Your sleep tracker can provide insights into more than just your sleep patterns. It can also offer information about chronic conditions like diabetes and sleep apnea, as well as illnesses such as COVID-19.
This discovery comes from a study that examined data from 5 million nights of sleep from approximately 33,000 individuals.
Study Reveals 5 Main Types of Sleep and 13 Subtypes
A recent study analyzed data from 5 million nights of sleep among approximately 33,000 individuals. The researchers have identified five primary types of sleep, referred to as sleep phenotypes, which can then be further categorized into 13 subtypes.
Furthermore, the study found that the frequency and manner in which a person transitions between sleep phenotypes could provide significantly more information for detecting health conditions, ranging from two to ten times more than relying solely on a person’s average sleep phenotype.
The findings of this study were published in the journal npj Digital Medicine on June 20, 2024. The data used for this study was collected from Oura Ring, a smart ring.
In a study utilizing a tracking device for sleep, skin temperature, and other data, researchers observed individuals over several months to determine if they had chronic health issues like diabetes and sleep apnea, or illnesses such as COVID-19 and the flu.
The study revealed that people often transitioned between different sleep patterns over time, indicating a shift in their health conditions. This resulted in a data-driven representation of an individual’s journey through the landscape of sleep, resembling a travel log.
The study showed that even minor changes in sleep quality could help us identify potential health risks. These subtle changes may not be noticeable through traditional methods, but they are significant. Benjamin Smarr, a faculty member at the University of California San Diego’s Jacobs School of Engineering and Halicioglu Data Science Institute, stressed the importance of wearables in detecting risks that may otherwise go unnoticed. He emphasized that wearables can provide valuable information about sleep patterns, which could lead to new insights for public health. The researchers also pointed out that monitoring long-term changes in sleep at a population scale could help identify potential early warning signs for chronic conditions.
The research team’s work is grounded in recent analyses from the TemPredict dataset at the University of California, San Francisco. This dataset was compiled from information gathered from individuals wearing the commercially available Oura Ring during the COVID-19 pandemic in 2020. The analyses were spearheaded by Smarr, who is also a faculty member in the University of California San Diego Shu Chien — Gene Lay Department of Bioengineering, and Professor Edward Wang in the University of California San Diego Department of Electrical and Computer Engineering. They collaborated with the study lead at the University of California, San Francisco to delve into the connection between physiological signals and the risk of illness or susceptibility to infection.
sco, Professor Ashley E. Mason, a practicing sleep clinician. Varun Viswanath, a graduate student in the Department of Electrical and Computer Engineering at the University of California San Diego Jacobs School of Engineering, was the lead author
The five sleep types
The researchers identified five sleep phenotypes based on data from 5 million nights of sleep across approximately 33,000 people. The study took into consideration various factors and also found trends that help distinguish the 5 sleep phenotypes.
- Phenotype 1: Commonly referred to as “normal” sleep. This type of sleep entails Phenotype 1: According to the National Institutes of Health, the recommended sleep pattern is getting about eight hours of uninterrupted sleep for at least six days in a row, and this was found to be the most common sleep type by researchers.Phenotype 2: People in this category sleep continuously about half the nights, but on the other half, they only sleep for short periods of time in bouts of less than three hours.
Phenotype 3: Individuals in this group mostly sleep continuously, but they experience interrupted sleep around one night each week. The interrupted night consists of one period of relatively long sleep of about five hours, and one period of short sleep.less than three hours.
- Phenotype 4: Individuals once again experience uninterrupted sleep for the most part. However, there are occasional nights where they experience long periods of sleep followed by a waking period in the middle of the night.
- Phenotype 5: Individuals only sleep for short periods of time each night. This particular phenotype was the least common among the participants and indicates severely disrupted sleep patterns.
Monitoring changes in sleep patterns
In order to track changes in sleep phenotypes over time, Viswanath developed a spatial model that represented all 5 million nights as different islands, each composed of mostly similar weeks of sleep.leep. The researchers were able to model each individual’s routes between islands, revealing different patterns over time.
What distinguished people with chronic conditions like diabetes and sleep apnea was not their average phenotype, but rather how frequently they switched between islands in the sleep landscape. Even rare switches in phenotypes could still be informative about their health.
The data indicated that most people rarely go multiple months without experiencing a few nights of disrupted sleep.sleep. “We discovered that the small variations in how sleep disruptions occur can provide us with valuable information. Even if these occurrences are uncommon, their frequency is also significant. So it’s not just a matter of whether you sleep well or not — it’s the trends in sleep patterns over time that hold the key information,” stated Wang, a coauthor and member of the electrical and computer engineering faculty at UC San Diego.
In contrast, individuals did not tend to stick to patterns characterized by fragmented sleep. However, the frequency with which they experienced specific disrupted sleep patterns reveals a lot about their overall well-being.
“If you envision a spectrum of sleep patterns, then it’s not so much about where youReferring to the study, Viswanath, the corresponding author of the paper, emphasized the importance of focusing on the frequency of leaving a specific area rather than just living in it. The research team made modifications to the technique utilized in the previous study, which was the largest investigation of sleep at the time and involved approximately 103,000 nights of data from the UK biobank. The previous study analyzed sleep timing, awakenings, and related features to create a “landscape” of the nights’ positions in relation to each other. However, the prior researchers failed to carry out two crucial tasks. They were unable to accurately determine the landscape and frequency of leaving the area.This new study differs from previous large-scale sleep analyses in that it focuses on the changing dynamics of people’s sleep over time, rather than just simple sleep characteristics. Previous studies only looked at two to three nights per person, which made it difficult to tie sleep patterns to health outcomes. The findings of this new study suggest that changes in sleep patterns may indicate a higher risk for a variety of health conditions. This research also provides insights that can help people better understand their sleep health.
