Artificial intelligence products such as wearable health devices, as well as AI sleep trackers can be effective in helping people with insomnia. To a large extent, this is a market that will be promoted. That's because about 70 million people have reported that they have sleep problems. And, more than ever, they are inclined to utilize technology to help them with their current predicament.
We spend all this money on these seemingly advanced sleep technologies, but do they really help us sleep better?
Currently popular technologies
According to Adam Blacker, communications leader at Apptopia, nearly 11.5 million people (11,490,230 to be exact) downloaded one of the top 10 most popular sleep apps on Apple's iOS platform in the past six months. And that staggering number represents just a fraction of the users of sleep trackers worldwide.
According to Dr. Jeffrey Durmer, data suggests that about 37.8 percent of people in the U.S. are using wearable technology. He is the chief medical officer of FusionHealth, a company that aims to help employers address employee sleep problems. He says, "In general, sleep tracking technology has helped raise the importance of sleep for most people. This is critical for Americans, given the growing public **** health problem of poor sleep quality."
Promising AI innovations
Artificial intelligence-enabled sleep trackers can monitor sleep behavior and help consumers change their poor sleep habits. Products such as SleepWatch and Rythm that are available to consumers use machine learning models to provide predictive diagnostics for common sleep disorders. An innovation from the MIT Sleep Lab takes this idea a step further by using advanced artificial intelligence algorithms to track sleep habits without connecting participants to machines or devices.
The goal of MIT's technology is to wirelessly monitor patient behavior and use the results to diagnose sleep disorders. While MIT's algorithms are trained to help researchers produce more accurate results than the apps and devices available to consumers, even advanced technology like this one may not accurately diagnose sleep problems.
The problem with artificial intelligence and sleep tracking
The problem with AI algorithms, said Michael Larson, founder and president of the Sleep Guardian Association, is that "most AI algorithms today have pattern matching at their core." "The problem in the field of sleep technology is that the patterns used in the algorithms are flawed because they don't describe sleep well."
For example, while AI has mastered the game, Larson noted that sleep data "is not as simple as the patterns commonly found in games." Artificial intelligence is flawed because its algorithms can become detached from motion sensors, thus providing inaccurate data. As a result, sleep-tracking technology has a long way to go before AI becomes useful.
Sleep tracker data provides a starting point
If realizing a problem is the first step to solving it, then AI-enabled sleep trackers set us on the path to finding a solution. While an algorithm isn't precise enough to fully diagnose and recommend treatment for a sleep problem, the results from a tracker can help you be more informed when consulting your doctor.
Buying a sleep tracker and relying solely on its results to guide you through the process of improving your sleep is like buying a specific mattress - one that statistically has been shown to work for others. Just because 40% of Americans find a full-size mattress to be the most comfortable, doesn't mean it's the most comfortable for you, too. By the same token, just because a tracker who tracks other people's results says you might have a specific sleep problem doesn't make it true.
You'd need to go through a lot of trial and error to really determine what's going on with your sleep problem. The results of an algorithm could be just as flawed as relying on statistics to guide your sleep decisions. Sure, they'll provide a good starting point for evaluating your problem, but further research will be necessary to fully understand what's going on.
Experimentation is key, as well as tracking overall trends for each user.Dr. Benjamin Smarr, a sleep researcher at Reverie, encourages people to go to bed at the same time every night and record how they feel in the morning. Control group experiments will experiment with caffeine and alcohol intake, changing bedtime routines, etc. Comparing it to tracker data will provide valuable feedback.