This article is an adaptation of the interview with Marco Altini for episode 31 of Sitkotalks, which you can watch at the following link.
Sebastian Sitko: Welcome everyone to another episode of SitkoTalks. Today we’ll be discussing heart rate variability (HRV). I’ve invited one of the leading experts in this field, Marco Altini. I believe most of you are very familiar with him. If not, Marco holds a Ph.D. in data science, as well as a master’s degree in training and computer science. Many of you know him not only as a well-known sports scientist, but also as the founder of HRV4Training, the famous app most of you use to measure heart rate variability. Thank you very much, Marco, for accepting the invitation.
Marco Altini: Thank you, Sebastian. It’s a pleasure to be here.
Sebastian Sitko: Ok, first, I’d like to start by talking a little bit about HRV with a brief introduction. Can you explain what HRV is and its connection to the autonomic nervous system?
Marco Altini: We’re referring to the variability between heartbeats. When your heart is beating, there are always differences in the time intervals between consecutive beats. Let’s say your heart is beating at 60 beats per minute; you won’t have a beat exactly every second. There’s variability in the timing. We can quantify this over at least a minute if we measure our HRV in the morning and follow a specific protocol. And the reason this is important, as you mentioned, is that HRV is linked to the autonomic nervous system. In particular, this variability isn’t random or due to chance; it’s related to how the body responds to stressors. So, when we encounter stress, in any form, the body responds in terms of the autonomic nervous system, typically with an increase in sympathetic activity, which we can refer to as the fight or flight response, where we need to mobilize resources. This is associated, for example, with an increased heart rate, which is something we see when we exercise or engage in physical activity.
This response also causes a reduction in parasympathetic activity, which is another part of the autonomic nervous system. This is usually associated with recovery. So, as the autonomic nervous system adjusts to deal with the stressors we face, it influences the heart’s rhythm. Essentially, as we said, the heart rate increases a bit, but the variability also changes. When the heart rate increases, the variability decreases. In those cases, the heart beats a little more consistently. On the other hand, when we are calm and relaxed, parasympathetic activity is higher, and the variability increases. Since we can’t directly measure stress or the autonomic nervous system, we can look at heart rate variability as a proxy for the stress response, because when we face a stressor, the autonomic nervous system reflects this in the heart rate and HRV, which is something we can measure. We measure heartbeat variability as a way to frame the stress response.
Sebastian Sitko: Ok, Marco, you’ve mentioned different stressors here. I believe there are many stressors that can impact our variability. Some of these could be food, alcohol, or sleep. Can you summarize the main stressors that can impact our HRV, specifically when talking about athletes?
Marco Altini: I’d say the good thing, and maybe also the bad thing, about the body’s variability is that it’s sensitive to many stressors—basically, I’d say, all stressors. Typically, if we have a negative stress response, meaning when we look at HRV and, for example, we see a suppression in HRV indicating more stress, if we measure it following best practices and good protocols, what we are capturing there is the stress response. It’s not the stress itself. So, if I feel stressed today and measure my HRV tomorrow, I don’t necessarily expect it to be low just because today I did a high-intensity workout. If my body has responded well over the hours and through sleep, then I would expect my HRV to be normal the next day. It’s not the stress itself, because we already know the stress—it’s what we apply through training and other things. But it’s really the response to the stress, and that’s why it’s interesting. If my HRV is suppressed tomorrow, maybe today I overtrained a little, maybe I did a workout that’s too much for my current fitness level, or something like that. So, that’s something I think is important to understand. It’s really about the response, particularly the response to cumulative stressors.
I mentioned high-intensity training, but as I said, it could also be poor sleep, alcohol, illness, or travel, which can be common for athletes as well. I think the relationship with sleep is particularly interesting because it’s a bit different from other stressors, meaning it works both ways. If tonight, for example, my HRV is low—meaning I’m very stressed, or even psychologically or mentally due to work or other issues, or maybe even in a positive way—I could be very excited. Maybe as an athlete I had a race or a good workout late in the day and I’m still in a heightened state, let’s say, which is associated with an increased heart rate and a more suppressed HRV. This can also impact sleep in such a way that we might not sleep as well as we would if we were in a more relaxed state, with a higher HRV, for example.
