Exploring The AI Running Revolution with Dr. John Holash and Dr. Cody Ray van Rassel

Guest [00:00:01]: You can look at cyclists in the Tour de France right now. They're producing power outputs in an extraordinarily tightly regulated sport, was what I would assume now because of all the drug scandals that we've seen in the past. And they're producing performances that are superior to what people were doing when we knew that they were cheating. So, yeah, we're starting to see a lot of those things, especially with training and nutrition, really, really improve. And we're starting to see those results carried forth.

Cory Nagler [00:00:35]: AI and data is changing the game when it comes to maximizing running performance. I find it fun to look through stats like VO2 max and race predictors from my Garmin, but my guests today take running data to a whole new level. Dr. John Hollasch is an associate professor of exercise and muscle physiology at the University of Calgary's Faculty of Kinesiology. My other guest, Cody, is a postdoc at the University of Calgary with a focus on wearable tech and exercise physiology. Both of them are experts in movement science and translating lab results into really fast times on the road or on the track. They joined me to explain how new innovations like super shoes or science backed diets are leading to faster times and what new innovations we might look forward to seeing in the future. They might just be up there for some of the smartest people that we've had on the show and I hope that you enjoy my conversation with Dr.

Cory Nagler [00:01:26]: John Holash and Dr. Cody Van Rassel. Hello runners, and welcome to the Run to the Top podcast where our goal is making you a better runner with each and every episode. I'm your showrunner, Cory Nagler, and I'm not an elite runner, but together we'll explore new strategies and topics to take your running to the next level. This podcast is created and produced by the expert team ofcoaches@runnersconnect.net where you can find the best running information on the Internet as well as training plans to fit every runner and every budget. Okay, this is probably going to be one of the more academic episodes we've done because I am joined by Dr. John Holash and Dr. Van Rassel from the University of Calgary, here to talk about all things technology and sports.

Cory Nagler [00:02:21]: Thank you both for joining me on the show.

Guest [00:02:23]: Thank you very much for inviting us.

Guest 2 [00:02:25]: Pleasure to be here.

Cory Nagler [00:02:27]: Absolutely. John, maybe I'll start with you, but I want to get both of your opinions. What is it that got you interested in this field in the first place?

Guest [00:02:35]: Oh, I think I'm. Well, I'm a longtime computer nerd, so I'VE always been fascinated with the way that computers work and can manipulate data and can do a lot of the calculations that it's taken us a long time ago to, to do by hand or doing different things. And it can automate that process. I remember, I remember the very first time somebody showed me about a. Like a batch script where you could do, write one program and it would process a hundred files. And I was sold in that instance. It's like, wow, you know, I don't have to open up each file and do this process for each one. This is great.

Guest [00:03:17]: So I started getting into computers for that. I also was just fascinated that you could, you could tell it to do something and then it would do your commands. Like, I was fascinated with the input output aspect of it controlling robotic arms.

Cory Nagler [00:03:35]: And yeah, there's kind of like a chicken or the egg. I was curious whether it came first, like the interest in sports brought in some of the more AI applications of the reverse. It sounds like maybe you were more interested in the modeling and then the sports came later.

Guest [00:03:49]: Yeah, I was definitely more interested in the computer modeling and the computer aspect first. It's actually, it's probably an interesting story because, like, when I was a kid, when I was really young, I got into. I had to build my very first computers because they didn't sell like, fully working personal computers at that time, or if they did, they were too expensive. So my very first computer was like a TI94, I think it was. It was made by Sinclair and it came in a box. And in the box there was just all these chips and things in a motherboard and you actually had to solder your chips to your motherboard to make it work. Then you had to kind of patch it into your TV and, and get that to work. And of course, you know, like every kid, I was interested primarily in doing it to play video games, because, hey, what are computers really for, right? And so I was fascinated by that.

Guest [00:04:42]: And then it, the need to interact with it and then, you know, break the video game or, or kind of cheat on it led me saying, like, well, how is this character controlled? How is this movement controlled? And so I always wanted to, to win. So I would go into the program and I would alter it and give myself superpowers and then, you know, destroy my friends. And they were like, how do you do that? And it was great until one day when I think When I was 15, 14 or 15, I got spinal meningitis. I got rushed to the hospital and it was touch and go for a Little while because I was really, really sick. And as I was there, I was looking out and I was watching all these kids like ride their bike and play in the river and do all these things. And I thought, I haven't done any of that stuff in such a long period of time. You know, if, if I make it out of this, I'm going to spend more time just being active. And so obviously I did make it.

Guest [00:05:40]: I came out of there, I put the computer in a box and I didn't look at computers 15 years until I got into my graduate studies and then had to use them again. And then kind of that old love and passion kind of came back and yeah, I, I then used them as a, as a tool and I've always had the computer superpower to, to put that aspect to work.

Cory Nagler [00:06:05]: Yeah, very cool. Very full circle.

Guest [00:06:07]: Yeah.

Cory Nagler [00:06:08]: And then Cody or Dr. Van Rassel, as I enter, introduced you as, or I understand you're okay with the more casual use, is that right? Yeah, that's fine on your end. So how did you get into your field of study?

Guest 2 [00:06:23]: All. Yeah, all by chance. To be honest, if I take a look at where I was 10 years ago, I would have never predicted that I'd be in the position that I'm at now. I think my background, playing sports, growing up and knowing I needed to do something, you know, I went to school and I thought, hey, like, I understand the body, I like exercise. Like, let's, let's jump into the kind of kinesiology field. So my background comes more from very exercise physiology focus. And going through grad school, I had some opportunities to add some wearable sensors to my project. And then that kind of blew it from there.

Guest 2 [00:07:03]: And I see I'm a little bit late to the game with artificial intelligence. I chat with John and I, you know, I'm jealous of all the knowledge he has, so I'm hoping to pick his brain and extract as much as I can. But I'm looking at all the opportunities and especially in the field of exercise physiology, I think, I think we've come a long ways and from here it's going to be, you know, small incremental steps. There's, there's probably not too many massive discoveries that are, you know, I'm sure like any true discovery, you don't know that a discovery is going to happen. But I think we have a firm understanding of a lot of things. And I think from my perspective, where the opportunity lies is, or at least where the interesting aspects are is by incorporating things like wearable technology by taking, by integrating sensors, by implementing all these exciting new AI technologies, it's really going to open up a lot of opportunities and it's really exciting to me. So that's kind of where I've peeled off in this last little while and where I've directed most of my time and attention.

Cory Nagler [00:08:11]: Yeah. So, Cody, for those on video, I've got my Garmin on right now. Would you have played any part in any of the metrics included in those kind of watches?

Guest 2 [00:08:19]: That's a great question. I have no idea. I assume a lot of what Garmin does, they'll look into the research and they'll apply some. The available concepts into their technologies. But that, that's. Yeah, John and I were talking about that earlier, and that's a, it's a really interesting question because, you know, the Garmin technologies and what shows up in your watch. I think Garmin's really good at creating a user interface that works well. They're, they're a technology company first and sometimes that's a bit of a limitation because they, you know, they may not leverage experts in the field to, to figure out what to do with the data.

Guest 2 [00:08:57]: Right. So a lot of what these wearables do is they'll tell you what you already know. Like if you had a bad sleep, they'll say, you know, yeah, you were up late and you had a few too many drinks, or if you went for a long walk, you'll say like, you got a lot of steps in today. At least from my perspective is I think that's where there's a lot of exciting opportunities. It's trying to figure out how we can leverage a lot of these wearable sensor, or all the data from wearable sensors, and trying to come up with kind of a nuanced perspective to try and give people information about things that they may not already know.

Cory Nagler [00:09:29]: Yeah, I think that last piece is really interesting, the things they may not already know. Because if you're coming back after a late night of drinking and then you've got the garment buzzing telling you you didn't sleep well, it's probably not as informative as some of the other metrics. Maybe looking at like your, your pace or heart rate variability or I'm sure you've looked into a lot of those.

Guest 2 [00:09:47]: Yeah, so that's, that's, I guess, a bit about what I have done. So more from the wearable technologies perspective, I've incorporated some of the running wearables like the Stride, the Coros, some wearable inertial measurement units. So kind of accelerometers that measure changes in acceleration in three directions and try to see kind of what can I extract from that data. I've looked into the stride pod to kind of, to actually try to validate it. You know, what is that metric in relation to cycling power? Is it giving something similar to what a cycling power meter might give and using that raw data and applying different analyses to see what's something different or something new that we can take from that signal that a runner may not already know?

