Episode Transcript
[00:00:24] Speaker A: Welcome, everybody, to yet another episode of Shitcode Talks. Today we are going to get some insights on the topic regarding heart rate variability. And I have brought today one of the leading experts in the field, Marco Altini. I think that most of you know quite well, Marco, if that's not the case. Marco is a PhD in data science. He also has a master's degree in high performance sports coaching and in computer science. He's a very proficient researcher in data science, especially related to sports. And many of you will know him because he's the founder of HRV for training this famous app which most of you are using to measure your heart rate variability. Many thanks, Marco, for accepting the invitation.
[00:01:15] Speaker B: Thank you. Thank you, Sebastian. Pleasure to be here.
[00:01:18] Speaker A: Okay, so, first of all, I would like to, you know, talk a little bit about HRV, like a short introduction. Could you explain us what is HRV and its relationship with the autonomous nervous system?
[00:01:38] Speaker B: Yeah, for sure. So when we talk about HRV, we are referring to the variability between heartbeats. So as the heart is beating, there's always some differences in time between the time that you have between consecutive bits, right? So your heart is beating, let's say, at 60 beats per minute, and still you don't have a bit. Exactly. Every second. There is some variability in the time we can detect heartbeats, and that is something that we can quantify over a period of time. Let's say a minute at least. If we measure our HRV in the morning, the protocols may be something we can talk about later, but still over a period of time, we look at this variability in heartbeats. And that is what we call heart rate variability. And the reason why we look at that is, as you mentioned, it's linked with the autonomic nervous system. And in particular, the fact that this variability is not random or due to chance, but it's actually linked to how the body responds to stressors. So as we face stress, any form of stress, we have a response of the body in terms of the autonomic nervous system, which typically increases in sympathetic activity, which is what we can call also the fight or flight or, you know, we need to use resources. And that is typically associated to an increased heart rate, for example, something that is obvious if we exercise or do anything with physical activity involved.
This response also typically brings a reduction in parasympathetic activity, which is the other part of the autonomic nervous system. And that is what is normally associated to rest recovery. So as the autonomic nervous system changes its function, let's say, to cope with the stressors that we are facing, it has an influence on heart rhythm. So basically, as we said, heart rate increases a bit, but also the variability changes. So when heart rate is higher, the variability is lower. So heart rate is a bit more constant. While when we are rested, we are relaxed and the biosympathetic activity is higher, then typically we have more variability between heartbeats. So since we cannot really measure stress and we cannot measure the outcome nervous system either, we can look at heart rate variability as a proxy of the stress response. Because as we face a stressor, the autonomic nervous system will impact heart activity and heart rhythm. And that is something we can measure. So we measure heart rate variability as a marker of the stress response.
[00:04:31] Speaker A: Okay, Marco, you have talked here about different stressors. I guess we have many different stressors that can impact our hydrogen variability. Some of them may be the food you eat or the alcohol you consume or the sleep you get. Could you summarize the main stressors that can impact our hrV, specifically when we are talking about athletes?
[00:04:57] Speaker B: Yeah, yeah. Well, I would say the good thing, and maybe also the bad thing about heart rate variability is that it is sensitive to many stressors. I would say basically all stressors because it's just the body's stress response that impacts quadrant variability. But it's not specific of anything. So typically if we have a negative stress response, and maybe that's also something we can clarify first, just a second, meaning that when we look at HIV and we see for example, a suppression in HIV that indicates more stress. If we measure according to best practices and good protocols, then what we are capturing there is the stress response. It's not the stressor itself. I think that is why it is useful. If I experience stress today and tomorrow morning I measure my hiv, I don't expect it to be low because today I did high intensity exercise. If my body has responded well in these hours and also through sleep, then I expect HIV again to be normal tomorrow. So it is not the stressor because we already know the stressor. We applied with training or other things, but it's really the response. And that is why it is interesting. If tomorrow it is suppressed, then maybe today I overdo it a bit. Maybe I was, you know, something doing a workout that is too much for my current fitness level or things like that. So that is something also, I think important to understand. It's really about the response and it's about cumulative stress. Also, I mentioned high intensity training, but as I said, it could be nutrition, it could be sleep. It could be alcohol intake, could be sickness, could be travel.
