Oralytics Reinforcement Learning Algorithm

Dental disease is one of the most common chronic diseases in the United States, particularly affecting disadvantaged communities. While scientific evidence indicates that healthy oral self-care behaviors (OSCB) (i.e., systematic, twice-a-day tooth brushing) prevent dental disease [Löe, 2000, Attin and Hornecker, 2005], this basic behavior is not consistently practiced [Yaacob et al., 2014]. We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimizes the delivery of personalized intervention prompts to improve OSCB. These prompts, delivered via push notification from the Oralytics app, are designed to supplement clinician instruction and consist of engaging content tailored to participants, such as brushing feedback and motivational messages. This paper describes the methodology used to design and develop the online RL algorithm. To make quality design decisions, we leveraged prior data, domain expertise, and experiments in a simulation test bed.