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This paper presents MediAlly, a middleware for supporting energy-efficient, long-term remote health monitoring. Data is collected using physiological sensors and transported back to the middleware using a smart phone. The key to MediAlly's energy efficient operations lies in the adoption of an Activity Triggered Deep Monitoring (ATDM) paradigm, where data collection episodes are triggered only when the subject is determined to possess a specified context. MediAlly supports the on-demand collection of contextual provenance using a novel low-overhead provenance collection sub-system. The behaviour of this sub-system is configured using an application-defined context composition graph. The resulting provenance stream provides valuable insight while interpreting the `episodic' sensor data streams. The paper also describes our prototype implementation of MediAlly using commercially available devices.