Alistair Brownlee engagement report

Best tweets

• Apr 27, 202615x their median

  Yesterday in London, two men ran under the two-hour marathon. Sawe in 1:59:30, Kejelcha in 1:59:41. A barrier that has stood for decades, gone. None of their watch data is on Terra. But 571 other runners on the same streets are, and their data turned up something I wasn't expecting. The spread of the field alone is fascinating. Finish times ranged from sub-2:12 to nearly 7 hours. Heart rates told a counterintuitive story too: sub-3 runners averaged 167 bpm for the entire race, while six-hour finishers averaged 154. But the calorie data is what really jumped out. Garmin and Coros watches agreed on heart rate. They agreed on distance. They disagreed on calories by 12% at the median, and the gap got much worse for slower runners. Here's the part that I think matters: kcal/km should be roughly flat across finish-time bands on the same course on the same day. The fact that one device produces a flat line and the other produces a steep one is a self-contained plausibility check on the calorie algorithm. Calories from a watch are a model output, not a measurement, and the slower you run, the further the model can drift from physiology. This is exactly the kind of question we're tackling at the Terra Research Run Club this Thursday, built to advance our understanding of wearable data in the real world, and ask how well our watches actually capture what's happening. Link for the Research and Run club below @TerraAPI

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• Apr 23, 202614x their median

  It is my birthday today, so I allowed myself a completely self‑indulgent data analysis. I have had the “what is the hardest endurance sport” argument in so many changing rooms and cafes that I lost count years ago. Swimming feels psychologically hardest for me. Cycling feels highest risk. Running just feels brutally honest. So this time I tried to answer it with data. I pulled nearly a million sessions across nine endurance sports and looked at what each one does to the cardiovascular system, both per minute and per session. Here is what I found: - Every sport has a distinct heart‑rate “fingerprint”. Running is a tight, right‑shifted bell around 145 bpm. Walking and ski touring sit broader and lower. Downhill skiing is all peaks and troughs. - Running really is hard on the heart. It has the highest session average, peak HR, and sustained intensity ratio. - Walking’s “high” intensity ratio is a statistical trick. Low average, low peak, very flat sessions that only look hard on paper. - Downhill skiing has the biggest swings. Peaks rival outdoor cycling, but average HR sits near walking. That 47 bpm gap matches the feeling of short bursts and a lot of standing around. - Cross‑country skiing behaves like running at the top end and like cycling on average. Huge peaks, long gliding recoveries. - Indoor cycling is the purest steady effort after running. The sustained profile is similar in relative terms, but the absolute load is lower because seated cycling simply costs less than weight‑bearing running. Within the same person, running still wins. Among 1,480 people who both run and ride outside, 93% hit a higher fraction of their personal max HR when they run. Same body, same heart, different biomechanical demand. Then I changed the question. Because intensity is only part of the story and I recently cycled for 35 hours at a low Heart Rate, but it certainly felt pretty hard! Do you want to reward time on feet, or time in the red zone? Full research below.

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• Aug 4, 20269.7x their median

