Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol.

Dental disease continues to be one of the most prevalent chronic diseases in the United States. Although oral self-care behaviors (OSCB), involving systematic twice-a-day tooth brushing, can prevent dental disease, this basic behavior is not sufficiently practiced. Recent advances in digital technology offer tremendous potential for promoting OSCB by delivering Just-In-Time Adaptive Interventions (JITAIs)- interventions that leverage dynamic information about the person's state and context to effectively prompt them to engage in a desired behavior in real-time, real-world settings. However, limited research attention has been given to systematically investigating how to best prompt individuals to engage in OSCB in daily life, and under what conditions prompting would be most beneficial. This paper describes the protocol for a Micro-Randomized Trial (MRT) to inform the development of a JITAI for promoting ideal OSCB, namely, brushing twice daily, for two minutes each time, in all four dental quadrants (i.e., 2x2x4). Sensors within an electric toothbrush (eBrush) will be used to track OSCB and a matching mobile app (Oralytics) will deliver on-demand feedback and educational information. The MRT will micro-randomize participants twice daily (morning and evening) to either (a) a prompt (push notification) containing one of several theoretically grounded engagement strategies or (b) no prompt. The goal is to investigate whether, what type of, and under what conditions prompting increases engagement in ideal OSCB. The results will build the empirical foundation necessary to develop an optimized JITAI that will be evaluated relative to a suitable control in a future randomized controlled trial.

Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.

While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.