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 sur-roundings and general online behavior. Concretely, we look at which social media features are predictive of physical sta-Tus, such as body weight at the individual level, and activity patterns at the population level. We show that it is possi-ble 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 mod-els 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 knowl-edge, the only one that uses public data.