  Sleep loss has been linked to heart disease, diabetes, cancer, and an
increase in accidents, all of which are among the leading causes of death in
the United States. Population-scale sleep studies have the potential to advance
public health by helping to identify at-risk populations, changes in collective
sleep patterns, and to inform policy change. Prior research suggests other
kinds of health indicators such as depression and obesity can be estimated
using social media activity. However, the inability to effectively measure
collective sleep with publicly available data has limited large-scale academic
studies. Here, we investigate the passive estimation of sleep loss through a
proxy analysis of Twitter activity profiles. We use "Spring Forward" events,
which occur at the beginning of Daylight Savings Time in the United States, as
a natural experimental condition to estimate spatial differences in sleep loss
across the United States. On average, peak Twitter activity occurs roughly 45
minutes later on the Sunday following Spring Forward. By Monday morning
however, activity curves are realigned with the week before, suggesting that at
least on Twitter, the lost hour of early Sunday morning has been quickly
absorbed.
