[00:00:00] Speaker A: Welcome to the heart rate variability podcast. Each week we talk about heart rate variability and how it can be used to improve your overall health and wellness. Please consider the information in this podcast. For your informational use and not medical advice, please see your medical provider to apply any of the strategies outlined in this episode. Heart rate variability. Podcast is a production of optimal LLC and optimal HRV. Check us
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Welcome friends to the heart rate variability. Podcast. I am Matt and I'm here today with Boss who gave me that gift of not trying to pronounce his whole name. So I want to thank him right off the bat for that.
I came across his work around readiness and it's such an important topic and one we haven't touched on nearly, I think enough in our podcast. So when I saw his work, his research with the group that he was working with, I just like, I got to talk to this guy, dive into this research because I think we throw around the term pretty loosely in the HRV field and I like to get really scientific with Boss. So Boss, can you just give a quick introduction of yourself and maybe just kind of how you came to heart rate variability?
[00:01:26] Speaker B: Yeah, sure. So I'm a PhD researcher at Maastrick University Department of Nutrition and Movement Sciences. So that's in the Netherlands for those who don't know. And I'm an athlete myself also in addition to the research. And I guess it's motivated the whole study a bit by just being an athlete and also my background as a coach and also just as a researcher because both as a coach and athlete and as a researcher. One of the things that is just very interesting indeed is just to see what should we doing today for training, like how intense should it be, what should the volume be ideally, should I train at all actually, or should it better take a day off? So the whole concept of defining a measuring readiness of course then is very important because that might provide us some clue like can we train and if so, how hard should we be training, what volume? And one of the things we potentially might be measuring there is heart rate variability. So traditionally that has been done in resting conditions to get some idea, but more recently it had also been done during exercise and there's not a lot of data there but potentially be even more sensitive and especially with the detrend fluctuation analysis of heart rate variabilities, just like sounds very complex just for those who are listening. Just one way to analyze heart rate variability data.
So yeah, just another metric essentially that we might derive from the heart rate variability that might give us some clue into training readiness. So perhaps I'll just leave there for now and leave you to yeah, because.
[00:02:59] Speaker A: I love this topic because as an athlete myself, many, I can say decades ago at this point when I was an athlete, you just pushed yourself literally to if you didn't vomit, you weren't working hard enough. That was sort of the mentality. Whether you threw up or not, the coach would like it. If you went over the trash can and pretended and you got up the next day at 05:00 A.m. And you did it all over again.
It would have been a total different experience. Probably not just I would probably have performed better when I needed to, but I probably would have enjoyed my sport a lot more if I actually was in a healthy spot to engage in my sport. And so I would just love maybe we can start out by talking about that, the metrics, your study that I found in the AAPB journal, kind of maybe walk us through your study, your protocol, because it is a little bit different from the resting that were maybe typical. And then I'd love to get practical about how you use this as a coach as well.
[00:04:07] Speaker B: Yeah, okay, clear. So what we did in the study is we measured a few just recreational athletes, essentially, and they came to the lab twice. I think there was about a week between two of the measurements. And what they did in each of the measurements is they went on the treadmill. Then they performed first about exercise, a very low intensity exercise, let's say it was about 10 km/hour on average for these individuals, so below their first ventilatory threshold. And then after like five minutes of running at that exercise intensity, we slowly started to increase the speed of treadmill to perform so called V two max test until exhaustion. And right after that first ramp, we did another ramp to have them also being really fatigued. And that's exactly what we also repeated on the second day.
So we published two studies from those experiments. So the first one is where we looked at the between session reliability of heart rate variability metrics and also of vu two and just heart rate in general to have some comparison, like how accurate is our heart rate variability metric compared to other metrics that we could be measuring at least in a lab setting.
And in another study, we also looked at the agreement between the Detrended fluctuation analysis of heart rate variability and then if you use that to analyze the first ventilatory threshold, how well does it agree with what we can measure with the gas exchange data in a non fatigue condition? So during the first ramp and also when we are fatigued. So during the second ramp. So that's the sort of two studies that we've been doing there.
