Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages

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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.