Making sense of your Oura ring Heart Rate Variability (HRV) data using HRV4Training
As we continue our quest to make heart rate variability measurements and data interpretation easy and meaningful for everyone out there, today we talk about our latest step, the integration between HRV4Training and the Oura ring.
A few things to cover or pointers for you in case this is all new, before we dive into the integration:
- What’s heart rate variability (HRV) and why would you care? HRV is the best non-invasive measure of stress we have, and as such, it can be extremely valuable to better balance training and lifestyle (when properly measured and interpreted, a big issue today due to poor standardization). Learn more in this slides deck.
- What’s HRV4Training? HRV4Training is the first and only validated app that lets you measure your HRV using the phone’s camera. This is how it started, but it’s all about helping you making sense of the data right now. See an example here or a case study here.
- What’s the Oura ring? The Oura ring is a sleep and activity tracker which is very good at measuring your physiology during the night. Learn more here.
During the past year, we have received countless requests to integrate Oura and HRV4Training. According to our users, Oura reports HRV but provides little value around this feature. This makes sense as Oura’s target application is sleep tracking, which also relies on HRV, but the value reported, or trend, lacks contextualization and makes it hard for a user to effectively use the information to make meaningful adjustments on a day to day basis.
For someone interested in training (professional or recreational athlete), HRV4Training’s insights tend to be more useful (obviously I’m biased on this one!). Hence, integrating HRV4Training and Oura would allow you to make better use of the ring’s data to assess physiological stress and recovery needs, without having to take an additional HRV measurement in the morning using our app. Best of both worlds, according to some.
As we have covered many times, context is key and the only moment that should be as good as the morning routine in terms of measuring your physiology is the night. No point measuring any other time of the day as acute stressors would be picked up and you’d learn little about your chronic physiological stress level, which is what matters in order to make meaningful adjustments that can lead to better health and performance. The past years of research keep backing these statements, as morning measurements and night recordings are used in different studies resulting in the same outcomes in terms of the relationship between HRV, coefficient of variation (see later), training load and recovery (see an overview here).
However, there are a few important differences to discuss. Let’s go a bit deeper into these two topics.
Making sense of your Heart Rate Variability (HRV) data: HRV4Training’s approach
Seven years of work in this field made it quite obvious that dealing with HRV data is a bit more complex than dealing with other metrics.
First, we need to learn to deal with the inherent day to day variability of this metric, which is much higher than the variability of other parameters that we are more familiar with (e.g. your heart rate or your body weight).
This means we need to always interpret your HRV data with respect to your historical data, and also be able to determine what changes are trivial, or just part of normal day to day variability, and what changes do matter and might require more attention or simply truly represent positive adaptation to training and other stressors (don’t worry, we do that for you).
Secondly, HRV analysis requires a mindset shift. First of all, we need to understand the nature of the data and the constant re-adjusting of the autonomous nervous system, and therefore take all the necessary steps to acquire a reliable measurement. This is typically addressed by the morning routine: the importance of context, limiting external factors, measuring as soon as you wake up and in the same body position every day — with the only viable alternative being as previously introduced, the night measurement. Then, we need to shift from a “higher is better” to a “normal is better” mentality, as physiologically speaking, being in a stable condition is typically a good sign.
The inherent variability of HRV measurements is something that your app or software of choice, needs to deal with. This is something we have spent a lot of time researching and designing in HRV4Training, starting with the way the daily advice is built.
A software that interprets any HRV increase as a good sign, or any HRV decrease as a bad sign, is failing to correctly represent the fact that there are normal variations in physiology, and that only variations outside of this normal range, should trigger concern or more attention or simply be interpreted as actual changes.
Here are a few links to learn more about some of these aspects, as recently we have talked a lot about normal values and the big picture (here), how to use deviations from normal values to guide training (here) and the importance of looking not only at baseline HRV with respect to normal values, but also the coefficient of variation (here). Finally, it all comes together with the automatically detected trend (here).
Alright, on to night measurements and some important points when using the Oura ring to measure HRV during the night, instead of your typical morning measurement.
Morning measurements vs night measurements
Morning measurements have been used for a long time in the context of tracking chronic physiological stress in response to training and lifestyle stressors.
On the other hand, mainly because of the difficulties in acquiring such data, night data has been used a little less. This being said, as scientists have been active in this field for decades, you can find several papers looking at the relation between nocturnal HRV and training load, for example here, or here, similarly to what we have shown for morning measurements here — we have also put together an overview of recent research in the context of team sports, with a lot of insights useful also at the individual level (what we want to do in a team, is still to individualize training based on a player’s response), that you can find here.
In our opinion, there is little doubt that night HRV is representative of physiological stress, similarly to morning measurements, and therefore we believe both approaches are valid in terms of acquiring data representative of chronic stress. It is of course key that the sensor used to measure night data is reliable, and this is the case for Oura, which shows extremely good agreement with ECG in this validation where rMSSD was computed from night recordings.
However, while both methods are able to capture changes in physiology relative to your baseline and normal values over time, the absolute values will most likely differ. What does this mean? Simply put, that you cannot interchangeably use one method or the other, but you have to stick to one, either morning measurements or night measurements, and then use always the same method so that data can be analyzed meaningfully over time relatively to your historical data.
An extra bit of information that is important when using night data is that the time at which you work out matters. For example, if you work out in the evening, your HRV will take some time before going back to normal, and therefore your average will be lower during the night, even if in the morning it’s all back to normal. This means that if you work out at different times of the day (sometimes in the morning and sometimes in the evening) a morning measurement might be better suited for you. On the other hand, if your training schedule is fairly similar across days, then night measurements will not reflect any differences.
Here is an example of a few weeks of data collected using the Oura ring, leading to a half marathon race:
We can see:
- a relatively stable period in terms of HRV leading up to the race, with a small increase in HRV in the last days before the race (tapering)
- a big post-race dip (March 18th, race day is what we have highlighted above, March 17th)
- a few days with lower values following the race, before things go back to normal
Similarly in HRV4Training:
Above we can also see a relatively stable period in terms of HRV leading up to the race and a positive trend (tapering), a big post-race dip (March 18th, highlighted in the figure) and a few days with lower values following the race, before things go back to normal.
Here we also have the added benefit of HRV4Training’s trend analysis, which looks at HRV, HR, the coefficient of variation and training load to determine how you are responding to your current training block, and indeed highlights a bit of post-race struggle in this specific case (orange highlights maladaptation, while green “coping well with training” as shown by the positive trend during tapering).
Wrapping it up
In this post, we highlighted our latest integration. In this case, more than ever, we decided to move forward due to the overwhelming feedback received by our community.
Thank you, everyone, for taking the time to provide your input and appreciation for how we analyze and interpret the data in HRV4Training. It is our belief that helping you making sense of the data is what we do best here, and therefore we are happy to expand the set of compatible devices for the ones that prefer to collect data passively in the night.
The main point that I have been trying to make is that context and your historical data are key for data analysis and interpretation. The software you decide to use needs to be able to contextualize your measurement with respect to your historical data, so that you can easily determine if a score is within your SWC or normal values, or if it is not and you should pay a little more attention to it, potentially implementing changes in your planned training. HRV4Training and HRV4Training Pro provide very intuitive visualizations of your historical data and contextual information, that we hope can make it easier to correctly interpret physiological changes for you and your team.
There is a lot to learn from being a little more aware of our physiology and how we respond to different life stressors.
Our goal is to help you build a little more on top of the daily measurements so that you can read through the noise and make meaningful changes based on relative, significant variations in physiology and associated parameters.
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering and is currently enrolled in a M.Sc. 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 and loves running.