[00:05:50] Speaker A: I feel like there's a semester long course to unpack there that is really interesting, like everything that you're looking at. So I just can't hold back. What were some of the things, what are some of the findings and again maybe with that if we've got maybe the weekend warrior but maybe an athlete who wants to use heart rate variability, what are maybe some tangible things coming away. So let's talk about maybe the findings first before I get real practical about how I can implement this in my own life. So what were some of the findings that you came with?
[00:06:32] Speaker B: I will start there with the study that's not directly related to the readiness but I think that will eventually lead us to the readiness study. So that's the study where we looked at the agreement between the DPHA, so the three trend of fluctuation analysis of heart rate variability, then the first ventilatory threshold. So that's a specific value of zero point 75 that agrees with the based on previous research with the gas exchange first ventilatory threshold and we indeed found also when we measured that method with the heart rate variability and compared it with the gas exchange data, we found quite close agreement. And in fact, the agreement was within the same variability that you would have if you use, for example, the gas exchange data and have two different people determine the ventilatory threshold. So essentially you get the same information if you're not fatigued. So that's I guess quite an interesting finding because that suggests if we want to get some real time feedback on our training intensity because we also use a heart rate like a chess belt actually polar H ten so we didn't use the ECG and everything. So that's another, let's say, novel thing of our study.
So it can then potentially be used in field just you just run with a chess belt, you get an app, might even be in the future on your smartwatch. There's some computational issues there but it can be done by an app on your phone and you can just in real time see what is your training intensity relative to, well specifically for you. So are you below your first ventilatory threshold, below the second one, anywhere in between?
So that's what we then show to be quite close agreement with the gas exchange data. But this is also where the interesting part of the study comes in. There have been a few studies that also found this but they all investigated this in non fatigue conditions and that's where the second ramp comes in. So remember we had the participants perform a first ramp until vutamax. So we increased peer until they were exhausted. We gave them a short rest period just from the top of my head, again five minutes and then we performed the second ramp so they were still fatigued from the prior ramp. And then in the second ramp we again looked at the agreement between the first ventilatory threshold with the gas exchange data and the heart rate variability and there we actually saw that there was quite a large difference. So that would suggest indeed if you're not fatigued which is well in a lab setting you might have this scenario that you're not fatigued. But in field setting, I guess if you're training regularly, you might have some residual fatigue. And then it would suggest well, that method of defining your training intensity based on your heart rate variability might not be that valid because what we found in the second ramp, at the same speed, the heart rate variability so the DVA value was quite suppressed. So if you would just look at the DVA value, it would suggest you're running above your second ventilatory threshold. But the gas exchange data clearly shows you're actually just running below your VT one. So again, that shows in theory it's nice to use and if you're again not fatigued it can be used, but if you're a bit fatigued then it really becomes questionable whether it can be used to determine training intensity. So that's the disadvantage. But it also opens up actually like a door to some other avenue to use that metric because it's sensitive to fatigue. It means we can also potentially use it, of course then to assess fatigue. And therefore if we just use that metric during a training session, again, there's no training studies on this, but that's just what we suggested. If you would monitor your DVA value during a training session and you see it becomes below a critical value for a consistent period of time, you might just say, well, I've been fatiguing my body so much, this is probably sufficient fatigue to get a sufficient training adaptation. And if I go even further, I might not be well adding much in terms of training adaptations, but I might be adding a lot of, let's say, mechanical damage to my system. So in terms of injury risk, that might be not beneficial.
And of course we could use it just prior to a training session to determine training readiness because it's sensitive to fatigue. And that then brings us to the second study, of course, where we looked at the realiability, because if you want to use it prior to a training session to assess if you are fatigued and if you should potentially modify your training program, then of course we want a metric to be reliable. So essentially, if you are in exactly the same condition, so you are exactly the same level of fatigue, you have the same hydration status, the environment is exactly the same. You would, of course, expect a DVA metric to show you exactly the same value, because if it doesn't, then it's not really useful because then it's just random variability independent of your fatigue level. So that's what we then investigated in that sort of second study that you started with, where we looked at the between session variability or reliability of this DVA metric. And there we also looked at the reliability in non fatigue conditions and in fatigue conditions, and in the non fatigue conditions we showed it to be quite well exhibit, quite reasonable. I think from top, we had zero point 78 on average, like ICC intracrance correlation coefficient for those who want to know. So it's like a good level of relative agreement. And that's comparable to what we showed for the between session reliability of heart rate and Vo two. So oxygen consumption, but in the non fatigue conditions. Well, as expected, I guess we showed that the ICC values so the reliability was a bit reduced and it was a little bit lower also than what we saw for the heart rate and for the oxygen consumption. But still it showed acceptable levels of reliability.
