A Field-Validated Architecture for the Collection of Health-Relevant Behavioural Data

Human behaviour is an underlying factor in many diseases. Behavioural data has traditionally been collected through interviews, surveys, and direct observation. While these methods offer significant insight, they have drawbacks including bias, limited recall accuracy, and low temporal fidelity. Automated data collection devices such as GPS trackers have helped to reduce these problems while increasing objectivity and fidelity. Modern smart phones provide sensors that can replicate the functionality of dedicated devices while providing ubiquity, near-perpetual presence, and the ability to perform ecological momentary assessment. This has spurred researchers to envision or deploy smartphone data collection tools. Not all of these tools, however, are well designed, thoroughly tested, or easily extended. To realize the potential of this technology in the health sphere, careful attention must therefore be paid to the underlying software architecture and its robustness. To this end, we present a highly flexible, reconfigurable, and verifiable software architecture for monitoring health-related behaviours constructed using modern software engineering principles. We detail here the process-stream abstractions that underlie its data collection and management processes. Efficacy is demonstrated through retrospective analysis of deployments of the system, which include targets as diverse as studying flu transmission and gamified interventions for sedentary behaviour.