The Big Sleep: Big Data Helps Scientists Tackle Lack of Quality Shut Eye | Articles | Big Data | Innovation Enterprise
Did you know that a third of U.S. adults get less than the recommended amount of sleep? (It’s 7 hours, in case you were wondering.)
Getting enough high-quality sleep is not a luxury we can afford to skip. Lack of sleep is linked to health issues such as type 2 diabetes, heart disease, obesity, and depression.
Sleep also has a major impact on brain functions, which affect judgment, productivity, mental clarity, creativity, motor coordination, and more. In fact, lack of sleep is a major cause of motor vehicle crashes and mistakes at work.
The good news is that more people are now aware of the health benefits of sleep, and we have access to more resources to help us sleep better and longer.
The use of big data is one of these major advances that enable scientists to understand how various factors affect sleep quality. It should help identify patterns and behaviors to make improvements on a large scale.
New tools, such as wearable trackers, allow data to be collected from a large pool of subjects without the expense and resources required to set up sleep studies in clinical environments. For example, Fitbit has nearly 10 million active users and has tracked over 6 billion nights of sleep.
Meanwhile, the use of artificial intelligence-driven technologies, such as machine learning, is helping scientists analyze the data to generate meaningful and actionable insights.
Here’s how big data is helping scientists improve our sleep.
Treat Sleep Problems
By analyzing data from a large population, scientists can better understand sleep problems such as sleep apnea, insomnia, restless leg syndrome, and narcolepsy (which causes extreme sleepiness and bouts of unexpected sleep). This knowledge allows scientists to design targeted treatments and help patients improve their sleep quality by alleviating various symptoms.
Understand Sleep Disorders
With the information collected from a large sample of the population, scientists can use genomics, proteomics, and molecular data to pin down the root causes of many sleep disorders. For example, studies have found that genes associated with narcolepsy are involved in the immune system, leading to the discovery that narcolepsy is an autoimmune disease.
Improve Treatment Plans
Certain health conditions, such as COPD (chronic obstructive pulmonary disorder), can impact a patient’s sleep quality. Connected devices are used to collect and compare data, track patients’ conditions, and monitor vitals as they sleep. These devices then send data to caregivers so they can calibrate therapies, prescriptions, and treatment plans to quickly respond to a patient’s conditions and improve their sleep quality.
Link Behaviors To Sleep Quality
The use of monitoring devices and wearables, as well as apps that facilitate self-reporting, allows scientists to collect and analyze a large amount of data to understand how habits and activities affect sleep quality. For example, we now have a better understanding of how nutrition and physical activities throughout the day impact sleep, as well as how bedtime routines and bedroom environments improve sleep quality.
Improve Mattress Design
What we learn about sleep patterns can inform mattress design. With built-in sleep-tracking technologies, some mattresses can now collect data on a user’s sleep patterns and compare it with information from existing databases. These mattresses will then react to the user’s movement, body temperature, and sleep cycles by changing firmness and other properties in real-time to create a personalized sleep environment.
Deliver Personalized Guidance
A digital mental health company has developed an app that focuses on changing a user’s actions, thoughts, and behavior to improve their sleep quality. The app uses tools such as sleep diaries for users to report their sleep measures and collect relevant data. Matching the information with conclusions drawn from an extensive database, the app delivers personalized content and recommendations.
With the help of AI-driven machine learning technologies, sleep scientists can now leverage big data to identify patterns and match them to an individual’s conditions to deliver treatment, as well as recommend improvements on lifestyle choices, habits, and sleep environment.