So again, we don't know for sure if the reliability is sufficient to be actually useful for infield data, but the fact that it is acceptable according to that statistical contract suggests there might be some potential to use the DVA method in field to monitor training radars. So a lot of more details to discuss there, but I'm going to stop there to give you some chance to respond.
[00:12:57] Speaker A: The thing that pops into my brain, because with my journey with heart rate variability, it's that can it be used during a game? Can it be used, is there kind of real time during activity where it could be useful? And the answer, I think overall has been no. And this is why I really exciting to see your research, because we need someone in a consistent position, sitting, laying, whatever it might be. We need them breathing at a regular basis. We need to get a baseline there's, all those sort of even now that we have apps at optimal, it's like you've got to do this the same way every morning and you don't pay attention to your breathing, just all these variables to get you in a normal state, whatever that is with this. And so really exciting to see that there's the potential to bring this into these higher stress situations. Obviously, with athletics, it's both psychological and physical. We know both showing up in HRV. So let's say that confidence level, we do another study and it's high and you're feeling good about this, everybody likes your confidence levels, and I give you an unlimited grant to integrate this into sport.
What are we looking at here from a practicality level allowing you to dream a little bit? I know with researchers, I got to give that permission to dream. And you can say, hey, I'm dreaming here with this. But what are we looking at as you kind of look into how this could be applied or how you're applying this in the athletic arena?
[00:14:57] Speaker B: Yeah, sure. So before I start dreaming, I think it's good to note one thing that prevents us currently from dreaming too much about it. But I do think that's overcomeable. So I think we included from the top of my head, something like 26 subjects into the study. But eventually we were able to only get accurate data. So the DVA heart rate variability data from the top of my head, again 14 up to subjects, which means about half of the sample dropped out and mostly because the DFA method is very sensitive to artifacts and the heart rate variability. So if you miss one heart rate beat, then of course the time will be messed up and there are some correction methods to apply there. But again, the method is very sensitive. So that's why I think currently it's still quite tricky to apply damr, so that's good to just keep in the back of the head. But again, I think that's something that we can probably overcome, which is improvements in hardware specifications and potentially some software within a chess belt, for example, or we have multiple centers in a chess belt. So if we assume that our future chess belts are able to capture this heart rate variability with higher accuracy, then indeed I could foresee that we might be using this metric potentially to determine training readiness. As I explained before, just prior to the session we perform, let's say I'm an athlete. I'm going to do a track session. And prior to each track session, I do a warm up, probably anyway. And during that warm up, I just run three k. And during that three k, I just try and standardize the speed at which I run it and I measure my DFA value. And if I've done this, let's say for ten of my prior interval sessions, I know approximately what my normal DPA value is going to be if I didn't do any super intense training a day prior to that interval session. So I'm not too fatigued.
Like the environment is quite constant, it's not super hot or super cold or whatever. So I just know approximately my value with some variability and then I can use the value that I measure today prior to my interval session along potentially with some subjective measures like how do I feel, I could do some questionnaires for this, but also just subjectively, how do I feel? Combine this with objective data that I get from the heart rate variability and then use that information within an app, for example, to integrate what should I be doing with my training session if I feel very fatigued? My heart rate variability DFA value is very suppressed then it would perhaps suggest well, both objectively and subjectively the data is telling me it's probably not well best to do at least a very intense session. At least that's what we know. We can come back to this in a second from some heart rate variability training based studies or just to reduce the volume of the session. And if it's like very poor, I might even just completely skip the high intensity session and instead do a more low intensity session which we know has less effects on autonomic fatigue, glycogen depletion, mechanical bone damage for example. So for various reasons that might be beneficial to do in such a situation. So that's a training readiness example and we could also just apply it, as explained before, during a training session.
