Dr. Michael Grandner

And so it's, you got to interpret it with, with that caution.

So that's the caution with the sleep stages.

If I show you what a brainwave tracing of sleep stages and a wearable tracing that you can tell you could, if it's a good device, you can easily tell these are the same person on the same night if you looked at them.

But if you actually counted the exact number of minutes, you would probably find that 20 to 30%, at least of those minutes didn't exactly agree with each other.

but you can tell visually.

That's why clinically I look at it visually.

I don't actually count the minutes because I don't depend on it.

And actually one night of data, is it worth much?

It's more about the weekly trend or like trending and changing over time.

That's what I care about.

So that's the sleep stage of data.

The third bin is the metrics like the scores with very few exceptions.

Most of those scores are, I give almost no attention to those.

I can be ungenerous and say they're mostly made up nonsense anyway.

I'm talking about anything that's called like sleep score, sleep quality, sleep needs, sleep readiness, recovery, any of that stuff.

If I'm being ungenerous, I'm saying it's mostly made up nonsense to sell devices because telling people what they want to hear.

But that's not the truth either.

They're not nothing.

They have a lot of these companies, not all of them, but a lot of these companies have smart people working for them who are not idiots, who know how to work with the data and are trying to make prediction algorithms that are actually useful.

thing is none of these things are published none of these things have been vetted none of these things it's like it's it's it's kind of like at the trust us level of like well how do i know what you're putting in your algorithm and how to interpret those numbers so like if i drink alcohol the night before but i'm otherwise totally healthy and the number looks bad should i worry or not