用在线强化学习优化牙科护理提醒时机，提升患者依从性。
A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
- 通过在线强化学习动态选择最佳提醒时间，提升干预效果。
- 部署于真实临床试验，已解决美国临床场景中的实际挑战。
- 适合关注mHealth干预、行为激励与临床落地的研究者。
牙科疾病是一种普遍的慢性病，带来巨大的经济负担、个人痛苦以及系统性疾病风险。尽管普遍建议每日两次刷牙，但因遗忘和缺乏参与感，患者依从性仍不理想。为此，我们开发了Oralytics——一个针对高风险弱势群体的移动健康干预系统，旨在补充临床预防护理。Oralytics采用在线强化学习算法，自动确定最佳时机推送鼓励口腔自我护理的提醒。该系统已在注册临床试验中部署，需精心设计以应对美国临床试验环境的特殊挑战。本文（1）重点阐述了该强化学习算法的关键设计决策，以解决这些挑战；（2）通过重抽样分析评估算法设计的有效性。Oralytics的第二阶段（随机对照试验）计划于2025年春季启动。
原文摘要 · Abstract (English)
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.
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