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Tymewear’s co-founder and lead engineer, Arnar Larusson, discusses how his collaboration with Dr. Stephen Seiler and coach Espen Aareskjold has led to the continuing evolution of breathing measurement technology.
Episode Transcript
[00:00:00] Chris Case: Hey everyone. Welcome to Fast Talk, your source for the science of endurance performance. I’m your host Chris Case here with Trevor Connor, an engineer. A physiologist and an elite coach walk into a bar. Tell me if you’ve heard this one before. No, no, no. It’s not a joke. Arnold Larson, Dr. Steven Seiler and Vima Lisa Bike Coach Espin Air all descended independently on a similar problem, how to measure breathing in endurance athletes, but they came from three different angles.
When they came together, it led to a fruitful collaboration. The result has been the development of time, wear’s breathing strap, which could very well revolutionize the way athletes train and race today. All three of them join us to talk more about the evolution and development of the product, the good, the bad, the ugly.
They share the common goal of creating a scientifically validated product that provides useful training, information, and decision making guidance for athletes and coaches. Accomplishing that is a lot harder than it sounds. Getting the device to be accurate, reliable, and comfortable is tough enough and has led to dozens of iterations.
But then there’s also the challenge of understanding how it could change training prescription and the measurement of things like fatigue and cardiovascular drift, especially since these things still aren’t fully understood. So our discussion focuses on the difficulty of the development of wearable technology as a type of cautionary tale.
So many wearable fitness products are hitting the market, and many of them are making incredible claims that it’s hard to know who and what to trust. This conversation should give you a far deeper appreciation of just how hard it is for a company that’s doing it right. Ultimately the engineer, the scientist, and the coach sat at the bar and agreed on what this device needed to be.
It doesn’t make for a very good joke, but it does make for a great new tool. The same can’t be said with a lot of other wearables that have hit the market in recent years. But before we get into that conversation, today’s episode is sponsored by stages. Cycling stages makes power meters. Of course. Trevor, I know you’ve been riding their dual-sided power meter, and I think it’s very appropriate.
We’re talking about the reliability, the accuracy of the tools we ride with. The episode is about how difficult it is to make it. But Stages has done it. They’ve made a power meter that you don’t have to think about and it comes out with really good data.
[00:02:25] Trevor Connor: Yeah. So I’ve been using their dual-sided for a couple months now, and that’s what I love about it.
It is reliable. I don’t need to think about it, I just know I’m gonna get good data. And it’s great that they are sponsoring this episode. ’cause that’s really what this episode is about, is how difficult it is to make a device that is going to give you good data in all circumstances. In all conditions.
And that’s a huge thing to say, to say, I’m just gonna get on my bike and I trust the power meter. And I will give you an example. I just did the triple bypass this weekend. Anybody who knows that ride, you start at 7,500 feet, you climb up to 12,000 feet, the temperature’s gonna change 40 degrees over the course of the day.
10 years ago.
[00:03:12] Chris Case: Mm-hmm.
[00:03:13] Trevor Connor: On power meters, you would’ve had a whole lot of bad data, it just wouldn’t have kept up. But stages has made that reliable device, which is a difficult thing to do. And we know Pat Warner, he is their product developer, and he is the sort of guy that’s probably done three, 400 iterations of this to figure all these things out, and they’re coming out with things like adjusting to temperature as you go.
Mm-hmm. The ability to constantly calibrate the power meter. So you are getting that good data throughout your ride, even in something as crazy as what I did this weekend.
[00:03:47] Chris Case: Yeah. I think it speaks to the person behind the product in a lot of ways. Anybody, anybody, not anybody, but a lot of people could create a device that spits out a number, but you don’t know if it’s reliable or accurate.
Right. I don’t wanna relate them too much, but people at time where have a passion for this, the people at stages have a passion for this. They don’t want it to just spit out a number. They want it to be accurate, they want it to be reliable. They want you to use it every day and be able to trust it. Right,
[00:04:15] Trevor Connor: right.
And today we are talking about a new device that gives you an internal metric what’s going on inside your body.
[00:04:23] Chris Case: Mm-hmm.
[00:04:24] Trevor Connor: And you know, I’m a big believer in internal measures, but the best training is done when you combine that external measure with the internal measure. And when it comes to external metrics.
Power is absolutely king and it’s gonna do things that you can’t get with that internal metric. It’s what’s gonna allow you to execute those high intensity intervals the most effectively. It’s what’s gonna give you that race data that you and your coach can look at and see those moments. You seem to be going way too hard here when you shouldn’t have been going that hard, or you’re just too inconsistent, or whatever it happens to be.
Power’s gonna give you that data to really analyze the race and figure out how to race better. So it is an absolutely important tool to have. And what’s great is Stages is making it accessible to everybody.
[00:05:11] Chris Case: Yeah, they’re selling off Overstock inventory right now. Shimano XT Power Meter is $150 Crazy XTR 201 0 5 200.
In the dual side of that you referenced at the beginning, 400 bucks, amazing prices. You can find them all on the sales section@stagescyclingand.com.
[00:05:30] Trevor Connor: Yeah. And look, as I said, I’m about to go online and order another one for my winter bike. If you wanna take advantage of these deals, you need to do it soon.
’cause this is Overstock inventory. These sale prices aren’t gonna last long.
[00:05:42] Chris Case: Yeah. The best power reader is the one you stop thinking about because it simply works.
[00:05:46] Trevor Connor: Couldn’t agree more.
[00:05:47] Chris Case: So take a deep breath. You’re about to learn how the time wear sausage was made. Let’s make you fast. Steven Espin. Arner, thank you for joining us today.
All the way from Barcelona.
[00:06:00] Stephen Seiler: Glad to be here. Yeah, thanks. This is fun.
[00:06:02] Espen Aareskjold: Yeah, thanks.
[00:06:04] Chris Case: I wanna set the stage for everybody because the three of you effectively are working as a triumvirate, if you will, in the subject that we’re talking about under this wearable technology. If the three of you could describe sort of the role you see and how this process plays out.
We’ve got a scientist, we’ve got a wearable developer, and we’ve got an elite coach working with a professional team.
[00:06:29] Stephen Seiler: I think any of us could start, ’cause it is an issue of where does the issue start. In some ways I would say maybe it starts with Espin is the coach who has some problem he would like to solve, some question he wants to answer better.
But we can start with me. And I’m the science geek and I was trying to understand training and what goes on during the training sessions themselves. That was of interest to me and how the sessions evolve and get harder and tougher and feel worse and worse. And heart rate responses were not matching up with my own perceptions during interval sessions.
And I still distinctly remember saying, man, I’m breathing out my ears. I wish I could measure that. And so that was kind of a starting point for me. And then I started searching around and. Fortuitously got connected to a startup company that was trying to measure breathing. And so that’s kind of the starting point for me.
