Week 47: Intensity as the Primary Driver of Autonomic Change
In Team Sports Too!
Hi there 👋
I hope you are doing well.
Last week I was in contact with Jorge Abruñedo, author of the study “An Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players“ (full text here), in which, as the title says, they monitored semi-professional basketball players over a full season, combining daily HRV4Training measurements with training load and contextual variables, and then used an explainable machine learning approach (Gradient Boosting + SHAP) to understand which load components had the largest impact on day-to-day HRV, and how these responses differed between athletes.
I’ve talked about HRV in team sports a few times, and recently co-authored an article on HRV in football, highlighting how the same principles apply, but also how we don’t have much data due to the complexities of working in team settings. Hence, it was nice to see a new study being able to track HRV for a long time in a (small) group of semi-professional Basketball players.
In particular, findings were aligned with what we know from endurance sports: intensity is what challenges the system most, HRV captures this reliably, and morning measurements captured with HRV4Training offer a simple, cost-effective, and actionable way to keep training aligned with recovery.
Learn more in the blog below.
Personal Coaching for Runners
I have one opening in my coaching roster, please apply here should you be interested in working with me.
You can also learn more about my coaching, here.
Thank you.
HRV4Training Pro
HRV4Training Pro is the ultimate platform to help you analyze and interpret your physiological data, for individuals and teams.
You can find a guide and overview here.
Try HRV4Training Pro for free at HRVTraining.web.app
When using Pro, the app will also automatically recognize your account and add the Normal Range to the Baseline view, together with detected trends and additional annotations, which can help contextualizing longer-term changes.
You will also be able to pick rMSSD as the parameter to see on the homepage of the app.
Thank you again for your support and for allowing us to remain independent.
See you next week!
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 is a certified Ultrarunning Coach.
Marco has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.
He is co-founder of HRV4Training, Endurance Coach at Destination Unknown, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.
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