Year: 2025
Type: conference-paper
Abstract: Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.
Authors: Anna L. Trella, Kelly W. Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
Awards: Personalized Digital Behavior Change Interventions to Promote Oral Health, Data-driven subtyping in major depressive disorder, Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs, Personalized Digital Behavior Change Interventions to Promote Oral Health, Center for Methodologies for Adapting and Personalizing Prevention, Treatment and Recovery Services for SUD and HIV (MAPS Center), mHealth Center for Discovery, Optimization, and Translation of Temporally-Precise Interventions (mDOT)
OpenAlex ID: W4409348716