That could also be common for athletes. I think maybe the relationship with sleep is particularly interesting because it's a bit different with respect to other stressors, meaning that it goes in both directions, right? So if tonight I am, let's say, my hiv is suppressed, meaning simply that I am particularly stressed, or that even just psychologically or mentally due to work or other worries, or maybe even in a positive way, I might be particularly excited. Maybe as an athlete I had a race or I had would work out and they did it late in the day, in the evening, at a game or something like that. That more excited state, and let's call it this way, which is typically associated with slightly increased heart rate and more suppressed hiv, is also able to impact sleep in a way that we might not sleep as well as we would if we were in a more relaxed state, if we were going to bad breath with them, let's say, call it a high error.
Now the relationship is also in the other direction, meaning that as we sleep, if we sleep well and recover, then there is also a larger difference in our hiv in the night with respect to the day. So we might have it much higher in the night because again we are recovering, we are resting, we are sleeping, we are in a highly parasympathetic state. And then when we wake up in the morning, we might be in a better state also to deal with the stressors of that day. While if ours need disrupted or poor or not good, then it will likely impact also, for example, the HIV of the morning after, which might be a reflection of our inability to deal with stressors in the same way. So there is a more complex relationship there. Of course, a sleep is key process each day of our life. So it's important to try to basically prioritize that in a way that we can get a restorative sleep and possibly also be better prepared to deal with the stressors of the day after. And then Chevy. Also there can be a merger of how this process is going in terms of delta stressors. Typically it's easy to see acute stressors in HIV data, day to day changes. For example, again, I'm getting sick or I'm trembling or yes, I mean, anything else that is a negative stress for us can be seen in the data in the short term, in the longer term, things a bit more complex. But I think also there can be maybe even a better use. Maybe it's a particularly difficult period in work or other things, right? Nothing. Every athlete is professional. They can dedicate themselves fully to the sport. Even professionals, of course, have other stressors that are not just sport related or training related, or they might be even due to training or to the profession outside of the training itself, for example, being busy with contracts or sponsorships or other concerns in terms of financing your life as a professional athlete and things like that. All of these things might build up a bit slowly over time in a way that impact your data a bit more chronically. And so even in that sense, not just a day to day acute stressors, but chronically over longer periods of time, we might be able to see how we are responding to certain stressors and hopefully make some adjustments.
[00:10:03] Speaker A: Which one would be in your experience, the factor that impacts HRV the most.
[00:10:11] Speaker B: Well, acutely, typically, is factors that hopefully are quite rare, like sickness. You would have a much larger impact with respect to even training of high intensity.
Also, I think a lot to do with changes in HIV is the context, right? So if we talk about an athlete, a professional athlete that has a certain periodization in training over the year and typically knows what they are doing, right? So the stimulus is appropriate to the training phase, to the current treatments and things like that, even when they train really hard, typically we don't expect CF suppression. So the day after things should go again within the normal range. So only unexpected things tend to show up in the data. For example, again, maybe we travel or we eat a bit differently, or some no training related stressors, or again, sometimes we get sick for one reason or the other. So these things tend to be in the data more than the training itself. And I think that's why it's also useful because we know a lot about training, especially when you work with professionals.
You might also know a lot about their response if you've been following them for a long time. But there's always different factors and different things that can impact our physiology and that having a marker of your response that is sensitive to, let's say, overall stress, not just to training. I think it can be insightful, even though when things go wrong, you always have to bring up that context to try to understand what is happening and what might be causing this change in the data that you don't expect, because the data alone will not be able to tell you.
[00:11:57] Speaker A: Okay. Nowadays most people are measuring HRV with apps such as the one designed by you, HRV for training either with hard drive strap or with the camera on their phone. But I would like to know the details of the measurements, such as the frequency, the best duration, and also the conditions, the preferred conditions at which you will have to measure to obtain the best results.
[00:12:25] Speaker B: Yeah. So the thing about HIV is that it is something that is linked to the stress response, as we were saying at the beginning. But that is the case only under certain circumstances, because what we see a lot today is also that this relationship is being extrapolated in a way that doesn't always reflect what is really happening in the module or how we know we can use the data.
I will try to clarify with some examples. So if we measure HRV according to good protocols, and that would be either first thing in the morning or during the night, then we can capture this stress response. And there are some differences in these two protocols that we can discuss briefly later. But still, what we measure in these conditions, typically we measure far from other stressors, far from confounding factors. We can do it in a similar way every day, and that makes the data consistent across days, which allows us to basically capture how the data changes in response to stressors and to capture our stress response.
