Magnani Jared W, Schlusser Courtney L, Kimani Everlyne, Rollman Bruce L, Paasche-Orlow Michael K, Bickmore Timothy W
Division of Cardiology, Department of Medicine, UPMC Heart and Vascular Institute, University of Pittsburgh, Pittsburgh, PA, United States.
Center for Behavioral Health Smart Technology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
JMIR Cardio. 2017 Jul-Dec;1(2):e7. doi: 10.2196/cardio.8543. Epub 2017 Dec 12.
Atrial fibrillation (AF) is a highly prevalent heart rhythm condition that has significant associated morbidity and requires chronic treatment. Mobile health (mHealth) technologies have the potential to enhance multiple aspects of AF care, including education, monitoring of symptoms, and encouraging and tracking medication adherence. We have previously implemented and tested relational agents to improve outcomes in chronic disease and sought to develop a smartphone-based relational agent for improving patient-centered outcomes in AF.
The objective of this study was to pilot a smartphone-based relational agent as preparation for a randomized clinical trial, the Atrial Fibrillation Health Literacy Information Technology Trial (AF-LITT).
We developed the relational agent for use by a smartphone consistent with our prior approaches. We programmed the relational agent as a computer-animated agent to simulate a face-to-face conversation and to serve as a health counselor or coach specific to AF. Relational agent's dialogue content, informed by a review of literature, focused on patient-centered domains and qualitative interviews with patients with AF, encompassed AF education, common symptoms, adherence challenges, and patient activation. We established that the content was accessible to individuals with limited health or computer literacy. Relational agent content coordinated with use of the smartphone AliveCor Kardia heart rate and rhythm monitor. Participants (N=31) were recruited as a convenience cohort from ambulatory clinical sites and instructed to use the relational agent and Kardia for 30 days. We collected demographic, social, and clinical characteristics and conducted baseline and 30-day assessments of health-related quality of life (HRQoL) with the Atrial Fibrillation Effect on Quality of life (AFEQT) measure; self-reported medication adherence with the Morisky 8-item Medication Adherence Scale (MMAS-8); and patient activation with the Patient Activation Measure (PAM).
Participants (mean age 68 [SD 11]; 39% [12/31] women) used the relational agent for an average 17.8 (SD 10.0) days. The mean number of independent log-ins was 19.6 (SD 10.7), with a median of 20 times over 30 days. The mean number of Kardia uses was 26.5 (SD 5.9), and participants using Kardia were in AF for 14.3 (SD 11.0) days. AFEQT scores improved significantly from 64.5 (SD 22.9) at baseline to 76.3 (SD 19.4) units at 30 days (<.01). We observed marginal but statistically significant improvement in self-reported medication adherence (baseline: 7.3 [SD 0.9], 30 days: 7.7 [SD 0.5]; =.01). Assessments of acceptability identified that most of the participants found the relational agent useful, informative, and trustworthy.
We piloted a 30-day smartphone-based intervention that combined a relational agent with dedicated content for AF alongside Kardia heart rate and rhythm monitoring. Pilot participants had favorable improvements in HRQoL and self-reported medication adherence, as well as positive responses to the intervention. These data will guide a larger, enhanced randomized trial implementing the smartphone relational agent and the Kardia monitor system.
心房颤动(AF)是一种高度常见的心律病症,具有显著的相关发病率,需要长期治疗。移动健康(mHealth)技术有潜力改善房颤护理的多个方面,包括教育、症状监测以及鼓励和跟踪药物依从性。我们之前已经实施并测试了关系代理以改善慢性病的治疗效果,并试图开发一种基于智能手机的关系代理,以改善房颤患者以患者为中心的治疗效果。
本研究的目的是对基于智能手机的关系代理进行试点,为随机临床试验“心房颤动健康素养信息技术试验(AF-LITT)”做准备。
我们按照之前的方法开发了供智能手机使用的关系代理。我们将关系代理编程为一个计算机动画代理,以模拟面对面的对话,并作为特定于房颤的健康顾问或教练。关系代理的对话内容以文献综述为依据,聚焦于以患者为中心的领域以及对房颤患者的定性访谈,涵盖房颤教育、常见症状、依从性挑战和患者激活。我们确定健康或计算机素养有限的个体也能访问这些内容。关系代理的内容与智能手机AliveCor Kardia心率和心律监测器的使用相协调。参与者(N = 31)从门诊临床站点作为便利样本招募,并被指示使用关系代理和Kardia 30天。我们收集了人口统计学、社会和临床特征,并使用房颤对生活质量的影响(AFEQT)量表对健康相关生活质量(HRQoL)进行了基线和30天评估;使用Morisky 8项药物依从性量表(MMAS-8)进行自我报告的药物依从性评估;以及使用患者激活量表(PAM)进行患者激活评估。
参与者(平均年龄68岁[标准差11];39%[12/31]为女性)平均使用关系代理17.8天(标准差10.0)。独立登录的平均次数为19.6次(标准差10.7),30天内的中位数为20次。Kardia的平均使用次数为26.5次(标准差5.9),使用Kardia的参与者处于房颤状态的天数为14.3天(标准差11.0)。AFEQT评分从基线时的64.5(标准差22.9)显著提高到30天时的76.3(标准差19.4)单位(P <.01)。我们观察到自我报告的药物依从性有轻微但具有统计学意义的改善(基线:7.3[标准差0.9],30天:7.7[标准差0.5];P =.01)。可接受性评估表明,大多数参与者认为关系代理有用、信息丰富且值得信赖。
我们对一项为期30天的基于智能手机的干预措施进行了试点,该干预措施将关系代理与针对房颤的专门内容以及Kardia心率和心律监测相结合。试点参与者在HRQoL和自我报告的药物依从性方面有良好改善,并且对干预措施有积极反应。这些数据将指导一项更大规模、改进的随机试验,该试验将实施智能手机关系代理和Kardia监测系统。