Personal life style plays important role in a person’s health. It is now possible to analyze and understand a person’s life style. Most people use phones with myriad sensors that continuously generate data streams related to most aspects of their life. By correlating these multi-sensory data streams, it is possible to create an accurate chronicle of a person’s life. By correlating life events with health related events, obtained using wearable sensors and other common sources of information, one can build health persona of a person. Health persona of a person is a long-term objective characterization of a person’s health. By using health persona for a large group of people, one can analyze and understand health patterns and causes of different diseases in a society. In this talk, we present a framework that collects, manages, and correlates personal data from heterogeneous data sources and detects events happening at personal level to build health persona. We use several data streams such as motion tracking, location tracking, activity level, and personal calendar data. We illustrate how recognition algorithms can be applied to Life Event detection problem and then build an objective chronicle for a person. We show how this could be combined with situation detection and help people in making decisions in their every day life. In this talk, we will present our ideas related to health persona, its impact on societal health, and its use in making decisions.