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.
Abstract (translated)
口腔疾病是一种常见的慢性疾病，会导致严重的经济负担、个人痛苦和增加患全身疾病的风险。尽管建议每天刷牙两次，但遵守推荐的口腔自我护理行为仍然存在较低的满意度，由于诸如健忘和疏离等因素。为了应对这个问题，我们开发了Oralytics，一个专为有口腔疾病风险的弱势人群设计的mHealth干预系统，旨在补充临床医生提供的预防性护理。Oralytics采用了一个在线强化学习算法来确定最佳的干预提示时间，以鼓励口腔自我护理行为。我们已经将Oralytics部署在一家注册的临床试验中。部署需要仔细考虑如何管理美国临床试验环境中特定的挑战。在本文中，我们（1）强调了RL算法关键的设计决策如何解决这些挑战，（2）进行了一项重新抽样分析以评估算法设计决策。Oralytics的第二阶段（随机对照试验）计划在2025年春季开始。
URL
https://arxiv.org/abs/2409.02069
