Poor health-related behaviors represent a major challenge to healthcare due to their significant impact on chronic and acute diseases and their effect on the quality of life. Recent advances in technology have enabled an unprecedented opportunity to assess objectively, unobtrusively and continuously human behavior and have opened the possibility of optimizing individual-tailored, precision interventions within the framework of behavioral informatics. A key prerequisite for this optimization is the ability to assess and predict effects of interventions. This is potentially achievable with computational models of behavior and behavior change. In this paper we describe various approaches to computational modeling and describe a new hybrid model based on a dual process theoretical framework for behavior change. The model leverages cognitive learning theories and is shown to be consistent with mobile intervention data. We also illustrate how system-theoretic approaches can be used to assess the effect of coaching and participants' health behaviors.