[00:07:28] Chris Case: Yeah. And Arner, you are obviously in some ways working completely independently from Steven initially, so how do you play a role here? Why were you working on this problem as well?
[00:07:40] Arnar Larusson: Well, that’s a great question. So my name is a Arna Larson. I’m originally from Iceland. My background’s in mechanical engineering.
And I got into this space really through r and d projects and work that I was doing, developing prosthetics for amputees exo suits for the US military as a way to validate those designs. We were putting face masks on people and measuring their metabolic output and efficiency. And we do that by measuring their breathing.
So I got introduced to this way of assessing performance and output through kind of what we would consider the gold standard of doing that. And so I was just staring at this data all the time, collecting it, and it struck me that there’s a core truth to the ventilation about what it describes about not only human performance, but how much work we’re doing, how efficient that work is being done.
And as an athlete myself, I just. Why can’t I measure this out in the field? And that was really the starting point where ventilation became this thing that we could only access in the lab. Became it that way to me. And that’s how I got introduced to it and wanted to be able to get this type of insight, not just in the lab, but out in the field, and be able to better improve my training.
It was also a bit of a personal itch, a caustic case of someone that overtrained. So I had some personal experience with that, and that came about by just being too overzealous with training as a teenager. So I had experienced this limit of just doing too much. I think it’s fairly obvious that you have this sort of lower limit of doing not enough.
And so from an engineering perspective, that begs the question of like, is there kind of a happy medium there? Is there an optimal limit? And that’s actually when I was thinking about those things, seeing the data coming out of the lab that we were collecting, that I started going down the rabbit hole of effectively a lot of Steven Seiler work and got introduced to the whole notion of how we train properly, the concepts of ventilatory thresholds, the role they play in polarizing our training, and how important they are to understand.
And the only reason we measured heart rate in the lab was because we sometimes wanted to take those people outside of the lab and measure things out on the on, on the grassy fields. And otherwise there was really no. No practical use of her heart rate in that setting ’cause it wasn’t giving us the information we needed.
So all of that just kind of seemed like an oddity to me. Like, why doesn’t this exist? It just feels so basic and obvious that we should be able to measure this out in the field. Uh, it turns out it’s not so obvious to solve and we’ve been many years in the development process and we’ll probably talk through some of those as we go here, the trials and tribulations.
But I’ll leave it there and let Usman introduce himself as well.
[00:10:12] Espen Aareskjold: Thanks. Yeah. I’m Espin, I’m, I’m currently performance coach in Miss. I got into escalation, the use of the metric through Steven. We first met back in 2019 and I believe it’s like Spring 22 I got introduced to. And gradually, yeah, spent more and more time in digging into ventilation as a metric itself.
Like we’ve been around. Riders now for the last 15, 20 years are actually o often observing them breathing heavily, but never having kind of a tool or, or a metric to understand how it actually progresses within training and racing for that matter. And from being introduced to Timer and started working with that back in 22, we have just gradually learned more and more that it actually adds to the decision process and reduces the uncertainty of what we should do next, or how we should evaluate what they or just have done.
[00:11:16] Trevor Connor: So I think where I wanna start here, we’ve done a couple episodes in the past about the development of wearables and Dr. Sadler, you were part of one of those. And for anybody who’s interested, episode 2 66 with Dr. Chung, we talked about the increasing rate at which wearables and different data is being released.
And then Dr. Seiler, the episode with you was 3 0 8 and I think you summarized our overall position really well in that episode where you said a lot of these devices, a lot of these metrics. Might very well prove to be valuable, but the issue is the marketing cycle that you have companies that are coming up with a new wearable, a new metric, and within a year they have it out there and saying, this is valid, this is reliable.
You need to be using this. And it’s just too quick to be able to say, yeah, we’ve got this all solved and the reason we’re having you on this episode with time wear is because you’re really doing it right. I was talking to Chris about this yesterday that I read a study from 2021 that was looking at the validity and reliability of your device and comparing it to a very expensive metabolic car in a lab.
And five years ago, this study basically said. Not quite as valid as the metabolic cart, but we would expect it to be. This is for out on the road and for out on the road. This is a great tool that you can use very effectively for training, and yet since that study, you didn’t jump on that and say, okay, we have our one study.
Let’s market the heck outta this and get it out there. What you’ve been doing is iterating ever since and trying to make it more valid, make it more reliable, and really get the science behind it. So you are doing exactly what Dr. Seiler said in that episode 3 0 8, what we’ve been saying of taking the long lifecycle to make a product that is really usable and really valid.
So I’m really hoping in this episode we can dive into the challenges you’ve faced and how you’ve been going about this.
[00:13:28] Arnar Larusson: Happy to shed some light on the sausage making.
[00:13:31] Chris Case: Why don’t you start by telling us how right we are? Are you doing it that way? Are you trying to be more deliberate? Are you also making a lot of mistakes and learning from them, but in, in ways that other companies seem not to be?
How would you assess your performance?
[00:13:48] Arnar Larusson: Well, I certainly would never say that we’re not making any mistakes. ’cause I think every day we make mistakes and that’s also how we grow. I think. I mean there are a few things. I mean, there are some real constraints when building a business generally, right? So you have resource constraints, you have financial constraints, you have time constraints, and you’re always trying to optimize those to get to the next level, whether it’s to unlock funding or to release a product so that you can get revenue.
That’s the kind of Rubik’s cube that we’re trying to solve every single day. And there have been many times when we’ve suboptimally solved that and run into issues and nearly run outta money and gotten back up and launched a product that had many bugs and had to work our butts off to solve them. And all those things are true, and I think there’s a.
An added layer of complexity when you start to add human physiology or science. It’s not just an app that was a, is scheduling something or it is all just a, it’s all just software. It’s real measurement, it’s real people using it to change or affect change in their lives. And so there’s a lot more responsibility that comes with that and I think we’ll on the team kind of recognize that and try to be as accurate as we can, but it’s not a perfect world.
So of course mistakes get made. The fundamental thing to kind of the core point there where people kind of maybe release products too soon or tracking things in not the quote unquote right way. Is the big question there. Is there a fundamental truth to what you’re measuring? And I was fortunate to be in a scenario where I was exposed to this data set in a way that I just saw the practicality of it, the fundamental nature of it.
I knew nothing like of the fact then that breathing was the first thing we were measuring when we were assessing performance in the forties and fifties. If you go back to the, uh, the NASA space program, they were streaming breathing data from the moon, from sensors measuring, breathing, in addition to heart rate.
So there is this sort of fundamental aspect to it, and that was by design that we honed in on it. We could have gone down the road of measuring EMG or other things that could be interesting from a performance standpoint, but just didn’t feel like it had that same kind of. Core truth to it and sort of universal application that breathing did.