Again, there's no research so far, but potentially, again, I'm dreaming a bit. We have done quite a few studies in the future where we just show if you train until your DVA value has dropped until a certain threshold, we see certain training adaptations, but for a group that train even harder. So the DCA value dropped below that value. At the end of training session. We don't see any additional training effects, or they're very marginal, but we do see a higher risk of injury, for example, or higher overtraining effects. So that's how we then potentially might also be using such a value to guide a bit how much fatigue, systemic fatigue, have we been inducing onto the body. And perhaps that's sufficient for this training session. Or perhaps I'm super fresh today, I cope well with it. I can actually do another run, at least according to just the HRV metric.
[00:19:12] Speaker A: And I wonder as you start, because it's one thing for us to nerd out about this and get into this, I wonder as you talk to or as you think about relaying this to coaches or athletes, oftentimes from different generations. So I think old folks like me, you come in and say oh, Matt just needs to lay off a little bit because his HRV is not know. I just know some people would no, no, we're pushing it today because we push it every day that know, one of the things I think is interesting for folks like us is how do we talk about this to people, not all of which have maybe even heard the term heart rate variability with that. So I'm wondering, as you think about communicating this in a practical way, how open are people, how do you get that message across maybe especially to people who never really maybe have heard the term heart rate variability but aren't really up to speed on all the research that's out there.
[00:20:28] Speaker B: So what we know from the research on, let's say, technology adoption there in both users and coach is that we see that people that are just starting with running, for example, they typically use, let's say, very simple watch or app that just tracks their distance, for example, and their time and speed. But if they get more interested and more experience into training, then indeed we see they also get interested in using other metrics such as some kind of biomechanical loading, but also physiological data such as heart rate, heart rate variability. And I think that's the group that might be interested in such metrics and especially also of course, the well better trained athletes because they can integrate all that information they get from different apps systems to see how intense should I be training. And I think that's also then where I would see the utility of such a metrics just to combine it with other metrics such as, again, simply subjective, how are you feeling? And then you have some objective data to also tell you what should I be doing? And I think a key advantage potentially is there for coaches also, because if you ask athletes, how do you feel? Some athletes might just say, well, I feel great today because they want to train, because they think if I train today, I'm going to improve. But if then the objective data is telling you, well, actually this athlete is not really fit, then it might at least give you some extra opportunity to keep questioning, do you really feel fit? And then explain them to them, well, if you're really not fit, it might actually be better not to train that hard. It's not going to make any difference in terms of selection for a team at the competition this weekend. So I'm not sure if that answers your question, but I hope that gives some ideas on where to apply it as well.
[00:22:14] Speaker A: Absolutely. I wonder too, because one of the big I think the big messages we try to send is an increased risk of injury.
That's something you often hear in this readiness, especially when it comes to athletes. Injury and illness are kind of the mantras that you hear that if you have a low HRV, that is the risk factors that can happen. And I heard kind of that coming from you as well. So again, there's science behind injury and illness. Even though I think it gets repeated so much, we just kind of throw it out there.
One of the questions I don't think a lot of people ask is, okay, let's say my HRV is trash today. I went out drinking last night, I've eaten a whole bunch of inflammatory foods. Nothing I've done is well. So I show up to practice, my HRV is in the toilet.
Even with that, why am I more at risk potentially for injury? Because I would assume my legs would still work. Like my legs should work, my butt should still work, like my butt works, all those things. Maybe cognitively, if I'm playing basketball or need to be cognitively engaged, I can see why. But why is that increase of risk there? Like I said, it's a question I think we all just assume is true without kind of asking experts like you is, okay, how's my leg or other parts get injured just because my heart rate variability is in the toilet today?
[00:23:58] Speaker B: Yeah, that's a good question. So if we assume again that we have that ideal metric that perfectly reflects fatigue, systemic fatigue, then we could argue that indeed heart rate variability then reflects systemic fatigue. And if we have systemic fatigue, that means we know from some research that, for example, if you have fatigue due to sleep deprivation, that reaction time decreases.
