A formidable challenge in designing sequential treatments is to determine when and in which context it is best to deliver treatments. Consider mobile health behavioral interventions for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment. Here we discuss our work in designing and implementing an online reinforcement learning algorithm for use in improving physical activity among individuals with stage 1 hypertension.