him. During the drive out, he spent rest-stop breaks at pay phones. “I was calling my girl back east and not saying anything,” he says. “Just weeping on the phone for like ten minutes, that high school heartbreak shit.”

But on his first morning in Carlsbad, he discovered that his new house sat on top of a steep hill with a view of the ocean, a feature that he credits with shaping the trajectory of his life. “Imagine, you walk out of this house onto this lawn, and you look and you’re like, Oh shit we’re right here.”

Surfing became the focus of Masekela’s life. As a Jehovah’s Witness, he was discouraged from playing organized sports, but several of the members of his congregation surfed, and they loaned him a board and a wetsuit, which he put on backward the first time. He spent his downtime at school paging through back issues of Surfer, neglecting his schoolwork to study board sports. He refined the basic skateboarding skills he’d started developing back east, and he learned to snowboard. “Nothing else sounded as good,” he says. “I didn’t want to be around people who did it. I wanted to be around people who lived it.” He became a full-on disciple of what he would call the shred life.

The tension between his new passion and his commitment to the church began to mount. At 19, Masekela went to South Africa to meet up with his father, who had recently returned home for the first time in 30 years. It was 1991, and Mandela had just been released from prison. During the trip, Masekela explored life a bit too enthusiastically for the church’s standards. His sins were, in his words, “that I made out with a bunch of girls and smoked some pot.”

When he confessed, the elders chose to disfellowship him. “You have to keep going to church, to the meetings, but no one talks to you,” he explains. During his exile, Masekela remained close with his mother, but the social isolation was a brutal punishment. “It was without a doubt the most difficult time in my life,” he says. “I was severely depressed. I held a knife to my wrist in my kitchen many times.”

He moved to a new congregation in a nearby beach community called Leucadia. In 1993, while working at a restaurant, he crossed paths with several employees from TransWorld Media, which produces board-sports magazines and films, and he charmed his way into a job as a receptionist. In no time he worked up to sales jobs and small-scale announcing gigs for skateboarding competitions. His circle of friends expanded to include the pros he was interviewing at contests. By 1996, he was the team manager for Boks, the nascent action-sports division of Reebok, where he helped build the brand’s surf, skate, snowboard, and BMX teams.

The more entrenched he became in action sports, the further he drifted from the church, leaving religion behind for a new gospel.

Masekela’s big break came in the winter of 1997, at a snowboarding conference in Vail, Colorado. Boks had just folded, and his future was uncertain. He knew he had to do some networking.

The event took place in the wake of the first X Games, which was an embarrassment to everybody who cared about action sports. Purple skateboard ramps and clueless commentators left the community and industry furious at how their lifestyle and products had been represented.

During a Q and A session that included executives from ESPN and MTV, Masekela decided to speak up. “At a certain point, I don’t even know what happened, but I was ­standing on top of my chair in the back. I said, ‘You know, I watch all these things—the X Games and what you guys are doing on MTV—and you don’t have any voices that represent our culture to tell people about what they’re seeing. Bill Bellamy doesn’t fucking snowboard. Here’s the deal: I’m young, I’m black, I surf and I snowboard, and I know that I could get in front of the camera and do that.’ ”

He got a standing ovation. “People were buying me beers all night like I had just given some weird ‘I Have a Dream’ shred speech.” At an after-party, an executive from MTV gave him a business card. The next year, Masekela was commentating the MTV Sports and Music Festival, offering the insider’s perspective he’d cultivated since landing in California years before.

By 1999, Masekela had landed a job as a reporter for the Winter X Games. The following summer, when Tony Hawk landed the first 900, Masekela was standing at the top of the ramp. From there it was pretty much game on. The action-sports wave was barreling into the mainstream, and Masekela was pitted as its chief evangelist.

Masekela’s presence on Tavarua is conspicuous for many reasons, but even if he was less gregarious, he would still stick out. Other than the Fijian staff members, he is the only black person on the entire island. By contrast, the kids on the trip are named Chili, Coast, Country, Fin, Hazel, Jet, Lyon, Oz, Rider, River, Roman, and Tashen. That list may not be exhaustive or spelled exactly right, but the point is: the only thing whiter than the sand here is the people.

Tavarua, like many tropical-island resorts, is a destination for people with money. There are spa treatments. There’s a yoga space. There’s an artificial-turf tennis court. Speaking of tennis, Masekela loves tennis. He also loves golf. When you grow up as a skateboarding Jehovah’s Witness, perhaps adding golfer to the list becomes easier.

