Adopting healthy behaviors can prevent the onset of many adverse health conditions. However, behavior changes are difficult to make, and often, people who like to improve their behaviors do not know how to do that. Personalizable intervention systems could assist them to achieve healthy behavior change. These systems decide what would be the optimal intervention for the target user based on his or her characteristics, including current and past behavior patterns. In this thesis, we propose novel solutions that address the main challenges in building a personalizable intervention system to promote healthy behavior change. First, we propose a system based on a Bayesian mixture model to identify subpopulations with different behavior changes from longitudinal data. This system is especially suitable when the amount of data is limited, and when there are unobserved factors that might affect behavior change. Second, we propose CLINT, a system based on a latent-variable model, to discover and predict behavior change patterns from fine-grained sensor data. The novelty of this system is that it produces interpretable patterns that could be used to suggest successful behavior change strategies from the existing users similar to the target user. Third, we propose a personalizable intervention system to improve the physical activeness of senior adults. The main novelty of this system is that it uses historical time series fitness data to decide which intervention to recommend. Finally, we propose ACFR, an adversarial approach to reduce intervention bias in observational data. This approach learns a balanced representation of the covariates that allows personalizable intervention systems to make a better estimate of the intervention effect. Our solutions turn existing human behavior data into actionable insights for future users who may have unhealthy lifestyles.