  final climb of Norseman felt like I was slowly falling apart. The data says I was doing almost exactly the opposite. On Saturday, I finished second at the Norseman Extreme Triathlon in 10:18:19. The race combines a 3.8 km swim, a 179 km bike with more than 3,000 metres of climbing, and a marathon finishing at the summit of Gaustatoppen, 1,831 metres above sea level. It is often described as the world’s toughest triathlon. Whether it is or not is subjective. It was certainly very tough. The day itself felt messy and unpredictable. Once I looked at the files, however, a much more coherent story appeared. I paced the swim sensibly, completing it alone in around an hour at roughly 1:35/100 m. I desperately wanted to follow the swimmers ahead, but I knew my limited swim training meant I could probably hold their feet for three to five minutes before paying for it. On the bike, that restraint disappeared, and I was far too optimistic. I rode the steepest part of the opening climb at around 354 W normalised. Later climbs of a similar character were closer to 285–300 W, a massive decline of roughly 15%. The numbers are fairly blunt: I started too hard. The most interesting finding, though, came from the run. I averaged 4:15/km over the flat opening 25 km. On Zombie Hill, that fell to 6:43/km, and on the final off-road climb to the summit it dropped to 11:51/km. Looking only at pace, it appears to be a spectacular collapse. But horizontal pace is a poor measure when the terrain changes so dramatically. My vertical ascent rate was about 726 metres per hour on the road climb and 716 metres per hour over the off-road summit section. A difference of only 1%. Even during the final grind to the summit, when I was hiking more than running, my climbing rate was still around 766 metres per hour. What felt like a dramatic deterioration was actually remarkably consistent output on increasingly difficult terrain. That is probably the clearest lesson from Norseman. It does not simply reward the biggest engine. It rewards the version of your fitness that remains after eight, nine or ten hours, and the mental flexibility to adapt when the race you planned is no longer the race you are doing. Kristian Grue was in a league of his own and defended his title in 9:53:46. Hans Christian Tungesvik finished just 36 seconds behind me, having forced me to find a little more speed in the final kilometre than I thought I had left! I am at least 5% below my old power for this kind of effort and more sensitive to high-intensity efforts than I used to be. But the residual fitness is still there, doing enough to let me take on adventures like this. That feels like a pretty good version of retirement. Link to the full research article below @TerraAPI #Norseman #ExtremeTriathlon #PerformanceData

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• Mar 20, 20265.6x their median

  1/5 Heat is a silent killer in endurance sport. I’ve felt it destroy races and even ended up in hospital after. You think you’ve got pacing, fueling, everything sorted.. then the temp climbs and your body just says no. I've been obsessed with beating it ever since. Acclimation helps (sauna, heat blocks), but it takes weeks. What if you could cheat the system a bit before the gun? Enter taurine. Not hype, some real human studies have me genuinely excited.

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• Apr 9, 20264.6x their median

  601 km. 35 hours. No sleep. I’ve spent the last few days digging into the data from last week’s ride, and a few things really surprised me. My heart rate at the same power output dropped 15 bpm from early to late in the effort https://t.co/VOtU8RsBgc

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• May 7, 20264.1x their median

  "Any fool can make something complicated. It is hard to keep things simple.”Richard Branson I’ve always loved that line. But this one from Rutger Bregman might be even more relevant to endurance training: “It’s easy to make things hard, but hard to make things easy.” I think this principle applies perfectly to both training and how we analyse training. In this week’s analysis, I looked for simple, effective answers to two key questions: 1. Is there a simple way to measure someone’s running fitness? 2. Is there a simple training approach that actually works? To answer question 1. I looked at a very simple approach: track the pace an athlete runs at specific heart rates across multiple sessions and plot the curve. If that curve shifts right over time, fitness is improving. Then identify which training methods create the biggest positive shifts. I’ll be diving deeper into this in future posts using a novel approach for standardised performance testing. What’s your take? Do we overcomplicate endurance training? Full research in the comments below @TerraAPI #RunningScience #Simplicity

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• Jun 18, 20263.2x their median

  Does heat slow runners down, or make them slow down? I studied 3,361 outdoor runs from 878 users, matched against hourly temperature data. A run starting at 25°C was, on average, ~10 sec/km slower and 1.3 km shorter than a run starting at 5°C. But heart rate stayed broadly flat. I expected heat to show up as higher cardiovascular strain at the same pace. It didn’t. The data suggested something different: Runners slowed down slightly, shortened their runs, and kept their effort under control. Wearable data is often treated as pure physiology. But it also captures behaviour. The choice to go out, to go harder, to ease up, to turn back early, or to self-regulate before the body forces you, each reflects a conscious decision. It seems the heat does not make runners slow down; instead, runners slow down because of the heat

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• Jul 31, 20262.8x their median

