  It is well understood that an individual's health trajectory is influenced by
choices made in each moment, such as from lifestyle or medical decisions. With
the advent of modern sensing technologies, individuals have more data and
information about themselves than any other time in history. How can we use
this data to make the best decisions to keep the health state optimal? We
propose a generalized Personal Health Navigation (PHN) framework. PHN takes
individuals towards their personal health goals through a system which
perpetually digests data streams, estimates current health status, computes the
best route through intermediate states utilizing personal models, and guides
the best inputs that carry a user towards their goal.
  In addition to describing the general framework, we test the PHN system in
two experiments within the field of cardiology. First, we prospectively test a
knowledge-infused cardiovascular PHN system with a pilot clinical trial of 41
users. Second, we build a data-driven personalized model on cardiovascular
exercise response variability on a smartwatch data-set of 33,269 real-world
users. We conclude with critical challenges in health computing for PHN systems
that require deep future investigation.
