Building HRV4Training Pro
Helping you making sense of physiological data
In the past few months we’ve been busy building HRV4Training Pro, a web platform for individuals and teams aiming at better understanding how different stressors affect their body, so that adjustments towards better health and performance can be made.
In this post, I’d like to cover the main approach behind our new platoform, deriving from the past 5 years of learnings. Since we launched the first and only validated camera-based Heart Rate Variability (HRV) measurement a couple of years back, we had the opportunity to learn a lot through continuous iterations and feedback from our community as well as from top scientists in the field.
From the average guy just like myself, to elite triathletes that I occasionally enjoy slowing down, HRV4Training made it extremely easy for everyone to gather meaningful data points linked to physiological stress.
So, what’s HRV4Training Pro about?
Quick step back first. What’s HRV and why do we care?
As pretty much anything affects the autonomic nervous system, collecting and analyzing longitudinal data representative of these effects, typically reflected in changes in vagal tone, can provide insights on many complex mechanisms taking place in health and disease. The best non-invasive proxy to vagal tone is HRV, as the autonomic nervous system modulates heart activity in response to stressors. Hence, the motivation to use HRV to assess recovery from workouts, as well as the influence of the many other stressors affecting our lives on a daily basis (e.g. work, family, traveling, food & alcohol intake, etc. — it all counts).
Where are we at 5 years later?
We built the easiest and most cost-effective solution to acquire high quality physiological data, in particular HR and HRV at rest (yep, that’s HRV4Training). We built a solid community with evidence-based work at its core. We published a fair amount of work, from the validation of the camera-based measurement, to acute day to day changes in physiology (heart rate and HRV) in response to training, to methods to estimate VO2max from workout data, methods to estimate running performance and the relation between HRV, training load and injury in Crossfit. We’ve also partnered with countless universities to which we offer this platform for free, to facilitate their work. Transparency and solid scientific grounds are what we believe in, which is why we started documenting to the public and validating our work since day zero.
Alright, back to the original question: what’s HRV4Training Pro about?
Data -> Awareness -> Insights -> Actionability
From data comes awareness. However, we need to be able to interpret data correctly, read through the noise, in order to gain insights and make meaningful adjustments to our lifestyle, based on the data we collect. It’s easy to get overwhelmed when things are not put in the right context, and just add to the confusion.
HRV4Training Pro provides many insights, typically combining physiological data, subjective annotations and actual workouts data. Thanks to the many interactions with users and teams, we fine tuned the platform to highlight what we believe is the most effective way to analyze and interpret physiological data.
In particular, with our new platform, we strongly relied on the following principles:
- everything is relative
- going beyond day to day variability
- multiparameter is key
Let’s look into them a little more in detail.
Everything is relative
Physiology needs to be always analyzed with respect to an individual’s historical data or normal values. In HRV4Training Pro we take this approach to the next level, allowing users and teams to build their own set of metrics and track progress over time.
For example, with the new dashboard, you can pick up to 6 parameters, including physiological data, training load and subjective annotations, and see how this combination of metrics has been evolving this week, with respect to the previous month.
We don’t stop at the past week and month, but interpret changes with respect to all your historical data, so that relative changes this week with respect to the previous month, can be put into perspective, based on what are normal, long-term variations for you (alright, it sounds a bit convoluted, but it all boils down to a nice, easy to read, radar plot).
The entire platform is built around relative changes over different time scales, see for example a multi-parameter analysis of your physiological trends, in which significant changes in the past two weeks, are derived based on what are normal variations for you in the previous two months:
Going beyond day to day variability
The second most important point is the ability to abstract and go beyond day to day variability and acute changes, so that we can focus on baseline changes and the big picture. This is true not only for physiological measurements, but also other modeling techniques used for example to estimate freshness or injury risk.
HRV4Training Pro builds on our previous work on physiological trends to easily highlight how your baseline is changing with respect to your historical data and allow you to understand if variations are just normal or are consistently outside of your normal ranges, at a glance:
Similarly, you can see below how acute and chronic load as well as freshness and injury risk can be abstracted at the week level and in relative terms, to provide more meaningful feedback.
If you are interested in learning more about these specific analyses, please refer to the user guide that you can find here.
Multiparameter is key
Granted that HRV is a strong marker of physiological stress and that it can be extremely valuable per se to track it. However, it is obvious that the ability to collect and analyze multiple physiological (and not only physiological) parameters is key to aid interpretation and provide additional context around our measurements.
HRV4Training Pro makes it very easy to look at the big picture, for example in the dashboard view shown above, or with the resting physiology analysis and correlations analysis shown below:
Similarly, it should be no surprise that HRV data should be properly contextualized, and not analyzed alone. In particular, only by looking at objective data representative of physiological stress (HR and HRV), subjective data (your own or your athlete’s feeling, as well as aspects more difficult to quantify such as muscle soreness), and external / training load, we can make well-informed decisions.
That’s all for now.
I hope you found this read somewhat useful and it will help you make use of the new platform. For a more comprehensive overview of HRV4Training Pro, check out the user guide here.
There is a lot to learn from being a little more aware of our physiology and of 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.
Enjoy HRV4Training Pro
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.