The Ultimate Guide to Heart Rate Variability (HRV): Part One
Measurement setup, best practices, and metrics.
In this series of posts, I’ll provide an overview of best practices for your Heart Rate Variability (HRV) measurements (part 1), and tips on how to analyze and interpret your data over the short and long term (response to acute stressors, longer-term trends, etc. — in part 2), backed by scientific research in the field.
HRV is nothing new, and fairly simple to use effectively, but poor standardization and methodological inconsistencies make it difficult sometimes to make good use of the technology, understand what is reported in the scientific literature, and navigate overly optimistic marketing claims.
Hopefully, these posts will help, but please feel free to comment below should you have any doubts and I’ll try to help.
You can find the other part of this series at these links:
Part 1: Measurement setup, best practices, and metrics (this post!)
Measurement setup and first recordings
But first, Just a tiny bit of theory
HRV is a term that refers to ways to summarize in a number the variability between heartbeats.
Right, but why do we care?
We care because HRV is a proxy of the stress response (Altini and Plews, 2021).
What does that mean?
When we face a stressor (a perceived ‘threat’ or challenge), we have a physiological response that mostly involves the autonomic nervous system and the endocrine system (hormones). In particular, a reduction in parasympathetic activity (the branch of the autonomic nervous system in charge of rest and recovery functions) is common during acute stress. While we cannot measure "stress”, and we cannot measure parasympathetic activity either, the parasympathetic system modulates heart rhythm (i.e. it slows down or quickens heart rate).
Given that parasympathetic activity has a direct effect on heart rhythm (heart rate and HRV), measuring HRV (at the right time!) is an effective way to capture our body’s response to various stressors such as training, lifestyle, and more.
In particular, a reduction in certain HRV features (for example rMSSD, more on this later) typically means that parasympathetic activity is reduced, and therefore we have not fully recovered or in general, there is more stress on our body and we are unable to quickly jump back to our normal range.
This all assumes that data is collected using valid methods, and following best practices, which I’ll cover in the next sections.
How do you measure HRV?
You can measure your HRV with various sensors, either an electrocardiogram (ECG) with sticky electrodes, or a more practical ECG device that measures the electrical activity of the heart, such as the Polar H10 chest strap. You can also use one of a small number of devices that use optical technology. Keep in mind that the only way to measure your HRV is to use an ECG (and therefore a chest strap), and optical methods such as the phone camera, rings, or wrist bands, measure PRV (Pulse Rate Variability, as detectable by changes in blood volume at the periphery - finger or wrist). However, under most circumstances, PRV and HRV are very similar, especially when tracked in terms of relative changes over time for a healthy individual. Thus, either method can be used effectively, provided that the device used has been validated (Plews et al. 2017).
If you prefer to use an optical sensor, measurements can be taken either using the phone camera (HRV4Training is the only validated app that can do so) or a separate device, for example, the Scosche Rhythm24. The Apple Watch is also able to measure HRV reliably when using the Breathe app, and can be integrated with HRV4Training. If you are an Apple Watch user, please read this other post as well.
My recommendation would be to use the phone camera or a Polar H10, so that HRV4Training has full control of data quality, and can provide you with that information as well. These are two methods we have validated against ECG.
Protocols for data collection
Once you have a way to acquire accurate data (the easiest would be to use the camera-based measurement in HRV4Training, which is also what I use), the next step is to use a good protocol to acquire data that is not only accurate but can also be interpreted meaningfully.
Our application of interest here is determining chronic physiological stress level, which derives from combined strong acute stressors (e.g. a hard workout, intercontinental travel) and long-lasting chronic stressors (e.g. work-related worries, etc.). By measuring the impact of these stressors on our resting physiology, we can make meaningful adjustments that can lead to better health and performance (more on this later!).
What is the best protocol in this context?
Morning measurements allow us to measure our resting physiology as far as possible from the previous day’s stressors and after the restorative effect of sleep. Additionally, morning measurements are the only way to exploit the orthostatic stressor, which allows us to capture subtle training-related changes in resting physiology (Gronwald et al. 2024). As such, I consider morning measurements the ideal way to capture our stress response.
Here are some best practices for a morning measurement: you wake up, grab your phone, sit up, and measure either using the phone camera or a chest strap.
Your morning measurement will last between 1 and 2 minutes (longer is possible but not necessary) and return an HRV number. During the measurement, try to limit movement and breathe naturally, without forcing it. Avoid yawning and swallowing, as these would create large artifacts. If you need to go to the bathroom, please do so before your measurement.
My recommendation is to measure while sitting, to add a little orthostatic stressor, which makes the data more sensitive to stressors, especially if your heart rate is particularly low or you are an endurance athlete. Measuring while sitting is one of the main reasons why morning measurements are better than night data, together with the timing which is further away from the previous day’s activities such as dinner or exercising late.
