Marco is solving the “data confusion” problem: people are surrounded by wearable metrics and made-up scores, but don’t know what’s actually measured, what’s estimated, and what’s meaningful over time. His work helps people use reliable physiological signals (HR, HRV, temperature) longitudinally to manage stress, avoid bad training decisions, and improve performance and health.

In today’s conversation Marco Altini explores how wearable tech has shifted us from one-time lab snapshots to long-term physiology tracking in real life. He explains what wearables can measure accurately at rest (like heart rate and HRV), what they’re estimating (like sleep stages and readiness), and why the most valuable insights come from trends vs your own baseline. Marco also breaks down HRV as a practical stress marker, how wearables can flag “something’s off” (like infection), and the simple morning routine that makes HRV data useful.

You will learn what modern wearables measure well at rest (HR/HRV) and why movement still challenges accuracy. You will learn the difference between measured signals versus algorithmic estimates (sleep stages, readiness), and how to avoid being fooled by a single score. You will learn what HRV is (beat-to-beat variation), why it reflects autonomic stress load, and how to interpret changes day-to-day and across training blocks. You will learn why infection detection is usually non-specific (it flags stress, not the exact virus) but still useful for decision-making. You will learn how to start a consistent, one-minute morning HRV routine that produces actionable trends.

You will discover that the real superpower of wearables isn’t perfect accuracy—it’s longitudinal tracking: comparing today’s physiology to your history to spot meaningful change early.

Marco helps listeners solve the challenge of making better decisions under uncertainty—when training, work stress, sleep disruption, travel, or early illness is pushing the body toward overload—so they can adjust before stress becomes chronic.