A big-data view on sleep and travel - new paper

Sleeping at home and away. Sleeping at home and away

Very excited to announce we have a new paper out:

Essentially, we have access to a pretty incredible deidentified dataset covering wearables users who track their sleep. These data let us tackle questions at a scale far beyond the size of a typical sleep study. And along with sleep indicators, like when sleep begins and ends, we can tell whether an individual is at home or traveling, so we can see, observationally, how travel affects sleep.

With that in mind, we went into the data with a rough expectation of quantifying how travel harms sleep, leading to less sleep for example. Certainly most travelers have all experienced (maybe not recently) serious jet lag. But in fact we found something more nuanced. Here’s the abstract:

Travel is expected to have a deleterious effect on sleep, but an epidemiological-scale understanding of sleep changes associated with travel has been limited by a lack of large-scale data. Our global dataset of ~20,000 individuals and 3.17 million nights (~218,000 travel nights), while focused mainly on short, non-time-zone-crossing trips, reveals that travel has a balancing effect on sleep. Underslept individuals typically sleep more during travel than when at home, while individuals who average more than 7.5 hours of sleep at home typically sleep less when travelling. The difference in travel sleep quantity depends linearly on home sleep quantity and decreases as median sleep duration increases. On average, travel wake time advances to later hours on weekdays but earlier hours on weekends. Our study emphasizes the potential for consumer-grade wearable device data to explore how environment and behaviour affect sleep.

Besides the paper itself (and apologies for hitting any paywalls), we also put together a fun little “Behind the paper” blog post.

Congrats to Sigga Svala Jónasdóttir, our fearless leader on this journey, and my long-time collaborator Sune Lehmann, who were both fantastic to work with.

Please check out the paper, the data and the code for more.

Jim Bagrow
Jim Bagrow
Associate Professor of Mathematics & Statistics

My research interests include complex networks, computational social science, and data science.