Cory Nagler [00:10:38]: Yeah, and there's one more thing I want to cover on the intro fronts before we move on, John. I know that we had a little back and forth over email and you mentioned that you had actually done some research that might have had some involvement in the Kipchoge sub 2 hour project. For anyone who's not familiar, not an official world record, but the first time any human had broken two hours. Can you tell us a little bit about what your research was and how that might have helped an attempt like that?

Guest [00:11:06]: Well, okay, first of all, it wasn't my research directly. It was just research that was going on in the Human Performance Lab while I was a student. And I guess my participation in that research wasn't so much as a scientist at the time. It was just another grad student that, you know, wore some variation of shoes who stood on force platforms, who did some of that testing. And so as a grad student, I knew the other grad students at the time who, who were involved in those, who eventually went on to be involved in those initiatives. And I know that some of the ways that those shoes were developed and what the initial research question was and, and how that took a particular form and then morphed over time and it just, it produced more and more and more fruit. And it, it's one of those questions where before and, and Cody and I were talking about this, and I'll include the audience now in this, is that when any you, whenever you look at a situation, you can look at the parameters that define it and say somewhere in that area space, you will have a local minima. And that local minima will produce the best performance because it'll take the least amount of energy to produce that.

Guest [00:12:29]: What we don't always know is we don't know where to set those parameter values. And so while we might, what we might think is the local minima or the global minima might only be a local minima in that if we expand the search space, there might be more information in There and maybe there's just over the next little hill, there's a lower minima there, which allows you to improve performance. And that's kind of what they found in a very general turn with the, with the Nike running shoe.

Cory Nagler [00:13:01]: Yeah. And John, when we talk about either global or local minima, are we talking about efficiency, are we talking about effort? What is it that you're trying to either maximize or minimize for?

Guest [00:13:13]: Well, it could be any number of those things. Right. You could look at something as simple as power output and you could say, okay, within this cycling metric, let's say we're having people cycle for a change and they're moving their pedals around and we think that crank sizes of 172 millimeters are the best because that's what everybody uses. And so we can do simulations where we can do actually measurements on what the muscular force is and what the muscular force production is. And we can see what works best in that space. But at the same point in time, we haven't checked out to see if maybe we had a system that varied the crank length. Right. Which is kind of what the Shimano BioPace system tried to do, was try to change that crank length not by changing the crank length, but by changing the, the length of the chainrings, effectively increasing and decreasing the, the crank length.

Guest [00:14:11]: But maybe more muscular performance is found in that area because you've now altered that space. And so that's kind of what I mean. So when I look at local minimas or local maximas, you could say it could be whatever metric that you're trying to optimize for. So a local minima is just normally a term and an optimization so that you've reached a particular threshold so it allows you to extract the maximum performance out of it.

Cory Nagler [00:14:45]: Yeah. So to use an example that runners might be familiar with, right now I think we're in the middle of the super shoe era or boom, and everybody's a putting carbon plates, but also these thick stacks of, of PEBA based foams. And I'm really curious your thoughts on why it took us so many decades to realize that, hey, if you actually put more foam into the shoes, it might protect you over the course of a marathon. Whereas the norm was, you know, these sort of paper thin, lightweight shoes until very recently.

Guest [00:15:16]: It's an interesting discussion because I think we've gone back and forth with that technology a few times. Back in the, in the 80s, I think the late 80s, there was these big super cushiony shoes. But the problem was is that People were kind of punching into the shoe or punching through the shoe. And so a lot of the energy that you were producing muscularly was being kind of absorbed in this foam and it wasn't being released really quickly. So now the way that they've kind of combined those two things with the carbon plate, which kind of adds a specific type of stiffness to an area of the shoe that minimizes kind of joint movement and you've added some cushioning, you're kind of, you're preventing the loss of energy into the shoe, but you're still using that cushioning. And it took us a long time probably to optimize into that area from getting both those things right because we knew that chop shoes would be great. But at the initial soft shoes, when we started to do them, they were too soft. And you know, you can imagine running in a pair of crocs, they're soft, they feel great on your feet, but you're not really going to go that fast because they're, they're too kind of squishy.

Guest [00:16:36]: So it's that magic of getting that right where you've got something that's improving stiffness and response, but then something that's also soft and cushioning.

Cory Nagler [00:16:47]: All right, so I do want to try one more example because I think this one's a little bit less of a balance and that's carb intake. I think it's probably within the last few years as well that a lot of pro marathoners have been trying to up the amount of carbs that they can take in and realizing that you can actually get more performance that way. Is there a reason, in your opinion that people haven't tried it other than maybe just it hadn't been done before?

Guest [00:17:12]: I don't know if it hasn't been done before. The concept of carb loading has been around for a really long time. Right. People used to go on specific diets where they would try to deplete their, their overall carbohydrate levels so that in order then when they had these large meals, they could super saturate their system with carbohydrates and then perform better. Because for a long time we've known kind of the limitations on carrying carbohydrates with you for a long term performance. Like there's. There seems to be an absolute maximum kind of timeline that the body can hold available carbohydrate sources. So again, it's probably getting that right mixture there to prevent yourself from doing something that caused bloating.

Guest [00:18:02]: And, you know, lots of carbohydrates also tended to cause people to feel full and weighted down and then maximize the performance. So lots of times we know where the research will go. We just can't get the secret sauce. Right. Right.

Cory Nagler [00:18:24]: And how do you know? And maybe this is a question for both of you, John, and then I want to hear from you, Cody, but when it comes to any new technology, how do you know when you found the secret sauce? Because obviously having tested it in a lab and seeing, hey, this is very efficient, or hey, X number is very large or small, depending what you're maximizing for, is great. But it's another thing to actually know that this is going to translate to real world performance.

Guest [00:18:51]: Yeah. And that's probably the biggest one. Whenever we test something in a lab, we're testing it in very specific conditions because, well, you need to control variability. Because variability, although it's everywhere in the modern, you know, in the real, in the quote unquote, real world variability is everything. It's, it's a, a difficult thing when you're doing a controlled experiment because the more variability that you get in, the more noise or confounding variability that you get in your study. And so we try to really hold things close, knowing that it's an artificial situation and then measuring changes within there. And then the hope is, is that it will be a global improvement in that performance across all of those variations. But we know that it won't.

Guest [00:19:44]: But that also leads into that kind of thing where, where we end up potentially finding a localized minimum instead of the global minimum. Right. We find something that works, but maybe it works in just those situations. And then if you move the parameters, you get into a better situation where things work in a more nuanced way and give more profound results. Because lots of the changes that we see in the lab, they just don't pan out into the real world because there's too much variability.

Cory Nagler [00:20:19]: And then, Cody, whether it's from either a sort of smartwatch perspective or any other technology you've worked on, what do you think it is, in your opinion, that tells you that you found something that's that secret sauce, or that's actually going to translate to runners getting faster performances. And one example that comes to the top of my head is, I know in your bio you mentioned working on like, readiness scores, but I imagine it would be quite difficult to actually know, like, what is the optimal time to resume training to balance that recovery with trying to get in more training.

Guest 2 [00:20:51]: Yeah, that's the, that's the golden Ticket. Right. It's, I think, you know, as, as athletes here, often there's a. There's a discrepancy between how you feel and how you perform. You know, some of your best performances, you might show up and you feel awful, and then you go after you perform great, or alternatively, you may not feel that tired, you feel okay, and then you go and actually start exercising, you feel miserable. Some parameters that I work with are things I try to estimate are an athlete's readiness to train and something called durability or resilience, which seems to be becoming a more popular term in, in our field. And so I, I assume most of your listeners will probably be somewhat familiar with their exercise thresholds, whether that's blood lactate threshold or your exercise intensity zones on a five zone model 1, 1 to 5, or three zone model 1 to 3. So from a physiological perspective, we're pretty good at identifying these transitions and kind of the body's adaptation to a given exercise intensity.

Guest 2 [00:21:57]: And those are really good.

Cory Nagler [00:21:58]: And Cody are talking, you're talking about training zones in terms of, like, easy zone threshold, zone VO2 max, that kind of thing.