But still, as a black man at the center of a nearly all-white industry, Masekela has encountered racism many times. In the early nineties, the owners of a surf shop where he was working let him go, telling him that business was slowing down and they needed to cut back on staff. But a friend who was still working there told him that the owners didn’t think Masekela matched the image of what a surf-shop employee should be—which is to say, white.

“Even though I had gone through all sorts of fucking racist shit as a result of starting surfing and snowboarding—people making fun of me and calling me a nigger and telling me that we don’t even swim—I still didn’t think something like that would happen,” he says. “It really, really fucked me up.”

When he got the job as the host of the X Games, the racism became more pernicious. People would assume he was a marketing choice made by network executives—that he had studied up on the difference between a heel flip and a pop shove-it after he got the job, when in reality he could do both of those tricks. “There were people who started to be like, ‘Wow, that’s really gutsy of ESPN to pick a black guy to do this. So smart. You don’t really do this stuff do you?’ ” The same authenticity that got him the job was suddenly being questioned because of his skin color.

“I didn’t have an agenda to be like, I’m the fucking Great Black Hope of action sports. I wanted to be the best commentator. I wanted to be seen as on par with the greats in broadcasting and entertainment.”

One warm summer afternoon on his couch in Venice Beach, Masekela was in a reflective mood. We were surrounded by boxes that he hadn’t unpacked since he moved to the house 12 months ago. The front door was open, and sunlight streamed in.

He told me about his split with ESPN, back in late 2012, saying that the network had wanted to renegotiate his contract. He said that a big reason he left was a feeling that ESPN had begun to devalue action sports in general. For Masekela, this was unacceptable; they were his life. A few weeks after quitting, he cut off his dreads.

“I was kind of wrestling for identity,” he said. “I cried while doing it. There were people who told me, ‘You just lit your career on fire.’ And I’d be like, ‘If you know me and consider me a friend, and you’re telling me that my hair is my calling card, then you’re telling me that you don’t hear what it is that I have to say.’ ”

As a host and announcer, one of the greatest strengths Masekela brought to action-sports events was his credibility. “We had a lot of these bro-type announcers who didn’t really capture what was going on,” says snowboarder Shaun White. “Sal knew us personally, so he could kind of talk about how a guy has been wanting to do this trick for so long and what it would mean if he did it during this run.”

Today, though, being respected by core board-sports athletes doesn’t do much for a guy’s résumé. Masekela is eager to begin a new chapter but admits he doesn’t know what that will look like yet. Which is why he’s trying a little bit of everything. He’s starting a podcast, tentatively called What Shapes Us, for which he’ll interview the deep well of exceptional friends he’s made over the years, and possibly broadcast conversations with his father posthumously. He’s touring with his band, he’s hosting more traditional adventure and travel stories for National Geographic, and he’s trying to do more acting. He says he’d like to host another TV show, but only if it feels right.

One impediment to Masekela’s career reboot is the fact that he’s not the most organized person. He doesn’t like budgets or spreadsheets. He has a tendency to lose things, forget stuff, and miss flights.

Case in point: he arrives on Tavarua a day later than planned, after a fundraising event for his charity, Stoked Mentoring, ran long and he didn’t catch his plane to Fiji. But after he finishes unpacking, he hops on the evening boat to Cloudbreak, an infamous wave that detonates two miles from the island on a barrier reef. Just about anywhere else, you’d call the conditions good to great, but by Cloudbreak standards things are looking somewhat pedestrian. The wind isn’t quite right, the lulls between sets are long, and the wave isn’t barreling like it should.

Then, just before dusk, the wind dies a bit, and the reef starts to grab the swell. All of a sudden, Masekela is on an absolute gem—green and gold, backlit by low-angle tropical sun. Miraculously, the inside section gets hollow, and he tucks into the barrel. You can hear him whooping with joy. Finally, just before the wave ends, he kicks out the back. He’s probably 100 yards or more down the reef, but he reels in his board and heads straight for the lineup.

The sun is setting, but Sal Masekela is paddling back out.

David Shultz () is a freelance writer in Santa Barbara, California. This is his first feature for ���ϳԹ���.

The post The Unlikely Preacher of Action Sports appeared first on ���ϳԹ��� Online.

