Abstract: Probability models of user behaviour are generated by tracking and recording user progress through health programs consisting of individual activities. Historical user engagement with a program or activities is aggregated into probability models that estimate future user behaviours and engagement with different activities. Using probability models, a next best engagement engine identifies and suggests health programs that have been determined to maximize the aggregate overall expected value to the user of being presented with an activity sequence. To model the probability of a user completing different activities in the future given past user behaviours, machine-learning techniques that train on input data sets observed over time are utilized.