Now, the relationship also works in the other direction, meaning that when we sleep, if we sleep well and recover, then we also see a greater difference in our HRV at night compared to during the day. We might see it much higher at night because, again, we’re recovering, resting, sleeping—we’re in a very parasympathetic state. When we wake up in the morning, we may be in a better state to handle the day’s stressors. Whereas if our sleep is poor, it will also have an impact, for example, on our morning HRV, which could reflect our inability to cope with stressors. Of course, sleep is a key process in our daily lives, so it’s important to aim for restorative sleep, possibly to better prepare us to handle the next day’s stressors. HRV can also be a marker of how well this process is working.
In terms of other stressors, it’s typically easy to spot acute stressors in HRV data—day-to-day changes. For example, if I’m sick or traveling, any short-term stressor can be seen in short-term data. In the longer term, things become a bit more complex, but I think this can still be useful. Perhaps it’s a particularly difficult period at work or in other areas, right? Not all athletes can focus solely on their sport. Even professionals, of course, have other stressors not related to sport or training. It could be your profession outside of training, for example, dealing with contracts, sponsorships, or how you make a living as a professional athlete. All these factors can build up over time and affect your data chronically. So, it’s not just about day-to-day stressors but also chronic ones over longer periods, where we might see how we’re responding to certain stressors and, hopefully, make some adjustments.
Sebastian Sitko: In your experience, what would be the factor that most affects HRV?
Marco Altini: Well, acutely, it’s factors that are less frequent. For example, an illness will have a much larger impact compared to high-intensity training. I also think there’s a lot to be done regarding changes in HRV and its context, right? So, if we’re talking about an athlete—a professional athlete who has a periodized training plan throughout the year and typically knows what they’re doing—the stimulus is appropriate for the training phase, their current fitness level, and so on. Even when they train very hard, we typically don’t expect to see HRV suppression, so the day after, things should return to their normal range.
Unexpected things only appear in the data acutely. For example, maybe we travel and eat something different, or some training-related stressors come into play. Or, again, sometimes we get sick for one reason or another. These factors tend to show up in the data more than the training itself. I think that’s why HRV is useful, because we already know a lot about training, especially when working with professionals. You can understand their response very well if you’ve been tracking them for a long time. But there are always different factors and various things that can impact our physiology, and having a framework for your response that is sensitive to general stress—not just training stress—is quite useful.
However, when things go wrong, you always have to bring in that context to try to understand what’s happening and what may be causing these unexpected changes in the data. The data alone won’t be able to tell you.
Sebastian Sitko: Nowadays, most people measure HRV using apps like the one you’ve developed, HRV4Training, or with a heart rate monitor, or even using their phone’s camera. But I’d like to know the details of these measurements, such as the frequency, the best duration, and also the preferred conditions for obtaining the most accurate results.
Marco Altini: Yes, the idea behind HRV is that it’s something connected to the body’s stress response, as we mentioned earlier, but only under certain circumstances. What we see a lot today is that this relationship has been extrapolated in ways that don’t always reflect what’s actually happening in the body or how we know the data should be used. I’ll try to explain with a few examples. So, if we measure HRV according to the best protocols—which would be first thing in the morning or at night—we can capture the stress response, and there are differences between these two protocols that we can discuss later. But even so, when we measure in these conditions, we’re typically measuring far from other stressors and confounding factors. We can do it consistently each day, and that consistency allows us to capture how the data changes in response to stressors and understand our body’s stress response.
This isn’t the case if we measure at random times. For example, if I’m talking, that will impact my breathing and in turn affect the HRV data in a way that doesn’t reflect stress. Similarly, many activities will disrupt the data in ways that don’t represent our actual stress levels. For example, simply drinking water can cause an incremental response—basically, the more water I drink, the higher my HRV will be in the next hour or hour and a half. But that has nothing to do with my recovery or stress response. That’s why I believe it’s not meaningful to use HRV outside of controlled settings.
Even in research, when we conduct studies on HRV—say, before and after exercise to observe the impact of different exercise intensities—some of the most interesting studies on HRV and how to use it for monitoring training come from research where people are studied before and after exercise. Some of this work was done almost 20 years ago, including studies by Seiler. But what’s interesting is that this is nearly impossible to do outside the lab, because there are so many confounding factors. No one in real life can finish a workout and then essentially do nothing for the next two or three hours. You can’t shower, you can’t eat, you can’t drink, you have to sit still, no talking, no moving—and then we measure your HRV to understand how the workout affected you. This isn’t feasible in real-world settings for obvious reasons. And if we try to measure HRV with wearable devices throughout the day, we’ll just capture noise. Any activity you do will disrupt the data in ways that don’t reflect your stress response. Unfortunately, the relationship between parasympathetic activity, stress, and HRV only holds under specific conditions, and now it’s being used in situations where it doesn’t work, which creates some problems with how the technology is used today.
