June 2023 recap 🗓️
a monthly newsletter
hi there 👋
I hope all is well.
Here is my May recap, broken down by topic, with links to the individual articles.
I hope you’ll find them useful.
Please feel free to comment below or in the individual articles should you have any questions, and I will try to follow up.
Take care!
Heart rate variability (HRV) 🫀
abnormally high heart rate variability: In a previous blog, I’ve discussed the importance of the normal range, when interpreting HRV data. While it is quite easy to understand that a suppression in HRV is associated with higher stress (see our paper here for many examples), the situation gets a bit more complex when we are looking at abnormally high HRV, i.e. HRV scores above your normal range. In this blog, I discuss my current view, hoping it is helpful to you when analyzing your own or your athlete’s data.
Heart rate variability (HRV) numbers: what do they mean? When measuring HRV, we end up with a number. This number is what we normally call an HRV feature. An HRV feature is a mathematical way to translate a series of beat-to-beat (or peak-to-peak, when using optical measurements) intervals collected in a certain amount of time (ideally, 1 to 5 minutes) into a single number that represents your HRV. In this blog, I discuss the main HRV features and their use.
Altitude and heart rate variability (HRV): A few years ago I went back to University to study Sports Science (it’s never too late!), and had the opportunity to work with the Dutch Triathlon Federation, looking at some of the data they had collected using HRV4Training during training camps at altitude. In this blog, I look at some of the literature, including my own study, and show how altitude (and weather) can be tracked automatically in HRV4Training, so that you can use that information in various analytics we provide (e.g. as context, in correlation analysis or as filters when looking at aerobic efficiency in HRV4Training Pro).
Podcasts 🎙️
How to use HRV to quantify stress, balance training and lifestyle and improve performance - Vert.run. A few weeks ago I had a really good chat with Francesco Puppi about heart rate variability (HRV) and its use. I think we touched on many important points in this one. As Francesco puts it, "we approach the topic of training embracing its complexity, without giving easy answers, but trying to understand the context and reason behind some choices and patterns that we observe, and how technology can be a great tool to increase our knowledge and awareness". Link here.
Building 🛠️
New HRV4Training Pro: I have been busy building the new HRV4Training Pro and have written various articles that can serve as a user guide and overview of the different features. You can learn more about the main principles and you can find a user guide here. Any user can now try the new Pro. Learn how to migrate, here. I hope you’ll like it!
Wearables ⌚️
Insights from continuous glucose monitoring: In the past years, I’ve used a few continuous glucose monitors (CGMs). As a scientist working with physiological data, I find our ability to measure our body’s responses quite fascinating, from HRV, to exercise physiology, to glucose levels measured with CGMs. While curiosity has mostly driven my initial experiments, a few aspects have shown repeatedly to be impacted by my behavior, and as such, have led to what I consider useful insights, in terms of health, stress, and training. Here, I cover these aspects.
Training talk 🏃🏻♂️
Bieler Lauftage – Courses de Bienne 100 km: race recap. A few weeks ago I ran my first official ultra (race data, here), one hundred kilometers at night in the Swiss countryside at the Bieler Lauftage - Courses de Bienne, an historical race that dates back to the 50s. It took me 9 hours and 50 minutes, finishing 36th / 693. It has been quite a journey, nothing I had planned or imagined many years ago when I started getting interested in how technology can be used to monitor our physiology. A journey that never ceases to amaze me when it comes to how we can transform our bodies. Learning from the science of endurance training, and adjusting based on individual responses, often through self-experimentation, remains a challenging and fascinating area for me, and I look forward to continuing this journey. This post covers my preparation, race execution, and learnings.
changing periodization: my running year ends in May, the time of what I now consider my main event. At this point, I normally evaluate how things went, and try to make some adjustments, with two goals in mind: 1) staying healthy (in broader terms, and specifically in the context of running as well, i.e. injury-free), 2) possibly improving as a runner. Point 2) is nice to have, but comes quite far after 1), which motivates some of the changes I will implement this year, despite having made a lot of progress in the past 12 months. More info, here.
That’s all for today, see you next month, and thank you for reading!
Previous newsletters:
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.
Social:
Thanks Marco. I'm not on Twitter, so hope you don't mind the question here. I've been using HRV for 6 months daily now and think it's an incredible bit of software that does what I want a ridiculously low cost, when compare to Whoop etc. I have read and re-read all the articles and my dumb brain just can't compute what rMSSD actually is, and why that number matters and then how you compute HRV and what relation your HRV number has to rMSSD? If I have a HR daily of 40 which doesn't change, my one day my rMSSD is 80 or 120, what does that actually mean, actually? In simple terms there between each heart beat, there is a pause of 80 or 120 milliseconds, is it an average over the minute and why is it generally a lower value might mean something is wrong? Maybe a page is needed as if a 10 year old needs to read it? I might get it then!