Dr Christopher Depner Discusses the Challenges, Benefits of Wearable Sleep Devices

There is a need for more research on integrating wearables with health monitoring systems and data regulation, says Christopher Depner, PhD, professor of health and kinesiology, University of Utah.

These findings were presented at SLEEP 2024, the annual meeting of the American Academy of Sleep Medicine and Sleep Research Society..

Transcript

What are the primary challenges in accurately measuring sleep with wearable devices?

A couple of the key challenges are one, just getting the research participant; from a research perspective, getting participants to reliably wear the devices and report when they are and are not wearing them. After the fact, a lot of devices do tell you if it was on wrist or off wrist, and you can tell if the participants weren't wearing it, which is helpful information. But that's lost data. Oftentimes, people aren't super adherent with wearing the devices, so that's obviously a huge issue.

And then for our actigraphy, specifically, I think another second challenge is—assuming that the person is wearing it—determining if the person is in bed trying to sleep or if they're perhaps in bed doing something relaxing, watching something on their phone, or not trying to sleep but in bed, not moving, with the lights off. Seemingly at the level of the wrist, those 2 activities look pretty much the same. So, it's really hard to disentangle those.

I think that's kind of the key challenge is figuring out typically we call it quiet wakefulness, which you might be in bed or actually in bed trying to sleep. And so, that's really common in today's society, with everybody's phone and everything. So that's a big challenge.

How do wearable sleep devices address the variability in sleep patterns across different individuals?

Especially with wrist actigraphy, there's a couple of metrics where you can sort of monitor or calculate sleep variability or sleep regularity. One of the more recent ones is something called the Sleep Regularity Index. That's an algorithm. It was developed by Andrew Phillips [Andrew J.K. Phillips, PhD, sleep scientist, Flinders Health and Medical Research Institute] and Elizabeth Klerman [Elizabeth B. Klerman, PhD, MD, professor of neurology, Harvard Medical School]. It basically can go back over an entire duration that someone was wearing a watch to track their sleep and say how regular their sleep was or was not. Perfectly regular sleep would mean that you're going to bed at the exact same time every night and waking up at the exact same time every morning, and you're asleep for that exact same time every day. Obviously, no one has perfect regularity, but it's a really nice metric because it basically just goes from a scale from 0 to 100, and you can easily quantify how regular someone is with their sleep.

I do think that does, on some level, overcome some of the challenges with the wearable devices, because it can be a little bit easier to determine how regular people's schedules are within a 24-hour day, using these types of metrics, and it can be pretty obvious if someone's very regular or very irregular. And then, there's a lot of data coming out showing that people with highly irregular sleep schedules have a higher risk of overall mortality, cardiovascular disease, and diabetes. That's all independent of sleep duration. So, not only is that a useful metric for health outcomes, but it does, on some level, help overcome some of those challenges that we were talking about with wearables. So, I think these are really powerful devices for determining that regularity in a 24-hour timeframe.