  Mobile health (mHealth) technologies empower patients to adopt/maintain
healthy behaviors in their daily lives, by providing interventions (e.g. push
notifications) tailored to the user's needs. In these settings, without
intervention, human decision making may be impaired (e.g. valuing near term
pleasure over own long term goals). In this work, we formalize this
relationship with a framework in which the user optimizes a (potentially
impaired) Markov Decision Process (MDP) and the mHealth agent intervenes on the
user's MDP parameters. We show that different types of impairments imply
different types of optimal intervention. We also provide analytical and
empirical explorations of these differences.
