The Ultimate Guide to Heart Rate Variability (HRV): Part 4
This is the fourth (and last) part of my series of educational posts on heart rate variability (HRV). You can find the other posts at these links:
- Part 1: Measurement setup, best practices, and metrics.
- Part 2: You measured, now what? (on interpreting your data)
- Part 3: Show me the data (case studies)
- Part 4: Common misconceptions (device accuracy, resting heart rate, strength training, night data, etc. this post!)
HRV is nothing new, and fairly simple to use effectively, but poor standardization and methodological inconsistencies make it difficult sometimes for people to make good use of the technology or understand what is reported in the scientific literature.
Make sure to follow the tips listed in part one, and you’ll be able to benefit from available technologies on the market.
You can also find common ways to easily analyze and interpret your data with respect to training and lifestyle stressors, in part two.
Finally, in part three I’ve shown plenty of case studies so that you can clearly see how the effect of different stressors (for example training, travel, work-stress, menstruation, etc.) is captured by measurements of resting HRV, and what to do about it in the context of improving health and performance.
Part 4: Common misconceptions
All you need to know to make effective use of the technology and data is already covered in the previous parts of this guide. However, there are a few misconceptions that keep popping up, and it can be beneficial to try to clarify a few points.
In particular, in part 4 we’ll see:
- Misconception 1: Phones or watches are less accurate than straps in measuring HRV. Follow up, misconception 1b: Watches are all accurate in measuring HRV
- Misconception 2: Measuring during the night is better because you are unconscious
- Misconception 3: Measuring before working out is more informative on my readiness than measuring in the morning
- Misconception 4: HRV is the same as resting heart rate
- Misconception 5: HRV should improve over time
- Misconception 6: I cannot benefit from measuring HRV if I do not care about training
- Misconception 7: HRV is less useful than subjective data to capture how an athlete responds to training
- Misconception 8: HRV is not useful if I do strength training
Hopefully, this post will help to clarify most of the doubts you might have, but please feel free to ask questions below should you have any additional points.
I can certainly add more to the list as they are most likely beneficial to others as well.
Misconception 1: Phones or watches are less accurate than straps in measuring HRV
This one has been debunked a million times already, but there is a lot of confusion due to how inconsistent different devices are, even when seemingly using the same technology. There can be good reasons to use a chest strap (see the end of this section), but accuracy is typically not one (also note that not all chest straps are equal, you need to get a good one that has been validated, such as the Polar H7 or H10). Finally, when using a chest strap, depending on the artifact correction method used, you might get less accurate results than using a phone camera.
Let’s look at some data. Below you have the raw ECG (in gray) and PPG collected using HRV4Training (in black). The data is a bit packed because we are showing a full minute, as this is the recommended measurement duration.
We can also see detected ECG peaks (used to compute RR intervals, these are the black dots), and detected PPG peaks (these are the gray dots). As you can see, they perfectly align over the minute of data.
Needless to say, if the detections are perfectly matched between PPG data and ECG data, then the resulting HRV will be the exact same. This plot should make it easy to understand that both methods are equivalent, and HRV data can be acquired accurately using optical devices. Note that not all optical sensors are able to do so, hence it is important to make sure the device you use has been validated (more on this in the next section).
Here you can see also the RR intervals over time, so the beat to beat differences as computed from ECG data, from PPG data and also as received from a Polar H7 strap. Again, they match:
For measurements at rest, you can use one or the other.
Then, it is, of course, true that PPG and optical measurements are more prone to noise due to motion artifacts, so if you have issues keeping your finger still, it is better to use a strap. Similarly, if you get 10 WhatsApp notifications while you measure, the signal will be disrupted (these are time-critical operation).
But if you do it right (which is fairly easy!), there is no difference in accuracy between methods when it comes to HRV measurements of resting physiology. The best method is really what works best for you, and what has the higher chance of you sticking to it for several months, which is the only way to benefit from the data (a “perfect” measurement taken once per year will be of no use!).
Follow up, misconception 1b: Watches are all accurate in measuring HRV
From what I have just explained above, you might then derive that all watches or optical devices can measure HRV accurately since the PPG peaks match the ECG RR intervals. I can’t blame anyone, after pushing so much to make it clear that PPG can be used for HRV measurement, now I get weekly emails asking about how to use this or that watch for HRV analysis. Well, more often than not, you can’t use it.