That is not the case. If we take measurements at other random times, for example, if I'm talking, then this is also disrupting my breathing a bit, and it will impact the, the data in a way that doesn't even reflect stress. And all sorts of things will impact the data in ways that doesn't reflect stress. For example, even if I just drink water, and this is even a dose response relationship with the more water I drink, the higher would be my HRV in the next hour, hour and a half. But that has nothing to do with my recovery and stress response. That is why I think it's not really possible to use HRV outside of this very controlled settings in a meaningful way. Even in research, when we do studies where we look at HRV, for example, before and after exercise, to look at the impact of exercise of different intensities, some of the research that was done, even cider about maybe 20 years ago, almost some of the actually most insightful research about HIV and how to use HRV for training comes from these studies where people measure before training and after training. And something that is so simple is actually almost impossible to do it outside of the lab because there is so many confounding factors that nobody in their life can end the training and then basically not do anything for the next two, 3 hours because you cannot shower, you cannot eat, you cannot drink, you have to sit, you cannot talk, you cannot move. And then we measure out your HIV changes over this period of time to understand the object and that is just not possible for practical reasons to do it. And if we still measure, like with wearable devices, then measure all the time. We just collect noise, because any sort of thing that we do will disrupt the data in a way that is not representative of our stress response. So unfortunately, this link between parasympathetic activity stress and HRV holds true only under certain conditions. And it is now being used in all sorts of situations in which doesn't really hold true. So that is a bit of an issue that we have with technology today. But if we were to really try to use this technology properly in the context of monitoring our stress response or working with other athletes, my recommendation would be either you measure first thing in the morning or you measure in the night. If you want to use a device that as your agent, sleep. The main differences there are that in the night, basically you can measure.
You end up measuring a bit closer to the stressors, right? Because the night comes before the morning. So if you exercise in the evening, or if you had some ankle in the evening, or if you ate a late dinner, all of these things might show up in the daytime. That might look like there is more stress in the body. Even though that stress is basically irrelevant in many situations because you are not looking at the response anymore. You're just looking at data collected too soon after the stressor. And it is perfectly normal that it takes time for the body to renormalize. After we have done a number of different things in the morning, after the restorative effect of sleep, I believe it is a better time. And we can also use a measurement in a different position. For example, you are not constrained to measuring while lying down, which is of course what you do when you sleep. If you measure when you sit up, typically you introduce a bit of stress called the orthostatic stressor. Basically, you sit up, your body has to readjust. And that if you measure within a short time frame from sitting up, that allows you to capture a bit better the stress response. Basically it is amplified, right? If you actually are sick and you can you try to do that, you will see that your heart rate after sitting up is a lot higher than it is when you're lying down. And you know that amplification of the stress response is what allows you to also to capture in a more sensitive way how you're responding to training and other more subtle stressors. So I think that is another advantage of doing it first thing in the morning. Obviously, this requires that you do something as opposed to just wearing a device so that is not always possible. Maybe there are situations in which, from a practical point of view, it's not possible. Maybe you have, I don't know, young children or anything that makes your morning routine a lot more hectic, and then it's just not possible to measure that way. So you might want to use a device, assuming you sleep through the night, because if you're also awake during the night to attend the same young children, then that also doesn't work. So I think practical considerations are always important and we need to see what works best for our lifestyle, for our routine. But there are some differences that I tried to highlight there. Once we start collecting the data with one of these protocols, I think ideally we want to collect it every day. In the worst case, I would say three, four days literature had been reported as the minimum to get at least an understanding of long term changes.
If you measure in the morning with an app, then you would again sit up, take your measurement. Even a minute is sufficient. And that's it. Use a device that over time will show you what is your normal range. So what is normal day to day variability for you? Because not all changes are meaningful, right? Data will always differ, right? Today it will not be exactly like yesterday. It might be a bit higher, a bit lower, but many of these changes are actually irrelevant. So we need to understand what is normal for you. And when you're outside of that, then we might want to implement some changes.
[00:19:13] Speaker A: How many days or measurements do you need to get a nice picture of your, what is your normal?
[00:19:21] Speaker B: So I would say normally more than a month.