Guest 2 [00:22:05]: Yeah. So we're really good at identifying those in a labs setting, and we're getting better to measure those in a practical setting using different wearables. Stride power, for example, will, you know, monitor your power over time, and you can kind of get that power duration relationship where as it flattens out, that's your critical power. And we know that that's a good indication of, you know, an exercise or running intensity you can sustain versus one that you can sustain. So although we can kind of identify these different exercise intensity domains or identify zones, one thing that we're. We're really bad at, and we really, we don't have a good metric even within a lab setting, is determining how long somebody could sustain each of those intensities. So we know if you're kind of In a zone 2, you can sustain it for so long, but we don't know we can physiologically measure why one athlete might be able to sustain that for 40 minutes versus one that can sustain it for 70. And we know that with training, your lactic threshold may not change much, but your ability to sustain a given intensity can improve.

Guest 2 [00:23:11]: And so that's some of the metrics I'm trying to, to look at in my research. And I do that using heart rate variability and something a little bit different than what most people kind of think of as heart rate variability. So we look at some nonlinear analysis of heart rate variability where essentially we're trying to look at kind of the. The absence or presence of fractal patterns within kind of your. Your heartbeat rhythm. And if you can, you kind of actually think of it like, you know, frozen fractals from. What's that Disney movie where I can't even think of it right now, but you kind of look at a snowflake, you can kind of see these embedded patterns within it. Or if you take a branch off a tree, you kind of blur your eyes.

Guest 2 [00:23:59]: That tree or that branch looks or resembles what the whole tree looks like.

Cory Nagler [00:24:05]: So interestingly enough, Cody, for those of us who haven't taken a geometry class in a very long time, that repeating pattern. That's what you mean by a fractal, right?

Guest 2 [00:24:14]: Yeah, absolute. And what's really interesting. So you see them in nature and all the time, you see them in how river beds are formed. You see them in trees, you see them all over the place. What's interesting is they're actually present within human physiological signals. So how your heart. What your heart rhythm is like. We can evaluate how that presence or absence of these fractal signals or fractal patterns slowly remove with exercise intensity or remove themselves as you go for longer, longer periods of.

Guest 2 [00:24:46]: Of running. And there seems to be relationships with kind of your fitness and kind of the presence of that signal. Or if you're very fatigued and unrested, there's the absence of that. That fractal pattern. And whether or not that's a secret sauce is kind of what I'm hoping to discover.

Cory Nagler [00:25:03]: And when you refer to a fractal in this sense, do you mean like a literal shape? Are we just referring to any type of repeated pattern in the way your body responds?

Guest 2 [00:25:14]: Depending on how you actually kind of take that data and try and extract meaning from it, you could in some sense resemble that shape of these continuing patterns that resemble themselves over time. So in some senses, yes, it is that fractals pattern, but it's these presence or absence of these complex signals.

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Cory Nagler [00:27:36]: I'm glad that I did because they arrived in the mail that same week and didn't disappoint. I was hesitant to try them because frankly, most insoles feel bulky and rigid, but these were almost more like having a layer of super shoe foam underfoot that fit me perfectly right out of the box. I could see visually that they captured the long and narrow shape of my foot. All I had to do to use them was slip out my old insoles and then put these in. It took some getting used to the way they hug my foot, but it's pretty amazing how great they feel after swapping out the original insoles. I haven't gone back since and you kind of talked about having a hard time perhaps predicting how long runners would be able to sustain certain efforts just to pick one very specific one being your lactic threshold. I want to isolate that because a Lot of the time when people talk about how do you know you're training at lactic threshold? I hear the definition thrown around. A lot of that's the pace that you can sustain for about an hour.

Cory Nagler [00:28:35]: But if. If I understand right, you're saying that even at a specific training zone, people have very different thresholds for how long they can maintain that. I guess, one, am I interpreting that right? And then two, if that's the case, kind of flips on its head, the definition there. So how do we then use these training zones to inform the way we get better as runners?

Guest 2 [00:28:56]: That's actually a fairly big topic. I wish it was simple. There's a lot of scientists in the background arguing about what lactate threshold is and how you measure it. And that's very true. You can identify this threshold, but that may not mean you can actually sustain it for 60 minutes. So from a cycling perspective, you know, cyclists have their functional threshold power, which is the power output that they can sustain for 60 minutes. But in some cyclists, that might be, you know, above their lactate threshold, where others, it might be well below. So in runners, obviously, power is more difficult to measure.

Guest 2 [00:29:39]: You'd have to go off, or most runners go off the pace. But it's the same thing. It's your pace.

Guest [00:29:43]: You can.

Guest 2 [00:29:44]: The pace you can sustain for 60 minutes may or may not be the same as the pace at your lactate threshold.

Cory Nagler [00:29:51]: And maybe I'm going to stray a little bit from technology here, but just because I'm really interested in this training zone idea, if you have somebody who, say, can maintain lactic threshold for much longer than the average person, is that an indication that you're more fit because you can buffer lactic acid? Or does that mean you're. You're less fit because your lactic threshold is. Is not as fast? Maybe.

Guest 2 [00:30:14]: I guess that depends on what you consider to be fit versus not fit. For sure, somebody that is training for longer durations at lactate threshold will probably have a enhanced ability to sustain that intensity at lactate threshold. But their ability to run a fast 5k might not be as good as somebody else whose ability to sustain their lactate threshold is shorter. If that kind of makes sense.

Cory Nagler [00:30:45]: Yeah. And. Well, I'll. I'll say it makes sense at a high level. I'm sure we could have an entire podcast just on this one topic.

Guest [00:30:51]: Oh, absolutely. A lot of that ability to maintain lactate is also going to relate directly to the fiber composition of the muscle and how the muscle ends up adapting to the training load. That you're exerting on it over a greater period of time. So it doesn't necessarily relate to an absolutely better performance. Sometimes it can actually, it can decrease performance because it decreases that flexibility to respond to different exercise demands. Right. And it may impede your ability to change that and run in, you know, maybe a higher, a higher production stage for a short period of time when you reach a hill. Right.

Guest [00:31:37]: Which might throw you off that steady state performance. And if it does and your, your muscles have been adapted to operating at, below the lactate threshold or just at the lactate threshold for a long period of time, your ability to recover that, that energy is going to be impaired.

Cory Nagler [00:31:58]: And in that instance where say that ability is slightly impaired, but you still have say a higher VO2 max, you can take in more oxygen. Do those offset each other? Or how do you actually define whether an athlete is better? When you're in a lab context?

Guest [00:32:15]: That's, that's another can of worms, right? You get to be kind of like this economy of exercise versus the efficiency of exerc and those kind of questions which are more kind of teasing away the nuances there, like is it better to get a really good miles per gallon or is it better to have a more efficient engine which runs at a same power but uses less fuel?

Guest 2 [00:32:44]: Or just a bigger engine.

Guest [00:32:45]: Right, or just a bigger engine, yeah.

Cory Nagler [00:32:49]: So when you have Nike coming and saying, hey, we've got this great new shoe, it's going to make you 4% more efficient, what does that actually mean?

Guest [00:32:58]: So when you have that, it's essentially saying that for a given power output, you're going to travel a distance or a percentage of the distance further. Right. So for a given energy consumption, you'll be able to do more things when you have more efficiency.

Cory Nagler [00:33:20]: And is that the same as saying I can go 4% faster? Or are those two different things?

Guest [00:33:27]: Those are two different things. Now they might relate, they might relate, but they might also not.

Cory Nagler [00:33:36]: I'm, I'm so intrigued. But the way you frame that answer tells me that this could be a five hour discussion just to unpack that question.

Guest [00:33:43]: It can be, it can be because we start, you know, we start looking at the way that those things translate to tendon stretch, to the muscular activity patterns, to compliancy with the ligaments, to a whole bunch of different things which, which improve that economy. Right. A lot of the economy that you're seeing in that, in that improvement with that shoe is, is due to a reduced mechanical load from the joint. That's occurring in the foot. And that's, that's occurring due to that carbon fiber plate. That adds a little bit of, of strength there. That re. Returns a little bit more energy or it doesn't allow that energy to be evaporated within the foot.

Cory Nagler [00:34:33]: Yeah. So energy evaporated. When we say that, is that kind of what you meant when you talked about like the thicker sold shoes where you aren't actually using the energy to move forward? Is that what kind of what you mean there?

Guest [00:34:46]: Yeah. Within any closed system. Right. You're going to, you're always going to conserve the amount of energy that you've got into it. But whether that, how that energy is returned from that system will, will show you how useful that energy is. Right. So if you're, if you're running and you can return that energy really quickly in a dynamic way, that will actually improve the performance of the running. If the energy is returned really, really slowly, it won't necessarily improve the overall performance.

