A study recently published in the journal npj Digital Medicine from the University of California-San Diego has identified 5 sleep types which could be further divided into 13 subtypes, and suggests that the way people move between the different types could provide insights about chronic and acute health conditions.
According to the data from the sleep study analyzing 5 million nights of sleep from around 33,000 people, your wearable may be giving you information about more than just your sleep, it could give you information about other chronic health conditions such as diabetes, sleep apnea, and illnesses.
The data was collected by the wearable Oura Ring which is a smart ring that monitors and tracks sleep, skin temperature, and other information. The researchers found that people often move between the different sleep phenotypes over time, this often reflected changes in an individual’s health, and this created a sort of travel log through the data-driven sleep landscape.
“We found that little changes in sleep quality helped us identify health risks. Those little changes wouldn’t show up on an average night, or on a questionnaire, so it really shows how wearables help us detect risks that would otherwise be missed,” said Benjamin Smarr, one of the study’s senior authors and a faculty member in the Jacobs School of Engineering and Halicioglu Data Science Institute at the University of California San Diego.
The 5 sleep types
Five sleep phenotypes researchers singled out based on data from 5 million nights of sleep across roughly 33,000 people. While many factors went into the study, the researchers also identified some trends that help to intuitively separate the 5 sleep phenotypes.
Phenotype 1: What we think of as “normal” sleep. In this phenotype, people get about eight hours of uninterrupted sleep for at least six days in a row. This is the type of sleep recommended by the National Institutes of Health and was the most common sleep type researchers found.
Phenotype 2: People sleep continuously for about half the nights, but they only sleep for short periods of time in bouts of less than three hours the other half.
Phenotype 3: People sleep mostly continuously, but they experience interrupted sleep around one night each week. The interrupted night is characterized by one period of relatively long sleep of about five hours and one period of short sleep of less than three hours.
Phenotype 4: People again sleep mostly continuously. But they experience rare nights, in which long bouts of sleep are separated by a mid-sleep waking.
Phenotype 5: People only sleep for very short periods of time every night. This phenotype was the rarest the researchers found and represents extremely disrupted sleep.
Tracking changes
A spatial model of all 5 million nights was created to track changes over time, in which the phenotypes were represented as different islands, composed of mostly similar weeks of sleep. Different patterns emerged over time that allowed the researchers to model each individual’s routes between islands.
How frequently they switched between islands in this sleep landscape helped to distinguish people with chronic conditions. In this way, even if someone switched phenotypes only rarely, the fact that they did switch could still provide useful information about their health. For example, the data showed that it is rare for most people to go multiple months without a few nights of disrupted sleep.
“We found that the little differences in how sleep disruptions occur can tell us a lot. Even if these instances are rare, their frequency is also telling. So it’s not just whether you sleep well or not — it’s the patterns of sleep over time where the key info hides,” said Wang, a coauthor and electrical and computer engineering faculty member at UC San Diego.
The researchers also found that people did not tend to remain in patterns defined by broken-up sleep. But how often they visited specific disrupted sleep patterns says a lot about how well they’re doing.
“If you imagine there’s a landscape of sleep types, then it’s less about where you tend to live on that landscape, and more about how often you leave that area,” said Viswanath, the paper’s corresponding author.
This article was written at the WHN News Desk
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