In my view, two months is a good time frame because you are nothing changing what is considered normal for you too quickly, right? Because if we think about, okay, let's just use a few weeks to be in this normal range. But then if you're sick, for example, and it's unfortunately something bad and you are sick for, I don't know, ten days or something like that, that deviation will basically make your normal being the situation in which you're sick, because you've used just too little data to create your normal range, even if you give updating all the time. So a longer time frame avoids that. When you have these acute changes that are a bit longer, they impact too much, your data. So I think a longer time frame is good. At the same time, we cannot use it too long. We don't want to use, I don't know, six months or something like that, because still there's a lot of variation in physiology, even just when it's a different season, right? If it's winter, it is summer. Our date is different, so we don't want to get stuck with it. It is so old. But, you know, 45, 60 days, I think it's good in research. Often they use just 30 days. I think that's also a bit for practical reasons. Right. Sometimes you need people to take their measurements, to enroll in a study, to do a study that is already 812 weeks and you want to start maybe a month earlier, two months earlier. So there's always practical considerations in your research. And that is why I think Wolf then unwant is used, which is what I would consider at this point, minimum, to do this.
[00:20:55] Speaker A: Okay. I think that here we have some nice insights to perform our measurements. One of the most interesting concepts that derives from the HRV measurements is the training readiness.
This will inform us as to when do you need to push even harder in your training or back off a little bit? At which metrics would you look at before deciding one way or the other?
[00:21:25] Speaker B: All right, so when it comes to using metrics from wearables or HIV measurements and things like that, in terms of adjusting training, I think the keyword there is really adjusting. So meaning that we need to start with a plan and it might sound obvious to you and maybe an audience that is into training, but many people approach this technology and they expect it basically to provide guidance, even without a plan. Meaning that it will tell you every day if you need to go hard, if you need to go easy and things like that. But the body doesn't work like that. So what we can see is how the body has responded to the stimulus. But if again, we have responded well and everything is normal, even after a high intensity session yesterday, that doesn't mean that we should go hard every single day until it's not normal anymore. Right? So it's important to start with a plan where we will have our hard days, our easy days, the structure that is best for us based on what we have planned. And then we can make small adjustments based on physiological data. In particular, I think what is useful is your physiological response in terms of HIV and of course your subjective field and I, a series of subjective parameters that you can easily track, like how sore you are, something you cannot measure with any device, right? And at the same time also your motivation to train that you also cannot measure, like all sorts of things that only you know and you know, despite the fact that, again, wearables and devices are marketed to you as the ones that will make this decision for you, what is your readiness, what is your recovery? But that is not really possible to do. And again, to me soreness is the best example, as in Gorad's athletes. Typically when we train hard, then for a day or a couple of days, we just can't do that again and we might be perfectly fine. Our physiology is perfectly fine, our heart rate is normal, our survey is normal, everything is good, but the muscles just can't do it. And that it cannot be measured by any wearable device.
If a device claims to have this holistic overview of your state so that it can give you a readiness or recovery score, but then fails to capture something that is so important in the context of having recovered from the previous session, then it's just not something that we can trust. So on my end I try to do it a bit differently. We only report the physiology. If the physiology is within normal, then everything is okay. And the advice is not to go hard, it's just to proceed as planned, which assumes you do have a plan. So if today I went hard and tomorrow my physiology is normal, great. It means I responded well and then my plan for tomorrow is anyway to do something easy and I will do something easy. So that is a bit how I think about the data and the subjective data also obviously plays an important role. There can be situations in which maybe that becomes more important and there can be situations in which maybe the physiology is also something that gives us some useful insights. Maybe we don't feel so great, or maybe we feel okay, but the physiology is suppressed for a day or two. It might be picking up that we are getting sick or something is off. So it could be a good idea to make a small exhaust. So that's a bit the framework in which I would think, and then in the way we actually use the data comes back again to the paper that Steven Seidel wrote, looking at the intensity of the stimulus and its effect on autonomic activity. Again, this was done by measuring before and after exercise at different timeframes. And you could see that for training of a certain duration, even when the duration was doubled, if the intensity is low and low, we can say something below the first ventilatory threshold. For example, they threshold zone, what we call, you know, zone one, two and five zones. Easy training. If your training is easy, even higher volume training typically does not impact your autonomic nervous system much, meaning that you will bounce back to normal rather quickly after exercise. But if the intensity of the stimulus is high, so moderate intensity between thresholds or high intensity even above the second threshold, then in these cases we have that autonomic nervous system is disrupted for much longer. So it takes a long time for HIV to renormalize. And interestingly silent split. Also the results based on fitness level of the athlete. So you could also see that the fitter you are, the quicker you bounce back to your normal. And that speaks also to what we were saying more anecdotally about elite athletes having their data almost always within their normal, even if they do train very hard. It's just that they recover also very quickly. So the idea behind adjusting training, given this data, is typically to adjust the intensity more than the duration. So if HRV is suppressed, we are already in a state that maybe is not ideal. There's already a negative response to stress and, and we might still train, but then we scale down the intensity. So if we had hard session, maybe we postponed that for a moment in which we might be able to absorb the stimulus better as opposed to still doing the hard training with suppressed HRV and being in a negative state. While for the volume, typically that could stay or even be longer and it should not impact it as much again, depending on our fitness.