So that’s kind of where we started. And that was probably met by the fact that breathing was kind of like a lost child of science or physiology. It’s basically been ignored since the eighties, so to say. And it’s funny, if you go back to the literature and you look at like publications, you type in heart rate and breathing and you search for those, whether it’s scientific publications or patent applications, and you take those search terms and the frequency that they appear and you go back to like the 1920s and you’ll see they’re kind of trending together.
All the way up until Polar releases their first heart rate monitor In about 1978, by the mid 1980s, heart rate starts taking off in terms of research publications and patent applications and breathing kind of just kind of plateaus, kind of just starts to fall off a bit. And why? Because all of a sudden we have this rich data set that we can access not just in the lab, but because we can access it everywhere.
And that’s how heart rate started. And it took 50 years to, for it to come to kind of a certain conclusion through all the different companies that have proliferated around that data set and what it can do for us on a daily basis, whether it’s tracking our activities or sleep and so forth. And breathing is this other fundamental thing that describes many of those same things better in other things that it completely misses.
And so we’re at this very early, early stage, and I think that was a, there was a recognition there. Of that, that we are early and this is something that many people have either forgotten or thought didn’t matter. And that’s a, that’s an uphill battle in terms of just turning minds and thoughts and gaining interest in what it can be doing for us.
And so we were definitely kept humble just by that fact in the early days. I mean, it was often that we were told that breathing didn’t matter. And so that was more of an external constraint on our growth than anything else. But the more you kind of stick to it and the more you start to engage with researchers that can really start to understand what it’s telling us and what it can tell us, that’s a big part of creating the sort of the momentum that we need in order to really understand how we can use it and for people outside of a core group that’s starting to adopt it and how they adopt it and so forth.
So I think there was just a fundamental understanding that was a, a significant part of this with respect to the history of where it all came from, the fundamental nature of the metric that we were collecting, the gap that existed in just the knowledge and how to apply it. So it was just a very natural fit to start working with researchers and to start really understanding how that can be applied.
And so that’s kind of where that really started.
[00:18:22] Trevor Connor: Dr. Seiler, there’s a lot of science behind this, correct? I mean, recent science has been showing that breathing rate is actually controlled by a different part of the brain than heart rate.
[00:18:33] Stephen Seiler: Yeah, and you’re mentioning the work of, and Andrea Nicolo, and he was formerly connected with Louis Passfield who passed away a few years ago after a cycling accident.
And I came into it and I was trying to understand, I said, well, yeah, I’ve, I learned in my exercise physiology courses that in a sense that breathing takes care of itself unless you have a particular pathology like asthma, or you’ve lost a lung, had a lung removed or something. And that was the ground truth that we were taught in basic exercise physiology class.
But I think they got it wrong because fundamentally they said, well, breathing is over dimension, meaning that we have a bigger reserve for breathing or ventilation than we do for cardiac output. And so cardiac output was seen as the limiting factor for performance and breathing was over dimension. But where they got that wrong I think, is that yes, if you look at it from the perspective of oxygen delivery, then breathing is over dimension.
But if you look at it from the perspective of something absolutely critical, which is CO2 removal, CO2 is a poison to the body. It is part of metabolism, but it has to be removed from the circulatory system or else it accumulates. You die to make it really dramatic. And if you look at ventilation relative to CO2 removal, then suddenly it’s not over dimension at all.
Then that 20% difference between peak CO2 removal rates and O2 uptake rates is suddenly not an over dimensioning. It is aligned with the fact that we gotta get rid of more CO2 than we actually consume in the form of oxygen. So anyway, so that’s a ground truth that turns out to be pretty darn interesting.
And so suddenly the, when I started thinking through that and getting connected to that, I said, well, wait a minute. This makes breathing way more interesting. And so that also fueled my fire as a scientist to think, okay, I, I want to be able to measure this. Let’s engage here and we want to talk about this is that process of how do you go from idea, from a ground truth, some science that makes sense to a product that becomes a daily tool.
And man, that’s hard. I have developed a great respect for how hard it is to measure in a correct way. Pardon my French. And so we started super small. I got a shirt because at the time. I wore it created a shirt where the sensor was built into the textile.
[00:21:27] Arnar Larusson: So we, we started with a shirt simply because we started with a sensor that was suboptimal.
And so we needed the shirt to keep everything in place. And this is a classic kind of resource constraint. Like it is really complicated to develop a shirt. You’re basically developing six different sizes that are supposed to go on 6 billion people and they’re not all the same.
[00:21:45] Chris Case: Wouldn’t it be nice if you did sell 6 billion of those products?
[00:21:48] Arnar Larusson: Yeah. Well we didn’t, and there’s some real reasons for that. But then the sort of the momentum that we carried, so we’d spend all this time to create a shirt and then by the time we got a better sensor that was actually doing the job that we needed to do and could have gone right away into a strap, we could have gone to the back to the drawing board and just developed it from scratch.
We didn’t, because we already had this foundation, we’re like, oh, just slap the sensor on and let’s go. And that is an example. There’s a classic example where we could have taken a few extra months. To go back to the drawing board and develop it from scratch. But we didn’t have the money, we didn’t have the time this needed to get out.
Now we needed to show proof points. And I think that’s a classic thing that that companies run into. Whether it’s a suboptimal version that you release because you’re constrained or because you’re in a certain momentum and you’re, the momentum carries you forward. But eventually we got to the place where we were getting, which by the way, serve has a tremendous value because if not for that version of the product, Steven wouldn’t have been able to measure anything.
Aspen wouldn’t have been able to measure anything and tell us, and both of them to tell us, man, you gotta figure out this shirt thing. But at least getting some data to say it’s worth solving the next step.
[00:22:50] Stephen Seiler: And we gotta talk about how we did that. So at first, I started up in my little lab at home. I’ve got the bike and the Swift and everything and I’ve tried out all kinds of technology.
So I put on their darn shirt. And there’s a kind of an app and it’s pretty basic. Let’s just say it that way.
[00:23:09] Arnar Larusson: Thanks for being polite.
[00:23:12] Stephen Seiler: And I see, well, yeah, I’m getting a frequency measurement there. I can measure breathing frequency and it seems about right. And so I got just enough belief in it. I said, all right, I think I’m gonna take this into the lab.
So I start with me. ’cause there’s no risk there. I’m nobody and if it doesn’t work for me, it’s not gonna hurt anybody. So then I go into the lab, I get some of my students to try it and we try it on a, a couple of different modalities, cycling and running and it seems to work. So we get a little bit of confidence.
And then I have a master’s student that’s already going to a camp to look at a an EKG product. And that’s his master’s thesis. I said, but while you’re there. Can you see if you can slip this shirt on a few riders and let’s see what we get? And he does that. And my goodness, it sucks.
[00:24:00] Arnar Larusson: It hits the fan big time.
[00:24:02] Stephen Seiler: It sucked. This product sucked. Well, I mean, because suddenly we were out in the real world where you needed battery life and you needed connections, wifi, and you needed all these different things that altitude and remote places don’t have the same way that ER’s lab does.
