How We Interpret Wearable Data

This page explains how we think about the numbers your wearable gives you, so you can read them the same way. It's the closest thing we have to a worldview. If the methodology page is about where our facts come from, this page is about how to read a number without fooling yourself.

A single reading rarely means much

Wearable numbers move every day, and a lot of that movement is noise: how you slept, when you ate, how the sensor sat on your skin, what time the reading was taken. One low HRV morning or one high resting heart rate is usually just a day, not a signal. The thing worth paying attention to is rarely today's number on its own.

Trends beat days

What carries information is the direction over time. A number drifting in one direction across weeks is telling you something a single day cannot. This is why we keep pointing you back to your own baseline and your own trend rather than to a universal "good" number. Your normal is more useful than anyone's average.

Your number is read against people like you

A resting heart rate that's perfectly ordinary for a 55-year-old might be low for a 25-year-old, and the reverse. Age, sex, and fitness all shift what's typical. This is why so much of what we publish is organized by age and sex, and why we treat "is this normal" as "is this normal for someone like me," which is a different and better question.

A number rarely moves for one reason

Sleep, stress, alcohol, illness, training, hydration, and the device itself can all push the same number. When your recovery score drops, there is usually no single cause to point at. We try to lay out the full range of what the research connects to a metric, rather than pretending one tidy explanation covers it, so you can weigh which factors actually apply to your life.

How we judge the evidence

Not all research is equal. A finding from one small study is not the same as a finding repeated across many. We weight stronger evidence more heavily, we flag when something is preliminary, and we say when good studies disagree. The goal is not to sound certain. It's to give you an honest picture of how solid each piece of the picture really is.

What this adds up to

Read trends, not days. Compare yourself to people like you, not to a universal number. Expect several causes, not one. And hold each claim as firmly as the evidence behind it, no more. Do that and you can read your own data well. We interpret. You decide.