We also know that muscle, especially rapid force development. And muscles also tend to decrease when you're fatigued. And there are several mechanisms by which that could increase injury risk. So if we look at, let's say, distance running, if you have muscles that are fatigued, the muscles for some lower limb segments, for example, this is getting a bit more into the biomechanics. But if you run, you have certain forces acting on your bones, on your tendons, and let's take bones as the example there you have so called bending moments on, for example, the tibia. So essentially it means that your bone, each time you hit the ground, it's being bent a little bit. And we know that this bending force causes just a little bit of micro damage and it's not a problem. But if you just keep running, if you do a marathon, you have quite a lot of damage accumulated. And if you do that the next day, more damage. Now, what's important part there is that muscles can actually counteract a part of these bending forces. But if your muscles are fatigued, then of course that means these bending forces will be greater because we have less of the muscle forces contributing to the other direction. And then that's one of the, let's say, more mechanical reasons why fatigue might increase injury risk. And then it could be other factors, like I explained before, this reaction time, which might be a bit more relevant in, let's say, high intensity sports situations, agility, where you're quickly changing direction, not a mechanism if you have eaten, let's say, bad food qualities, you haven't really refilled your glycogen storages. There is also some research that if you then exercise the day after, you have higher turnover of bone damage markers. So again, suggest that you're inducing in some way more damage to your bones. So that just various reasons, both, let's say, mechanical and physiological that can explain why fatigue in general that might not be reflected by the heart rate variability can increase your injury risk if you're exercising especially high intensity when you're fatigued.
[00:26:24] Speaker A: Awesome. Let me throw out just a scenario here.
Even though I have an athletic background, I came to heart rate variability through the mental health space as a measure of that sort of fatigue.
Let me give you a scenario because maybe we're hitting something that we need to be talking more about in our field is let's say we got that athlete who's going to go through your assessment. That two stage assessments. But right before, and I don't know how you would measure this in a laboratory, I get in a horrible fight with my spouse. So I've got in other words, psychological, let's not call it trauma, but I'm psychologically distressed as I enter your laboratory well controlled setting. We know that fight will probably lower my heart rate variability as well. We've got all that research from the mental health space and I'm going to make a jump from your research because just for the audience, I'm not saying you're saying this.
Will we get to the point where we just are separating the physical fatigue from any of the mental health components? And I'm trying to think about a sport which I'm just going to go to arena I know nothing really about, which is like track and field where you're just running and I know their strategy, so please don't hear but you're just like you got to sprint, that's what you got to do. Does that fight with the spouse?
Does that impact your data anyway? Is that going to be carried over to the physical performance or is just that exertion getting it out of your system in a positive way? So I guess I'm trying to think about this metric that kind of measures everything but can also be impacted by things. Do you think we'll start to separate the mental and the physical in some way or do you think about that differently in your work?
[00:28:30] Speaker B: No, that's a very good point. So indeed there's quite a few studies showing that heart rate variability is just sensitive to essentially just a lot of things. Indeed, like you mentioned, psychological stress, physical stress, even just slight dehydration, there's a lot of factors that can impact the heart rate variability which again, it's sort of a good thing because it means it's sensitive also to a lot of these stressors. Because again, psychological stress is a stressor, physical stress is a stressor. But in some situations like the one you're describing now, I guess we can argue that your physical, let's say your muscles, tendons, bones are really impacted by just having a conversation with your spouse or whatever. But mentally, of course, you might not be prepared that well and that's also then being reflected in your heart rate variability. So there I think it's good to again combine for example, heart rate variability with both subjective and potentially even other objective measures. So then you can better differentiate between your heart referring ability is low, but this data says it's high, this data says it's high, this data says it's high. As a coach you can then ask, gives you an opportunity to ask do you know what happened that might explain why this is lower. And then indeed if that's the reason, then you can argue, well, okay, if you're feeling mentally ready to still perform well or anyway to just do a training session, well, we can just go on because I don't see any, let's say physiological reason, muscle, bone, tendon reason why you wouldn't be able to perform.
So that can still get you that training session. But if you only have the heart rate variability data, then of course that's getting a bit more tricky. But as long as you just are aware that it's sensitive to a lot of these metrics, then even as an athlete or a coach, then you can try and reason why might it be. Lower. And if you then have explanation, okay, it's just probably this part which doesn't impact my training. I can just continue training. Or it's actually this part which might be relevant actually for my injury risk or for my performance ability, that it actually might be good to adjust my training a bit.