Yeah.
[00:27:18] Speaker A: One of the main setbacks associated with HRV measurements for me is that as with many other numerical data that can be incorporated into athletes training, is the fact that once you measure and you get a number, there is a risk of bias for the athlete in a way in which when you are going to train today and you get a numerical number that shows you that your values today are below what they should be or above what they should be, you can get biased in a way in which you try to train and you start saying to yourself, okay, today is not my day, etcetera. I have seen this many times with athletes and I would like to get insight from you regarding this because I have always thought that it would be a good idea to blind the result from that lead. I don't know what you think about this.
[00:28:18] Speaker B: Yeah, for sure. I think that's a factor to consider. Meaning that it's obviously there one thing to remember that I think might be helpful also for the athlete or the person that is just maybe using the data or self coached and therefore they are looking at their data, is that HIV doesn't determine what you can do. So it's more about how you will respond to the stimulus than about what you can do today. So if your hiv is suppressed, we exclude of course, a situation in which you're sick, then obviously something is wrong and you might not perform well. But let's say suppressed for any other reason, some stressor or whatever, then you can still perform at your best, right? It's not really an issue with the performance of that day. That's why we shouldn't get stressed. If it is slow or is day actually in these situations it might be even normal for it to be suppressed because maybe you're just a bit excited about the event or a bit more nervous and it's not a problem.
But where it comes in, I think it's really in the ability of the body then to respond positively to this stimulus.
And that is why it can be useful in training, I think to make adjustments. But then it's not necessarily particularly relevant if we are racing or the day of the event or things like that. So if you are concerned that psychologically could bother you in those situations, I think it's okay not to measure or just to keep it line. The issue one I will look later. There's in the tools. Typically you can do that in a way you don't look at the data today and then after the fact you might need to queue is to overlook. But it doesn't really matter because anyway you are raising well during training you might want to provide the stimulus at the best time. Right. We all understand that not only the stimulus but also the timing of the stimulus matters. And this could guide a bit hmv in terms of how you provide stimulus. In that case could be, should look at the data daily. But again we need, I think to understand that it's just a market of your stress response, that on most days it should actually just be normal. So I think here an issue could also be with some of the tools that try to maybe engage too much the user, right. There's numbers from zero to 100 or percentages and they tend to stress the fact that higher is better and things like that. While I think the conversation should shift a bit towards, you know, is this normal or it's maybe abnormal, meaning there is an important suppression and that is rare. So on most days you shouldn't be really boring. Like you take your measurement, you look at the data, it's normal again like just every other day. So I think a bit of the issue is maybe how the tool presents the information. And of course there is also a fact that numbers and advice and colors might impact us and impact the way we approach our training and things that we are doing. So that is something to think about. I think that in general a layer of education can always help, right? So if the athletes know these things that we talk about today. I think the they also understand better that there is, you know, nothing deterministic about it. It doesn't decide what you can do, what you cannot do. There's just a way to track your stress response. And maybe today is slow and you ignore it and you do your hot session and tomorrow it is perfectly normal and that is okay. It's like it doesn't mean anything in a way. Right? So that is why also research has shifted a bit from focusing a lot on the daily value. Before it was like this, even the HRV guided trainings based on the value of today, we decide what to do if we make an adjustment or not. Right now we look at the baseline, we call it, it's just a seven day moving average with respect to your normal range. So by definition, since it's a seven day moving average, it takes more time for it to go outside of your normal. So if today is suppressed, tomorrow is suppressed, a third day is suppressed, then the moving average starts to go lower, low, low. And then it might make sense to make a change because there is clearly a stronger stressor and we have not any more overly reactive on what happens every single day. I think the truth is in between, because if you are sick, then you don't want to wait for days to make an adjustment. So research, studies and real life need to meet somewhere. We need to look a bit at both these things and build a bit of nuance. And then I think the tool can become useful.