[00:24:22] Arnar Larusson: Not to mention that these cyclists are going out for six hour rides.
Up to that point, I think 45 minutes was about the limit that we could do. And so it was basically like a week. I think we slept six hours the entire week, us on the engineering team, and we rewrote the entire tech stack because it wasn’t handling any of the data. Wow. They were while they were collecting.
[00:24:43] Stephen Seiler: Yeah.
[00:24:44] Arnar Larusson: And so it was this very, very frantic period where we basically realized that we needed to redo a lot of things. And that’s another classic example of just like you, if you’re not from a certain world, but you’re developing a product that can be used for that application, you’re not gonna know. What to develop or how to develop it until it’s really in the user’s hands, which inevitably means it’s gonna break in their hands.
And so it’s a tightrope to walk, but it has to be done. And it’s one of the hardest things for people developing products to do is to release your product before it’s ready. ’cause you know it’s crap. You just don’t know which parts are crap. And so the only way you’re gonna solve that, and it works perfectly, like Steven was saying, it worked perfectly in the lab.
We used it a bunch in the lab and gone out for some runs with it. And it was, it was working fine. We thought it was great. But obviously when you really stress test it in that normal, in that natural environment, things start to break. And that’s what you need to focus on to fix. So. That helps with the optimization of like, what should we work on next?
What should we develop and fix and so forth. So it’s a critical kind of interplay of kind of, yeah, stress testing it and understanding where it breaks and that’s where Harass comes in
[00:25:48] Espen Aareskjold: from our side, like both in my previous team and, and now currently in, in, in this Malis bike, it’s mostly like that solid foundation of the ventilation as a metric that will help us make better decisions and the literature that was there, or reading frequency towards fatigues.
Different kinds of fatigues as an example, but also I like to say working with people that has a big passion for what they do. Kinda rubs off on those you talk to. So meeting Al and Steven, having that enthusiasm for the product also makes you kind of stay in there for the long run. As long as we have an open and honest conversation where they are transparent, where we share the same values, curiosity like I spoke about, and that we have this common interest in having something to solve to create win-win situations.
Because if solves measuring the ventilation, that will help us a lot. And then I can go to my riders or the riders within a team to say, why trying this technology that’s has its faults and, and we need to go through different iterations I create, or you kind of create a situation for me where I can help you even better in the future.
So it’s always like getting across that hurdle of things being perfect and kind of allowing the process to run its course that. I think has been really important in the process that we have gone through.
[00:27:23] Stephen Seiler: So let’s think about that sweaty shirt. ’cause that was early days and it’s kind of a success that’s underlying a failure because the reason the initially it was a shirt and we thought that’s okay, was the original kinda use case or business model was around periodically putting that shirt on and doing a ventilatory threshold test.
And then we come along, me and I think Espin pretty quickly and we said, well, we’d like to be able to wear this more often. And initially maybe for key workouts. And then after a while some were saying, well, why wouldn’t I wear it every day?
[00:28:01] Arnar Larusson: Mm-hmm.
[00:28:01] Stephen Seiler: Well then the sweaty shirt is a pain in the ass because it’s hard to wash.
And so it was a success wrapped up in a failure. In the sense that you’ve convinced us that this is not only good enough or interesting enough to use periodically, but we would like to be able to use it regularly, but you gotta do better than this sweated shirt.
[00:28:20] Arnar Larusson: Exactly. Yeah. And that’s something that, this is now five, six years ago, if we go back to that point, like there was no real reason to think that regular tracking of breathing would be useful.
I mean, that’s not necessarily something that was obvious or proven by any stretch of the imagination, but now we were actually putting it on people and tracking it and having issues with that and redoing our whole backend infrastructure and sweating a lot in the shirt in the process. But it gave us that sort of inkling of a clue that like, wow, we’re seeing.
Things with the breathing data that we’re not seeing in the heart rate data, in the power data. And those are really kind of the critical aha moments that like, now we’re unlocking a door right now, we’re go, now we’re back in 1980s, heart rate land where they’re finally seeing like, oh, we can actually start to maybe apply some training zones to this and use it to improve the training process.
And that’s just continued to go on. And, and we’ve been responding to those kind of aha moments through development, through improvements, whether it’s hardware or algorithms or software and ui, uh, that’s a continuous process with all of our users.
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Every pair is designed with premium features like lightweight, flexible frames, hydrophilic nose pads for secure fit, and shatterproof lenses delivering performance and protection without the premium price tag. For more information, go to to fosi optics.com. So I just wanna take a quick step back for our listeners.
Just emphasize everything that you guys are talking about, because if you know nothing about product development and you think about a device that’s measuring breathing rate, this sounds incredibly simple. You just put a stretch strap on somebody, it expands, it contracts, there’s one, it expands, it contracts, there’s two.
This is the easiest thing in the world. You could have a product in four months and be marketing the heck out of it, but that’s kind of that naive view that I would have not being in product development. What I love about this conversation is you’re raising all the things that if you just quickly tested this product in a lab, you wouldn’t see.
But once you get it out in the field, there’s just a host of issues that you haven’t even thought of, and even something that sounds simple. Is actually remarkably complex. And that personally makes me really think about some of these products that are trying to measure something far more complex. How long have they been testing the feasibility of these things out in the field, and what are the challenges that we don’t even know about yet?
If you’re one of the first people to buy this product?
[00:31:15] Arnar Larusson: Well, I call it the Dolly Parton effect.
[00:31:18] Stephen Seiler: Oh, I don’t know where this is going. Yeah, let’s, let’s
[00:31:20] Trevor Connor: see from it. Everybody loves Dolly Parton. Be careful.
[00:31:23] Arnar Larusson: Joel, your most famous quote is A is the perfect application for anything that’s done well is that it takes a lot of money to look this cheap.
And that applies to startups in a profound way because especially consumer products where. You’re trying to simplify and make simple the application of something that can be incredibly complex. Whether it’s us developing a sensor that measures things reliably as close to a, as a, a lab as possible, or just a user interface and, and making that simple and accessible.
These are all non-obvious things that, you know, if you describe them with words, it’s like, yeah, I mean, okay, we just do that and it will be done. Like, that’s what I also thought when I started this. I, oh, this would be super simple and easy and we’ll just be done in a few months and we’ll call it a day.
And then you run into all the reality of the complexities of the minutia and the little details that wouldn’t have or couldn’t have been un uncovered until you really get in front of them and have to contend with them. And there’s a million of those, the unknown unknowns. And then you’re also layering that onto the unknown and of how people will receive it and use it and break it.
And just like there are things that people do with products that you couldn’t imagine, you know? Mm-hmm. Yeah. And you have to solve for all those things. And that has to be factored into the design in ways that is re resilient to those things. And, and that just takes time. ’cause especially when a human is in the loop, you need to expose yourself to those cases.
