Heart rate variability, or HRV, refers to ways to quantify the variation in heartbeats. This variation between heartbeats is mostly due to how the body responds to stress, and as such, we can try to use this information to better understand how we are responding to training or how our lifestyle or behavior is impacting our stress response. This can be easier said than done, given the many different options available on the market, the inconsistencies between the protocols recommended to collect data, and the methodologies used to interpret the data. In this article, we try to clarify some of the most important points, so that you can possibly use HRV more effectively.
A proxy of stress
When we experience stress – in any form, physical, or psychological, due to lifestyle or the environment – our body responds in standard ways, typically a combination of changes in autonomic nervous system activity and the release of hormones. In turn, the autonomic nervous system, which innervates the heart, will impact heart rhythm, both in terms of heart rate and variability. Due to these mechanisms, measuring heart rate and HRV gives us insights into how our body responds to stress. We can view HRV measurement as a non-invasive proxy of our body’s response to stress, and as such, when properly implemented, HRV measurements can allow us to better understand how we are responding to training and other stressors, and enable us to make meaningful adjustments.
How should we measure HRV?
Given that HRV is influenced by the nervous system, pretty much anything will impact it. This can be an issue at times, as when we measure HRV, we want to make sure we are capturing our overall physiological stress level and not just some transitory stressor that will be gone in a few minutes. For example, we are not interested in capturing our response to walking up the stairs or drinking coffee, but we want to capture our body’s response to larger, long-lasting stressors, that impact our health and performance. Examples of these larger stressors would be big training blocks, travel, sickness, the menstrual cycle, psychological stressors, and many of the other aspects that impact our daily lives.
How do we make sure we are capturing our overall physiological stress level and not just some transitory stressor? To address this issue, we simply need to measure in what we call a reproducible context, something we can do every day in the same way, without being impacted by transitory stressors that confound our measurements. There are two ways to do this: a morning measurement, as soon as we wake up, or a night measurement, using a wearable device that can provide the average of the full night.
Morning measurements can last even just one or two minutes, and should be taken after a visit to the bathroom (if needed!), and while breathing naturally. For endurance athletes with particularly low resting heart rate, it is, in general, a good idea to measure HRV while sitting, as this little extra stressor allows them to better capture their stress response, with respect to a measurement taken under conditions of complete rest (sleeping or even just lying down). The morning measurement is also the most inexpensive way to try this out, as apps like HRV4Training can use the phone camera for the measurement, hence no sensor is needed. Measuring as soon as you wake up, while relaxed, and before breakfast of exercise, ideally while sitting, is the optimal way to collect meaningful data, used in research and applied settings.
If you prefer to use a device that measures automatically during the night, make sure the data is collected during the entire night, and not just for a few minutes as some devices do, otherwise depending on the sleep stage, the data will vary wildly, without a clear association with your overall stress level.
Technology for HRV measurement
Technology for HRV measurement at rest is getting better every day. At HRV4Training we have developed the first camera-based approach to collect HRV data accurately, which has been validated (2), and independently validated (3). For morning measurements, an alternative would be to use a Polar or Garmin chest strap paired to the same or a similar app. Additionally, other devices have been recently validated for night-long recordings, for example, the Oura ring, and Polar watches. In my experience, Garmin watches also work reasonably well for night measurements. This is not the case for the Apple Watch however, as it provides sporadic measurements highly impacted by transitory sleep stages, and therefore becomes ineffective in capturing overall stress level.
Interpreting the data
Depending on your preference (cost, interest in wearables, etc.), you might go for a morning or a night measurement. Both can be effective (5), but there are some important differences: while night data might better reflect the previous day or evening’s stressors, morning data can be more actionable, and more representative of daily readiness (4), as the measurement comes later and after the restorative effect of sleep. Long-term trends will be similar, but make sure to evaluate these differences before choosing a device or app. My recommendation for endurance athletes would be to measure first thing in the morning while sitting, unless unable to do so for one reason or the other. In that case, a device that captures the full night of data is certainly a valid alternative, at least for longer-term trends.
Assuming that we use an accurate sensor, and we either measure first thing in the morning or our sensor is able to provide an average of many hours during the night, we miss only one final step: interpreting the data. Unfortunately, most tools out there still provide naive, “higher is better” interpretations. However, HRV, similarly to other physiological signals, should not be interpreted just in one direction, but in the context of what is your normal range.
Differently from other parameters, for example, blood pressure, HRV does not have population ranges, hence once we take our first measurement, we really do not learn much as we have no frame of reference to compare to. However, as we keep measuring, we can start learning what is our normal range and our own frame of reference, which will allow us to analyze relative changes over time, in response to various stressors. Below is an example where you can see the normal range (shaded area) and response to a stressor (suppression following a day of travel), in HRV4Training.
Once we have established our normal range, an optimal area in which we expect our HRV to be unless we face major stressors, it is easy to understand that a stable HRV highlights an ideal response. This is true even when training hard. A stable HRV does not represent a lack of stress but highlights a great ability of the system to respond and renormalize quickly after stress. On the other hand, an unstable HRV or low HRV the day after training highlights a mismatch between the stimulus and the athlete’s fitness (possibly time to rethink the training plan) or the presence of non-training-related stressors (psychological stressors, sickness, or else). Let’s see what we can do when we find ourselves in such a situation of suboptimal HRV, with a suppression or unstable profile, in response to stress.
If HRV helps us to quantify individual responses to stress, we should be able to use this data to manage stress a bit better. The idea behind HRV-guided training is therefore that by providing the most appropriate training stimuli in a timely manner, when our body is ready to take it, positive adaptations will occur and we will be able to improve performance. Normally a timely manner means that we should probably avoid adding high-intensity training stress when our daily HRV or weekly HRV is suppressed, with respect to our historical data. For example, if our HRV is below our normal range, it is a sign that we have not responded well to previous stressors, and we might not be in the right state to assimilate additional stress..
In their research, Alejandro Javaloyes and co-authors showed how HRV-guided training could lead to better performance, and reported: “hypothesis for this greater adaptation to training for the HRV guided group is in line with the idea of performing high intensity training when the athlete is in optimal conditions to perform it. Therefore, these differences … may be due to a better timing in the programming of high-intensity training” (6).
Needless to say, our capacity to handle stress is limited, and while periodization is an important starting point, we need to be able to add flexibility and provide the right stimulus at the right time, which is something HRV might allow us to do.
Conclusions and takeaways
As the body tries to maintain a state of balance so that it can function optimally, heart rhythm is influenced by a series of processes that reflect the level of stress on the body. Thus, we can use HRV as a generic proxy of stress.
To make use of HRV data, we need to collect accurate data, at a meaningful time, and interpret the data with respect to our individual normal range. Make sure the tools you use are validated and analyze the data with respect to your own normal range, aiming for stability. Strong stressors, associated with our health and lifestyle (e.g. training, sickness, alcohol intake, travel, etc.), can have a dramatic impact on resting physiology. Positive responses will be associated with a stable HRV profile, or slow increase over time, while negative responses will result in less stability and more frequent suppressions in HRV.
Capturing stress responses before they develop into negative chronic states can be key in making adjustments leading to improved health and performance. For these reasons, HRV can be a useful tool for day-to-day load and stress management.
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.
About the author: Marco is the founder of HRV4Training, a data science advisor at Oura, an Editor at IEEE Pervasive Computing (Wearables), and a guest lecturer at VU Amsterdam. He loves running.
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