Yeah.
[00:33:11] Speaker A: One of the other issues I have found frequently with athletes is that they doubt the reliability of the app or whichever method they are using, and you will encounter the typical athlete that sits on the sofa and measures four times in a row. And he tells you, I got a different result each time I measured. What, what. What would you tell him in these cases?
[00:33:36] Speaker B: Yeah, yeah, I think this happens. Part of it is normal. Meaning that even if I just wear a chest strap and I measure while sitting there for a long period of time, and then I look at all my HRV changes, it's not always the same value. There is variability. It is partially due to breathing, it's partly due to whatever is going on in our head.
Given this context, this is why the normal range is important. Right. So after many days, then depending on the day to day variability, this range can be wider or smaller for an individual based on how, let's say, variable is there yet. Right. So the more jumping around, the wider also the range. So that basically you can be outside of this range only on situations in which the valuation is still even higher, then I think part of the repeatability issue typically is simple artifacts as well. So things that we need to know that we shouldn't do and typically we don't think about it or we don't know. For example, even if I just swallow saliva, which is something that we do a million times in a day, that can change your hrpenne by a factor of two. So it will be twice as high if you do that in a minute while you are measuring. So once you know that, then of course you make a conscious effort not to do it. Otherwise it is perfectly normal that in a few measurements you will do it in some and not in others. Other small things, I don't know, yawning, things like that will all impact the dead. And obviously these things are not impacting your stress. I'm just impacting the bit to beat variability. And therefore the impact of stress is co founded by all these other things. So I think, you know, try to just relax, sit there, take your measurement, try not to do other things. Once you've taken your measurement, that's it. Sometimes if you go back and measure again, then even mentally, you can influence it because you might get frustrated or you might start, you know, getting in this line of thinking. And that is also not helpful because of course that is a stressor and that will impact the rate as well. So we try to keep it really simple, just keep measuring everyday, simple protocol. In the same way, try not to swallow, to yawn.
You can make it two minute long, a bit more time reduces this repeated measures variability. Right. Because it's a bit more data and also your breathing, that is important. Try to keep it, you know, relaxed. Do not force it, particularly for some measurement and not for others.
Yeah, it sounds complex, but once you start doing it every day, I think it's pretty simple.
[00:36:24] Speaker A: What is, in your opinion, the most common mistake that at least do when attempting to recover their HRV data? And what will be a single piece of advice to, you know, address this, this mistake?
[00:36:40] Speaker B: So I think maybe in terms of using the data, it would be a bit rhetoric about having higher values or aiming even for higher values, especially for athletes that typically already exercise a fair amount and might have a good lifestyle. In terms of, you know, eating well and trying to prioritize, read and other things.
I think the data is more useful if we think about stability in the data. So over time, having responses that are quick, that you get back to normal after a stressor and things are quite stable within your normal range, on most occasions, more than trying to optimize it or increase it or change it in a way, which is a bit, you know, there. The common rhetoric about HIV should be higher than this. And that, I think that was questionable in general because there is a strong genetic component. But especially in this context of, you know, healthy, active individuals, I think it's. It's very unlikely that when you start measuring, there will be changes that lead to different absolute values. And on the other hand, again, there are seasonal changes and all sorts of things that can actually change your normal range, even in the opposite direction. And that is okay. But keeping your day to day data and weekly data within your normal range typically means that you are responding well to stressors. So I would try to look at the data that way over time and understand the limitations that we discussed so far, and that should make it useful in the end.
[00:38:24] Speaker A: It's the same piece of advice that you could give someone in other aspects of training, because in the end, you are looking for increasing adherence. And this routine that, you know, allows you to not get injured, not to get sick, and in the end, you will, you will, you'll have more training days behind your back. No, it's the same story.
[00:38:44] Speaker B: Exactly. Exactly. Sometimes people get really into the metric as the thing to optimize, but we need to remember that, you know, health and performance is our outcome and it's just a tool.
Yeah.
[00:38:56] Speaker A: Okay, Marco. So I think that we got several very nice insights regarding HRV for training. And many people who are either initiating or have been doing this for years, I think that may change some of their perspectives after this podcast. Many thanks again for accepting the invitation and hope you see you soon again. Here to discuss a little bit in more advance this HRV topic.
[00:39:22] Speaker B: Thank you so much.
[00:39:23] Speaker A: Bye.