And so that does take time and you have to be quite kinda humble in releasing something. Again, like I said earlier, like releasing something that you know is probably gonna break, but you just know which part. And so that’s the only way you’re gonna uncover it.
[00:32:51] Stephen Seiler: But if we get back to the basic idea of we wanna measure breathing, we wanna measure that, uh, both in terms of frequency and maybe depth of breathing.
And so those are those two big goals. It’s not straightforward because if you just think a little bit about a cycling ride, what are you doing while you’re riding? You’re not just breathing rhythmically, you’re talking, you’re drinking, you’re coughing, you’re holding your breath occasionally while you drink.
There’s all kinds of behavioral noise going on. And so now I introduce this term noise. And in this world of music or video or whatever, we always talk about signal to noise, trying to tune in, maximize the signal, the thing you’re trying to actually listen to or measure or see and dealing with the noise.
Even on this podcast, we’ve gotta deal with a bit of that noise. So signal to noise is a ubiquitous way of understanding the technology world, and I think that’s what we’ve been working with. First, it’s an engineering problem, and then it’s a filtering problem around getting rid of some of the noise. But then there’s other kinds of noise.
There’s the noise of adoption. There’s the noise of humans that if they have to push more than two buttons, they’re not gonna do it. And so there’s this long iterative process. So far, we’ve just been talking about the ground truth and getting the biosensor right, but that’s not even close to enough. To getting what is necessary for this to be a tool.
And we moved from shirt to a brief period with a vest, and then the real breakthrough was the strap. ’cause it turned out the shirt didn’t stabilize or it created lots of force vectors and now the strap had less noise.
[00:34:47] Arnar Larusson: It’s like the overnight success that took 10 years. It really winds up because once you solve the foundational thing, which can take a long time, then you can start to build really quickly kind of the application layer on top of that.
And that’s kind of in the place we’re in now where we’ve really stabilized in many ways, the hardware and the sensing. And it’s quite easy to use. It’s just now in a strap form factor. Like any other chest strap, we’ve resolved a lot of the issues about how you connect to things and stream data. And so there’s a lot of kind of robustness now in all that.
And that just takes time to figure out all those cases and solve them. And then once you have that, then you can really start to deliver some really great insights, great automations. You can build nice algorithms that automate all those things. And so I would say like the progress that we’ve made in the last six months, if you like.
Put it on a scale, it’s, it probably looks like 10 times in terms of the progress that was fundamentally made in many years prior to that, but, but self-built on that foundation. And I think that kind of goes back to the initial point where it’s like, why would we go through this process? Like you’re saying, it’s because once you solve that core, why do we take the, I guess quote unquote, the longer approach, go through the validation and the research and so forth, and not, instead of just kind of ignoring it and rushing something to market, it’s because without that foundation, we can’t reliably produce any of these other valuable things.
And that was always kind of intuitive to me also, like you keep the main thing, solve that first, and then everything else can follow. And that is what we’re seeing and we’re lucky to have made it through. It’s not often the case that that happens or, or that people make it through. And, and we’re certainly not through the woods yet.
I mean, there’s a lot of, a lot of ground yet to cover, but it’s certainly trending in the right direction.
[00:36:24] Trevor Connor: So to that point, that study that looked specifically at your device from 2021 made a really interesting point in their conclusion about if you’re trying to make the product more and more valid, there’s two issues that have to be addressed.
And this is true of any device. So they’re just talking in general. One is the quality of the data that you’re recording, and then the second one is the algorithms. How effective are the algorithms at using that data? So. Question I have for you is, which of those two has been the bigger challenge for you, and what have been the biggest challenges that you’ve been facing with both?
[00:37:04] Arnar Larusson: The data is the most fundamental and the biggest challenge. All these issues that we’ve uncovered and solved is they’re all fundamentally in. Collecting good data and securing a good data pipeline, a good data flow. So the fundamental thing is if you wanna analyze something, or if you want to tell someone something, you better have a good foundation on which that data is built.
So you have to have confidence in the data that is creating that assessment or analysis. And because, you know, maybe we bit off more than we could chew at the beginning, it was never quite good enough. So we always just kept iterating and getting it better and better. And we kinda got s. Not stuck, but we just got kind of like, we’re like a dog with a bone with that.
And it goes into electrical engineering and signal processing and understanding the mechanics of what we’re measuring. And so there’s a lot of factors that our team is really good at, has become really good at over the years. And because we were so focused on that, like Steven mentioned, like the app sucked because we were just not focused on it.
We were just trying to get the hardware to work right with the intention that once the hardware works, then we can really start to move and build something on top of it. But we never really built the competency while we were doing that. So that’s something that we’ve really tr seen a transition within the team over the last.
Year, year and a half or so since kind of landing the strap and getting that through the production cycle, which by the way, only took about a year. So that was actually quite quick for product cycle because we had all that built-in knowledge prior that that helped speed that through and started to shift more over to the sort of the data layer or the sort of what sits on top of the data layer, which is the algorithms, the insights that we can glean from this information.
[00:38:42] Trevor Connor: So that raises a good question. That’s for you and Aspen, I’m sure. When you started this whole adventure with this product, you had your idea of what data would be really valuable working with Aspen. Has he come back to you? With any surprises where he said, yeah, we’ve actually been finding it really useful for X or Y, and you went, oh, we didn’t even think of that, but that’s really cool.
Has there been any of that?
[00:39:08] Arnar Larusson: The role of breathing rate, for instance, was certainly underappreciated at the start and has become increasingly appreciated for telling us something that. If we look at the stress of an athlete and the breathing rate response that they get, and independent of heart rate or power, and that’s exactly what Steven’s been showing in the lab is that it has a very different response and we can tell that the stress is increasing at a certain point when the heart rate would suggest otherwise or power would suggest otherwise.
So that would be certainly one big one. And I’d say that one is a combination of Stephen and Espin, the riders and seeing the data, Stephen seeing it on himself in his own lab. The other one is the stability of the ventilation signal as it relates to internal workload. There was a hunch there, but to see it actually borne out in ways that is quite robust is has been not necessarily like a big aha moment, but like a, wow, this actually works, which is nice to see.
[00:40:04] Stephen Seiler: It’s just been a discovery and for me it’s been, now I have a tool. It’s a tool that’s not perfect yet, but it’s getting good enough that I can now go back to some. Research from decades ago with new eyes, new tools, and again, reminds me of how much good science has been done decades ago. The way breathing gets connected to our movement cadence and so forth, and all this optimization that happens between breathing and force signature and cadence and all of that.
And man, there’s a lot of data on that from decades ago. So now we’ve got this new tool that it gives us a lot more data generation capacity. We can collect it from world class athletes, from regular folks that are hardcore recreational athletes and from untrained people. And so we can build out reinvestigate things.