Guest [00:35:23]: It comes back into the system, but it's, it's the speed at which that energy is, is released back into the system which integrates into its power. Right, got it. Because power is the amount of energy over a period of time.

Cory Nagler [00:35:37]: Yeah. So I've spent a lot of time maybe harboring on these carbon plated super shoes and I think obviously a very impactful technology, but probably not the only big advancement in running or sports in general that we've seen. Are there any others that really stand out to you as interesting advancements in the sport?

Guest [00:35:57]: I think the big way, in the way that we're training again, we've known the fundamentals of training for a long period of time, but we're starting to see how all of these things that we, we used to train or think about individually, how they integrate and how sometimes the reason that we didn't see a gain is because while we, we improved one thing, something else kind of crept up that took away those grains as well. But now as we understand them a little bit more, we can kind of maximize the maximizing things and minimize the minimizing things a little bit more. So we're actually starting to reveal the true potential of some of those gains. The way that we eat, the way that we kind of look at nutrition is changing. The way that we do in space. Training is, is changing quite a bit. You can look at, you know, cyclists in the Tour de France right now, they're producing power outputs in a, in an extraordinarily tightly regulated sport was what I would assume now because of all the drug scandals that we've seen in the past and they're producing performances that are superior to what people were doing in when we knew that they were cheating. So, yeah, we're starting to see a lot of those things, especially with training and nutrition, really, really improve and we're starting to see those results carried forth.

Cory Nagler [00:37:33]: Is that something that you study yourself? Training, nutrition?

Guest [00:37:38]: No, I don't. That's, that's a peripheral side to, to my research focus, which tends to be more on muscle physiology, exercise physiology, and the integration of computers and computer technology into understanding. Although all those things work. I'm aware of those things, but that's not what I study.

Cory Nagler [00:37:59]: Can you model that in a computer? Like, is there a way to program in. Hey, X participant. They, they spaced out their carbs and protein perfectly. How is that going to impact their running performance in that 5K tomorrow?

Guest [00:38:12]: It's, it's been difficult in the past to monitor because we lack the skills to do the integration. But this is one of those areas where I think that AI is coming into the field because we've got people that are now really diligently recording when they eat and how much they eat in food apps. And we've got the same people that are potentially wearing a Garmin watch and going for a run. And those same people have a phone in their pocket. And that phone is potentially allowing us to bridge that nutrition, the timing and the sequencing and the calorie amount and density and the, the food types with that performance. And you know, those, those pieces existed before, but it was hard to link them together. We had to do very, very focused research. And then, you know, we were doing relatively small populations because it was, it, it tends to be a little bit hard to control.

Guest [00:39:19]: But now we've got AI that can basically look out, it can start to integrate those things. And the first thing it can do is it can start to look for correlations and performance or correlations within values or within data states. And correlations obviously don't cause causation. We know that from very young first year university. Hopefully everybody got that message drilled into them. But where you do see correlations, maybe they're kind of. I say to people that while we know it's not causation, maybe it's something that we should investigate why those things are correlated. Where we see this, this, this overlap and why are those two things being associated together.

Guest [00:40:07]: And sometimes those are false flags, but sometimes they're new insights into the way that the body's working.

Cory Nagler [00:40:14]: Very cool. Now, I'm kind of surprised that we're not there already because I feel like the moment I've, say, poured myself a bowl of Rice Krispies, my phone is already throwing ads at me. So I find it hard to believe we can't have it. Tell us, hey, you know, maybe you're a little bit more tired or not training as well after you've had XYZ food.

Guest [00:40:32]: I don't think that they've realized that they can start optimizing that, especially for a particular audience and giving them that information. Definitely, you know, if, if you're, if you're looking up at Rice Krispies, you're going to get lots of various Krispies related information. You know, the, we start to see that already in some of these algorithms just by the, the programmings and the information that we watch, right? People listen to particular podcasts, they listen to particular periods of times. We have listening behaviors. We're suggested new podcasts that are along the same topic, along the same ideas, maybe have the same people, maybe have the same topics, right? And we get kind of fed that information, which is, to an extent it's good, right? Sometimes it's really good to get that targeted information, but sometimes it's also really bad because again, you get trapped in a localized minima, right? Which is, could be an echo chamber of your own perceived biases, right? Yeah, I've got lots of friends that years ago or a couple years ago started really looking into ketogenic diets. And you know, how if you're in ketosis, you start metabolizing different products, right? You kind of shift away from a lot of the glucose metabolism. And we have this whole secondary energy system that runs on ketone bodies. And you know, for a couple years there, we experienced and said, hey, maybe if we give people ketone bodies, it's a way of carbohydrate sparing because we know that we can only load so much carbohydrates in this period of time.

Guest [00:42:13]: So if we give somebody a boost of ketones within a race, within an activity, maybe that's going to improve their performance because, hey, they can shut down carbohydrate metabolism for a bit and produce their energy that way. And so, you know, we start to look down that rates. But sometimes people get so convinced of that that they fail to see, okay, maybe. But then when we look at the big studies, that kind of washes out. It doesn't seem to have the effect that we were hoping it would.

Cory Nagler [00:42:45]: This is very timely because this, this episode is actually coming out two Weeks after my interview with Michael, who's the founder of Ketone iq, which are drinkable ketones. So it's interesting to get kind of the exercise physiology perspective on that type of fueling.

Guest [00:43:01]: Yeah. Have you ever tried the ketones yourself? Did you try one of the Ketone that IQ before the show?

Cory Nagler [00:43:07]: I have, yeah. They're a sponsor of ours and I've tried a couple of theirs. I even said this on the podcast. They taste terrible. They are not an easy supplement to get down, but it's an interesting feeling. It is a different type of energy than like a caffeine or curve that I haven't experienced before.

Guest [00:43:24]: Yeah. I find the mental acuity that you get from it is really quite astonishing. But, yeah, they taste like dirty feet. Yeah.

Cory Nagler [00:43:35]: Yeah. And I'm somebody who's like, experimented with beet powder. So when you have supplements that don't. Don't taste good, if they work, they work. But yeah, sometimes runners will put up with a lot.

Guest [00:43:45]: Yeah. Actually, I've got a great beet powder recipe for you if you're interested in that, where you mix it with a little bit of vanilla protein powder and then you have a little bit of honey and then you put a little bit of mushroom m there.

Cory Nagler [00:43:58]: That actually sounds pretty good. I might have to hit you up for that.

Guest [00:44:01]: It. It tastes. It tastes pretty good. Like, I was surprised. I got this beetroot powder tea to take before beds, and I compete in. In. In biathlon still, so I go and do these hard races and so I was taking that, and by itself it was terrible. But one day I thought, oh, mix it in with a little bit of my protein powder.

Guest [00:44:22]: And it was like, wow, like in protein powder, this is a whole different game. This actually tastes pretty good.

Cory Nagler [00:44:29]: I believe it. I've had some of those, like, kind of vegetable green powders, and they're definitely a lot better if you mix them with like a vanilla or chocolate protein powder than just straight up.

Guest [00:44:43]: Yeah. I always used to mix one of those with like, lemon juice and garlic and stuff like that when I got sick. Placebo effect. Right.

Cory Nagler [00:44:51]: It does sound like a natural home remedy.

Guest [00:44:54]: Yeah. The worse it tastes, the better it will do.

Cory Nagler [00:44:58]: Cody, we kind of were talking about, like, all of this kind of like measuring and starting to get more deliberate with fueling. I know that you mentioned having a track and field background, but also weightlifting, which I feel like is a sport where there's been a lot of attention for maybe a bit longer of a time around how you're feeling when and exactly what macronutrients you're taking in. Do you feel like you had earlier exposure than most to that kind of optimization of your nutrition?

Guest 2 [00:45:30]: I have no idea. I believe a lot of crazy things in the past, Right. Even as a researcher, right. You, like, you, like, you'll hear all this stuff and you're like, oh, that sounds really cool. Like, maybe. Maybe they're right. Maybe they're onto something new. Right.

Cory Nagler [00:45:46]: So you weren't taking ginger and lemon back when you were weightlifting?

Guest 2 [00:45:49]: You guys are crazy. I wasn't taking. Yeah, I wasn't mixing my own stuff in the back room. Yeah, like weightlifting and sports, where you have to cut weight. That's. That's a whole different beast. It seems like sports, you have to cut weight, they're getting a little bit more extreme, and people are willing to cut more and more weight to make weight and, you know, trying to get that weight back on. We were talking about carb loading, and, you know, it's.

