UCLA Oralytics

Functionality & Mechanism

Developed at UCLA, Oralytics is a mobile health intervention system engineered to promote adherence to oral self-care behaviors. The system leverages an online reinforcement learning (RL) algorithm to determine optimal times for delivering personalized intervention prompts. This mechanism aims to improve engagement and consistency with recommended tooth brushing habits by personalizing the timing of behavioral cues. The intervention is designed to complement clinician-delivered preventative care for individuals at risk for dental disease.

Evidence & Research Context

• The system's reinforcement learning algorithm was developed and refined using prior data, domain expertise, and extensive experiments within a simulation test bed, as detailed in its design papers.
• A re-sampling analysis was conducted to validate key design decisions of the reinforcement learning algorithm for deployment in a clinical trial setting.
• The Oralytics intervention system and its associated algorithm have been deployed in a registered clinical trial focusing on oral health for at-risk populations.
• Associated research protocols outline a planned second-phase randomized controlled trial to formally evaluate the system's efficacy in improving health outcomes.

Intended Use & Scope

Oralytics is designed for use by individuals, particularly those at risk for dental disease, as an adjunct to professional preventative care. Its primary utility is to reinforce and support adherence to oral self-care behaviors between clinical visits. The system does not provide clinical diagnoses or treatment recommendations and is not a substitute for professional dental consultation.