Myths And Facts About Heart Rate Variability (HRV)

Is there seasonality in heart rate variability? Can you use HRV as a real-time stress monitor? And is a higher HRV always better? These questions (and more) answered.

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In the previous articles of this series, we have seen how heart rate variability (HRV) is a marker of stress that we can use to capture our response to training and lifestyle stressors.

We then looked at available technologies and best practices in terms of protocols for data acquisition.

Once we have collected some data, we can build our normal range and understand if, on a given day, our score is showing a positive response (e.g., an HRV within our normal range) or a negative one (e.g., a suppression below our normal range).

This is the basic mechanism behind HRV-guided training. For example, on occasions in which our HRV is below our normal range, it might be a good idea to reduce training intensity.

In this final article, we look at a few myths and facts about HRV, with the goal of helping you make the most of the data. 

Ready? Let’s jump in!

A person looking at their smart watch.

A Higher HRV Is Always Better: Myth  

We often hear that a higher HRV is a good sign. However, this is not always the case.

In fact, in certain situations, strong stressors might lead to an acute increase in HRV (and suppression of heart rate), highlighting a highly parasympathetic state.

This highly parasympathetic state does not reflect “being recovered” as much as it does reflect “being busy recovering.”

Naive interpretations typical of wearables tend to always consider an increase in HRV as positive, often missing the mark when it comes to these responses and providing incorrect advice to users.

This is why when I built HRV4Training, I decided not to flag suppressions below the normal range but also particularly high values above a person’s normal range.

This being said, chronically, over months, you might want your HRV to increase in response to a certain intervention, but this is different from acute (day-to-day) change.

Finally, remember that stability is the ideal response, highlighting how we can quickly re-normalize after stressors, regardless of absolute values or long-term changes. 

A person looking at their smart watch.

There Is Seasonality In Heart rate variability: Fact

I just wrote above that we might want HRV to change chronically in response to an intervention.

We should, however, remember that there is seasonality in resting physiology, and as such, there might be changes outside of our control or simply not necessarily driven by our behavior or intervention.

For example, resting heart rate tends to be lower in summer and higher in winter, and heart rate variability tends to be a bit higher in summer and lower in winter.

Seasonality can be more marked in certain people and is not necessarily obvious to see in our individual data (while it is clear in aggregate data from large populations).

The practical implication is that we need to understand that if we start a positive behavioral change when winter is approaching, it could be that the expected changes in our data are not present or reduced, and we shouldn’t really worry about that.

Remember that metrics like heart rate and heart rate variability should help us manage stress on a day-to-day basis so that what we improve is health (and performance), not necessarily the metric itself. 

A person looking at their smart watch data.

We Can Use Heart Rate Variability As A Real-Time Stress Monitor: Mostly A Myth

This one is a bit more complex to answer.

There is room for using HRV outside of the morning routine or at night, but this use requires careful planning and contextualization of the data, which is very far from what wearables companies are offering today.

For example, heart rate variability has been used in research to assess the impact on autonomic activity of training of different intensities and durations by measuring it before and after a given workout.

This research was key in understanding how to manipulate training based on HRV, as it showed that low-intensity training would not cause a disruption in autonomic activity, even when doubled in duration (something I have discussed in part 3 of this series).

However, this type of protocol requires participants not to drink or eat anything in the hour or two before the first measurement.

Then we exercise, and then again, we sit still, without moving, eating, or drinking (or showering!) for about 2-3 hours to take another set of measurements that allows us to capture the body’s response to exercise right after the fact.

A person running.

Any deviation from this protocol will artifact the data.

For example, it has been shown in multiple studies that even something as simple as drinking water will increase HRV for more than an hour, making any interpretation of that data meaningless.

Unfortunately, most wearables today “use HRV” to provide real-time stress monitors that have little physiological meaning and are easily artifacted by all sorts of things we do daily, from drinking water to eating.

We cannot control these confounding effects outside of a laboratory study.

As such, practically speaking, I highly recommend intentionally measuring our heart rate variability following the best practices and protocols described in part 2 of this series instead of using wearables’ automatic and decontextualized interpretations.