And now I think we are able to say, Hey, wait a minute, breathing matters. Just like in the hospital, it’s a vital sign and we’re going there.
[00:41:08] Chris Case: To that point, Aspen, you’re working with one of the best cycling teams in the world, some of the best riders in the world. The stakes are very high for you, and as Dr.
Seiler has said, the product isn’t perfect yet. So how have you used this product to change the way that you are both training the athletes on the team and maybe on race day, how you are using the data on race day, if at all?
[00:41:39] Espen Aareskjold: Yeah. Also, back with my previous team, a lot of the time was spent on data collection and almost just trying to understand and looking for patterns.
Things gradually evolved into being more HandsOn even within the session, so, so now we even prescribe. A training session based on ation metrics or we give them guidance like, if x, y and set occurs, then you need to take a certain action. And mainly way we use it now is mainly kind of monitoring and handling fatigue and making sure that they don’t do too much on any given day.
Yeah, and especially for, like, if you think about the threesome model in the, in the first zone of the, in the threesome model, we feel we can more actively prescribe training and analyze training and give the rider insights and also making decisions on the road when things are not going according to plan.
Either they are stronger than we were planning for, or they are. More fatigue than, you know, planning for. So almost like we can ize what they’re able to do at the given days.
[00:43:00] Stephen Seiler: I have two things to say. Signal to noise can be applied directly to the development of technology, but signal to noise can also be applied to the the training process.
The signal is what we’re trying to achieve, the adaptations. The noise is the cost, the stress responses, the autonomic nervous system overloading the immune system dysfunction and things like that. The glycogen depression, the heap stress, and all of that plays in. So you can think of signal to stress as signal is the adaptation and stress is the cost of trying to turn on those adaptations.
And the management process that we’re trying to help is getting that balance to be sustainable. We’re not saying that train ever gets easy and that we eliminate stress, but we’ve gotta manage it. Keep our athletes healthy and espin, they’re my goodness, the stress is high for the coaches because these athletes are trying to make choices about who’s gonna do this tremendous effort of the Tour de France, which athletes are ready, which are healthy, which are, and so they’re looking for every kind of information they can get to make those good decisions comes down to that management process.
So it’s fascinating to be on the outside looking in and understanding the, the circus of the Tour de France is amazing to watch. It’s one of endurance sports’, most demanding test test cases for management of health and capacity. And so now maybe they’ve got a little bit of an extra tool in that, and I think it’s really fascinating to be able to be part of that.
[00:44:39] Espen Aareskjold: Yeah, well say like Tower has helped us all, like ventilation in general. For me, I feel comfortable saying that some of the training. It has removed more of the uncertainty than I had in the past. I think the imagination for me, and also makes me do better decisions, it creates a better understanding of what’s happening internally and it actually gives more insight in my discussions with the rival.
So there’s no kind of ventilation data that makes me make a decision, but I’m more confident in the meeting with the rival, which probably also rubs off a bit, to be honest, and I get it earlier. So it’s not like waiting for some HIV data the next day. Often, that actually confirms what I have seen, especially towards the end of rights where the pattern or the construction of the ventilation, if you like, is altered towards what I see as baselines.
So then I can act earlier. I can reassure. It also open doors to actually do more if the circumstances are there.
[00:45:50] Stephen Seiler: And you said something that I think is a take home message for any of the listeners is why would I adopt a technology, a new technology? Well, I would adopt it if it helps me make a decision that I already need to make, but it helps me make it with more confidence.
It’s a tool that helps me make better decisions over time. And if a new technology doesn’t pass that truth test, then probably it doesn’t belong in your toolbox. If a technology is a solution looking for a problem, then that’s, it’s not what we want. We have enough problems. And now we’re saying, does this technology make us better at solving that problem or making decisions?
And for me, that’s been a good way of truth testing. Whether or not I’m going to get motivated about breathing because I’m convinced that this can help coaches make better decisions. And not just coaches, but regular folks in their own daily training that don’t have a big coaching staff around them.
They’re self coached. There’s a lot of us, and if that tool is gonna help us, then that’s what it needs to do is can it help me make better decisions about do I need a day off today or should I train?
[00:47:04] Chris Case: Do you think in the grand scheme of things, clearly you three are believers in this. Not just as a product, but as a method for identifying that problem and giving more information to make better decisions.
How significant an impact will this have on sport science training practices in a decade from now? Is it going to replace anything? Is it going to just be a much better supplement to things we currently have? How would you frame that?
[00:47:36] Arnar Larusson: I can speak to how our users are using it now. And so we do these regular interviews where we reach out to all of our users and ask them to come in for an interview or answer surveys.
So we’re very proactive about that. And one of the red threads that that keeps coming up now is this scenario where someone has been training, actively training five to 12 hours a week, depending on their overall fitness level. They’ll land somewhere they’re actively pursuing fitness, uh, whether they’re training for a triathlon or road race or a run.
And they’ll often have been banging their head against whether it’s FTP or some percentages of heart rate max, some formulas that have been used to decide where their training thresholds or their training zones are. And they’ll come into these interviews and they’ll explain like, this is how I used to train.
I’d do my FTP, it would tell me to train this in this way, and that’s what I was doing and I wasn’t making progress for months or sometimes years. I’d run into a rut. They take the test that our product allows, the threshold test that uncovers where their first and second ventilatory thresholds are.
Fundamentally, you can think about it as VT two or the second threshold is basically what FTP is getting at and VT one is this transition that in the three zone model, it’s that zone one to two transition. In that, in the five zone model, it’s your sort of, it’s the notorious zone two, top of zone two, roughly speaking.
It’s an incredibly important threshold to know and it’s an incredibly hard one to intuit or to back into for many of these other measures. And what invariably has been happening in these cases is that the estimates based on the sort of the standard way of doing things, whether it be FTP or an all out 5K or whatever it might be.
Miss the mark on this very critical first threshold. And so they’re either overdoing it or they’re doing too little in some cases. And so by identifying where this sort of transition is for them shifting their training to focus on it and appropriately, if that was the, in the cases where that was their limiter, then they start to see dramatic improvement.
And we’ve seen cases where FTP as a, as a sort of by proxy measure goes up, having stalled out for months or years, all of a sudden jumps up 40 watts. In the course of weeks or a couple months, race durability starts to improve. You start to see this just like shift in all these numbers and what’s happening there is not super complicated.
There was a truth to their fitness level. It was just not being measured appropriately. And by identifying where those markers were, they were now able to orient their training around. Their individual fitness level. And that’s when progress started to happen. And this is happening today across our entire user base.
And we’re hearing that back from users. And some describe it as an epiphany or in some dramatic cases, life changing because it’s something that they were dedicating themselves to frustratingly not making any progress because of this simple fact that they were just measuring it incorrectly. And so it becomes this tool that just uncovers this roadmap to improvement.
