  The proliferation of consumer health devices such as smart watches, sleep
monitors, smart scales, etc, in many countries, has not only led to growing
interest in health monitoring, but also to the development of a countless
number of ``smart'' applications to support the exploration of such data by
members of the general public, sometimes with integration into professional
health services. While a variety of health data streams has been made available
by such devices to users, these streams are often presented as separate
time-series visualizations, in which the potential relationships between health
variables are not explicitly made visible. Furthermore, despite the fact that
other aspects of life, such as work and social connectivity, have become
increasingly digitised, health and well-being applications make little use of
the potentially useful contextual information provided by widely used personal
information management tools, such as shared calendar and email systems. This
paper presents a framework for the integration of these diverse data sources,
analytic and visualization tools, with inference methods and graphical user
interfaces to help users by highlighting causal connections among such
time-series.