A person running wearing a heart rate monitor.

HRV Is A More Sensitive Marker Of Stress Than Heart Rate: Fact

Both heart rate and heart rate variability reflect changes in parasympathetic activity. As such, some stressors will similarly impact both metrics.

However, typically, only the strongest stressors have a meaningful impact on heart rate. For example, sickness or excessive alcohol intake will show up clearly on heart rate data.

Other stressors, such as training, tend to be better captured by HRV.

This is due to how the parasympathetic nervous system impacts heart rhythm: as parasympathetic tone mostly modulates heart rhythm during the exhale, changes in parasympathetic activity are somewhat averaged out when computing heart rate, while they are more obvious when computing HRV.

In practical terms, both heart rate and heart rate variability are useful markers and should be used together, in my opinion.

While your heart rate will most likely be within your normal range every day, unless you get sick, HRV will likely show larger changes (e.g., suppressions) in response to things like training, environmental stressors (heat, altitude), travel, psychological stressors, and more.

When both heart rate and HRV are outside your normal range, it is a strong signal that something is off, e.g., you might be getting sick, and it is probably wise to adjust to training (and life in general).

When only heart rate variability is suppressed, we are probably dealing with a more subtle response, which allows us to make adjustments before it is too late, for example, adjusting training intensity. 

A person running.

Readiness And Recovery Scores Are Effective At Capturing The Stress Response: Myth

It is quite common for HRV to be used in readiness or recovery scores provided by wearables and apps.

However, there are issues with this approach, especially for athletes. Wearables are useful for capturing raw physiological data, but they do a disservice to athletes when interpreting the data providing readiness or recovery scores.

As athletes or coaches, we really want to see how the body responds to (training and other) stressors.

Muddying the waters mixing behavior (e.g., sleep and activity) with physiology (e.g., heart rate and heart rate variability) makes it so that the data reflects assumptions made by a generic algorithm (e.g., less sleep or more activity requires more recovery) as opposed to what the athlete’s physiology actually showed (e.g., a good response to an increased training volume).

For anyone who has a plan (training or other), it is more effective to look at the physiology than to rely on an overly reactive approach guided by scores that tend to make assumptions more than evaluate the actual state of the individual.

These are important nuances if we want to use the data effectively, as opposed to only capturing very large stressors that probably don’t require a wearable for us to notice (e.g., sickness or excessive alcohol intake).

When I wake up, I want to see how my body responds; I do not want to be penalized because an algorithm assumes that having slept a bit less, I need more recovery. 

Heart rate data on a screen.

I Can Measure My HRV If I Have An Arrhythmia: It Depends

Arrhythmias cause a disruption in the beat-to-beat data used to compute HRV. Depending on the type of arrhythmia and, most importantly, its frequency, it might or might not be possible to compute HRV.

If you have an arrhythmia, even just some harmless premature ventricular contractions (something that seems to affect between 40 and 75% of the population…), it is key to use software that can deal with artifacts in the data and still provide an accurate heart rate variability measurement.

My recommendation in these cases is to measure your HRV in the morning, as you are awake and can easily determine if there was any ectopic beat during the measurement (either because you can feel it or because you can see its impact on the raw data, for example, the PPG data streams shown in HRV4Training).

On the other hand, measuring at night leaves you wondering if the data is accurate or artifacted. Remember that disruptions of this kind in the data can only result in an artificially higher HRV.  

A person reviewing their phone.

Heart rate variability: a key metric

That’s all for this series on HRV.

Hopefully, it is a bit clearer now what HRV is, how we can collect data, and how we can interpret it meaningfully.

There is a lot to gain in becoming a bit more aware of how different stressors impact our bodies.

HRV can be a key metric to get us there, but make sure to measure heart rate variability in a meaningful context and to use HRV for what it can actually do: an assessment of our resting physiology, sensitive to many stressors but not specific to any in particular.

A person running.
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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 has published more than 50 papers and patents at the intersection between physiology, health, technology and human performance. He is the co-founder of HRV4Training, advisor at Oura, guest lecturer at VU Amsterdam, and editor of the Wearables department of IEEE Pervasive Computing. He loves running.

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