Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages

描述(申请人提供)：未受控制的高血压是发病率和死亡率的主要原因，许多患者没有按照处方服用降压药。我们建议使用人工智能(AI)来允许短信服务(短信或短信)干预，以适应患者的依从性需求，并大幅改善用药情况。这项研究的目的是：(1)开发在以人为中心的环境中进行适应性决策的人工智能方法，并论证由此产生的人工智能增强的短信服药依从性干预的可行性；(2)证明干预可以通过根据患者服药时间的变化调整短信消息流来“学习”；以及(3)检查潜在的干预影响，通过改善服药依从性和收缩血压来衡量。我们将招募100名高血压失控和降压药物治疗不合规的患者。依从性和其他协变量将在基线、3个月和6个月时通过调查进行测量；血压将在基线和6个月时测量。参与者将获得一个电子药瓶依从性监测器。参与者将收到旨在激励抗高血压药物坚持服用的短信。消息内容和频率将使用人工智能算法自动适应，该算法旨在自动优化预期的药瓶打开。对于目标1，前25名患者将被招募来开发和测试替代的RL算法，并微调系统参数。对于目标2，我们将检查消息类型的概率分布的变化，并将该分布与基线报告的患者不遵守的原因进行比较。对于目标3，我们将检查自我报告的用药不依从性和血压以及自动报告的药瓶开度的变化。这项先导研究将确立 这种用于自我管理支持的移动健康消息的新方法的可行性和潜在影响。结果将被用于支持R01申请进行更大规模和更明确的干预影响试验。

DESCRIPTION (provided by applicant): Uncontrolled hypertension is a major cause of morbidity and mortality and many patients fail to take their antihypertensive medication as prescribed. We propose to use artificial intelligence (AI) to allow short message service (SMS or text messages) interventions to adapt to patients' adherence needs and substantially improve medication taking. The aims of the study are to: (1) develop AI methods for adaptive decision-making in human- centered environments and demonstrate the feasibility of the resulting AI-enhanced SMS medication adherence intervention, (2) demonstrate that the intervention can "learn" by adapting the SMS message stream according to patients' medication taking over time, and (3) examine potential intervention impact as measured by improvements in medication adherence and systolic blood pressures. We will recruit 100 patients with uncontrolled hypertension and antihypertensive medication non-adherence. Adherence and other covariates will be measured via surveys at baseline, 3- and 6 months; blood pressures will be measured at baseline and 6 months. Participants will be given an electronic pill-bottle adherence monitor. Participants will receive SMS messages designed to motivate antihypertensive medication adherence. Message content and frequency will adapt automatically using AI algorithms designed to automatically optimize expected pill bottle opening. For Aim 1, the first 25 patients will be enrolled to develop and test alternative RL algorithms and fine-tune the system parameters. For Aim 2, we will examine changes in the probability distribution over message-types and compare that distribution with patients' reasons for non-adherence reported at baseline. For Aim 3, we will examine changes in self-reported medication non-adherence and blood pressure and automatically-reported pill bottle openings. This pilot study will establish the feasibility and potential impact of this novel approach to mobile health messaging for self-management support. The results will be used to support an R01 application for a larger and more definitive trial of intervention impacts.