So that’s already making quite dramatic improvements to people.
[00:50:31] Trevor Connor: I was gonna say, using your product for a while now that has been. I think one of the most valuable features of it. I’ve been asked by athletes all the time, how do you determine my VT one or my top of that zone two, just using heart rate and power?
And my answer’s always been, we got pretty good ways of figuring out VT two, what you think of as your anaerobic threshold, but there’s no good on the roadway to figure out that VT one just using power and heart rate. But your device actually allows pretty valid way of testing to find out where that is.
And a lot of people wanna get into the zone two training and without knowing where that point is, it’s actually very hard to do effective zone two training.
[00:51:15] Arnar Larusson: And it’s a very awkward intensity. It’s like the type of intensity you want to go a little bit harder for it to be hard enough or you wanna back off for it to be truly easy.
It’s like this sort of like in-between that it’s so hard to fall off the edge on either side, that when you can target it and it’s quite easy to target it. Once you have the number to track against the training, improvement becomes dramatic.
[00:51:37] Espen Aareskjold: Yeah. And also to add this, like one thing is the intensity or the power you’re doing it at, but we also see changes within the duration you are keeping it at.
So you deteriorate as you go, and then if you keep on that intensity power-wise. Then your internal load will increase dramatically and maybe you’re not hitting the targets that you actually want to hit. So you actually need to kind of reduce the intensity to still maintain that signal if you like.
[00:52:10] Arnar Larusson: And that’s a slippery slope to over training, right?
And that’s the classic case where, you know, the five hour ride, where the first hour is gonna be much easier than the last hour. It’s not just perceptually easier, but your legs are just tired. And so the power at this threshold changes. It drops over time and the more durable you are, the less it drops.
Right? That’s what we see in like the GC contenders is just, they have incredibly resilient kind of durability or, or high durability, but there’s a whole spectrum to this and ventilations tends to stay true to that internal workload. Giving you a rabbit to chase. While allowing power maybe to drop a bit while allowing heart rate to drift a little bit or shift around based on your fueling and hydration, that all is extremely rich information.
The fact that heart rate is suppressed, the fact that it’s drifting, the fact that it’s elevated, these are all incredibly valuable pieces of information that if we’re controlling for heart rate. We’re not getting that information right. We’re trying to keep it controlled, and it’s not telling us then what it’s actually trying to tell us, which is that, oh, we’re actually under fueling there.
That’s why it’s drifting. Or we’re actually a little bit overtrained at this point. So that’s why it’s blunted with ventilation. As this moderator or this sort of normalization factor that you can target, you can then see, oh, power is dropping this much, therefore I can now quantify my durability. Heart rate is drifting this much, therefore I probably need to look at my fueling strategy or my hydration strategy.
And you can literally like, like I’ve done this myself, I tended to go for fasted runs in the morning, and my heart rate was always high in drifting, and it just like it never settled. And then I started fueling before those runs and all of a sudden my heart rate’s just locked in and drifting towards the end and then shifting normally based on how my fatigue status.
So it becomes very easy to just gauge this yourself and in fact learn a lot in the process.
[00:53:57] Chris Case: Well, as we start to close things out, it’s really interesting to hear all three of you speak to this subject and this product in this new realm from three different angles. And it’s really fascinating to understand how you collaborate and maybe you could shed a little bit more light on how that happens, the communication that’s taking place between all three of you, how often you’re talking, what those conversations are like.
It would be fun to be a fly on the wall for some of those conversations as you work through problems offline and probably share beers too.
[00:54:30] Stephen Seiler: Well, there’s been hundreds, if not more than hundreds of emails. There have been countless Zoom or teams meetings. There have been multiple face-to-face connections.
So it is about trusting each other and communicating with each other, and I genuinely believe in aspen’s. Heartfelt desire to be the best possible coach he can for his athletes. I do not doubt that. One second. I genuinely believe that Arnold Larson. Yeah. Does he wanna make money? Yeah. Does he want to pay his bills?
Absolutely. Does he deserve to? Yes. But I also believe that he is just genuinely interested and fascinated with solving this problem. And so I can get behind that. Do I ever get frustrated that it’s not going fast enough or So Yeah, because I’m easily frustrated. That’s my thing that I have to deal with.
[00:55:31] Chris Case: It’s good that you can admit that.
[00:55:32] Stephen Seiler: Yeah. You know, and, and I can be a pain in the ass to work with, so, but I just love working with these guys because we all have the same goal. In mind that we wanna solve this and we wanna help more athletes have a great experience with endurance training.
[00:55:50] Chris Case: I think that speaks volumes about how you feel about this as a metric.
You’re talking about a very, very good coach with one of the best teams in the world, a very well recognized sports scientist, and then there’s AOR over here developing this product. But you all believe in it, and I feel like that speaks a lot about how you anticipate it will change the way people train, how much it will improve the accuracy with which they might.
Define their zones and so forth.
[00:56:22] Arnar Larusson: I think that me and most of the people on the team, on on the development side, were driven much more by impact than money. I mean, it sounds maybe trite to say that, but because we are in a business and we are trying to sell this product, but it’s only gonna sell if it’s having an impact.
And I think that’s where we start. I think that’s how we generally think about things and what motivates us. I think if I wanted to make a bunch of money, then I probably just would’ve gone into banking or private equity and called it a day. I wouldn’t have struggled through almost a decade of banging my head against the wall with very little payout, if any.
So it has to start from a place where you’re fundamentally interested and curious about the thing that you’re solving. Otherwise, you’re just not gonna make it. I mean, the thing that causes startups or businesses to fail is. Not really the lack of funding because funding is not the end all be all. It’s the thing that enables certain things.
It’s not the success or release of a product ’cause you can always improve that and release. Again, it’s just people giving up and there are extremely fair reasons to do that. At any time there’s an off ramp to stop doing what you’re doing and go do something else. And there are any number of reasons that we get as we kind of get these frustrations and try to get this product to work.
And then the successes. Build that confidence. And, uh, me and my co-founder, we’ve developed this way of thinking that like there’s really nothing we can’t solve. And that’s built through having solved all the crap that we’ve solved to date. And that is confidence building. But it’s also, it enables us to then just continue.
And every time we solve something, we see a new door opens and that’s exciting. So it’s a love of that process. The end goal should be that if we’re successful, if we’re having an impact, we’re gonna build a really big and successful company. But if that’s where you start, man, we would’ve given up two months in.
[00:58:06] Stephen Seiler: And when I look across right now, ’cause we’re sitting kind of around each other, I can see a little glint of water in honor’s eyes. So it does mean something doesn’t get you
[00:58:17] Arnar Larusson: emotional here.
[00:58:18] Stephen Seiler: No, but I mean, it, it says something about that. He does get emotional talking about that process. And I’ve seen that and I use him as a guest teacher for that reason.
