Communications of the ACM
Brown Researchers Developing New Interactive Sleep App
A new app developed by Brown University computer scientists and clinical psychologists uses sleep analytics to generate personalized recommendations informed by the scientific literature on sleep.
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Brown University researchers developed SleepCoacher, a cellphone app using sleep analytics to generate personalized recommendations informed by scientific literature.
SleepCoacher guides users through a self-experimentation framework to help them find the recommendations that best work for them.
The researchers used SleepCoacher in two pilot studies, which found 80% of people who followed the recommendations at least 60% of the time reported improvement in their sleep.
"This could be personalized for whether you are a night owl or morning person, a light or heavy sleeper, or even someone who needs more than the usual eight hours of sleep," says Brown professor Jeff Huang.
As part of the two pilot studies, participants entered a rating of how refreshed they felt in the morning, and noted other factors that might have affected their sleep. The SleepCoacher algorithm used that data to determine what factors were correlated with three key sleep outcomes: how long it took participants to fall asleep, how many times they woke up during the night, and how refreshed they reported feeling in the morning. When a strong correlation is made, the algorithm generates a recommendation based on a collection of 117 recommendation templates developed with psychologists and psychiatrists.
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