Let’s try to understand why.
Most wrist-worn trackers are facing many challenges in providing reliable heart rate (HR) data, mainly due to motion artifacts. This is the case every time the intended application is the measurement of heart rate during exercise (which is typically the case for heart rate watches). For this reason, these trackers include heavy filtering which aims at dealing with artifacts and providing reliable HR data during exercise. Some of these trackers do work quite well in this context. However, the filtering and signal averaging necessary to provide reliable HR make the data unusable for HRV analysis. By definition, we are interested in beat to beat variations that get highly reduced or canceled out by the averaging. Long story short, most watches are simply not designed for HRV (see later for some exceptions).
Let’s look at an example, here we have RR intervals from our trusted Polar chest strap:
While here we have RR intervals from a mio alpha (older device, just to make a point):
As you can see the trend is the same, showing that the average heart rate will be very accurate. However, there is much less variability in the second figure, due to heavy filtering necessary to be robust to motion artifacts.
Indeed when we compute various HRV features, we see that HRV for the mio alpha is artificially decreased for all features:
Note how AVNN matches, as it is computed as the average of the RR intervals for a minute of data (this feature does not tell us anything about variability, it simply tells us how accurate is the sensor in measuring average heart rate).
In terms of technology, however, it is feasible to use photoplethysmography (PPG) to extract HRV features as we have seen in the previous section (here is our validation paper with respect to chest straps and electrocardiography: Validation paper accepted for publication in the International Journal of Sports Physiology and Performance: PPG vs Polar H7 vs Electrocardiogram).
When using camera-based or optical solutions the user needs to be steady and limit movement as much as possible during the measurement. Thus, suffering as well from motion artifact problems, but with the advantage that spot-check measurements can be feasibly taken while steady since we are talking about short periods of time (typically 60 seconds). Similarly, during the night it is possible to use the same technology (as the Oura ring does) since you are also not moving.
At the time of this writing, only the following devices can be used to accurately measure HRV (excluding apps and chest straps):
- Scosche Rhythm24 armband (not the Rhythm+)
- Corsense finger sensor by Elite HRV
- Apple Watch (with the caveat that you need to use the Breathe app to measure, as explained here)
Note that these devices are not “particularly good at measuring HRV”, but they simply included 2 modalities: 1) a regular modality that is used for exercise 2) an HRV mode (that you either set via the Scosche app or when you use Breathe on your Apple Watch), so that in mode 2), no heavy filtering is employed, and you can collect accurate data at rest.
As far as I know, no other device apart from the Oura ring has been validated, but of course, there are new ones every day, so this is certainly not a comprehensive list, but only includes the ones I am certain about and I feel comfortable recommending.
Misconception 2: measuring during the night is better because you are unconscious
However, it is important to know that a night measurement needs to be analyzed in a certain way (you need the whole night of data to start with), and sporadic measurements taken during the night (for example the random data points that the Apple Watch records while you sleep) are of absolute no use despite the fact that you are unconscious.
Why is that? I’ve stressed how the morning routine is all about avoiding confounding factors, and it does make intuitive sense that the night might be a good way to get all sorted out without effort. Well, not so fast.
This can be counterintuitive, but if you measure during the night, your HRV will depend on your sleep stage. This is exactly the principle behind using HRV to detect sleep stages. Basically, HRV changes during sleep stages, and therefore devices that can measure your heart rate rhythm during the night, use this information to estimate sleep stages. It follows that of course if the sleep stage affects HRV, the HRV reported in the morning is also affected by when during the night it was measured. Additionally, the circadian rhythm will also play a role, as shown below for an article I put together with Oura:
This is why the sporadic measurement taken by certain devices during the night are not useful. Similarly triggering a measurement just before waking up or at a certain hour would make no sense — the sleep stage becomes an additional confounding factor over which you have no control. What data shall we use then to trust a night measurement? We have a few options: the average of the entire night, the average between certain hours, or only measure during deep sleep as some suggest.
