You are still in a light sleep stage at this point, but it is here where your brain produces sleep spindles or a sharp increase in brainwaves frequency. You are entering light sleep at this point where your brain is somewhat alert and you can be jolted out from your sleep easily. This stage can be referred to as the introduction to sleep and may last up to seven minutes or so. At the end of the day the goal of wearables is to improve our health and help us feel better not just give us numbers on a screen.Your brain begins to produce alpha and theta waves as your eye movement is reduced. If your device is telling you that you are getting enough high quality sleep but you still feel like your sleep is inadequate you should listen to your body first. More importantly, correlate the data with how you feel. For example, if my total sleep time is getting worse, I would expect my heart rate variability to go down and possibly a rise in my resting heart rate. It is always best to correlate metrics with other related markers of health. It is rare in medicine to make decisions based on one specific metric or test no matter how accurate it may be. Long term trends (7 days or more) can give you feedback on longer term lifestyle changes such as starting a new job or moving homes. Short term trends (one day to another) can give you feedback on a specific behaviour you may have undergone the day before such as drinking alcohol. There are two types of trends, short term and long term. Notice if the trend is in the right or wrong direction. The main things to consider when understanding your data are the following. In a sense, we forgo accuracy of a single snapshot in favour of more data that shows change over time. The whole point of continuous monitoring is to show us continuous data that changes over time. "Sleep is so significant to our health, you really can't sacrifice non-REM deep sleep or REM sleep without damage" This suggests to me that there is a lot of value in the data when taken in context. From my experience working with users of Span, I have seen meaningful changes in sleep metrics after implementing certain lifestyle changes. When Matthew Walker, a renowned sleep expert was asked by Peter Attia about this he seemed to concur. This suggests that the data we are getting is meaningful enough to make decisions. There are studies showing that using these devices to track sleep and gather feedback does improve sleep quality. We actually don’t know the answer for sure. The next question is, is this bias constant? If my deep sleep is off by a certain percentage, is that percentage fixed every time? If yes, then there is utility in tracking the trend instead of the specific number. It means we have to put them in the correct context. This means that when looking at our data we need to keep in mind that there will be a significant amount of bias. Both devices however, are significantly flawed when measuring sleep stages such as deep sleep and REM sleep, with Whoop being the better of the two. Oura was less accurate for deep sleep with an agreement of only 51% with polysomnography and 61% for REM sleep.įrom these results, we can confidently say that in general, both devices are accurate enough in measuring basic sleep metrics such as time spent asleep and time awake. However for estimating minutes awake after sleep onset it was only similar in around 51%. Whoop was the more accurate of the two at 68% similarity to polysomnography when measuring deep sleep and 70% for REM sleep. When it comes to more detailed metrics the accuracy of these devices starts to get less reliable. Oura was mostly accurate in estimating the time it takes to fall asleep (sleep onset latency or SOL), how long you spend awake after falling asleep (wakefulness after sleep onset or WASO) and total sleep time. Whoop was shown to give a small overestimation of total sleep time on average but did not vary significantly. In this article I focus primarily on Whoop and Oura as they are the most common devices owned by Span users.īoth Whoop and oura give reliable measurements of simple sleep measurement such as total sleep and wake time. There have been a few studies comparing different wearables to polysomnography. In order to assess the accuracy of sleep metrics obtained from a wearable device, they are compared to that gold standard. The gold standard for sleep tracking is a lab test called polysomnography. ![]() How accurate are my wearable sleep metrics? Always correlate your data with other related metrics and more importantly, how you feel.When interpreting sleep stage metrics such as deep and REM sleep you should focus more on the trend rather than the number .Wearables are accurate enough when measuring basic sleep metrics such as time spent asleep and time awake .Wearables are not as accurate as lab tests yet but they can still be useful .
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