[00:30:37] Speaker A: Interesting. That's where I just could sort of wonder, because I seen this on the other side as well, too, is, hey, from a mental health perspective, you doing a workout, getting a jog in, great for your mental health as well. But knowing afterwards your HRV is probably going to be lower, probably doesn't mean your cognitive performance is going to decrease. It might actually increase because you've got a good workout in. So it's those tricky gray areas with heart rate variability that I think your work and others are really like, okay, what exactly are we measuring? How do we support this with those subjective measures as well? Because there's the limitation of I think whether I'm doing it for mental health or you're doing it more for athletic performance, that there are all these variables that come in, which is a good thing. But again, we've got to kind of at least sparse those a little bit in some ways, which I think is the tricky thing that a watch might not be able to always do for you, even if that watch is getting really good as we go on.
[00:31:47] Speaker B: Yeah, absolutely. I guess the key is with whatever data you're collecting, you still have to give context to the data. And the context is king, essentially. So that lets you know, well, is this good? Is this expected? If not, can I explain it? And what does that mean for my training? Or can I train? Shouldn't I train, adjust intensity, volume, et cetera.
[00:32:09] Speaker A: And I wonder how as you look at we've talked primarily about training, but let's think about then the performance, how to get people performing and showing up to a game or a meet at peak performance. How do you see your science? Obviously, training is going to be a big part of that, but leading up to a peak performance event, how can we start to use some of the great data that you're collecting to help ensure that, hey, on Thursday, on Monday I need to train. Well, on Thursday I need to be my best self. How do you start to utilize some of this great work that you're doing to help me show up in my best state for the game that I have or the meet that I have on Thursday?
[00:33:03] Speaker B: Yeah, that's another interesting question. So I guess there are two parts to it we could use again, the heart rate variability prior to the session on Monday just to see how well, how intense should that session be. And we might then adjust the training intensity a little bit. And again, there is some research also, at least when heart rate variability is measured in resting conditions that if you compare it to just doing training as planned, then doing training based on heart rate variability tends to increase certain measures of performance and physiological outcomes.
So that's one way Mitri might use it. Again, as I explained before, second way could at least in the future be to just see how intense, how far should we be going during that session on Monday. So should we get the DVA value all the way to the bottom? Or should we say, well, we have a race on Thursday, so actually you want to stop at a certain level of systemic fatigue because I don't want to fatigue myself too much. And then how you could use it potentially during the days after is there is some work, again, measured with resting heart rate variability, just case studies. So it's not really strong, but I now have to remember, try and delve deep into my head, but I think they've been looking at the ratio between heart rate variability, so not necessarily DCA, but just some Rssmde and just normal heart rate to see how well someone is coping with a certain taper period, for example. And that might not help you directly too much during that specific taper period, but it can help you potentially in the future to see, well, that taping period, everything went well. So I might try and use that same taper period for this particular event because my sort of whole level of systemic fatigue or all measures that I was measuring there were suggestive of being beneficial or perhaps actually not beneficial because the ratio was not that beneficial. So that's another way, I guess, by which we could at least in theory, apply such a metric.
[00:35:05] Speaker A: Great. Let me just ask you about a word that I'm hearing, and there might not be a whole lot to add here as you talk about really that the readiness piece. How do you think about recovery? I'm hearing this in what you say, so I just want to make sure that I'm not leaving any stone unturned here. How do you see your research informing the recovery process or just kind of how HRV has helped you think about, I did have the big game on Thursday. I've got another game next Thursday. Recovery is part of readiness. If there's just anything we haven't kind of covered there with that.
[00:35:46] Speaker B: Yeah, I guess it's indeed sort of two sides of the same coin. So there, again, if you have something planned, again, if you were just focusing on the DFA now, you could use again the DFA value during a low intensity bout just to see, am I recovered well enough? So is my trading readiness essentially again, the same sides of two sides, the same coin. So is that sufficient to do another training session or might I actually want to wait until the next day or just reduce the intensity a bit? Just to ensure at least from a systemic, let's say more cardiovascular perspective, which I guess it's primarily reflective of, am I recovered well enough? So perhaps again, good to add there just a little bit.
As I just mentioned, of course we're not really, I guess, at least reflecting or the heart rate variability not really reflecting recovery of, let's say, bones and tendons. That well, would be my expectation at least. So that's always good to just keep in mind that what are we really measuring? And if the heart rate variability suggests we are fully recovered, does that mean the same for bones, for tendons, and also the other way around? Of course.