Unfortunately, there isn’t much standardization, as most studies in research these days use morning measurements and therefore it’s easier to rely on those insights to understand how to interpret the data. My recommendation is to use the whole night of data, as there is little doubt that using the entire night reflects your physiological stress level. The same cannot be said for measuring during a specific sleep stage, say deep sleep. Note that deep sleep is always estimated, not measured, and the estimate can have errors (otherwise, sleep studies would not wire people’s heads with EEG headsets). Hence, cherry-picking a certain sleep stage requires making several extra assumptions on our ability to correctly identify it. On top of this, selecting only a few minutes over the night, confounded by far-from-perfect deep stage detection, brings us back to the initial issues, this is basically a random data point collected at variable times during the night, which results in an ineffective way of assessing resting physiology (see figure above).
Finally, the idea that collecting data during deep sleep is better, is merely a theory, as there is no “reference check” that you can do to determine if your final value is “better” (what does better even mean? consider that a recent study identifying deep sleep as one of the most effective moments to measure HRV derived this conclusion based on the fact that the data was highly correlated with morning rMSSD!). As we have seen in the previous post, morning measurements can capture stress from different sources very well (no need to take my word, just look at the data), hence there is really no better way (since the task is just to capture stress effectively), just use what you prefer.
Another important consideration when measuring outside of the morning routine is that the time at which you worked out, will have a large impact. For example, if you work out in the evening, your HRV during the night will most likely take some time to go back to normal, and therefore your Oura (or other night tracker) data might think you need more rest, while a morning measurement might reflect better your full recovery. If your workout schedule is more consistent (for example you train every day in the morning), then it does not really matter what method you use, and both morning and night measurements should show the same trends.
Misconception 3: Measuring before working out is more informative on my readiness than measuring in the morning
As covered in part one, the morning routine and full night measurements are the only reliable ways to measure HRV in the context of understanding baseline physiological stress in response to training and lifestyle stressors.
Why is that? Variability during the day due to acute stressors, for example even light activity, coffee intake, diet, getting upset, and so on, will result in continuous changes in HRV as the autonomic nervous system adapts to maintain a state of balance. Measuring your HRV all the time will simply reflect these transitory stressors which most likely do not have any chronic impact and are not actionable (it is normal to have your HRV lowered because you had coffee, not knowing context, so that you did have coffee, will prompt a device to tell you to rest or relax, which is not meaningful). On the other hand, the cumulative effect of all of these stressors as well as larger ones (hard training, travel, etc.) will impact your chronic stress as measured when relaxed, first thing in the morning, and this is the actionable information which has been used consistently in the last 30 years of research in the field, and that we would recommend relying on.
The morning routine is all about avoiding confounding factors (coffee, diet, even light activity, psychological stressors, etc.), hence it is the only moment where it makes sense to measure (apart from the night)
Sometimes the confusion comes from the fact that while taking several measurements, you might see that some data points result in higher HRV during the day. The first important bit to consider here is to determine even if these scores are really higher or just within your normal range (see part two of this guide). But even if the score is indeed higher, it does not really mean anything. Let me elaborate on this. The point of the measurement (both morning and night measurements) is to capture your level of baseline physiological stress (in response to strong acute stressors, and long term chronic ones). This is not necessarily the moment in which your HRV is highest. You could have “positive stressors”, like doing a mindfulness session to increase acutely your HRV, still, the effect of this session on your baseline HRV might be none, so if there are major stressors creating problems, they will still be there the morning after.
The morning (or full night) is simply the moment in which your HRV is representative of what we want to measure.
Misconception 4: HRV is the same as resting heart rate
Let’s start with physiology. Heart rate modulation (the actual differences between consecutive beats) is driven by your autonomic nervous system in terms of (para)sympathetic activity. In particular, parasympathetic activity (the part of your system that takes care of recovery, also called rest and digest) is something that we can capture using heart rate variability as it acts really quickly (in the order of milliseconds) on heart modulation (as well as another 80–90% of all processes in the body).
When there are really big stressors, the sympathetic branch also is clearly captured by heart rate metrics (e.g. just resting heart rate) as this is the fight or flight response and stress is so high because you are facing some life-threatening challenge (or at least that’s what your body thinks, then you are just probably reading some really annoying comment online).