Last November, 54-year-old Conrad Anker feet while climbing in Nepal. He was roped to an ice wall at the time of the attack and had to rappel down to base camp before a helicopter could take him to the hospital. (he made a full recovery), Anker said:

At about 9:30 a.m., I was seconding [following] with a pack on, and I felt tired and out of breath. Initially I thought, ‘The sun will come, I’ll warm up, I’ll feel good, and then we’ll finish the next two pitches to get to our bivouac site.’��Then all of a sudden it was like, bam, what the heck is that?…Every time you go into the doctor’s office, they always have these charts: How to recognize a heart attack, how to recognize stroke, how to perform the Heimlich maneuver. You’re not committing them to memory, but with repeated exposure you understand them, so that was sort of how I self-diagnosed.

Altitude wreaks havoc on our bodies in many ways, but can it increase the chance of a heart attack? Was the altitude a factor in Anker’s experience?

In a strange twist of irony, for the past five years on a series of studies investigating altitude’s effects on the human body. Mayo’s medical scientists have been looking into how altitude affects everything from breathing patterns to gene expression. In the past, researchers have struggled to show through epidemiological studies that extreme altitudes can cause a heart attack. This is because ����’s tough, both ethically and statistically, to design something like a randomized controlled trial, where you’d transport large groups of people from low to high altitude and wait for one to have a heart attack. Also, there aren’t large populations of people who live at extreme altitudes to examine in natural experiments. (A study of Sherpas would be incredibly interesting, and a , but studying those who were born at and are genetically adapted to altitude��creates��a sampling bias.) The Mayo Clinic’s work has not yet been peer reviewed, and currently there is no published research conclusively linking heart attacks to altitude exposure, but we do know a few things about altitude’s effects on our bodies.

Altitude exposure causes changes in our blood—specifically, it affects our hematocrit, or the ratio of red blood cells to the total volume of blood. As altitude increases, the total volume of air above your head decreases. As this happens, air pressure—including oxygen—decreases as well. Think of it like coming up from the bottom of a swimming pool. Less oxygen gas pressure reduces how much oxygen is absorbed in the blood. So to compensate, the body adds more oxygen-carrying red cells to the blood. Think: 20 half-full boxcars can carry as much as ten full ones. (This, in short, is how athletes dope—erythropoietin, or EPO, is a hormone that can push red blood cell count far higher than training at altitude.)

But there is a cost to adding more red blood cells. “The blood is thicker and doesn’t flow as smoothly and may tend to clot easier, particularly if it runs up against a partial obstruction,” says , a professor of medicine, physiology, and biophysics at the University of Washington in Seattle and editor of the academic journal . Anker told National Geographic that, at 54 years old, after a lifetime of mountaineering, he has thick blood with a high red blood cell and hematocrit level. He usually takes an aspirin while he’s climbing as a prophylactic blood thinner, but Anker forgot to on the day of his heart attack.

There isn’t a specific altitude threshold��at which these changes start coming into play. Red blood cells gradually increase with height: the higher you go, the more your body makes to compensate, and everyone responds a bit differently. At what elevation you’ll notice symptoms depends on what you’re acclimated to. If you live at sea level, for example, you’ll notice some differences if you drive to the Rockies and ride the��chairlift��to the top of Peak 8 at Breckenridge (13,000 feet) the same day.

So ����’s hard to finger altitude as the cause of Anker’s heart attack—there are so many other risk factors present in mountain sports. Cold weather constricts blood vessels, and thinner vessels are more susceptible to clotting. Climbing is also an intense upper-body workout guaranteed to increase heart rate and further tax the cardiovascular system.

High places—and the pursuits of reaching them—are generally stressful. These stressors change the balance of hormones, like adrenaline and glucocorticoids in the blood, leading to responses like narrowing arteries and blood sugar changes. The is . Anker’s attack also occurred early in the morning, when stress hormone levels are naturally high. Then there’s the unavoidable risk of aging. No matter how healthy you are, plaque accumulates in your arteries over time. Anker was 54 years old at the time of the arrest—not old by any stretch but well into the age where risks start to increase. All of these factors greatly overshadow the role of altitude in Anker’s experience.

One study, however, gets us close to understanding what might cause a heart attack at altitude. In 1993, physiologist Martin Burtscher and colleagues that described his research tracking how many people experienced sudden cardiac death (a fatal heart attack) in the Australian Alps between 1985 and 1991. Then, by analyzing how many people used the ski lifts during the same time period, they calculated the odds of experiencing sudden cardiac death while exercising in the mountains compared to sea level. “As compared with the overall risk of sudden cardiac death, the risk during mountain hiking for men over the age of 34 was increased by a factor of 4.3, and the risk during downhill skiing was increased by a factor of 2.1.” A 4.3-fold increase for death might sound like a lot, but overall the risk was low either way: “one sudden cardiac death per 780,000 hiking hours and one sudden cardiac death per 1,630,000 skiing hours.”