  Most people still think cycling to work is the slower option. Better for your health, better for the environment. But slower. I wasn’t convinced, so I looked at the data. I matched commute looking cycling activities against both TfL journey times and Google’s rush-hour driving estimates. The result surprised me. On 1,628 fully matched weekday commutes: • Cycling beat @TfL estimates on 92% of journeys, with a median saving of 15 minutes. • Cycling beat Google’s rush-hour driving estimate on 75% of journeys, with a median saving of 5 minutes. The busiest commuting corridors into central London were the most striking. Routes from places like Hammersmith, Wandsworth and Clapham to the City consistently favoured the bike, often saving well over 10 minutes. One result I didn’t expect was that the advantage didn’t disappear on longer commutes. I assumed the Tube would become increasingly competitive. Instead, cycling continued to perform remarkably well. That may reflect a group of committed cyclists, or the fact that people are more likely to ride longer distances when the transport network is disrupted. There are important caveats. This data only tells me about the recorded bike activity, not necessarily the entire door-to-door journey. Some people may combine cycling with walking or public transport, while the TfL and Google estimates are complete origin-to-destination trips. So this isn’t a perfect race between transport modes. But the overall pattern is hard to ignore. For many London commuters, the bike isn’t just the healthier choice. It’s the faster one. I’d happily ride even if it took a few minutes longer. I used to commute to school by bike, and still ride E-bikes as much as I can. Fresh air, a bit of movement before work, and seeing London from street level beats sitting underground. It turns out I often get there sooner as well. What’s your experience? Has cycling changed the way you commute? Full research linked to below #activetravel #healthdata #commuting

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• Sep 17, 20262.5x their median

  How accurate is the VO₂max number on your watch? Help me find out properly. I’ve been digging into what VO₂max really tells us about performance and longevity, and why the number on a wearable is ultimately an estimate built on a chain of assumptions. I’ve written up what I’ve learnt. But rather than stopping at the theory, I want to run an open VO₂max experiment. You can connect your wearable data, see different VO₂max estimates, and help me understand how these methods perform across lots of real people and real-world data. I’m especially looking for people who have had a proper CPET with a measured VO₂max in the last six months. If that’s you, you can add your lab result so we can compare the wearable estimates against a genuine physiological measurement. All data used in the analysis remains anonymous. If you’re curious about your own numbers, or you’ve recently suffered through a VO₂max test in a lab, I’d love you to take part. I’ve added the article and experiment links below. You get to see your numbers. I get some data to do science with. Hopefully we all learn something. @TerraAPI

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• Mar 30, 20262.1x their median

  If you're like me, you probably think that the first glass of wine helps you drift off. And honestly? It probably does. But then what? At Terra Research, we analysed 15,000 nights of sleep data from people who logged alcohol before bed vs. those who didn’t, using a mixed-effects model to control for individual differences. What did we find? Alcohol makes you restless, but it barely touches your sleep stages. Here's what we found: → +398 seconds extra time spent awake in bed (SE: 116s) → More wakeup events throughout the night → Increased respiratory rate (p=0.02) → No significant change in light, deep, or REM sleep proportions That last point is the one that caught my eye. Decades of sleep lab research consistently show REM suppression and increased slow-wave sleep in the first half of the night after alcohol. So why don't we see it in 15,000 real-world nights? I think there are a few reasons. Lab studies typically use controlled doses administered at precise intervals. Real-world drinking is messier. Timing varies. Dose varies. Some people have one glass of wine, others have several pints. The wearable devices capturing our data also measure sleep stages differently from polysomnography, which picks up fine-grained EEG changes that a wrist sensor likely won't. But where wearables are most accurate in measuring wake events, fragmentation, and respiratory rate, our findings align with the literature. So the practical message? That glass of wine probably isn't destroying your deep sleep or REM in a way your wearable can detect. But you are setting yourself up for a restless, fragmented night. Your body has to work harder to breathe, you wake up more, and that "guarantee" of falling asleep within 15 minutes vanishes. Full analysis in the comments below #sleep #healthdata #wearables #terraresearch @TerraAPI

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