1/ How wrong can your wearable's VO₂max be? I compared lab tests with rest scores for 21 people. The biggest miss: a very impressive 87 in the lab vs 51.

Apple may have just made one of the most important moves in consumer health. Not because Health Age or Readiness will be perfectly accurate. They won’t be. The interesting shift is from wearables recording data to interpreting it well enough to change behaviour. I really enjoyed digging into the science behind it, especially the population problem, and the fact that inputs like wearable VO₂ max are already estimates. The real innovation may be making complex health data simple enough that people actually do something with it. Link below. @TerraAPI #DigitalHealth #Wearables #HealthTech #Longevity #SportsScience

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

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

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

Did you know that eating late meals affects not only your sleep but your heart rate, too?! This was quite hard for me to wrap my head around. At @TerraAPI Research, we analysed thousands of nights of wearable data to explore whether late eating meaningfully impacts sleep. Not in a lab, not in a controlled setting, but in the messy reality of everyday life. There is a relationship between late eating and sleep. Eating closer to bedtime is associated with slightly worse recovery, as evidenced by higher heart rate, later sleep onset, more awake time and small changes in sleep structure (although sleep structure measured with wearables should be taken with a pinch of salt), but the magnitude is modest. If you’re interested, link to the full research is in the comments below

Do people actually sleep better alone or together? Conventional wisdom says having a partner is good for long-term health. In theory a big chunk of that benefit should show up during sleep, our prime recovery window. So I decided to test the hypothesis: Does sharing a bed actually improve sleep and recovery? I analysed a large dataset of wearable sleep records where users had labeled whether they slept alone or with a partner. So what did I find…The results were more nuanced than I expected. • Shared-bed nights showed more awake time (roughly +8-11 minutes) and slightly less total sleep time! I’ll leave it to the reader to hypothesise on this. • Resting heart rate tended to be lower on shared nights in population views, but RMSSD (HRV) was modestly lower. • Alone-dominant users showed more stable night-to-night sleep (lower coefficient of variation). • The patterns held across population, within-person, and lifestyle cohort views, though effect sizes were generally small. The data suggests that, on average, sleeping alone is associated with better sleep continuity (less awake time, more sleep), while the recovery marker story is mixed. Of course, this is all observational. There are clear limitations and potential confounders: people who diligently label their sleep data might not be representative, alone nights often happen during travel, and we can’t cleanly separate causation from correlation. I wouldn’t rush to the spare room just yet. Unless you’re a hardcore recovery optimiser willing to run your own 10+ night experiment (controlling what you can), the data doesn’t scream that sharing a bed is ruining your recovery! Full research in the comments below