The biggest predictor of a heart attack wasn’t the altitude at which they occurred, but rather the age and sex of the individual and whether they’d taken time to acclimate. “No increase in risk was found for men who participated regularly in mountain sports,” Burtscher wrote.

“If ever there was a heart that was adapted to low oxygen, it was Conrad’s heart. I’m sure the heart attack he had would’ve gotten rid of me quickly up there,” says Bruce Johnson, a researcher at the Mayo Clinic who researches altitude’s effects on the heart and has conducted studies (some including Anker) on Everest and Kilimanjaro. “But he’ll probably have a complete and full recovery.”

Even though Swenson and Johnson both agree that risk of a heart attack from altitude exposure is relatively small for healthy people, it can be minimized by following the classic advice for adapting to altitude. They both emphasize acclimating slowly, which, in addition to minimizing stress on the heart, will allow the rest of body to gradually adapt. It will also help to stave off other noncardiac issues associated with climbing, like altitude sickness and swelling in the head and lungs. Also, staying hydrated is always a good idea when you’re aiming to keep blood viscosity down.

“The reality is the human body is amazing and adapts to these extremes,” says Johnson. “There’s probably added risk at extreme altitudes, but for the average guy in the continental U.S. who takes a moderate approach to altitude, life is pretty safe.”

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The income gap is , and it turns out that it may be making us less healthy.

In a published this past August in the British Journal of Sports Medicine, researchers combined data from 177 previous studies conducted around the world to better understand the link between a country’s income inequality and youth fitness. Specifically, the researchers compared a country’s Gini Index, which measures how income is distributed throughout a nation, with children’s performance on the 20-meter shuttle-run test in that same country. They found that the greater a country’s income disparity, the less likely their children were to do well on the shuttle run.

The premise of the shuttle run test, which you might remember taking in grade school, is simple: Two parallel lines are drawn 20 meters apart. The children must run back and forth between them, reaching the next line before a beep sounds. The time between beeps decreases as the test goes on, forcing the kids to run faster. If a child fails to reach the opposite line before the beep sounds twice in a row, he or she is eliminated. Because the standardized test is popular around the world and because many children can be tested at once, scientists can draw conclusions about a country’s level of youth fitness by pooling enough shuttle-run data.

“This is a really powerful study,” says David Lubans, a researcher at the University of Newcastle Australia who was not involved in the study. “We can’t infer causation, but we’re looking at such a large number of data points that you can have some confidence in what’s being said.” (As Lubans points out, ����’s important to note here that the researchers have discovered a correlation between income inequality and fitness.)

“We know that when there’s a large gap between rich and poor in a country, there tend to be large subpopulations of poor people within that country,” says Justin Lang, the first author on the new paper and a PhD student studying population health at the University of Ottawa. “Poverty, we know, is linked with a whole bunch of poor health outcomes. One of those outcomes is poor aerobic fitness in children.” The link between obesity and cardiorespiratory fitness lies at the heart of this discussion, and while ����’s perhaps unsurprising that being overweight has a negative impact on fitness, the real question at the center of the matter might be, “What does income inequality have to do with obesity?”

The answers to this question are simultaneously complex and intuitive. “The most obvious and commonly put forward suggestion is that when you’ve got a group of people with low income, then they’re more likely to be in an obesogenic environment—one where they don’t have access to healthy food, for instance,” says Timothy Olds, a researcher the University of South Australia who has been studying the link between income inequality and fitness for more than a decade. “They have access to cheap but very high-calorie, energy-dense food, and they don’t have access to things like parks or walkable neighborhoods.”

This idea—that poorer people don’t have access to healthy lifestyle choices—isn’t revolutionary, but as the figure from the paper below shows, kids from plenty of poor countries (like Tanzania) scored extremely well on the shuttle-run tests. “One thing we do know is that those countries that are generally quite poor, like a lot of the African nations, particularly the developing ones, is that they don’t have the good parks and playgrounds and equipment and facilities,” says Grant Tomkinson, also from the University of South Australia. “They’re almost physically active out of obligation. So they have to walk or cycle to and from work. They have to walk over a greater distance to access fresh water or groceries, for example.”