If we want to use this technology correctly in the context of monitoring stress responses or working with athletes, my recommendation would be to measure either first thing in the morning or at night, using a device to measure throughout the night. The main difference is that at night, you can measure further away from stressors. If you worked out in the evening, drank alcohol, or ate late, all these things could show up in the data, making it seem like there’s more stress on the body, but that stress is largely irrelevant in many situations. You’re not measuring the body’s response anymore—you’re just capturing data collected too soon after the stress occurred, which is perfectly normal since the body needs time to return to baseline after a series of different activities. In the morning, after the restorative effects of sleep, I think that’s the best time. You can also take the measurement in a different position. You don’t have to measure while lying down, which is how you sleep. If you measure after sitting up, you’re introducing a little bit of stress called orthostatic stress. Essentially, when you sit up, the body has to adjust. If you measure shortly after sitting up, you get an amplified stress response, right? If you’re sick and try to measure, you’ll see that your heart rate after sitting up is much higher than when you were lying down. That amplification of the stress response allows you to capture changes more sensitively. So I think that’s another advantage of measuring first thing in the morning.
Of course, this requires that you don’t do anything else but use the device, which isn’t always possible. Maybe, for practical reasons, your morning routine is too busy—maybe you have young kids, for example. In that case, you could use a device to measure overnight, assuming you’re able to sleep through the night. But if you’re waking up frequently, for instance, to attend to those same young kids, then the night measurements won’t be useful either. Practical considerations are always important, and you need to find what works best for your lifestyle and routine. But these are some of the key differences I try to highlight.
Once we start collecting data using one of these protocols, ideally, we want to collect it daily. At the very least, I’d say three or four times a week, which has been reported in the literature as a minimum to get an understanding of changes over time. If I measure in the morning, I wake up, take the measurement, and one minute is enough. Over time, a device will show me what my normal range is and what my day-to-day variability is, because not all changes are relevant. The data will always differ—today won’t be exactly like yesterday. It might be a little higher or lower, but many of these changes are irrelevant. So it’s necessary to understand what’s normal for you. When you’re outside that range, that’s when we can start considering making adjustments.
How many days or measurements do you need to get a good picture of your baseline? I’d say, typically, more than a month. In my opinion, two months is a good amount of time, because your baseline doesn’t change quickly. If you use just a few weeks to establish a normal range, but you happen to get sick during that time, and you’re ill for 10 days or so, that deviation will skew your baseline, because you’ve used too little data to create your range, even if you keep measuring regularly. A longer period prevents these acute changes from having too much impact on your data. So, I think a longer period is better. But at the same time, we can’t use a timeframe that’s too long—we don’t want to use six months or more, because there’s too much variation in physiology, even with seasonal changes. Whether it’s winter or summer, or if your age has changed, you don’t want to be stuck with an outdated baseline. But 45 to 60 days is good. In research, they often use just 30 days, but that’s mainly for practical reasons. People need to take measurements and participate in studies that last 8 to 12 weeks. If you ask them to start two months earlier, it becomes too long. Practical considerations play a role in research, which is why we often use the minimum required.
Sebastian Sitko: I think we have some good advice here on how to conduct our measurements. One of the most interesting concepts derived from HRV measurements is “readiness,” which tells us when to push harder in training or when to dial it back. What metrics would you look at before deciding one way or the other?
Marco Altini: Well, when it comes to using metrics like HRV to adjust training, I think the key word is *adjust*. This means we need to start with a plan. This might seem obvious to you and an audience familiar with training, but many people approach this technology expecting HRV to be the guide. They think HRV will tell them every day whether to go hard, go easy, and so on. But the body doesn’t work like that. What HRV can show is how the body has responded to stimuli. But even if everything looks normal after a hard session yesterday, that doesn’t mean you should go hard again today until something doesn’t look normal.
It’s important to have a plan that includes hard days, easy days, and a structure that suits your goals. Then, you can make small adjustments based on physiological data. In particular, I think what’s useful is the physiological response reflected in HRV, along with subjective feelings and other subjective parameters. How you feel, for example, isn’t something any device can measure, right? Also, your motivation to train can’t be measured either—these are things only you know. Despite the marketing of gadgets and devices claiming to make these decisions for you, like determining your readiness or recovery, they can’t fully capture the reality of how you feel.
