CEO Moxy Monitor: Roger Schmitz

So, for the people that are seeing this for the first time, can you briefly give us an introduction on what you're building?

Yeah. So we make a muscle oxygen sensor that's for athletes. It's a wearable sensor that measures oxygen levels in the tissue of the muscle in real time while the athlete is working out. This provides feedback that they can use to train more effectively.

I was really excited when I learned about the technology because as a weightlifter, I was using a heart rate monitor. Heart rate monitors show cardiac output as a percentage, and many companies correlate this with muscle oxygen. But my heart rate monitor cannot deduce the oxygen my biceps are depleting during exercise. It's interesting to understand each individual muscle while training.

How did you start in this field?

I'm a mechanical engineer by training. I used to design automated machinery for computer disk drives and then moved into optical operations. I started designing near-infrared spectroscopy devices for medical applications. After being laid off, I developed a way to make the technology smaller, less expensive, and more accurate. The previous devices were costly and cumbersome. My initial thought was to make it a medical device, but a cardiologist suggested making it for athletes to avoid FDA issues. We launched it for athletes in 2013, and it's now used in heart failure research.

How did the first 10 users learn about the product?

It was a bit haphazard. We put up a website and worked with a marketing company that suggested writing blogs about training with heart rate and power meters. Jorg Feldman, a trainer in Canada, found us and was excited about the technology. He helped us develop methodologies that are still used today.

Was blogging the first marketing approach you tried?

Yes, it was. We worked with a startup marketing company, and those blogs are still up on our site. Blogging has been a good method for us to find new users, but word of mouth is the most significant source of new users.

How does the sensor look today, and how can people use it?

It's a small wearable sensor, about the size of a book of matches. You wear it on the working muscle you're interested in. It's muscle-dependent, not systemic. Cyclists wear it on the vastus lateralis, runners on various leg muscles, and swimmers on the back. We use multiple sensors to measure different muscles and assess systemic responses.

What will I see if I place the sensor on my calf while training?

You'll see the balance of supply and demand for oxygen in that muscle. SMO2, the percentage of hemoglobin and myoglobin carrying oxygen, will go down as you start working out. As you warm up, your cardiac output increases, and vasodilation occurs, causing SMO2 to rise again. This helps optimize warmups and intensity control.

How should a cyclist train with the sensor?

Cyclists use it for precise intensity control. After warming up, they find the highest load they can maintain with stable oxygenation. If SMO2 is steady, they're producing power aerobically. If it drops, they're using more oxygen than they're delivering, indicating a load they can't maintain.

How can weightlifters use the sensor?

It depends on their goals. For strength without hypertrophy, like my daughter, they should avoid long desaturation periods. They should pick lighter loads, do reps until oxygen hits the low limit, then recover. For hypertrophy, heavier loads and staying at the bottom of desaturation are needed.

What other use cases have you seen with the sensor?

Endurance sports are the most straightforward applications, but strength training and physical therapy are growing areas. In physical therapy, we can assess recovery and symmetry, ensuring movements stress the intended muscles. Injury prediction and prevention are also potential applications.

Can you share some athletes using the product?

Christian Blumenfeld and Gustav Iden, both world champions and Olympic medalists, use it. Their trainer, Olav Alexander Buu, has done significant development work with the sensor. They started by observing the data without changing their training, learning what SMO2 values worked for them.

And so then they, just by looking at their head unit, you know, they didn't do any mathematical analysis or anything, but they just, when they were doing the intensity that their trainer wanted, that's where their SMO2 was. And if they kept it there, then they were getting the correct intensity. And so they've really used it for that precise intensity feedback. So when they go to altitude, they keep their SMO2 at that level, depending on the stress they want of that day.

If they want a high stress day, they'll run it lower. If they want more of a recovery day, then they'll stay away from that level. So that they've really used it for that precise intensity feedback. And then they've used it for things like they've tested out shoes. You know, how does the shoes change their oxygenation? They've done things like with bike fitting and other things. But the reason they're wearing it, you know, every day for training is really for that precise intensity control.

You mentioned shoes. How does this correlate?

There's, I haven't seen the results from that. I know they did some testing with it, but there's some shoes claim to have higher running efficiency than others. And if that's true, you should see that show up in the oxygenation data. Now that would have to be, these are really small changes in the running efficiency. And so you'd see really small changes in the oxygenation. But if you did a really controlled test, you should be able to find that.

