Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages

描述（由申请人提供）：不受控制的高血压是发病和死亡的主要原因，许多患者未能按处方服用抗高血压药物。我们建议使用人工智能（AI）来允许短消息服务（SMS或短信）干预，以适应患者的依从性需求，并大幅改善药物服用。这项研究的目的是：（1）开发用于在以人为中心的环境中进行自适应决策的AI方法，并证明所得到的AI增强的SMS药物依从性干预的可行性，（2）证明干预可以通过根据患者随时间服用的药物来调整SMS消息流来“学习”，以及（3）通过药物依从性和收缩压的改善来检查潜在的干预影响。我们将招募100例高血压未控制和抗高血压药物不依从的患者。将通过基线、3个月和6个月时的调查测量依从性和其他协变量;将在基线和6个月时测量血压。将为受试者提供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依从性的短信。消息内容和频率将使用AI算法自动调整，旨在自动优化预期的药瓶打开。对于目标1，将招募前25名患者，以开发和测试替代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变化，并将该分布与基线时报告的患者不依从原因进行比较。对于目标3，我们将检查自我报告的药物不依从性和血压以及自动报告的药瓶开口的变化。这项试点研究将建立 可行性和潜在的影响，这种新的方法，移动的健康消息的自我管理支持。研究结果将用于支持R 01申请，以进行更大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.