Factors Associated with Sustained Engagement with ECG mHealth Technology in a Post-Intervention Atrial Fibrillation Population

项目摘要 房颤(房颤)是临床上最常见的心律失常。这种心律失常 可能是零星发生的，并且在频率和持续时间上因人而异。然而，AF认为 如果没有发现和治疗，可能会导致生活质量差、住院、中风和死亡。新的, 需要使用简单易用的移动健康(MHealth)方法检测社区内的房颤，以便于及时 检测和治疗旨在改善症状和临床管理，并最终得到改善 结果。 由于广泛使用，mHealth是一种很有前途的自我管理疾病的平台，如房颤 移动技术的发展，特别是在少数群体中，以及移动心电图机的复杂性 允许用户记录和传输心电以区分正常节奏和房颤的软件。 然而，纵向数据表明，mHealth的使用量随着时间的推移而下降。人们对以下因素知之甚少 与持续参与mHealth相关，但了解这些因素以优化 通过增加依从性来提高mHealth的疗效。这有可能改善儿童的健康状况 那些活着的自动对讲机。初步研究表明，个人用户特征，如年龄和疾病 国家地位，可能会在持续接触中发挥作用；然而，这一概念尚未得到彻底探讨。 我们的目标是采用个性化的方法来了解与可持续发展相关的因素 与心电mHealth技术接触。在这项混合方法研究中，我们计划使用登记的参与者 在iHeart试验中，正在进行一项由NINR支持的针对房颤患者的随机对照试验。我们会 将重点放在随机接受iHeart干预的参与者身上，因为他们将使用心电mHealth技术 六个月了。这项研究的持续参与是自愿使用心电mHealth技术PER IHeart方案(在六个月内每天至少两次)。IHeart研究协调员敦促参与者 忘记一周后发送心电图机。初步数据显示，大约有一半的人 参与者没有参与，因为他们需要每周进行提示。 我们将在六个月期间测试心电移动健康技术使用轨迹的差异 使用个人增长模式的活跃用户和非活跃用户之间的关系。的预测者和主持人 持续的参与将来自适应的技术接受和使用模式，该模式使用和 考虑到独特的用户特征，可以使用在以下过程中收集的心电和调查数据进行测量 艾哈特试验。我们将与iHeart mHealth心电参与者(干预组)进行焦点小组讨论 从用户的角度更深入地了解这些因素。该项目有可能提供 对房颤高危人群中与mHealth参与度相关的因素有价值的洞察。

Project Summary Atrial fibrillation (AF) is the most common arrhythmia encountered in practice. This cardiac arrhythmia can be sporadic in occurrence and varies in frequency and duration among individuals. However, AF that remains undetected and untreated can result in a poor quality of life, hospitalization, stroke, and death. New, easy to use mobile health (mHealth) methods for detecting AF in the community are needed to facilitate timely detection and treatments aimed at better symptom and clinical management and ultimately improved outcomes. mHealth is a promising platform for self-management of diseases like AF because of the widespread use of mobile technologies, especially among minorities, and the sophistication of mobile electrocardiogram (ECG) software that allows users to record and transmit ECGs to distinguish between a normal rhythm and AF. However, longitudinal data indicates that mHealth use declines over time. Little is known about factors that are associated with sustained engagement with mHealth, yet it is important to understand these factors to optimize the efficacy of mHealth through increased adherence. This has the potential to improve the health outcomes of those living AF. Preliminary studies suggest that individual user characteristics, such as age and disease status, may play a role in sustained engagement; however, this concept has not been thoroughly explored. We aim to take a personalized approach to understanding factors associated with sustained engagement with ECG mHealth technology. In this mixed-methods study, we plan to use participants enrolled in the iHEART trial, an ongoing NINR-supported randomized controlled trial of individuals with AF. We will focus on participants randomized to the iHEART intervention because they will use ECG mHealth technology for six months. Sustained engagement in this study is voluntary use of the ECG mHealth technology per iHEART protocol (at least twice daily for six months). iHEART research coordinators prompt participants to transmit ECGs when forgotten for one week. Preliminary data indicates that approximately half of iHEART participants are not engaged because they require weekly prompting. We will test differences in trajectories of ECG mHealth technology use over the six-month period between engaged and unengaged users using individual growth models. Predictors and moderators of sustained engagement will come from an adapted model of technology acceptance and use that uses and accounts for unique user characteristics, and can be measured using ECG and survey data collected during the iHEART trial. We will conduct focus groups with the iHEART mHealth ECG participants (intervention group) to gain deeper insight into these factors from the user perspective. This project has the potential to provide valuable insight on factors that are associated with mHealth engagement in a high risk AF population.