And I don't, like I said, I don't know the results of that, but those are the kinds of things that you can get this objective feedback on. You know, if someone says, here, these shoes will, these shoes will have you, you know, give you a better running efficiency. Well, that's hard to verify. You know, you think, well, they're brighter colors, so they make me run faster or something. But this way you get a much more objective measure of that type of thing.

That bike fitting is another one where, you know, does changing a bike fit, you may get yourself a little bit more aero, but have a big physiologic cost to doing that. And so you may choose to adjust the bike fit if you, if it doesn't give you, if it costs too much physiologically.

What other data can you combine here? So there are a lot of wearables out there with a lot of information. What other data points would you combine to understand better what's going on?

Yeah, that's a really good question because the Moxie data is useful by itself, but then it's really useful when it's combined with other data. And, you know, and that's where I'm super excited about the things you guys are doing there with Terra because it makes this more possible. You know, it makes it a lot more, it makes it more feasible. It's always possible to combine the data, but doing this on a large scale with large numbers of athletes where you're getting data from multiple sources is a challenge. And I'm super excited for what you guys are doing in this space.

So like right now we see a lot of people use their Moxie with VO2 data. So we have, you know, we have integrations like with the VO2 Master, with Core Medical, Cortex, Cosmed, a handful of other VO2 devices where you can get your VO2 data and your Moxie data all in one app, saved in one file, and that works really well. We just had a recent integration with Delsys for EMG measurements. Now that's mainly used in research. It's used sometimes in training, but used a lot in research. And that's another case where getting the EMG data along with the muscle oxygen data is super useful.

We don't have an integration with bar velocity yet, but I'd love, you know, if there's a company out there that has bar velocity, we'd love to work with you on doing some kind of integration with bar velocity, because I think seeing what happens when your bar velocity slows down, as you fatigue, what is your muscle oxygen doing? That could be really informative. And seeing, do they correlate? You know, do you see a physiologic response at the same time that you slow down, or is it more of a motivational issue?

You know, I think in strength training, that's always a little bit of a question. And so, you know, integration with bar velocity would be really tremendous. I think that would be a great application.

And then another one is GPS data, like they'll have these devices they wear on their back. We already have an integration with Waimu. They're a Spanish company that makes a GPS device, but then there's other GPS devices out there where it'd be really nice to get their acceleration data and, you know, their time at speed kind of data, their time versus how fast they're running. And in sports like soccer, basketball, you know, even American football, it'd be really great to have that information where you could see they just did a long, long run, their SMO2 is really low, and now we're starting to see all these elements of fatigue setting in.

And then how long does it take them to recover from that? So, you know, getting that data combined, I think would really tell a pretty amazing story, you know, in those kinds of sports.

For my last question, what should we expect for the next five years from Moxie?

Yeah, that's a really good question. And maybe another one, just to add them together, it's what do you think is the future of our space?

The future of the space?

Of our space, yeah.

Yeah, yep, so I think these are related. That's maybe the same answer, is I think what really needs to happen is this data needs to be seamless to the user. You can't have your data in separate places. And it needs to be, it needs to be, like some of the integrations we do now are very specific. You know, it works with this app, but not this other app. And so I think as much as we can do to make that data so that it's just where you want it without you even having to think about it.

And we're working on those things. You know, we follow some standards with how our data is saved, and we'll continue to do that. We have our own app called Moxie Portal. We have plans to make it a lot easier to get that data out of that system in an automatic fashion so that you can set it up so that when you're done, your data just shows up. It already does that. Like when we work with a Garmin, your data shows up in training peaks.

Just, you don't have to push a single button, your data's just there. And that I think is kind of the ideal, that your data should just be wherever you want it to be when you expect it to be there. And that is where our development is going.

And then the other area where, you know, this is more Moxie specific, but the science is so young here, it's really just getting started. I mean, this is like, we're just starting to scratch the surface of what can be done in this space. And we see it in research. You know, there's a lot of research going on. About half of our sensors actually go into research applications. I think we're gonna see more of that research, but we're gonna see that research get translated into practical applications for training more effectively.

And, you know, the Norwegians are really at the forefront of this. The Norwegian triathletes, they collect a ton of data, they're super meticulous, and they're really using that data to inform their training. And I think where they're a little bit of an anomaly right now, I think that's gonna become the standard. I think it's gonna become much more standard that you're gonna use all kinds of physiologic data in addition to Moxie, but Moxie's certainly gonna be an important part of it, to be able to inform the training so that you're getting more benefit for lower costs, more benefit in terms of performance for lower costs in terms of, you know, training energy that you have to put in, time and effort that you have to put in.