The distinction between the developed and developing world seems central to explaining the fitness trends that the new study reveals. While in developed countries the poorer people tend to be less fit, the opposite is often true in undeveloped ones. “There’s a thing called the physical activity transition,” says Olds. “In poorer countries, ����’s the richer people who tend to be fatter, and the poorer people tend to be leaner and fitter. Then, in the middle, you get countries that are in a certain point on their developmental trajectory—we found this with Colombia and with India, for example—where ����’s basically dead even; the level of fatness is the same in the poorer and richer people.”

It seems, in other words, that poverty tends to make people less fit primarily when they live in a relatively rich country. Being poor but surrounded by fast food, automobiles, and television is more detrimental than being poor in a rural environment where physical activity is a necessary part of daily life.

Again, these data reflect only a correlation, not causation. But performing more rigorous experiments, like a randomized control trial, can be logistically and ethically tricky for researchers. “You can’t take a kid from an environment of equal income and put them in an environment of unequal income and see what happens,” says Olds. Instead, he suggests they strengthen their findings by tracking the change in the Gini Index against the change in youth fitness over time—a project he hopes to start in the future. “If ����’s true that as societies become more unequal, kids become less fit, that would be interesting,” he says.

“Natural experiments could also help us shore up the findings,” says Lubans. “We could work with schools in terms of delivering whole-school interventions that try to get kids more active and try to improve their fitness levels, and then explore the impact of that on outcomes. There’s such a great opportunity to have an impact on young people’s current and future health by getting them more active.”

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So you want to read a study?

First of all, congratulations. Most people don’t make it past the media headlines,��which are often , rather than precise��truths,��in mind. Reading the original research paper can��give you a��detailed account of a new discovery and put the results in the proper context. A news story from a major publication should, in theory, do the same thing—and there are plenty of examples of��great science journalism these days—but even the best reporting likely won't go into the same detail as the scientists themselves.��

Trouble is, peer-reviewed studies are dense, nuanced, and all-together tough to navigate. As a freelance science journalist for��outlets like and , I’ve had the chance to report on everything from the , to , to . There’s no one correct way to read a research paper, but along the way, I've developed an appropriately exhaustive��guide to��the most important things to pay attention to while reading a study. Here are some of the habits and techniques that have helped me.

Before You Begin

First, understand that science is an incremental process: every study is built on previous work, and most advance our knowledge of��a subject by a very small amount. Landmark discoveries��like the DNA double helix or the ��are��rare and usually receive coverage from major media outlets—and maybe a Nobel Prize. Most science has an extremely narrow focus: one protein, one gene, one drug molecule in one group of test animals. The average study is only a single��piece of��a huge, incomplete puzzle. It can be an��invaluable piece, but people, including journalists, get into trouble by over generalizing the results, taking them out of context, or extrapolating the findings to situations the scientists never meant to address.��

Also, before you start reading, take a moment to Google the journal that the publication appears in. Academics love to talk about “,” or the relative prestige of various journals, but tons of great science is published in smaller, lesser known journals, and even giants like and are not immune from publishing��junk occasionally. You want to make sure you’re not reading from a pay-to-publish journal that will take money from anyone and print anything, regardless of its quality and without peer review.��Peer review is the backbone of the scientific process, and��the process is��vital, . Any journal that skimps on this should not be trusted.

To give you an idea of which publications to avoid, here's a��list of “.”��

Finally, Google the authors. What are their qualifications? What have they done in the past? Where do they work?

Where to Start��

Begin with the article’s abstract—the��one-paragraph introductory CliffsNotes version of the study. A good one will be written in plain language and should roughly explain what the researchers wanted to know, the experiment they conducted, and the gist of their results. Because ����’s supposed to be a short summary, the abstract probably won't��make much of an attempt to put the results into context or explain the limits��of the experiment—it prioritizes clarity and conciseness over nuance and the quantification of risk or uncertainty. So,��while reading the abstract first is helpful, it is impossible to write a thorough and measured article based solely on the abstract of a paper, though lazy journalists will sometimes try.