Guest 2 [00:46:15]: When you carb load, like when you. When you store glycogen, you're storing it with water, and there's different ways to try and maximize that or try and get your. Your muscles refueled as quickly as possible. But, yeah, I probably can't. I wouldn't be the best person to speak on that, to be honest. I. I like to. What's worked best for me is, like.

Guest 2 [00:46:38]: And I guess I think I'm fairly fortunate this way is I. I really try to. You know, there's still a lot to be said about listening to your body. And, you know, I eat according to how I feel sometimes. It's. It's, you know, you're stressed out. You tend not to eat as much as you. As you should.

Guest 2 [00:46:57]: So I have a tendency not to eat enough, which is a little bit difficult. And your body does really weird things. You know, we have this idea that it's energy in versus energy out. But I can tell you firsthand that, like, if I cut my calories by, you know, up to, you know, 5, 600 calories a day, sometimes I won't lose any weight. But my workouts suffer massively. I just don't have the energy. You know, I bump my calories back up, 5, 6, 700 calories, my weight will still miraculously say the same. But I finally have my energy back, especially with.

Guest 2 [00:47:31]: With weightlifting and more that speed, power.

Guest [00:47:34]: I just.

Guest 2 [00:47:36]: I don't have it if I don't keep on my diet. And that's just eating. For me, it's definitely been a balance as much Carbs as I can get, simple carbs, high quality proteins and some fiber. But yeah, yeah.

Cory Nagler [00:47:54]: And I think a lot of athletes are like that where they'll kind of eat intuitively or to feel and maybe for training too, which kind of brings up sort of a debate around that training by feel versus what the data says. And I think we haven't gotten yet to a point where we've fully replaced being able to listen to your own body. But do you think there's other situations where you have to kind of make that trade off as a researcher in your field in terms of when is it optimal to make specific recommendations versus when are runners or other athletes best off just listening to their gut?

Guest 2 [00:48:30]: I, I think with a lot of the stuff that we're researching, I think there are ways to optimize above which you kind of feel. And I guess my, my biggest suggestion would be to always try it out first. Don't try to, you know, implement a new carb load strategy or you know, experimental gel or ketone supplement the day of your race. Like definitely try it out with nerves and you're starting to.

Cory Nagler [00:48:59]: We've all made that mistake.

Guest 2 [00:49:00]: Yeah, right. Especially once you like, you have a big race coming up or whatever it is, then you know, the nerves can really play into your ability to self regulate sleep and food intake. So there's definitely something to be said about using that data, keeping a log, you know, leveraging some of these models, looking at different supplements that can help optimize your performance. But so it's what might work for somebody, might not work for somebody else. And I think that's a long standing message as well. And definitely try out some of these strategies first before just going in hot, eyes closed and hoping it's going to work.

Guest [00:49:44]: I can remember doing a race where I'd just been to a series of nutrition seminars where they were talking about carb loading and carb density and things like that. And I got it in my mind that the potato was like the, the perfect transporter of carbohydrates. So I was in a competition. I was, this was when I was a cyclist and doing really long term endurance rates races. So this is about a five hour ride and I brought potatoes covered in butter in, in my pocket for, for my refeeding.

Guest 2 [00:50:19]: To the back of your bed?

Guest [00:50:22]: Yeah. And you know I had these potato slices that were just baked, packed in butter and like, oh my goodness, to get one of those things down in a race, like I was, I was gulping down a huge water bottle every Time I did it and it was just a disaster. So yeah, never do it the day before your race, no matter how compelling the evidence.

Cory Nagler [00:50:44]: What was going through your mind to think it would be a great idea to carry around a bunch of potatoes during this bike ride?

Guest [00:50:49]: I, you know that potatoes are really, really carbohydrate dense. They, they release them fairly slowly. They're, they're not a high glycemic index food. You know, I just, at that period of time I, I'd been to a seminar on potatoes essentially and potatoes is a nutritional source. And this was when I was really, really a young researcher and still competing in, in cycling. And so I thought, hey, just got the new, I've got the cutting edge research right today. I'm, I'm running with it on the weekend. That was not a good idea.

Cory Nagler [00:51:25]: So having moved past the potato phase, what is now the cutting edge fueling that, that you rely on?

Guest [00:51:33]: Oh, right now it's a mixture and of course a lot of it depends upon exercise duration and timing. And for me, like competing in biathlon and then the odd 10k race, I'm really, hopefully, I'm keeping those, those endurance events under an hour and so I'm not really too worried about replacing energy during that period of time. What I've got at the starting line will be good. You know, I might need a little bit of water or a little bit of moisture along those ways. Sometimes a little bit of dehydration is show it to improve performance. So you don't want to over hydrate. You obviously don't want to dehydrate, but in an hour that's not going to happen. So I try to optimize things like, you know, the, the beetroot powder so that I've got enough nitrates in my system to hopefully cause some vasodilation to hopefully improve my profusion of my muscles by that small little margin that they've shown that it'll do it.

Guest [00:52:35]: So I feel a little less tired, a little less fatigued. I can hit a hill or challenge something a little bit faster than I normally would and not pay the, the large repercussion of energy drops from it.

Cory Nagler [00:52:50]: So having seen that you compete in biathlons, I probably should have looked this up beforehand. But are we referring to biking and running or is this the skiing and shooting?

Guest [00:53:00]: The biking and running is a duathlon.

Cory Nagler [00:53:03]: Okay.

Guest [00:53:04]: The skiing and shooting is biathlon. That's what I'm competing in. Although I do over the course of summer I can compete in a couple of triathlons. And every once in a while a friend will goad me into doing a Spartan race or, or a duathlon. So I run.

Cory Nagler [00:53:20]: This shows you how much I know about winter sports.

Guest [00:53:22]: Come on, you're Canadian. You should. You should understand some of these. We've got, what, eight months of winter we got. We got to enjoy it somehow.

Cory Nagler [00:53:31]: You're right. I'm a hockey fan, but I can't say I've ever followed the biathlon. Maybe I should.

Guest [00:53:37]: It's. It's an exciting sport. Like, if you watch it from Eurovision getting broadcast over, it's a really, really exciting sport because there can be these huge changes in leads because you've got these athletes that are coming in super, you know, super fatigued and you're skiing really fast, but then they have to stop and they have to shoot. And if you can't, like, bring your heart rate down really fast and stop your arms from shaking because you've just been pulling so hard and like, manage that performance and you mess up your shooting, it can cost you three, four minutes. And everybody that you were, you know, 30 seconds ahead of or 45 seconds ahead of in the skiing, they're gone. And now you've got to catch up. So often in biathlon races, you'll see the lead switch, like, dynamically because of people's range, time and, and activity. It's.

Guest [00:54:34]: It's also a great sport because lots of times they integrate both the male and female athletes together in, in mixed races, so you'll men and two females. And then the way that you stack those people and their performances, like in. Throughout your race, adds another aspect of the strategy. And women are all, usually some incredible shooters and so their shooting performance is higher and it's, it's just, it's fun. You should watch it, take some time, watch it by Athlone race. They'll reward you with excitement dynamics when.

Cory Nagler [00:55:07]: You integrate men and women. Is this a relay or actually racing against each other?

Guest [00:55:12]: It's a, It's a. It's a rerated. It's a relay. But because you could run like one team could say, okay, our two women are going to ski first and our two men are going to ski the anchor leg. That's one strategy. Or it could be the men that ski first and the women that ski second. So in that situation, you have men and women on the competition field competing directly against each other.

Cory Nagler [00:55:39]: Yeah, it draws interesting parallels too, with, like, we have the mixed relays now with world athletics, and I think this is even going to be an Olympic Sport finally in 2028 and in LA. But I believe they've placed specific rules that require a certain order. But it's a cool sport to watch. When you kind of mix it up and you see massive lead changes.

Guest [00:56:03]: Yeah, it adds a little spice into the mix. And the more strategy that you can start to think about who's better at what part of the course and at what aspect of, of something and you know, psychologically, how much, how much a lead change can cause people to, to change their, their motivation. Like we were talking about before, one of the hardest things to understand when we're talking about sustainable sports is, is kind of understanding motivation in there as well. When Cody was talking about that, you know that we people can go for about 60 minutes. I, we were participating in one, one study that I was, I was doing where we had athletes and we, we were really good at predicting like how long they would be able to cycle at one particular performance. But then we introduced an incentive like within that last. These, these people were saying, you know, my rate of perceived Exertion is like 18, 19. I'm, I'm going to stop the test in a minute.

