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Wireless Body Area Networks (WBANs) promise to revolutionize health care in the near future. By integrating bio-sensors with a mobile phone it is possible to monitor an individual's health and related behaviors. Monitoring is done by analyzing the sensor data either on a mobile phone or on a remote server by relaying this information over a wireless network. However, the “wireless” aspect of WBAN is being limited by the battery life of the mobile phone. A WBAN designer has a range of options to trade-off limited battery with many important metrics. From the choice of programming languages to dynamically choosing between computation versus communication under varying signal strengths, there are several non-obvious choices that can have dramatic impact on battery life. In this research we use an in-field deployed WBAN called KNOWME to present a comprehensive quantification of a mobile phone's energy consumption. We quantify the energy impact of different programming paradigms, sensing modalities, data storage, and conflicting computation and communication demands. Based on the knowledge gained from the measurement studies, we propose an Active Energy Profiling strategy that uses short profiling periods to automatically determine the most energy efficient choices for running a WBAN.