  The advent of mobile health technologies presents new challenges that
existing visualizations, interactive tools, and algorithms are not yet designed
to support. In dealing with uncertainty in sensor data and high-dimensional
physiological records, we must seek to improve current tools that make sense of
health data from traditional perspectives in event-based trend discovery. With
Chronodes, a system developed to help researchers collect, interpret, and model
mobile health (mHealth) data, we posit a series of interaction techniques that
enable new approaches to understanding and exploring event-based data. From
numerous and discontinuous mobile health data streams, Chronodes finds and
visualizes frequent event sequences that reveal common chronological patterns
across participants and days. By then promoting the sequences as interactive
elements, Chronodes presents opportunities for finding, defining, and comparing
cohorts of participants that exhibit particular behaviors. We applied Chronodes
to a real 40GB mHealth dataset capturing about 400 hours of data. Through our
pilot study with 20 behavioral and biomedical health experts, we gained
insights into Chronodes' efficacy, limitations, and potential applicability to
a wide range of healthcare scenarios.