Background

Following the abstract, many journals will have a “background” section, which helps contextualize the study within the existing body of research. This section is useful��in figuring out how important��the new finding��is. For example, you might��see a news article trumpeting a huge breakthrough in��cancer treatment, but in reality��scientists have merely refined an existing cancer treatment and made it marginally safer or more effective. The background will help temper the hype.��

Materials and Methods

This section is usually pretty dense, and if you’re a non-expert, don't expect to understand every sentence. But it often contains vital information that will help you get an idea of the significance and particularities of the results.��

You’ll want to keep an eye out for particularly important information like sample size and the species of the animal model. Sample size in particular��can help you decide how much trust to put in the results. Studies on a dozen test subjects (rats, ferrets, people, rocks, cell cultures, celestial events) can be interesting and help guide��future research, but such a small sample increases the chances of both false positives and false negatives. A small sample size does not inherently mean a study is bad, but readers should be skeptical of any claims about what the results mean for the world at large. A study on how��gut microbes influence weight gain in a population of 15 Americans, for instance, may be useful as a research tool, but a resulting article that claims your gut microbes are making you fat should be viewed��as dubious at best and��irresponsible at worst.

You’ll also want to pay close attention to the models used in the research. It seems obvious that a lab mouse is not the perfect equivalent to a human, but ����’s easy to get excited when a new drug, diet, or operation looks promising in mice. Animal��models are testing grounds, not proving grounds; they inform researchers about what might work in humans. ��

Looking at the type of experiment the scientists performed is also useful. Randomized control trials, which organize subjects into groups that either do or do not receive the treatment in question, are often considered��the gold standard. However, these experiments are expensive and come with ethical challenges—you can’t assign people to treatment groups when the treatment is starvation or limb loss, for instance. All study designs, however, have their own strengths and weaknesses: determining which design is right for the job is a complex task that researchers and critical readers must evaluate on a case-by-case basis.��

Results

This is the part of the paper you’ll��want to pay the most attention to, as it lets you know what happened in the experiment. Look at what the scientists say they found, but also look at how they qualify and express their results via statistical expressions like��confidence intervals and p-values, which tell you how likely it is that the observed result��is actually caused by the factor being investigated. For example, a��p-value of .05��indicates that if the researchers repeated the experiment 100 times, they’d expect to see a result equal to or more extreme than what they observed at least 95 of those times. Generally speaking, p-values need to be less than or equal to .05 to be considered statistically significant—the lower, the better.

The confidence interval tells us what range of values we can expect the result to fall into with a given level of certainty. For instance, a study might say it found that a drug increased cycling performance by 10 meters-per-second (m/s)��with a 95 percent confidence interval of +/-2, which means that while they’re not positive the improvement is exactly 10 m/s, the researchers are��95 percent certain that it is��between 8 m/s and 12 m/s.��

While , they give us a measure of��how sure��we can be in a study's��results and are relatively easy to understand. Sure things do not exist in science—there’s always a chance that an observation is the result of random chance. Good science quantifies that risk and minimizes it wherever possible.��

Discussion and��Conclusion

Most scientists will use the discussion and conclusion sections,��often combined into one, to explain how they��interpret��the��results. Pay close attention to this section, and be on the lookout for information on the limits of the study. Most scientists are terrified of sensationalism and are thus measured and cautious in presenting��the ramifications��of their results. Take��a similar approach. If you’ve already read the��abstract and results, you should have a pretty good handle on what happened. Use these sections to confirm that you’re interpreting everything��properly.��

Figures, Graphs, and Charts

Don’t skip these. Often whole experiments or sections of experiments can be summed up visually. The graphics will give you another chance to wrap your brain around what the researchers discovered—some people even look at them first, after reading the abstract. Confidence intervals can be represented graphically��in the form of error bars.

These figures and charts��will typically be��sprinkled throughout the paper at relevant points, though some publications put them at the end. A helpful rule of thumb: if you can’t describe to a friend what is going on in the figures, there’s a good chance you don’t quite understand the study yet.��

Conflicts of Interest

All reputable journals require scientists to disclose any potential conflicts of interest in their work. Check for this every time; you can usually find the disclosure statement at the end of a study, but the exact location can vary. If a researcher does admit to a potential conflict—like receiving money or equipment from a stakeholder, for example—it does not immediately invalidate the study, but it is cause for a more critical examination of the work. Consider how the soda��industry funneled��lots of money into research of the health impacts of sugar: many of those studies have been found to��, even though they were conducted by “independent experts” at reputable universities.

���ϳԹ��� Comment

There’s no section in a scientific paper that allows other researchers in the field to make comments, so this is an area where journalism can add real value. If you’re reading a news article��about a new discovery, look for a comment from at least one outside expert. Many times, that person��will simply confirm that they don't��see any issues with the��study. Other times, they'll point out a flaw or concern that would be invisible to the uninitiated, or help put the results in context. All of this��is extremely valuable.

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