Guest [00:57:08]: And we said, how about we give you $10 for every five minutes longer that you can, you can ride for. Right? And suddenly, you know, hey, I'm, I'm a student. Ten bucks for five more minutes. I can do that. Maybe I could ride longer. And so, you know, we saw some people take away 30 bucks.

Cory Nagler [00:57:32]: It's a funny parallel when you're a broke student compared to a lot of people who don't enjoy running, who might spend 50 DOL on an Uber to avoid those five minutes of walking.

Guest [00:57:42]: Yep. I'm a big advocate for stairs, right. And every day I see people, people taking the elevator up and down. It's like, wow, you're giving away all that free exercise. What are you doing?

Cory Nagler [00:57:54]: One other interesting thing you mentioned was this idea of motivations. And I think that's interesting in terms of what you're measuring for because I think most of what we've been talking about and probably most of what you test out in a lab is how do we maximize efficiency and performance. Whereas if I go to a 5 to couch to 5k runner and I say, hey, I've got this beet powder, it's going to taste like crap, but it'll make you run insanely fast, the response is probably going to be no, that's the last thing I want. So is that something you look at too maximizing other things Whether that's maybe comfort or you know, I guess, are there any other ways beyond just getting faster?

Guest [00:58:35]: One of the things that we've seen in shoe development is that you will run the fastest in the shoe that you find most comfortable. And that that's, it's a very like comfort. It seems to always win when, when you absolutely ask people now, are people subjectively, is the comfort experience or what they're experiencing giving them that feedback that says, hey, I'm comfortable because this just feels right, because it feels right, I'm going to be fast in it, or is it they're picking up on what's making that shoe really fast for them? We don't quite quite know because those shoes can be very different for different people. Like it bounces around and what's, what's best for, for one person in terms of that. So comfort, comfort is a really, really interesting aspect and I don't know if it's a self kind of triggering of optimization. Well, this shoe feels best for me and because it feels that good and because I'm comfortable in it, maybe that shoe is doing all the right things for you. Or maybe it's just really comfortable and you love it. We haven't been able to kind of tease that out yet.

Guest 2 [00:59:46]: Yeah, I was thinking about that from a very different perspective. Like when we think of comfort, you know, there's, there's also comfort in your actual training session. Right. So at least from a research perspective, you know, if we've become more prone to have people do or say you can do high intensity interval training to get, you know, adaptations in a short amount of time, because longer periods of time our thought is thought to be more uncomfortable.

Cory Nagler [01:00:19]: Right.

Guest 2 [01:00:21]: But that's all, you know, it's very dependent upon the person. Right. If you're running at a low intensity, but for a longer duration, that might be perceived as more comfortable. And then it's the, you know, what's your environment? Are you running in a, on a treadmill in a basement by yourself? Right. Your perceived comfort might be very different than, you know, if you get a group of friends and you're doing a run club. Right. Or now if you're racing against your buddy that just got a new pair of shoes and you want to prove to him that it's his shoes aren't as good as he thinks they are. Right.

Guest 2 [01:00:51]: Your perception of the effort is totally changed. And I was actually working with somebody in University of Tokyo where that's what their whole study was on is there's just people's perception of effort and how it can change when running in a group. So that's such a big aspect is having some friends, certain situations or certain environments, even switching it up. So if you're running at the same gym over the winter all the time, you know, just going to a new venue could be very, very beneficial and very, very rewarding for small cost, bigger benefit.

Cory Nagler [01:01:32]: Is that something you can simulate in a lab, the effect of say, running with others or maybe even in the case of say, like pacing? I know a big thing on in women's sports is whether or not you have men there to pace you or others running around you. Whether that's kind of, I guess, blocking the wind or even just the feeling.

Guest 2 [01:01:49]: Of exertion, I think it's fairly difficult. So we usually go the opposite way where we try and we don't give anything that's, you know, perceived as comfortable. Right. We run in these not so nice laboratory settings. You know, there's no music allowed. You often will take away kind of motivation and we'll let the person, you know, self motivate to get through the exercise and we'll put, try and keep the conditions the same. So it's the same time of day, obviously the same laboratory. So we're, you know, we're not introducing any potentially, you know, any things that are going to confound the results because they're more motivated one day or more comfortable.

Guest [01:02:30]: We know it has a huge effect and that's why we control for all those things. Right. It's like if you're, if you're doing a really standardized VO2 max test and you say, come on, give me eye of the tiger in the last couple minutes there because, because that's just gonna, that's gonna really help me push it. We, we know that that will actually affect your performance. And so normally for research papers we remove that because we, we don't wanna see that in there, surprisingly enough, because we're trying to, to stamp out that variability. But does it affect it? Oh yeah. Oh yeah. Has a massive effect that we're running, listening to music.

Guest [01:03:10]: Right. We know that that changes the way that people push themselves or perceive the, or the effort and the energy. It can cause pacing, it can cause people to increase their pace or decrease their pace.

Cory Nagler [01:03:27]: What about something like Wavelet technologies? I know that's been a big thing, especially in track. Is that something which you think was studied or was that more an intuitive idea where somebody felt, oh, surely you can run faster if you have a technology telling you the perfect Pacing.

Guest [01:03:43]: Oh, you're going to have to tell me exactly what you're talking about because there's a number of different wavelet technologies that are kind of being introduced. There's red light therapies, there's sometimes there's blue light therapies, there's pulsed therapies, there's.

Cory Nagler [01:03:58]: Yeah. So when I'm talking about wavelet technology, I mean specifically like on the track and to help with pacing. So you'll see in a lot of the diamond leagues, for example, they'll set it to either the world record or the meet record. So you can kind of see what that pacing is and follow it along.

Guest [01:04:15]: Yeah, well, Nike did the same sort of thing when they were going for that, that two hour marathon. Right. They projected the time in front of the Kipchoge as he was running. So he knew whether he was ahead of that time or behind that time. Right. And we, we know it has, has a big motivating factor. Yeah, you know, it's great when you get that anticipation that maybe you're on a world record pace. You can sometimes push people to that extra, extra little bit of performance there.

Cory Nagler [01:04:49]: So is that something you think was studied in labs or do you think it would be really hard to mimic something like that in a controlled setting?

Guest [01:04:57]: Oh, we've definitely studied variations on that theme. Like I say, we've studied people running to music, which is a pacing strategy. We've studied people to work out specifically with a metronome. And then we set the metronome so it's faster. Right. Every time you, you touch a step, you should be hitting the ground. Right. And so, yeah, we, we've definitely done pacing strategies and, and pacing work and know that it has an effect.

Guest [01:05:27]: Again, you start running into the variations of human beings and what's best for their performance. We know that, you know, running isn't a steady state activity as much as people try to. If you look at their running periods of times, right. You'll see negative splits, especially towards the end, because people speed up. And so their pacing strategy alters. Even though they're fatigued and that they're at the most tiring part of the race, suddenly they speed themselves up. And the longer the race, the more of a pacing strategy is going to be effective in it. Right.

Guest [01:06:05]: You don't pace yourself in 100 meters.

Cory Nagler [01:06:09]: And assuming that there is no kind of elevation or changes or temperature and anything like that. Is it safe to say that even pacing is always, at least on paper, the most efficient?

Guest [01:06:21]: Even pacing? No, no, it Absolutely isn't, actually, because. Because there's lots of variations within people. We know that the best performances, like just historically, if you look at the fastest marathons ever, they've been run with a negative split. So it tells you dynamically there from the data that human beings do not like to run at an even pace and that the best performances are not achieved under those conditions.

Cory Nagler [01:06:48]: All right, so I'm gonna ask you a question that's maybe a little bit less backed in science, but knowing that a negative split is what our bodies like to do and how we run the fastest, why is it that most of us run positive splits? Because I haven't looked specifically at the data, but I think I'm probably not out of line in saying that there's a lot more runners who go out faster than they can maintain than those who are, like, really well paced and get that nice push at the end.

Guest [01:07:12]: Well, I think you hit on it right there, and that's probably because you see a lot more recreational runners that don't know where their limits are. They push themselves into some sort of state of fatigue, and then they try to survive. They're no longer trying to win, and they're trying to survive. And so once you get into survival mode, yeah, pace drops and activity drops and all those things. Right. And you see people stumbling with their knees and they're wobbling as they try to make to the finish line. Obviously, those people aren't. Aren't charging over the line.

