Marco holds a PhD in applied machine learning (cum laude) and MSc in computer science engineering (cum laude). He is currently leading data science activities at Bloomlife, a digital health startup using wearable technology and data analytics to improve prenatal healthcare. Marco is also the creator of HRV4Training, a mobile platform that helps you making sense of physiological data.
http://www.hrv4training.com/ (the blog is probably more interesting than the rest of the website: http://www.hrv4training.com/blog)
Overview of heart rate variability (HRV) & physiological stress, with focus on tools, best practices, caveats, practical applications and limitations of a very valuable but easily misunderstood metric. I believe we can do a lot with what is available today, but we can also get easily fooled when performing a study if we do not understand all the critical components of a system, either because of lack of transparency from technology providers, or just because of the inherit complexity of measuring and interpreting physiology. For these reasons I will focus on points that should help in critically analyzing available tools, studies, and implementing processes leading to valid, replicable, outcomes.
Intro / What is heart rate variability (HRV)?
- Physiological mechanisms mediating heart rhythm, homeostasis, CNS, definition of HRV
How to collect data
- Technology (ECG, PPG, wrist based sensors, issues with artifacts)
- Context, factors influencing HRV (confounders), features / metrics
What to do with the data / studies
- types of analysis, short term, long term, spot checks
- sports, medical, lifestyle, other research areas