There are plenty of cellphone apps on the market designed to help people monitor their sleep patterns. The apps generally record data on when people go to bed and when they wake, and many use the device’s microphone and accelerometer to take note of noises in the night and to monitor how much people toss and turn. A group of Brown University computer scientists, including Nediyana Daskalova, a doctoral student in computer science, and clinical psychologists have come up with an approach that takes sleep monitoring one step further. The approach, dubbed SleepCoacher, uses sleep analytics to generate personalized recommendations informed by the scientific literature on sleep. SleepCoacher then guides users through a self-experimentation framework to help people find the recommendations that best work for them." title="<--break-->">
“The idea is to not only present people with information about their sleep, but to give them some control over it by giving recommendations along with a step-by-step plan for improving their sleep,” said Nediyana Daskalova, a doctoral student in computer science at Brown who is leading the development of SleepCoacher.
Daskalova presented a paper on the SleepCoacher approach recently at the User Interface Software and Technology Symposium in Japan. The presentation included the results of two small pilot studies of SleepCoacher users, which found that 80 percent of people who followed the recommendations at least 60 percent of the time reported improvement in their sleep. The team is now working on a self-contained SleepCoacher app that they hope to make available to users early next year.
Daskalova and her team developed SleepCoacher under the direction of Jeff Huang, an assistant professor of computer science at Brown and leader of Brown’s Human-Computer Interaction Group.
Read more of Kevin Stacey's article on SleepCoacher.