It’s not strange to hear people complaining about being tired several times a day, but why? Sleep is fundamental to human health, but because it is so private, there are quite a few tools to broadly measure how much sleep everyone gets. Current methods use time diaries, sleep surveys, sleep labs, or, more recently, wearable technology to measure sleep. But none of these approaches is ready to tackle the global sleep loss pandemic.
A Monash University study asked a different question: Can the big data we all produce when we’re online and disconnected from it help researchers understand sleep?
When Internet addresses are online and offline during the day, they follow the daily cycle of human behavior: a trough in the early hours, followed by a spike in activity during the day to a peak in the evening, then a sharp drop overnight.
However, no two cycles are the same: day of the week is important (heading downtown on a Friday night lowers internet activity), stay-at-home requests certainly matter (we get online earlier and longer), and even a dip in activity during prayer times During Ramadan visible in traditional Islamic areas.
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The American Time Use Survey (ATUS) asks Americans about their previous day’s activities, including when they woke up and when they went to sleep. The Monash study used survey data of 81 US cities over a six-year period to calculate when residents sleep and wake up each year, and then used Internet activity data to do the same calculation.
The researchers then trained a machine-learning algorithm to track how changes in Internet use over the course of a day correlate with average waking and sleeping times in each city.
When the algorithm was asked to predict the expected average sleep duration for a city the algorithm had never seen before, it was accurate to within 20 minutes. When estimating the average morning wake-up time, it was accurate to within nine minutes.
The researchers repeated this finding when using daily electricity demand data instead of internet data to predict sleep. But there is something fundamentally different about measures of internet activity compared to electricity demand data: global availability. The United States has a high-performance electric bureaucracy, but not all countries do. On the other hand, internet activity can be remotely measured and continually measured for any internet connected device on the planet.
This suggests that the amount of sleep we collectively get for any city (connected to the internet) on the planet can be estimated in nearly real time.
This type of research has a wide range of applications, including impact mapping during natural disasters, documenting internet shutdowns linked to human rights abuses, and even providing internet availability assessments during the Russo-Ukrainian War.
It remains to be seen whether this approach can be applied globally. The technology and sleeping habits of Americans may be unique. If so, the artificial intelligence (AI) model that learns the Internet and sleep association in the US will fall outside its bounds. Another potential pitfall is that internet patterns will likely be influenced by the mix of technology in the game – the internet signature of a ‘mobile first’ continent such as Africa may be vastly different from North America, which relies heavily on fixed broadband internet.
Like many challenges in applying AI to the health sciences, the answer to both hurdles lies in broadening the model’s training base. The more measurements researchers get from traditional sleep studies, in more countries, cultures, and technological contexts, the more confident they are in any model prediction.
Should a global sleep observatory emerge (from internet measurements), population and sleep health scientists may benefit more.
If major shifts in internet use reveal similar changes in sleep patterns, researchers may flock to the field and use more precise tools for further research. Similarly, important global shocks such as epidemics and recessions can be studied in near real time for their impact on our sleep, sending the right public health messages about mental health and sleep, improving technology and application design, and timely education about the importance of sleep in times of stress.
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