  Wearable devices are increasingly used as tools for biomedical research, as
the continuous stream of behavioral and physiological data they collect can
provide insights about our health in everyday contexts. Long-term tracking,
defined in the timescale of months of year, can provide insights of patterns
and changes as indicators of health changes. These insights can make medicine
and healthcare more predictive, preventive, personalized, and participative
(The 4P's). However, the challenges in modeling, understanding and processing
longitudinal data are a significant barrier to their adoption in research
studies and clinical settings. In this paper, we review and discuss three
models used to make sense of longitudinal data: routines, rhythms and stability
metrics. We present the challenges associated with the processing and analysis
of longitudinal wearable sensor data, with a special focus on how to handle the
different temporal dynamics at various granularities. We then discuss current
limitations and identify directions for future work. This review is essential
to the advancement of computational modeling and analysis of longitudinal
sensor data for pervasive healthcare.