Yet, when we look at chronic stress and baseline chronic stress as captured first thing in the morning, changes in heart rate are minimal. The modulation of heart rate (heart rate variability) is more sensitive to stressors than just the average heart rate is (we can have even very different HRV at the same HR for example, when we talk about resting measurements). I think this is fairly obvious to anyone that has measured data longitudinally using HRV4Training or any other system, as you can see many days in which heart rate does not change while there are larger variations in HRV.
The higher sensitivity of HRV with respect to heart rate in detecting various stressors is also shown in one of our publications:
In this study, we looked at the effect of different stressors and you can see that while heart rate changes are in the order of 0.5–1%, HRV changes are in the order of 5–10%, and therefore much more useful.
Long story short, HRV is more sensitive to stress, due to how heart rate modulation and the nervous system work, and therefore almost all applications that focus on stress these days use HRV instead of HR as it was done in the old days when it was less practical to measure HRV.
Misconception 5: HRV should improve over time
Let’s say that yes, you can improve your HRV over time, but this is not how I would recommend making use of the data.
Let’s elaborate on this a bit. If you are coming from a situation of chronic stress which might have reduced your HRV or impaired the parasympathetic branch of the autonomic nervous system, or if your lifestyle is not ideal (if you are sleep-deprived, have a poor diet, do not exercise and drink often, then I would be fairly sure you can improve your baseline HRV), then most likely you can improve your baseline HRV a bit.
There is also research showing how individuals starting with physical activity, can improve their baseline HRV (pretty much any parameter improves in that case). Similarly, yoga and mindfulness have shown some benefits, even though typically HRV is measured during the practice itself or right after, and I personally think we should be measuring baseline chronic stress early in the morning, more than the acute effect of the practice.
Now, this being said, the way I see this and recommend using HRV is slightly different. First of all, our baseline value (say 7 days moving average or our absolute average in the past month or two), is mostly determined by genetic factors and age. Changes are possible but are small. HRV is different from for example heart rate or VO2max or other parameters clearly linked to aerobic fitness, which improve with training in most individuals.
As HRV reflects the level of stress on the body, the way I believe these parameters should be used is a continuous feedback loop. This is why also we do not think measuring sporadically is particularly helpful, as we are more interested in capturing the information every day and trying to make adjustments so that eventually we can improve health or performance.
In terms of HRV changes, in general, it’s easy to change things at an acute level (deep breathing or meditation practice, for example, will trigger an increase in HRV), but it’s a bit different when we talk about chronic stress or baseline stress which is what we try to measure in a known context first thing in the morning.
You can use HRV to make choices and adjustments to your day that will provide better outcomes (avoiding burnout, training better, etc.) but what you optimize or improve in the long run, is not necessarily your HRV itself, but your health and performance (by better balancing stress for example — see some examples in part three, case studies).
Misconception 6: I cannot benefit from measuring HRV if I do not care about training
Needless to say, training is hardly the only stressor in anyone’s life, no matter if you are a professional athlete or just went for your first run yesterday or if you cannot care less about training. There’s work, family, expectations, etc. — we need to deal with a lot more than just training, and it all affects us physiologically
As a matter of fact, HRV is the tool of choice of many psychologists and therapists as it allows you to capture how you are responding to stress, as well as to train your ability to better cope with stress (for example using biofeedback or mindfulness techniques).
Let’s look at some actual data with non-training related stressors, which is always the best way to make sense of these statements.
In mid-February, I suffered an injury. This was the second time I had a big setback while preparing a marathon, in just a few months, despite being injury-free for many years before. If you’ve been there you know what follows, you get a little depressed, start being less careful with your diet and alcohol intake, fitness goes down, etc. — you got my point.
What did my data show at this point? I am barely training (I am doing some cross-training cycling, not shown here), and yet my HRV keeps going down:
Of course, HRV reduces: mentally I am in the wrong place, motivation and lifestyle choices are far from optimal. HRV shows objectively how poorly I am dealing with the current situation.
It makes no sense to look at your HRV data decontextualized. HRV represents your response to training and lifestyle stressors, and you need to look at how things are changing with respect to those stressors, as shown in HRV4Training Pro for example.
Check out also part three for more case studies including travel and work-related stressors and different HRV responses.