So I guess. I know he is genuine because he can’t hide it. He sucks at hiding his emotions just like me,
[00:58:34] Chris Case: that’s rare for an Icelandic person who are known for their stoicism. And
[00:58:38] Arnar Larusson: I grew up half my life in the States. So I think that I know you’re soft. You’re soft what you’re saying. Yeah, I got some, I got some softness there.
Yeah.
[00:58:46] Trevor Connor: Well, I hate to say it, but I think that’s a great place to leave this and I hope we can continue this conversation at some point. But shall we move into our take homes? Absolutely. And we have a big group here. So for those of you new to the show, we always finish with one minute take homes. You have one minute to give what you think is the most important lesson for our, our listeners.
And since there’s so many of us, maybe I’ll start it out and then we can go from there. So my oddest take home here. Is just going back to all the challenges that you face to create a great product that you wouldn’t even think about. And I’m just going to use an example that you solved with me and really appreciate all the tech support you gave to me.
But early on, using a prototype of your device, this stretch strap detects movement and it needs to be sensitive enough to know, okay, now I’m being used, I need to turn on and I need to start recording. But had that issue that when I took it off and put it on the table by my door, any sort of vibration in my house would turn it on and turn it off, and would never even would’ve thought of this if I was trying to develop a product, it was causing the battery to go dead.
You had to do a whole lot with the algorithms to figure out how sensitive do we wanna make it, but still be able to protect the battery life. And I’m sure that’s when you started the product. That wasn’t even a thought on your mind. That was something you had to solve and you solved it. It now works great.
But just wanted to bring that up to, would you are developing a product like this? All the different things that have to be solved along the way to have a really good product. When you might go into it thinking, this is simple, I’ll have a product and in three months it’s not, there is so much to figure out to create a great product, and to me that’s the most important message of the episode.
So with that, who would like to go next?
[01:00:45] Stephen Seiler: Well, I’ll go, I’m making this up as I go, but I, in many ways, I would say this device, this ability to measure breathing fits hand in glove with my kind of career of trying to understand the training process and understand how do we have long-term management of stress?
How do we keep our athletes healthy? How do we keep ourselves healthy? So it’s the tool I wish I had 20 years ago that would’ve sped up my understanding of training a great deal. But I’ve got it now, so I’m gonna use it and I’m gonna try to push out some interesting or useful. Ways of using the tool. In my remaining years as a sports scientist,
[01:01:28] Chris Case: I don’t have too much to add, although I’m glad that Arner has the passion he has, and he met Steven and he met Aspen, and the three of you seem to be really driving the development here because I’m a little impatient too.
I’m like, Steven, I want this thing to be perfect now. I want the algorithm to tell me exactly what I need to do. I want it to be simplified and I know you have a lot of work to get to where you’re going and who knows what will come in a few years time when all these people are out there breaking your product and you’re refining it and refining it.
So yeah, I just wanna say both. Good job and good luck, because there’s probably a lot of work still to be done.
[01:02:09] Espen Aareskjold: I think for me, I draw like a similarity to the training process. So I also have writers that are impatient and want to do main expecting, or the heat session or the nutritional strategies or what have you, but.
Just like developing in this time where, and kind of follow both with Anna, but also seeing it from the outside. It’s like you have to solve the basics and you have to do them really well over and over again. It’s really boring stuff. But once you have that trickle down, then you can use the nitrates after a race or before a race or have that special training intervention.
But as long as we don’t do it the basics, well then yeah, you’ll probably also not perform as a writer.
[01:02:57] Trevor Connor: Good vest. Well Arter, you wanna take us out?
[01:03:00] Arnar Larusson: Yeah. So I think the way that I’d frame it is like going back to just why are we doing this? And the way I think about it is that what we’re solving is a way to deterministically improve fitness.
And if we think about the impact that can have, whether you’re trying to win the Tour de France or preparing for an age group weekend race, or maintaining your fitness level as you age,
[01:03:23] Stephen Seiler: why are you looking at me right now?
[01:03:27] Arnar Larusson: I see your data.
[01:03:28] Chris Case: Oh,
[01:03:33] Arnar Larusson: or I was gonna say if you’re fighting the onset or the rehabilitation of lifestyle diseases that are fundamentally at their core, the metabolic in origin, like type two diabetes, a lot of cardiovascular disease. All of these people have the same thing in common. The thing that will move them forward from left to right, from current state to progress is improved fitness.
Of course there are a lot of other factors and things that come to play, but this is the fundamental one that’s gonna have probably, I would say, the biggest impact. And it’s unsolved, right? So there’s so many things that are, we’re unsure about, that’s been touched on it where he has now more certainty.
And the reason he has more certainty is because he has the data layer that can help him make better decisions. And, and that’s true not just for a world tour cycling team. That’s true for anyone that’s trying to move their fitness forward. And so for me, that’s our mission. That’s what we’re trying to do, and takes a lot of people and a lot of hands to row in the direction of that goal.
Just very happy to have the resources and the people that we have along for this ride. So it’s been, uh,
[01:04:29] Stephen Seiler: it’s been great fun. He has noticed that he used Hands to Row. He’s referring to the row of the World Cup. I don’t know if you’ve been following along, uh, the
[01:04:41] Chris Case: Viking ships.
[01:04:42] Stephen Seiler: The Vikings, yeah. Rowing to Victory.
So he’s stealing that.
[01:04:47] Arnar Larusson: I would say that the Icelanders were the Norwegians that rode away from Norway, which is crazy because Norway has a much better climate than Iceland does. And that they decided that being in the North Atlantic where the growing season is about three months was a good idea. And now they were gonna stay there.
But they
[01:05:01] Stephen Seiler: do, you do have natural jacuzzis.
[01:05:03] Arnar Larusson: Well, I mean, well, the, the irony is that Iceland didn’t, neither had refrigeration nor running water, so un until like 1945. So if you were lucky to be next to a hot spring, then yeah, you could take a bath every now and then. But the rest of them were pretty. Pretty smelly, I think,
[01:05:20] Stephen Seiler: but we digress as we normally do.
[01:05:25] Chris Case: Thank you guys.
[01:05:25] Stephen Seiler: Real pleasure. Real pleasure. Thank you guys. Uh, this was fun. Thank you. Thank you.
[01:05:30] Chris Case: That was another episode of Fast Talk. Subscribe to Fast Talk wherever you prefer to find your favorite podcast. Hey, don’t forget, we’re now on YouTube. Give us a like and a subscribe there and help us grow our reach.
As always, remember that the thoughts and opinions expressed on Fast Talk are those of the individual. Join us on social media at Fast Talk Labs and for access to our endurance sports content and continuing education. For coaches, head to Fast Talk labs.com. For Arner Larson, Dr. Steven Seiler, Espin Ahold, and Trevor Connor.
I’m Chris Case. Thanks for listening.