Guest [01:07:48]: I know that was the biggest problem I had when I was a young athlete. Right. I felt so great at the beginning. I would go out, like, super, super hard, and then, you know, suffer tremendously for a very, very long time.

Cory Nagler [01:08:05]: I. I feel like you have to go through it a couple times before you learn your lesson, more than one.

Guest [01:08:11]: Would expect, especially being, you know, an intelligent human being. Hopefully, I. I've done that way more. It's just. It's so hard to combat that great feeling at the beginning. It just feels so good. It's hard to imagine that you're gonna feel so bad for so long.

Cory Nagler [01:08:30]: I know exactly what you mean. So I think it's been cool to hear about a lot of how these technologies come to form and what types of things you're testing for. Looking forward, I'm curious, are there any technologies you're actively working on or anything you're particularly excited about that we might see come to light as far as, like, technologies changing this board are concerned?

Guest [01:08:56]: Well, one of the biggest things that, you know, I. I kind of see moving in this direction. And I think we're living in unprecedented times and I, I think some people are starting to get the awareness of that right now. And what I'm talking about is, I'm talking about the effectiveness of these large language models that we have and AI and generalized AI and that, and how it's, it's revolutionizing science. The way that we do research, the speed at which we develop new technologies. It's like for, for a lot of the muscle work that I've done, I did very intricate modeling of protein structures within the muscle, specifically within, within myosin. I looked at myosin in the, the muscular lattice network and I, I tried to understand how the absolute structure of those proteins influence their function. Right.

Guest [01:09:52]: So what's, how much does the environment itself and the way that the environment is changing change the functional aspect of those proteins? And I, I did a lot of that work in computational simulation because we just, we lacked the tools to be able to see at that resolution right in nanometer, things moving and over a period of time. So when I was doing that, when I would go access the protein database, there were about, oh, there was, there was a couple thousand proteins, maybe 10,000 proteins that were list listed in the database. That the numbers is floating around in my head right now. But you know, to understand the shape of a protein was really, really a difficult process. And how the protein folded up and how it made its actual structure. And we would have to, we'd have to freeze these things and then examine them by diffraction. So we'd shine, we'd take crystallized forms of the protein, we'd shine specific types of light through it, and we'd look at how the light scattered and from the way that the slight scattered, we could reconstruct the structure. But it took a long period of time.

Guest [01:10:56]: Well, Google came in and they had this alpha fold, which was a derivative of AlphaGo, which they used to beat the world champion go player. And they kind of started to look around with it and toy around with it. And over a couple of years they managed to make one protein structure. And they said, wow, we folded our first full complete protein since that big year that they did that. They have now folded every single protein that we know of on Earth. You know, in the millions and millions of proteins, they've decoded all of these protein structures. You know, 50 years to do 20,000 and then 230 million within a year. Like the exponential growth of these models in the way that they can introduce information is, is Incredible.

Guest [01:11:48]: So what I see these models and how they're going to start to revolutionize the way that we're doing things. They can, they can understand different proteins. They, you know, there's so many different ways that we can start to use them. We can understand, like, protein structures, and then we've got these tools called crispr, which can then maybe introduce or edit genes so that they produce new protein structures or change protein structures or alter protein structures. So at a very basic level, we can do that, but then at a macro level, we have data that's sitting out there on the web that we like. There's so much data we don't know what to do with. Right. So many people have run a marathon with, with a chest strap or a watch or something like that, and that watch has recorded every single stride that they've taken and what their heart rate was during the marathon and, you know, what their speed was and what their overall marathon time was.

Guest [01:12:48]: And we potentially also have their training data leading up to that. Like I said, we might have their data on their smartphone for what they were eating, for that we might know their weight because they might be stepping on a smart scale. That smart scale might be doing bioelectrical impedance and telling you how much body fat and what their body fat ratios were. And so potentially, like, all of this data is out there, potentially, we can start using the computational power of these, these things to start just even to help us create the tools. So you can ask the model, look, I've got this enormous data set. How do I create a tool that's going to look at this? And it can help you then grab that secondary skill set that you don't have, maybe, which is Python programming. And it can help you write up a Python program that you can say, oh, I can get, I can get analysis with this, or, you know, how do all of these things relate? And, you know, the first thing is looking for correlations. We start to see correlations and then we start to dig deeper.

Guest [01:13:52]: Why are these things correlated? How are they correlated? Is it, is it just, you know, a random chance? Like, I always throw up a slide in one of my studies that says there was a study that showed that cheese consumption is extraordinarily highly correlated over this period of time with the rate of people that die by being wrapped up in their sheets at night. Right. Obviously a spurious correlation. I don't think that cheese consumption is making people to do night spins and lead to their death by suffocating in their bed sheets, but maybe There is something there that we should be looking at, right? Lots of times we don't know whether it's a spurious correlation or not or how to integrate those pieces of data together to give us a bigger picture and say, ah, I see it, it's when this is up and this is down and this is left and this is right and this has been done this, that's when you get your best performance. And I think potentially that's the next big, big frontier, right? It's performance and data and information for the average person. Because we do lots of research, right? We do, we primarily see two populations. We see, well, we see a ton of university students that are strong, healthy and fit because we're in kinesiology and we're in athletic science. And those are also the 10 people that tend to volunteer for running studies.

Guest [01:15:25]: We've got lots of data on high performance athletes, we've got lots of data on people with injuries and potentially disease platforms. But you put them all together and that's less than a tenth of the overall global population. If you look at every person that's ever been involved in a scientific research study and then you compare that number to the close to 8 billion people that we've got on this earth and we've done in depth examinations on 0.00001% of the global population, but now we have data for those people and information and what are we going to be able to do with it? Like we've got this gigantic blind spot about what ordinary people do. Recreational runners, age group runners, age group competition people, people. We really don't know what works best for them, right? Is it buying those new pair of shoes or is it losing ten pounds or is it, you know, doing something else like walking stairs, running stairs, like it might be strongly related to your stair climbing count. We don't know in the average population.

Cory Nagler [01:16:45]: So, so just tying that all up in a pretty bow and at the risk of kind of, of simplifying it too much, is the basic idea here that we're able to take all that massive amounts of data from everyday runners and actually tell them what are those meaningful? Not just correlations, but causations to actually improve their performance.

Guest [01:17:08]: Yeah, yeah. I think that the people are going to be able to start asking, well what about me? What's my data? And these things are going to be able to scrape. Maybe they even have all that data in their training data, right? These, these models are trained essentially on all the data that we see out on the world Wide Web. Well, I can tell you, every triathlon that I've ever been in is recorded on the World Wide Web. There's tons of my Strava data that's on the World Wide Web. And you know, if I ask these models about me, they can give me some pretty scarily precise information. And so I think that's going to be the, the, the benefit. It's going to be knowledge for the average person.

Guest [01:17:51]: And you know, we're gonna, we're gonna really be enriched by that as scientists, but the average person is going to be enriched by that tremendously too, because they're going to find more relevant and cogent information for them.

Cory Nagler [01:18:07]: And Cody, from your perspective, is this kind of exactly the type of proliferation of data that you're trying to achieve with wearables, or is there anything else in that mix that you see as being a huge step forward on the technology front?

Guest 2 [01:18:23]: Yeah, it's totally agree with John. And I think that's where a lot of my interests are starting to lead, is looking for opportunities and kind of the massive population that we're underserving right now. And that's kind of most people like the, you know, the more recreational runners that are asking, you know, what's best for me that are willing to wear a sports watch but aren't willing to go purchase, you know, a heart rate strap, a stride running power meter, you know, you know, a pair of $400 carbon plate shoes. Right. But they still want to know, they want, you know, I think that's where we're moving more towards and I think we can provide a better service for more of a generalized population that is looking, you know, we can do individualized training, coaching for people for cheap. I think, you know, people are willing to wear a sports watch, so we can get a lot of data from that. But in the future, I think it's going to change. You know, sports watches, I think right now John and I were discussing this is they're limited by computational power that you can put inside of that small device, but even potentially more by the battery power to operate that computational device.