For me, pain is the best example. As endurance athletes, after a hard training session or a couple of days of intense training, we simply can’t do it again right away. You might feel perfectly fine, with your physiology showing normal heart rate and HRV, but your muscles can’t handle the load again—and no device can measure that. So, when a device claims to provide a holistic view of your state, like recovery or readiness, yet it can’t account for something as important as muscle fatigue in the recovery process, it’s not reliable.
What I try to do is just report physiology. If your physiological data is normal, then all is good. The advice isn’t to go hard, but to stick to the plan—assuming you have one. So, if I went hard today and my physiology is normal tomorrow, that means I responded well, and since tomorrow is an easy day in my plan, I’ll proceed with it. That’s how I approach it. Subjective data also plays a big role. There are situations where this becomes more important, or where the physiology gives us useful information. Maybe you don’t feel great, or maybe you feel fine but the physiology suggests you should take a day off. This could be a sign of illness or soreness, and taking a break might be wise. That’s the framework I believe in.
There’s also a risk in how we use the data. I go back to Stephen Seiler’s work, where he looked at stimulus intensity and its effects on autonomic activity. This was done by measuring HRV before and after exercise at different intensities. They found that even doubling the duration of exercise, as long as intensity stayed low (for example, below the first ventilatory threshold—zone 1 or 2 in a five-zone model), higher training volume didn’t impact the autonomic nervous system as much. This means you’ll recover faster after exercise. But if the intensity was moderate (between the thresholds) or high (above the second threshold), the autonomic nervous system was affected for a longer time, meaning it took longer for HRV to normalize.
Interestingly, Seiler’s results also varied based on athletes’ fitness levels. The fitter you are, the faster you return to normal. This aligns with what we mentioned anecdotally about elite athletes: their data stays within normal ranges even after hard training because they recover quickly. So, the idea behind adjusting training based on HRV data is typically to adjust *intensity* rather than duration. If HRV is suppressed and we’re in a less-than-ideal state with a negative stress response, we might continue training but at a lower intensity. We could train at a time when we can absorb the stimulus better, rather than pushing hard when HRV is low and we’re in a negative state. Volume can usually remain the same or even increase, and it shouldn’t have too much of an impact, depending on our overall health.
Sebastian Sitko: One of the main limitations I find with HRV measurements, similar to other numerical data incorporated into athletes’ training, is the bias risk for the athlete. For example, when you get a number and see that your values are below or above what they should be, it can influence the way you train. Athletes might start telling themselves that today isn’t their day, etc. I’ve seen this many times with athletes, and I’d like to hear your thoughts. I’ve always thought it could be a good idea to blind athletes from their results. What’s your opinion on this?
Marco Altini: Yes, definitely. I think that’s a factor to consider. It’s important to remember—something that might help athletes or those self-coaching—that HRV doesn’t determine what you *can* do. It’s more about *how* you’re responding to stimuli. Unless you’re sick, where it’s clear something is wrong, you can still perform at your best even if HRV is suppressed due to stress or other factors. A low reading doesn’t mean you won’t perform well that day, so there’s no need to stress about it. In low-risk situations, it’s normal to feel off because you might be excited or nervous before an event, but it’s not a problem. What matters is your body’s ability to respond positively to that stimulus.
This is where training adjustments can help, but on competition days, it may not be relevant. If you’re worried about the psychological impact, I think it’s fine not to measure or keep the data hidden until you review it later. Many tools allow you to hide daily data and check it after the event. But it doesn’t really matter on race day—you’re competing regardless. In training, on the other hand, applying stimuli at the right time is important, so seeing your daily data might be useful.
We also know that timing the stimulus is as important as the stimulus itself, and HRV can guide this. But again, HRV is just a measure of how you’re responding to stress. In most cases, things will be normal. Tools that give overly detailed numbers, percentages, or ratings from 0 to 100 can overcomplicate things, encouraging the idea that higher is better. The conversation should shift toward whether values are in the normal range or not. Significant suppression is rare, so most days should be “boring” in terms of HRV readings—just normal data, like any other day.
How the tool presents this information can also impact behavior. Numbers, advice, and color-coded data can affect how athletes approach training. We need to consider that. Generally, education can help. If users understand the concepts we’ve discussed, they’ll see that HRV isn’t determinative—it doesn’t dictate what you can or cannot do. It’s just one way to monitor your response to stress. If you have a low HRV reading today and still push through an intense session, everything might return to normal tomorrow—and that’s fine. It’s not a big deal in isolation.
