Can a Wearable Really Tell You How Healthy You Are?
There's a familiar ritual that happens almost every time we visit a doctor.
At some point, the stethoscope comes out.
In fact, the stethoscope is so closely associated with medicine that it has become almost shorthand for doctor. Put one around someone's neck and we immediately know who we're looking at.
Then comes the familiar instruction.
“Take a deep breath.”
The doctor listens.
But what, exactly, are they listening for?
A stethoscope doesn't diagnose anything. It simply makes sounds inside the body easier to hear. The useful information comes from the person on the other end—someone trained to recognize what sounds normal, what doesn't, and what might deserve further investigation.
For most of medical history, accessing even these relatively simple signals from inside the body required an encounter like this.
Now I wear a device that measures a similar set of signals while I'm sleeping.
It tracks my heart rate without anyone putting a stethoscope to my chest. It follows changes in the intervals between my heartbeats. It estimates my sleep, activity, temperature trends, stress, and recovery.
And every morning, before I've even gotten out of bed, an algorithm is ready to tell me what all of this means.
It's extraordinary.
But it raises a question I've spent much of my career thinking about in one form or another:
Measurement isn't the same as understanding. So what does all this new measurement actually help us understand?
What Is Your Wearable Actually Measuring?
When my wearable gives me a recovery or readiness score, it seems like a measurement.
But it isn't. At least not exactly.
The device measures physiological signals: heart rate, movement, temperature changes, and the tiny variations in time between heartbeats that we call heart rate variability, or HRV.
Other metrics are inferred from those signals.
My wearable doesn't directly measure "stress" or "recovery." It collects information associated with those states and uses an algorithm to interpret what the combination might mean.
Sleep provides a good example.
A wearable can detect movement, heart rate, and other physiological signals while I'm in bed. From those signals, it estimates when I fell asleep, when I woke up, and how much time I may have spent in different stages of sleep.
The sensor measures signals. The algorithm gives them meaning.
More Measurements Don't Necessarily Mean More Information
This is where things get particularly interesting.
I've spent a good part of my career studying risk factors for disease, and one of the first things you learn is that measurement isn't simply about collecting as many variables as possible.
What you measure matters.
How you measure it matters.
When you measure it matters.
And how the measurements relate to one another matters too.
Imagine that a wearable tracks six physiological signals. If several reflect essentially the same underlying process, six measurements may not give us six independent pieces of information.
In that case, the whole can actually tell us less than the sum of its parts.
But the reverse can happen too.
Several measurements that tell us relatively little on their own may become much more informative when they change together.
Perhaps your resting heart rate rises, your HRV falls, your temperature shifts slightly, and your sleep changes—all at the same time.
No single measurement necessarily tells us very much.
But the pattern might.
And that's where the algorithm becomes enormously important.
The Algorithm Is Part of the Measurement Story
Wearable companies aren't simply collecting data and handing it back to us.
They're trying to make sense of it.
An algorithm may consider your heart rate, HRV, sleep, temperature, recent activity, and—importantly—how those measurements compare with your own previous patterns.
Then it distills all of that complexity into something beautifully simple:
Readiness: 72.
That simplicity is one of the things that makes wearables so appealing.
But it can also obscure how much interpretation has already occurred.
Two devices could collect similar physiological signals and reach different conclusions because they combine, weight, and interpret those signals differently.
So when we ask whether a wearable is "accurate," we're really asking several questions:
Did the sensors accurately capture the signals?
Did the algorithm interpret them appropriately?
And does the resulting score tell us something useful?
So What Makes Wearable Data Useful?
This is where I think wearables have an extraordinary advantage.
They keep measuring.
A physician might measure my heart rate during an office visit. My wearable can observe it every day, under similar conditions, for months or years.
That changes the question from:
Is this number normal?
to:
Is this normal for me?
One unusually low HRV reading or poor sleep score may mean very little.
But a change that persists for several days—or a group of measurements that begin moving together—may tell us that something has changed.
It doesn't necessarily tell us what changed.
That's an important distinction. A wearable isn't a diagnosis.
But it may give us something we rarely had access to before:
a picture of our own physiology unfolding over time.
And perhaps that's where wearable data becomes most useful—not when it gives us another number to optimize, but when it helps us notice patterns we otherwise might never have seen.
The Bigger Picture
The stethoscope hasn't become useless because my wearable can monitor my heart while I sleep.
If anything, the comparison helps clarify what measurement can—and can't—do.
A wearable collects thousands of data points across days, months, and eventually years.
No doctor can do that.
But collecting more data doesn't automatically mean understanding more.
The value still lies in interpretation.
Which signals matter? Which are redundant? Which become meaningful only when they change together?
The stethoscope taught us to listen to the body.
Wearables allow us to listen much more often.
The challenge now is learning what all that information actually means.