[00:37:00] Speaker A: Awesome. So as you look at moving you mentioned this a little bit already, but I want to make sure we don't leave this unexplored either. But looking at metrics, I've been on this kick and people who've been listening to this kind of have gotten that we haven't really created a whole lot of new HRV metrics in the last 30 years or so.
There's got to be more we can do. And then we've got the commercial grade products who give you a readiness score and they may generally tell you how they get it, but they're not going to release their algorithms to you. So we've got things happening in black boxes which make it kind of hard to study if they're not going to give us the exact things they're doing. So I just like as you look at this and as kind of we talk about maybe some of the limitations of are we measuring physical or psychological and currently we kind of are measuring all of it kind of how you're looking at the existing metrics. Is there any way this kind of the Matt question of the month right now anyways, that you see, hey, there may be new ground that we can start to explore here to really get better metrics of physical recovery or readiness as we go forward. But as somebody who's done a deep dive into this, I'd love to just get your impressions about the current metrics limitations and where we might is there any room for growth in this arena?
[00:38:31] Speaker B: Yeah, perhaps. I guess. One thing that might be interesting to discuss in relation to this specifically, so that's also one of the reasons why we use the DVA metric as compared to some other metrics is that if you look at most other metrics that you can derive from the heart rate variability. They tend to be very suppressed already at low exercise, intensities or just to reach an idea. So to stop at a certain exercise intensity or some of the other metrics might first require you to come to a lab and then you have to first do a maximum exercise test to get your sort of maximum, your lowest or either highest sort of heart rate variability metric. And then you can use some scaling to just see. Well, this would be if we're talking about exercise intensity, my VT one, my VT two, and the reason why we use D three and the fluctuation analysis is that it doesn't require a prior maximum exercise test and it scales also nicely over the full range of exercise intensity. So again, that's why it's really easy, at least in theory, again applicable to apply in practice, because you can just put on a chess belt, look on an app. There are many apps already available to compute a metric and just see in real time your DPHA value after you've been running like for two minutes or something, just to get stable heart rate value. So I guess that's one of the key advantages of the Detrended fluctuation analysis at least for determining exercise intensity. So if we look more onto, let's say, the recovery readiness side there, I guess you would need to compare different heart rate variability metrics with the detrend of fluctuation analysis to see which one would be most beneficial. And to my knowledge, it hasn't been done during exercise. There have been some studies comparing this during resting conditions and then if I remember correctly, typically they use the root mean squared of sustained differences as well, the sort of baseline measure. And that seems to be quite reflective of exercise readiness recovery there. But I wouldn't be able to say based on the current data if that's the same, if that would be better than deep friend of fluctuation analysis during exercise, if the other one would be better. I guess we really need a study to just answer that question, unfortunately.
[00:40:50] Speaker A: Well, I mean, in some ways this is what I love about heart rate variability is we hit those questions fairly quickly and folks like you take us another step.
Sometimes I think, like, your research sparked like 20 new questions that I have, probably. Yeah, so we hit this and the good thing I imagine for somebody like you is you're never going to run out of research questions in a career with where it's kind of the fun being and instill what's relatively a new field. I know it's been around for decades now, but I think with the technology that you're mentioning that we utilize, we can ask whole new questions and put HRV out there on the practice field, on the treadmill and get some new data, which I'm just excited. But there's, like, all these questions that run up against our wall of knowledge, which to me is a really exciting field to be in.
[00:41:48] Speaker B: Yeah, but you're correct. I mean, the field has been mostly centered around just resting heart rate variability initially and just different metrics to assess heart rate variability, which has been, well, let's say fairly established by now. And then they moved on a little bit to just using heart rate variability in resting conditions to determine training radiance. And then at parallel, there've been quite a few studies looking at heart rate variability metrics to determine exercise intensity. And it's just more recently that indeed the DFA method has been used in relation to exercise intensity and then even more recently to look at the DVA in relation to, let's say, just overall systemic fatigue recovery and training readiness. So now it's indeed just the next part is to combine all that to do it preferably also in field because now we have chess belts that we can use in contrast to just an ECG in a lab setting. So that's still useful, of course, but it just opens up a lot new avenues for different research questions.