Misconception 7: HRV is less useful than subjective data to capture how an athlete responds to training
This misconception is mostly deriving from a paper that a few years back stated that subjective metrics are better than objective ones in monitoring athlete training response.
But let’s look at what was actually analyzed in the paper.
The authors looked at how training load related to both subjective and objective metrics, hence according to the paper, the reference to determine if a metric is a valid metric, is how it correlates to training load.
In my opinion, the whole assumption that you should find the metric that “correlates the most” with training load, makes very little sense. Why? Because you are already measuring training load, so what is the point of having another metric that gives you the exact same information? Well, none. By definition, if a metric is perfectly correlated to training load, then it is a useless metric, as it does not add any information to the training and recovery equation (but ironically, it would have been interpreted by the study as the best metric).
I’ve already discussed before how the notion that increased load should trigger a reduction in HRV is very simplistic. As a matter of fact, we have seen we can have stable or increased HRV when increasing load (a sign of positive adaptation) as well as reduced HRV with low load because of other stressors (travel, work, etc.). If my training load is increasing and my HRV stays within normal or increases, that’s great, it means I am responding well to stress. Yet, I most likely feel more tired and sore, and can’t wait for my next recovery week (I’m pretty sure you can easily relate if you practice any endurance sport).
The point is that by measuring your resting physiology first thing in the morning, you can understand how you are responding to training (and other stressors), and use that information as part of your decision-making process. If you are coping well with stress, HRV will not be decreased. The data shown in the various case studies I have highlighted here should clearly show how important context is and how effective is HRV in capturing individual responses to various stressors (training included, of course).
Finally, don’t get me wrong, it is fairly obvious that subjective metrics are also extremely important. This is why we include a questionnaire after the measurement so that you can take a minute to pause, and self-assess how you are feeling subjectively, a key part of the process.
A smart coach, educator or athlete, understands that training load, HRV, and subjective metrics all provide important information that needs to be integrated daily, to decide the better course of action.
There is no winner between objective and subjective metrics, they all serve a purpose.
In the next section, I’ve reported some good input on this from Andrew Flatt, one of the main researchers in the field of HRV and training.
Misconception 8: HRV is not useful if I do strength training
Let’s start with some general considerations. A typical question we get a lot is the following: “does it make sense to use HRV if I do this or that sport”?
As it follows from the previous question on HRV in the context of non-training related stress, physiological stress comes from different sources, all having an impact on our ability to deal with additional stress (there is only so much we can take!) and therefore of maintaining or improving our health and performance.
At the end of the day, it does not really matter what sport you or your athletes do, stress is part of everyone’s life (there’s travel, work, family, you name it). Training is just one of the stressors, and obviously keeping stress in check is relevant way beyond endurance sports, if we care about our own and our athlete’s health and performance.
But let’s try to get more specific here. If you are interested in strength training, I would highly recommend following experts in the field that have a comprehensive understanding of the topics of strength, power, periodization, and stress, such as Carlo Buzzichelli (author of Periodization: Theory and Methodology of Training, together with Bompa) or Andrew Flatt.
In particular, Andrew wrote this piece for our blog years ago, everything he mentions there is 100% current, and therefore below I will mainly quote his words:
“A definitive training program or manual on how to improve a given physical performance quality in highly trained individuals of any sport does not exist. Nor will it ever. This is because of (at least) two important facts:
- High inter-individual variability exists in how individuals respond to a given program.
- The performance outcome of a training program is not solely dependent on the X’s and O’s of training (i.e., sets, reps, volume, intensity, work:rest, frequency, etc.) but also largely on non-training related factors that directly affect recovery and adaptation.”
While point 1 is clear to most coaches, point 2 seems to be absent from the conversation. Most research on strength training takes an extremely simplistic approach, for example, having ten young men with strength training experience doing hypertrophic and maximum strength loadings once and then measuring HRV following exercise, on a single occasion (example paper here).
Well, it is quite obvious to me that a 1 rep max done once is very different from training and competing over weeks and months while dealing with the cumulative effect of training, traveling, getting sick, menstruating, and all the other stressors that are part of real life, and here is where HRV becomes useful (note that even in the study mentioned, rMSSD was reduced after the single session, clearly showing that stress is captured by HRV — and will pile up when you take this out of the lab, on repeated days, etc.).
