As people age into their 60s and beyond, sleep can turn into a nightly disappointment. What once was peaceful repose becomes fragmented, unsatisfying, or simply evasive.
For some, the cause is chronic illness, or the medications they take to treat it. Or, it could be tied to depression and anxiety, the double whammy of aging. Also, some disorders, such as sleep apnea and restless leg syndrome, often worsen in old age.
It can be a vicious circle. Illness begets poor sleep which begets more illness.
So, with a big chunk of the U.S. population entering its senior years, there’s a pressing need to more clearly comprehend the correlation between sleep and physical and mental illness. And, a key is finding more efficient and less invasive ways to monitor older adults hoping to stay in their own homes.
Dina Katabi is helping to make that happen. A professor of electrical engineering and computer science at MIT, she and her team have developed a device that uses radio waves to track how well people are—or aren’t—sleeping. Specifically, it can measure when and for how long a person is spending in different stages of sleep, such as light, deep and REM.
And, unlike more conventional sleep tracking, where a person is hooked up to monitors or has to wear sensors, this innovative approach is built around a box that can sit, barely noticeable, in a home, not unlike a wi-fi router.
That’s possible because the researchers created an algorithm that enabled the machine to learn to identify different levels of sleep based on the reflection of radio waves in the room where the person is sleeping.
Put simply, the device was taught to recognize a connection between radio signals and the various stages of sleep. This was done by showing it many examples of sleep stage data from an FDA-approved monitoring device, while it tracked radio frequency signals in a room. As radio waves reflect off a body, the slightest movement, such as a person’s pulse or breathing, can change the frequency. Just this month the researchers were issued a patent for this motion tracking system. The algorithm also taught the device to ignore radio signal alterations that are irrelevant, such as those caused by reflections of radio waves off inanimate objects in the room.
“After many such examples, the machine learns the radio frequency pattern associated with each sleep stage,” Katabi explains. “At that point, there is no need for more examples. The machine can be taken to a new home and used by a new person. Once it sees the radio frequency pattern, it knows how to map it to the corresponding sleep stage.”
That gives the device a big advantage over current sleep-tracking methods, says Matt Bianchi, chief of the Division of Sleep Medicine at Massachusetts General Hospital. “It’s not just that it’s in the home, but rather it’s the capacity to perform repeated measurements,” he says. “Sleep quality and quantity can change from night to night, and this variation can hold important clues that can lead directly to decision-making related to health.
“For example,” he adds, “the effect of alcohol and body position on sleep apnea are well known, but don’t occur equally in each person. If we could measure sleep apnea over multiple nights, we could better understand the impact of different behaviors on a person’s sleep and provide more personalized feedback.”
Understanding Parkinson’s disease
Katabi sees another potential benefit to long-term sleep tracking—the ability to better understand the progression of conditions like Parkinson’s disease, which has been found to have a strong correlation to sleep problems. She notes that many people with a condition called REM-Sleep Behavior Disorder (RBD) eventually develop Parkinson’s. People with RBD can thrash around, flail their arms and legs, or even walk around while still in REM sleep.
“By understanding the relationship between RBD and Parkinson’s, we could have a better understanding of who might develop Parkinson’s and how it progresses,” she says. “That could help in developing drugs for Parkinson’s.”
To comprehend that kind of complex relationship between a sleep disorder and a chronic condition, however, requires lengthy analysis.
“You can’t really understand this unless you monitor it over a long time,” says Katabi. “Somebody who has REM disorder may take many years to develop Parkinson’s. The problem today is that if you want to do longitudinal studies of sleep, people would need to go to a hospital or clinic regularly for years. That’s not doable.”
Bianchi explains that while scientists have long been aware of a connection between RBD and Parkinson’s, they have struggled to determine how much the former can precede the latter. The best estimate at this point, says Bianchi, is 10 to 20 years.
“These are incredibly challenging studies to conduct precisely because they require many years and many individuals to be followed,” he says. So, Bianchi acknowledges, he is “very excited” about the prospect of being able to track key aspects of sleep without a person needing to wear monitoring equipment.
While Katabi believes the new device is likely to be used for research by pharmaceutical companies and sleep labs before it becomes available to consumers, she sees it as part of a larger goal of making homes “health aware.”
“For all the technology in our homes, there is very little to understand health and be able to detect health emergencies,” she says. “That’s particularly important for older people who are more likely to have multiple chronic diseases.
“Our vision is something we call ‘invisibles,’ devices that can sit in the background of your home and can alert a caregiver to health emergencies and also track progression of diseases,“ she adds. “That way a problem can be addressed before a person ends up in the emergency room.
“We need to rethink health care. In the same way that computers changed office work, we need a new system that can deal with the changes that can come with many more older people living alone. This is where technology can play a very big role.”