  An increasing number of people use wearables and other smart devices to
quantify various health conditions, ranging from sleep patterns, to body
weight, to heart rates. Of these Quantified Selfs many choose to openly share
their data via online social networks such as Twitter and Facebook. In this
study, we use data for users who have chosen to connect their smart scales to
Twitter, providing both a reliable time series of their body weight, as well as
insights into their social surroundings and general online behavior.
Concretely, we look at which social media features are predictive of physical
status, such as body weight at the individual level, and activity patterns at
the population level. We show that it is possible to predict an individual's
weight using their online social behaviors, such as their self-description and
tweets. Weekly and monthly patterns of quantified-self behaviors are also
discovered. These findings could contribute to building models to monitor
public health and to have more customized personal training interventions.
  While there are many studies using either quantified self or social media
data in isolation, this is one of the few that combines the two data sources
and, to the best of our knowledge, the only one that uses public data.