Maybe the misconception is again that in these studies researchers look for that kind of perfect relationship between load and “recovery”, but that makes little sense (the human body is more complex than that, and of course it adapts, so over time you get better at handling the stressor). I’ve discussed earlier how, as a matter of fact, a parameter that correlates perfectly with training load, is of no use as it does not add any information to the training and recovery equation (see misconception 6).
HRV tells you how you are handling stress. If we take well-trained individuals, we don’t expect HRV to be highly suppressed the day after aerobic exercise either, if that’s an exercise that they can handle well.
Back to Andrew’s article, he also explains in more detail how strength and power athletes can respond to very high acute stressors as well as how things differ with the cumulative effect of more sessions, in terms of HRV:
“In a study involving elite Olympic weightlifters, HRV was suppressed for 48 hours following a grueling 2-hour workout that was preceded by 10 days of rest. Once HRV returned to baseline, so did 1RM strength. This is a nice example of an acute bout resulting in an HRV response that takes several days to return to baseline. What happens when insufficient recovery between sessions is provided? In a recent study, daily HRV measures showed a progressive decreasing trend during a 6-day overload microcycle in 15 trained powerlifters. HRV trended back towards baseline in the 4 days following the overload. 1RM Bench and Squat followed a similar pattern with a decrease compared to baseline by the end of the overload followed by a return to baseline 4 days after.”
Obviously you cannot capture any of this if your protocol consists of doing 1 exercise and measure right after (no longitudinal measurements over weeks, no repeated loading, etc.). Unfortunately, there are many studies that have little to do with how things work in real life, and should not be used to provide recommendations to practitioners, in my opinion.
Andrew mentions three main reasons when HRV is useful for strength athletes (not surprisingly, these are as good for any other sport):
- The training stimulus is significantly greater than the individual typically experiences
- The training stimulus is novel or different from what the individual is accustomed
- Training is otherwise normal, but non-training related stressors are affecting recovery
I am sure you can see the link between these and what we have previously covered, or showcased in our case studies.
Back to Andrew, I’ll report point 3, which is easily applicable to anyone and really the main point that coaches and athletes should understand, no matter what sport they practice:
“Non-training related stressors are tremendously important to consider for trained athletes. These factors affect HRV and they affect your ability to handle and adapt to training. These factors likely contribute to large acute changes in HRV in response to otherwise “normal” or familiar training that typically wouldn’t cause such a change.”
“Gains in strength and hypertrophy largely depend on the balance between protein accretion and protein breakdown favoring the former. Chronically elevated stress results in catabolic activity and may shut off protein synthesis, thus shifting the balance in favor of protein breakdown. Also impacted from high stress is immune function. Combine your regular training regimen with high non-training related stress and your chances of getting sick go up. When you’re sick, you can’t maintain your normal training and naturally, progress is derailed.”
“if you believe that an important part of the training process is managing stress, then stress should be monitored. Training load gives us an indication of physical stress, wellness surveys give us a good indication of perceived stress and HRV provides a good indication of its effect. Thus, when taken together, each variable provides unique and meaningful insight regarding the amount and type of stress that is being experienced and importantly, how we’re handling it. Therefore, HRV is most useful when used in conjunction with these other recordable metrics.”
Isn’t that so obvious?
Objective data is not here to replace how you subjectively feel but to be combined with it and with training load information so that you can make better decisions, no matter what sport you do.
I hope this was a decent overview of common misconceptions about strength training and HRV. We have the technology to go beyond unrealistic laboratory settings or one-time measurements following isolated protocols, and we should really try to do studies that reflect how various stressors interact in real life.
Last few words
We live in a time where it takes only 10 USD and 1 minute in the morning to measure physiological data.
There are no quick fixes for lifestyle changes and physiological adaptations, but it all starts with a little more awareness.
Measure every day for a few weeks, gather contextual data (subjective parameters, lifestyle, training), then look at how things have evolved and you can start to implement meaningful changes that have positive long term impact on your health and performance.
I hope this guide can be a useful starting point.
Take it easy.
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching.
He has published more than 50 papers and patents at the intersection between physiology, health, technology and human performance.