A note on continuous heart rate variability (HRV) measurements

issues with the physiology, technology and data interpretation of a (falwed) emerging application

As it becomes easier and easier to capture HRV data (or at least PRV, the version of HRV captured when using optical methods via PPG), it follows that the interest grows in measuring HRV all the time.

In this blog, I’d like to argue against this approach or at least provide some pointers related to why we should be very cautious.

Some of my reasons have to do with physiology, others with the technology, and yet some more with the interpretation of the data.

Let’s get to it then.

Physiology

What’s HRV?

HRV is a measure of the variability between heartbeats. Under certain conditions, this variability is mostly due to the parasympathetic nervous system, which modulates heart rhythm in response to different factors, such as stress. Note that other factors impact HRV, for example, the baroreflex, mechanical stimuli, and hormones.

Under certain circumstances, HRV is a decent proxy of the stress response. These circumstances are: measurements taken under controlled conditions, at rest, while breathing normally. This is why HRV is normally measured first thing in the morning (or possibly, during the night, if we use several hours of data not to be fooled by sleep stages and other aspects causing variations in HRV during the night).

Outside of these circumstances, HRV might not reflect what we are interested in (i.e. parasympathetic activity), and as such, might be meaningless. The autonomic functions captured by HRV are the ones that regulate heart rhythm - by definition. However, the parasympathetic system has other functions that are not captured by HRV measurement.

For example, digestion is a highly parasympathetic state, hence after eating you’d expect to see high HRV if HRV was representative of parasympathetic activity. However, since heart rate is increased for the extra work required during digestion, HRV will be suppressed. A suppression in HRV (or even - if we wanted to extrapolate - in parasympathetic modulation of heart rhythm), does not mean that overall the body is not in a highly parasympathetic state, in this case. It follows that continuous measurements, or any measurement outside of the morning routine, would fail to capture this state correctly. Only by measuring far from meals, you’d capture a state that is not confounded by changes in parasympathetic activity that cannot be captured using HRV.

Additionally, outside of these well-defined morning protocols (or the night), any sort of irrelevant factor can impact HRV in ways that are not meaningful or actionable.

The figure below is from research carried out by James Heathers. You can see a clear dose-response relationship between HRV and drinking water. As most people seem really quick to associate behaviors or other factors to changes in HRV as if they were positively or negatively impacting our health, remember that this is often not the case, as shown in the dose-response between HRV and drinking some water.

The amount of confounding factors is endless, making it so that more data (i.e. data collected outside of very well-structured protocols) does not lead to more insights, but quite the contrary.

Technology

The technology for continuous HRV measurement is just not there. This is the least problematic point, as regardless of the technology, issues with physiology (discussed above) and interpretation (discussed below) will still stand.

However, it is important to stress that PPG technology, or optical measurements (the ones provided by wearables), cannot be used under conditions that are not of complete rest. Any minimal motion will trigger artifacts so large that HRV analysis cannot be carried out (even when heart rate can still be recovered with high accuracy).

Consider that just typing on your computer or slightly contracting a muscle makes HRV data collected from wearables completely unusable, artificially increasing HRV 3+ times, just because of artifacts.

Finally, the relationship between HRV (actual changes in heart activity) and PRV (changes in pulse rate variability at the finger, wrist, or other location due to volumetric changes in blood flow) is strong only under certain circumstances, again of complete rest and natural breathing. Outside of these settings, PRV, which is what you get from wearables, might depend more strongly on other factors (e.g. blood pressure, arterial stiffness, etc.).

HRV analysis is too prone to error for continuous measurements under conditions in which there is any motion, making the measurement quite unreliable (and I’m not even getting into artifacts, ectopic beats, or other cardiac abnormalities, something that up to 75% of the population experiences, which would also lead to inaccurate data).

When using chest straps or other sensors able to measure the electrical activity of the heart, this is less of a problem, as they tend to be able to provide more accurate data on most occasions. However, these are typically not the systems people are willing to wear 24/7.

Interpretation

Analyzing HRV continuously, even when done with sensors that can provide accurate data (i.e. chest straps), and in situations in which the data might reflect parasympathetic activity (e.g. at rest, with normal breathing, etc.), is often trivialized due to the disconnect between acute and chronic responses to stressors.

The simplest example here is exercise: acutely, exercise reduces HRV greatly both during and after exercise. Chronically, exercise will lead to a number of positive effects for your health, and might even increase your HRV. Similar considerations could be made for sauna or hot baths. The last time I had one, my Garmin went crazy because of the increased heart rate, a perfectly normal physiological response (blood vessels dilate to cool you off, blood pressure reduces, heart rate increases. HRV simply reduces as HR increases). Similarly, I had several people reaching out and sharing Garmin screenshots, worried about how these devices would detect long periods of stress post-exercise, basically turning a normal physiological response into something pathological.

No wearable will ever have the required context to understand what is going on beyond the most simplistic interpretations. While these issues are present also when taking resting measurements, they are amplified by continuous measurements.

Acute and chronic responses can often differ, and continuous interpretation of HRV is often trivialized as “low HRV is bad”, potentially leading to an unhealthy obsession with any sort of stressor, even the ones that are actually positive.

Even if we were to understand that low isn’t always bad, how do we discriminate which stressors are negative only acutely, and which ones are not, for stressors that are not as obvious as exercise? Think about how people might use HRV in the context of diet or else.

I could touch on other aspects that confound interpretation (e.g. circadian variation), but hopefully, you can follow my reasoning.

Wrap-up

The utility of HRV is in measuring our stress response, i.e. what happens in our body hours after the stressors. That’s why it is useful, if we respond well, it normalizes and is stable, if it stays suppressed, something went wrong or other stressors played a role.

Measuring in a known context (e.g. first thing in the morning, hours after stressors, and after the restorative effect of sleep), allows us to capture just that: the response. This makes the data meaningful and actionable (see an example below).

Measuring HRV all the time provides noisy, de-contextualized, and meaningless data (e.g. not linked to parasympathetic activity), risking becoming a tool to turn any normal physiological response into something pathological.

When it comes to HRV, less is more.

A single, morning measurement, taken according to best practices (a simple protocol consisting of waking up, going to the bathroom, and afterward measuring your HRV while sitting), is all that is required to capture individual responses to stressors in a way that is meaningful and actionable.

I hope this was informative, and thank you for reading!

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 co-founder of HRV4Training, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.

Twitter: @altini_marco

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