Why we all might want to wake up to the benefits of sleep tech
Ah, daylight savings.
It makes it easier to have one more drink, stay out a bit later, but it doesn’t cancel out the need for catching enough sleep. (Or in yesterday’s case, thinking we have an extra hour, leading some to accidentally lose two!)
In a society so consumed with ‘having experiences,’ it can be hard to justify bowing out early for the sake of an extra few minutes of sleep. It’s also really hard to appreciate the actual impact and deficits caused by a lack of sleep, as these effects often accrue over long periods of time (notably, making them more challenging to rectify). Until recently, many of us knew very little about how we sleep — the only opportunity we had to understand more was through a sleep clinic.
In comes the data
As in other areas and industries, technology, this time in the form of wearables in addition to sophisticated mattress pads and apps, is flooding us with data on [how poorly] we sleep. An increasingly wide range of sleep tech devices and diagnostic tools are slowly helping researchers (certainly with the help of users) uncover links between sleep and disease/disorders like Parkinson’s disease, esophageal reflux, hormonal dysfunction as well as how to address sleep apnea and insomnia, both of which are rising at at alarming pace.
A recent data release by Fitbit, contains data from 6 billion nights of sleep from its American user base — “It’s probably the largest biometric data set in the world,” according to data scientists at Fitbit.
Upon first glance, the data is telling — we’re not getting enough sleep.
According to the Centers for Disease Control, an adult should sleep at least 7 hours each night. Men and women are both missing the mark by about 30 and 10 minutes, respectively. But sleep quality is arguably as important as quantity — and the data shows that while women sleep longer, men are sleeping better.
Social Jet Lag
The Fitbit data shows that the average American goes to bed late — around 11.20pm, to be more exact — though bedtimes typically vary throughout the week. Fitbit and others have used the term “social jet lag” in reference to sleep lost from inconsistent bedtimes. They found that when a person’s bedtime varied by two hours over the week, they slept an average of 30 minutes less each night (compared to people whose bedtimes varied by only half an hour).
Bad things happen to good [sleep-deprived] people.
Sleep is good — for everything, and for everyone. It plays a critical role in our long-term health and wellbeing and in our short-term personability (“are you just hungry?”) and productivity. A lack of sleep has been associated with irritability and memory loss and can have even more profound effects insofar as increasing risks of heart disease and stroke.
More technically, rapid eye movement (REM) sleep provides us with vivid dreams, and is a catalyst for mood regulation and memory processing. Deep sleep, on the other hand, benefits memory, learning, and helps to keep our immune system functioning well.
The big guys are getting their foot in the game, too.
Workplace wellness has been having a moment with a proliferation of offerings aimed at exercise, nutrition, and, perhaps the least robust of the few, a good rest.
It is clearly in the best interest of a company to have healthy, rested, and alert employees; however, even with the advent of unlimited PTO, it’s a challenge to get employees to take time off work or to turn their mind away from work when they should be resting. While staff at NASA, Facebook, and Google can nap on the job in specially designed sleep pods, how employees can get a good rest to perform their best is still a difficult task.
Apple is not so subtly entering into the sleep market, recently filing a number of patents in the area. One of the patents contains an algorithm focused on calibrating your wake-up time according to when you were able to get to sleep. Though there are some impracticalities associated with this at a basic level (work doesn’t care how late you stayed up streaming shows), there is potential value for people who typically have a hard time falling asleep at night.
Apple submitted another patent geared towards monitoring vital signs. The technology leverages sensors placed in a bed to track sleep and provide feedback on how to improve your slumber. This patent emerges after Apple’s acquisition of sleep tracking company Beddit last year, though it’s unclear whether Apple was working on this technology before the buyout.
Similar tech is already being explored by other organizations — and it wouldn’t be outrageous to say smart beds are now a thing, recently highlighted by technology that was on display during CES 2018. By gathering data on metrics like heart rate, motion, and movement, users can learn about their sleep patterns and receive feedback, typically through a mobile app. Sleep Number, for instance, offers a smart bed that tracks ‘how well’ the user sleeps based on heart rate, breathing, and movement data. Pete Bils, Sleep Number’s vice president of science and research comments that using “smart bed” technology, “… when your heart beats, your body actually presses on the mattress.”
Incredible work is being done in the field — last November, three American scientists were awarded a Nobel Prize for their research on the circadian rhythm — essentially, our internal body clock that dictates when we feel the need to sleep and eat. While we have learned a significant amount in recent years in decades, driven forward by these scientists’ efforts, there is still a lot that remains unknown when it comes to our ability to hack the best sleep for ourselves.
There is also a particular irony in using technology to help curb the sleep problems that technology itself has created — but some promising strides being made to help promote better sleep in a world where technology and our devices are inextricably linked to our daily lives.
Sleep is as basic as a human need as they come, and, unfortunately, sleeptech faces a challenge familiar in other digital health areas — i.e. research has shown that users tend to be mostly healthy and affluent. How well we address this issue, along with more technical issues relating to accuracies of machine learning algorithms in our sensors will be critical in this fields’ successes.