#1 is a great idea for follow-up -- looking at variance, 90th percentile vs median, and so on. There's probably some very salient measure of sleep-affecting noi

#1 is a great idea for follow-up -- looking at variance, 90th percentile vs median, and so on. There's probably some very salient measure of sleep-affecting noise that we can derive from the data. On the accuracy of sleep metrics, when Apple Watch makes an error, it tends to misclassify deep or REM sleep as core (light) sleep, or waking as light sleep. It's relatively rare to misclassify deep or REM sleep as awake, or deep as REM (and vice versa). That's partly why we focus on those specific sleep metrics. You can see a full confusion matrix here: https://www.empirical.health/metrics/deep-sleep-percent#accuracy-of-apple-watchs-deep-sleep-estimate https://www.empirical.health/metrics/deep-sleep-percent#accu...