Guest 2 [01:19:39]: If you're trying to run advanced AI algorithms on a watch, it's probably not going to work that well. But as we start to develop technologies that pair with a phone or maybe directly connect to cloud computing, you can run these advanced models not on the watch, but give you the metric on your watch. As we start to integrate with more advanced textiles, where these wearables are integrated into your clothing, perhaps implantations in the near future, who knows what's going to happen, but we're going to have more and more data, we're going to start integrating signals and there's going to be a lot more opportunities to understand the human body, understand kind of more of the missing or that under service group. We have high performance and we have clinical and that's where most of our research attention has been directed towards. But you know, in a lot of ways, how do we know, how do we bring somebody from the general population and move them towards high performance? What do they need to do? And alternatively, and I guess this is, this is probably more relevant to most people is if you're in that kind of recreational area, kind of middle ground, how do we prevent you from moving towards chronic disease? And I think that's a lot of areas that people are interested in is it's not just about, you know, most people, they want to run the best marathon that they can run and they're doing in a lot of senses because they want to, they want to be healthy, they want to maintain longevity, they want to live into their late years of life being physically capable and you know, how, how can we better service that group and you know, director training, prevent injury, optimize their, you know, xyz. I think there's, that's, that's kind of where my attention goes and I'm really excited about all these, all these new technologies and platforms and yeah, it's really, really cool, really exciting time.

Cory Nagler [01:21:31]: Yeah. And I think that's exactly what I was trying to get to in that idea of like, what are your motivations? Because I think as much as us here might all be hyper competitive in the broader population, it's probably a pretty small number that are actually concerned with like, like, what's my marathon time going to be compared to, like, how can I live a healthier life that's free from disease and other discomfort. So it's pretty cool to see how we're moving in the direction that data that can actually help you in that sense being available to literally everyone at your fingertips or on your wrist.

Guest [01:22:05]: Yeah. And you know, that's another thing that I bring up often is that we can run people's. We know physiologically that at some point in time we get into a state that I hate to say that we're forced into a declining performance, but to some extent that's true a lot with exercise and exercise health. We've shown that masters athletes can maintain physical parameters that we previously thought were, you know, unmaintainable. Right. Motor neuron density, like so how many nerves you've got going to your muscle. We used to think that, that, that number, because we saw it in the data for years. That number just went down as you aged.

Guest [01:22:54]: Then we started to see a whole bunch of master's athletes where that number seemed to, like, the slope of that decrease changed quite dramatically, and it became a much more gradual decrease. And so these people, if they had stayed active and were really healthy, we still have people with, you know, motor unit density in their 50s, which would, you know, parallel somebody in their 30s normally. And that's, that's an enormous improvement. And so we know that to some extent, people are going to get worse as they age, but we also know at the same point in time that not everybody ages the exact same way. Right? We always will. We'll interview the person that's just turned 102 and still has all their marbles and say, say, you know, what did you. What did you do? Like, how did you get that way? And they'll, they'll say, oh, you know, I, I had a drink of a wet rind every night and I made sure I laughed with my friends and I, I did all these things and, you know, lots of anecdotal. And then you'll talk to the next person and they'll say, I never drank a day in my life.

Guest [01:23:55]: And I, I did this, but I allowed myself one cigarette a week. And, you know, the, the results vary, but we'll start to see, like, links between. The more data that we get on people, the longer period of time, we'll see more links, we'll know more like, we already know most of the things to do. We'll know more about that. But we'll also give. Be able to give people real life projections to say, hey, look, you want to keep your cognitive performance sharp. You want to be able to play with your grandkids, you want to be healthy and happy at this age. Like, here's the things that you need to do, and I'll tell you what the magic pill is, and everybody probably listening to this podcast already knows it.

Guest [01:24:37]: It's exercise. Exercise. And intense exercise is the number one best thing that you can do for your health, for your longevity, for your mental ability, for your cognition, for your immune system, for your bones, for your muscles, for everything that works in your body. Exercise beats every drug, every other thing that we know about by a margin that's so big, like, it doesn't even. It doesn't even compare. But, you know, at the same point in time, we're seeing Obesity rates like we've never seen before, right? So how can we, how can we integrate with these people's lives and give them motivation, awareness? You know, maybe your, your watch turns red on days where you, you've been a little bit lazy. It says, hey, you're heading in the wrong direction. Maybe, you know, it kind of glows a little bit yellow when you're doing things.

Guest [01:25:30]: Okay. And maybe it goes green when you're, when you're really rocking it and you think, wow, I want to see that green dial more, more and more. And it's integrating with, you know, large computation and large models, and it's giving you that feedback. And you know what? Things are rewards to people. Like we, we also know that that technology has a dark side. The infinite scroll, right? The, the, the, the. The technology that was designed initially to, to alleviate the problem, that you just couldn't present enough information in one page, suddenly became like the most destructive technology for, for, for human beings on Earth because there's this microburst of new information every time you scroll down. Oh, there's more new things.

Guest [01:26:20]: Oh, right. And that drove bad behaviors and people staring at their phones and, you know, somebody would look up and go, I can't believe I've just spent three hours scrolling through my Facebook feed. Maybe we can use those same types of motivations and tricks and things that we understand that work that way to push people towards more exercise.

Cory Nagler [01:26:41]: I'm going to take the takeaways this overall episode to be. Don't spend three hours scrolling through Instagram. Use it to exercise and spend 90 minutes listening to a great running podcast. So. So I can't really think of a better takeaway.

Guest [01:26:53]: That's a great idea. The other thing is that there's a lot of stuff that we just to harbor on this point that we don't know about the general population. I had a friend once come in and talk to me because people come talk to me when they've got issues. He said, they saw these strange things on my ECG measurement and they're thinking this and this and this and this. And I said, first of all, just take a big breath and relax. I said, I know you. You're healthy. This all looks good.

Guest [01:27:25]: But we don't know about natural variability in human hearts a lot. In the general population. We know about six populations and we know about really healthy populations. I said, normal variations in the way that the heart beats and things. We don't know a lot because healthy people don't go to the hospital and they don't come in to do research unless they're, you know, going for something. So relax, get more information, get more diagnostics, get more stuff, figure it out. And I think with a lot of this technology, especially wearable technology and the integrated sensors in the way that they're collecting data, we're going to start to understand that normal person a lot better.

Cory Nagler [01:28:10]: That's exciting, I think for a lot of people listening who are not going to be that just average sedentary person from a medical perspective or from a training perspective. Super cool to see, to see all that data become available. John Cody, this was a great discussion. Unfortunately we only have so much time, but I want to open it up for people to be able to find your research and read more if they're interested. Where can people go and learn more either about what you're working on now or anything new coming up?

Guest [01:28:39]: Well, I have a. I have a fairly good website on the university that talks a lot about me and a lot about my research. I've also got a website that's. I have the domain name, I haven't attached it to the website yet. So the domain will be called digitalathletelabs.com and they can go there hopefully within the next couple weeks and find out all about me in our lab and the research that we're doing and how to get involved and other things.

Cory Nagler [01:29:07]: Awesome. How about you, Cody?

Guest 2 [01:29:09]: I don't have a website or anything. Unfortunately, most of my research can be found. I think as researchers we try and we have our own little social network, if you will, on ResearchGate. It's a, it's kind of like a social media platform for, you know, researchers to connect with each other and a lot of our science is available on there. You can, you can find, just search my name on that website. You can see a lot of the research I've done related to running power and heart rate variability to look at readiness to train and durability and stuff like that.

Cory Nagler [01:29:42]: Yeah, awesome. Sounds good. I'll link all of that in the show notes. And once again, John Cody, thank you so much. I'm super fascinated by technology and sports and didn't realize just how much goes into creating a training environment to make this stuff available. So. So I appreciate you both joining me on the show to talk all about it.

Guest [01:30:00]: My pleasure. I really enjoy the topic. I do it every day, so it's great to share that with a broader audience.

Guest 2 [01:30:08]: Yeah, there's not many opportunities we get to actually discuss some of the things we do and have a group of people willing to listen. So yeah, thanks for having us on. That was fun.

Cory Nagler [01:30:17]: Absolutely my pleasure. I had a lot of fun too. Thank you both and good luck with whatever research come next and can't wait to read all about it myself. Thanks for listening to the Run to the Top podcast. I'm the showrunner at Runners Connect where as always our mission is to help you become a better runner with every episode. You can connect with me on Instagram at Corey Nagler or through Strava by searching Cory Nagler. And please consider connecting with the rest of our team@runnersconnect.net if you're loving the show. You can help us reach more runners by leaving a rating on Spotify or Apple Podcast.

Cory Nagler [01:31:04]: Lastly, if you want bonus content, behind the scenes experiences with guests and premier access to contests, then consider subscribing to our newsletter by going to runnersconnect.net podcast. I'll see you on the next show, but until then happy running everyone.