[00:42:48] Speaker A: Well, and I appreciate your work for taking us a step forward. That's the fun thing about being a part of this podcast is, okay, we hit a wall and now boss is looking at, okay, what more information, how can we push that wall a little further out with new knowledge? And just really exciting to talk to you. And I got to ask you my kind of standard innovator question as we sort of wrap up here is, where do you see us? 510, 15 years from now, assuming technology continues to evolve, that we're pushing that wall of knowledge further out as we gain more and more information, as one of those people pushing that knowledge further, technology is improving. Again, I'll allow you to dream a little bit here knowing we're looking in the future, where are you going to be pushing these boundaries in the future? What's your kind of wish list of future studies? Where do you think we'll be implementing this into sports? Again, think 1015 years out with where you see the field going.
[00:44:03] Speaker B: Yeah, sure. So where I would like the field seen going, and I guess that's also what I'm working on. So is to have some wearable ideally because again, we don't want to be bound to the lab is that tells us ideally, how intense and long should we be exercising today to get optimal health or performance. And both from, let's say, I always see it as a sort of cardiovascular perspective and a mechanical perspective. So the cardiovascular one could be something where we use heart rate variability and just subjective measures to tell us how fatigued am I today. So that's what we've been discussing a lot today and in parallel what I've been working on. So that's work that should be out quite soon as well is we've been collaborating as part of my PhD with a company in Aintover Atagir, it's called. They develop pressure sensitive insoles that you can just put in your shoes, running shoes, for example. And what we did in the lab is we trained with machine learning. We used to predict the load on the patella femoral joint. So let's say your knee on your TBR, so your chin and your Achilles tendon. And we are able to quantify with a reasonable accuracy it won't mention any numbers for now until it's published, but the loads and also sort of proxies of damage on these different tissues. So we don't know exactly yet if you have a certain level, is that going to be too much for you or is that still safe? But that's why I think we can go in the next ten or 15 years. So at least we now have a sort of wearable that can monitor loading on different mechanical loading and different tissues. And then if sufficient people use that something like such a wearable and get injured also and then tell the app, I got injured. Then we start to know at which thresholds do people get injured? And then we can, in real time, start to provide feedback based on both mechanical loading to prevent bone tendon injuries and also on again. I'm just calling cardiovascular loading to just optimize. Also the training stimulus that we might need to improve feed to Max running economy while also at the same time minimizing injury risk. So that's where I hope I can contribute just a little bit to in the next few years.
[00:46:26] Speaker A: I love an incredibly specific research description for this answer.
I think you won the prize on my final question because I love that and I love the idea. Mean, I've just been playing around with what can Chat, GBT and others know with all this information in a way where it's for Boss or it's for Matt. Knowing our demographics, our history, maybe our morning HRV score. And bringing all these different things in is what we're going to learn about human performance and what sort of individualized feedback, which I think has been such a limiting factor as somebody who's tried to figure out how do we give feedback to people in a halfway useful way, is like the error is on. Give them their score, give them a score, the higher the better. But we're getting to the point where we can just give so much more in depth feedback and I love that what you're doing with those insoles is so exciting because that just gives you that other huge data point to work with as well, which is really fascinating.
[00:47:39] Speaker B: Yeah, I also like integrating all these different fields essentially, so it's nice to be in a position where I am currently awesome.
[00:47:48] Speaker A: Well, I'm going to leave you an open invitation when you do get published on that next piece of research. Love to have you back on the show and talk about your insights that you've gained from that and hope to continue to have you back over the years to keep an eye. Because, again, like I said, it's just so fun to talk with people that are pushing that knowledge and just how we use heart rate variability and expanding that and asking some of these really again, questions that hit us back up against that wall of where hopefully our knowledge will expand in the future. So I just want to thank you for your work and just an open invitation to come back and continue to share your research as you progress as well.
[00:48:33] Speaker B: We'll do this for sure.
[00:48:35] Speaker A: Awesome. Well, as always we will put information about Boss in the show
[email protected]. Boss, thank you so much for joining us and really fascinating when I read the article and I'm going to be obsessing about this conversation for weeks to come. So thank you for my current obsession. I really appreciate you and your work.
[00:48:55] Speaker B: Yeah, thanks a lot for the invite.