Alternatively, you can use a night measurement. If your evening routine is rather uneventful, the data in the long term should be quite similar to morning measurements taken while lying down. For a night measurement, you wear a device as you sleep, and the device measures your HRV during the entire night, typically in chunks of a few minutes, then the average of these chunks is computed as your night HRV number. Note that if you are measuring just for a few minutes over the entire night, data will be affected by the circadian rhythm, sleep stages, and misdetection of sleep stages, causing reliability issues that are described in detail in this post.
Regardless of the sensor you use, you can enter your data in HRV4Training as part of the morning questionnaire, in order to obtain a proper interpretation (see how, here).
Useful resources:
Remember, consistency is key
The list of confounding factors for HRV analysis is pretty much endless. Why? Because HRV is nothing less than a mechanism triggered by our body in response to stress (and pretty much anything is a stressor!).
This explains also why it is absurd to think that HRV is irrelevant to a specific sport. Regardless of the type of sport you practice (or if you do not even practice any sport), stress is part of everyone’s life, and therefore we can use HRV to determine how we are responding to stress and possibly implement changes.
This also explains why it is key to either rely on the morning routine or on night data, something we can do every day in a similar way. Once you pick a protocol, try to stick to it and take your measurements consistently over time.
The goal is to capture your resting physiology far from large stressors, consistently, every day. Once that’s done, we can start looking at relative changes over time, and assess how we are responding to the various stressors we face. Without a good and consistent measurement routine, we cannot use the data effectively.
What does the HRV number mean?
Once we have a validated sensor and good protocol (e.g. first thing in the morning, sitting up), we can take our first measurement, which will return an HRV number.
HRV is determined by computing so-called features, starting from a series of RR intervals, or time differences between heartbeats, as we have seen in the figures at the beginning of this post.
This means that on the contrary to heart rate, which can be thought of as an almost instantaneous value, HRV requires a certain amount of data to be accumulated, before it can be computed. In terms of features, the sports science community through the work of many in the past 10–15 years, settled on rMSSD as the most meaningful and practical HRV value to use in applied research and real life.
Why rMSSD?
Because of how our physiology works. In particular, the vagus nerve (representative of the parasympathetic system) acts on receptors that modulate heart rhythm on a beat-to-beat basis. Hence, variability computed as rMSSD reflects parasympathetic activity, since the math captures high-frequency changes (you can learn more about HRV features here, should you be interested). Without going into another primer on HRV, parasympathetic activity represents our body’s rest and recovery system, and can be captured in terms of HRV (rMSSD): a stressor might, for example, induce a physiological response in terms of reduced parasympathetic activity, which translates into lower HRV as the nervous system modulates heart rhythm in response to such stressor.
The HRV number that you see in HRV4Training is a transformation of the rMSSD value, typically resulting in a score between 5 and 10. This score is entirely based on your HRV, to make sure you can assess how your body is responding to stress, physiologically. Your sleep, activity, and other questionnaire data is there for you to contextualize the data, but will not influence the number, as we believe the number should represent how your body has responded, nothing else.
What’s next?
Now that you know how to measure HRV and understand what the data means, we can look at how to analyze the data in a meaningful way.
I will cover in the next section the concept of the normal range, but the important bit to remember, for now, is simply that HRV data is highly individual and has inherently high day-to-day variability. This means that it is not meaningful to compare to others and that in your own data there can be large fluctuations between consecutive days. Similarly, HRV should not be interpreted as “higher is better”, a common issue with most other apps and tools out there.
What are the implications? To make effective use of the data, we need to be able to determine what changes are trivial, or just part of normal day-to-day fluctuations (what we call your normal range), and what changes do matter and might require more attention or simply truly represent a positive (or negative) adaptation to training and other stressors (HRV4Training does the math for you).
In the next post, you’ll learn to analyze how training and lifestyle impact your physiology, and potentially make changes toward better health and performance.
Check out part 2, here.
References:
Altini, M. and Plews, D., 2021. What is behind changes in resting heart rate and heart rate variability? A large-scale analysis of longitudinal measurements acquired in free-living. Sensors, 21(23), p.7932.
Plews, D.J., Scott, B., Altini, M., Wood, M., Kilding, A.E. and Laursen, P.B., 2017. Comparison of heart-rate-variability recording with smartphone photoplethysmography, polar H7 chest strap, and electrocardiography. International journal of sports physiology and performance, 12(10), pp.1324-1328.
Gronwald, T., Schaffarczyk, M. and Hoos, O., 2024. Orthostatic testing for heart rate and heart rate variability monitoring in exercise science and practice. European journal of applied physiology, 124(12), pp.3495-3510.
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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 is a certified ultrarunning coach.
Marco has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.
He is co-founder of HRV4Training, Endurance Coach, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.
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