Research has shifted away from focusing solely on daily values. Even in HRV-guided training, we used to base adjustments on daily readings. Now, we look at trends, like a 7-day moving average relative to your normal range. Since this average spans 7 days, it smooths out fluctuations. If your HRV stays suppressed for several days, the moving average will drop, and at that point, it might make sense to adjust your training because a stronger stressor is at play.
The reality is somewhere in between both approaches. If you’re sick, you don’t want to wait days before adjusting, so studies and real life need to meet in the middle. We should consider both the daily and longer-term trends, and I think that’s where the tool can be useful.
Sebastian Sitko: One of the problems I frequently encounter with athletes is the reliability of the application or method they are using. You’ll find the typical athlete sitting on the couch, measuring themselves four times in a row, and telling you they get a different result each time. What would you say to them in these cases?
Marco Altini: Yes, I think that happens. Part of it is normal. Even if I only use one sensor and measure myself over a long period, when I review all my HRV changes, the values won’t always be the same. There is variability. This is partly due to breathing and partly due to what’s going on in our heads. In this context, that’s why the normal range is important. After many days, depending on the variability from one day to the next, this range can be broader or narrower for a person, depending on how variable their day is. So, the more variable they are, the wider the range will be. Basically, you can only be outside of this range in situations where the stress is much higher. I think part of the reliability issue also comes from simple artifacts—things we should be aware of and avoid, but that we sometimes don’t think about or know. For example, if I just swallow saliva—something we do millions of times a day—that can change your HRV by a factor of 2. It will be twice as high if you do it in one minute. Once you know that, of course, you’ll make a conscious effort not to do it. Otherwise, it’s perfectly normal to get certain results in some measurements and not in others. Other small things, like yawning, will affect the data. And obviously, these things don’t impact your stress level; they just affect the beat-to-beat variability, so the impact of stress is confused by all these other factors. So, I think the best approach is to try to relax, sit down, and take your measurement. Try not to do other things. Once you’ve taken your measurement, that’s it. Sometimes, if you decide to measure again, you can even influence the result mentally, because you might get frustrated or start overthinking it. That’s not helpful either because, of course, that generates more stress, which will also impact the result. So, try to keep it very simple. Measure every day with a simple and consistent protocol. Try not to obsess over the measurements. You can do it for two minutes or a little more. This reduces variability in repeated measurements because you’re collecting more data. Your breathing is also important; try to keep it relaxed. Don’t force it in some measurements and not in others. Yes, it sounds complex, but once you do it every day, I think it’s quite simple.
Sebastian Sitko: In your opinion, what is the most common mistake athletes make when trying to track their HRV data? And what would be a piece of advice to address that mistake?
Marco Altini: I think, in terms of using the data, one of the most common mistakes is thinking that higher values are always better, especially for athletes who are already exercising a lot and generally lead a good lifestyle, eating well and prioritizing sleep and other factors. The data is more useful if we think about its stability. Over time, we should see quick responses that return to normal after stress, and that things stay fairly stable within their normal range most of the time. More than trying to optimize it, increase it, or change it in a way that is somewhat conventional—like HRV should be higher, and this and that—I think that’s the main issue because there is a strong genetic component, especially in the context of active and healthy individuals. I think it’s very unlikely that when you start measuring, you’ll see changes that lead to different absolute values. Additionally, there are seasonal changes and all sorts of factors that can shift your normal range even in the opposite direction. And that’s okay, but keeping your daily and weekly data within your normal range typically means you’re responding well to stress. So try to view the data in that way over time and understand the limitations we’ve discussed so far. That should make the data useful.
Sebastian Sitko: It’s the same kind of advice you could give to someone in other aspects of training. Because, in the end, what you’re looking for is to create adherence and a routine that allows you to avoid injuries and illnesses. With that, you’ll have more training days under your belt. It’s the same story.
Marco Altini: Yes, exactly. Sometimes people obsess over optimizing everything, but we have to remember that health and performance are our goals, and this is just a tool.
Sebastian Sitko: Marco, I think we’ve gathered some great advice on HRV for training, and many people who have just started or have been doing this for years might change some of their perspectives after this podcast. Thank you again for accepting the invitation, and I hope to see you soon here to discuss more about this HRV topic. Goodbye.
Marco Altini: Thank you very much.