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

项目摘要 心房颤动（AF）是临床上最常见的心律失常。心律不齐 可能是偶发性的，在个体之间的频率和持续时间不同。然而，由于 仍然未被发现和治疗可能导致生活质量差、住院、中风和死亡。新的， 需要易于使用的移动的健康（mHealth）方法来检测社区中的AF，以促进及时 检测和治疗旨在更好的症状和临床管理，并最终改善 结果。 由于广泛使用，mHealth是AF等疾病自我管理的一个有前途的平台 移动的技术的普及，特别是在少数群体中，以及移动的心电图的先进性 允许用户记录和传输ECG以区分正常心律和AF的软件。 然而，纵向数据表明，移动医疗的使用随着时间的推移而下降。我们对影响这些因素知之甚少， 与持续参与移动健康相关，但重要的是要了解这些因素，以优化 通过增加依从性来提高mHealth的功效。这有可能改善人们的健康状况 初步研究表明，个体用户特征，如年龄和疾病， 地位，可能在持续参与中发挥作用;然而，这一概念尚未得到彻底探讨。 我们的目标是采取个性化的方法来理解与持续性相关的因素。 与ECG mHealth技术合作。在这项混合方法的研究中，我们计划使用参与者登记 在iHEART试验中，一项正在进行的NINR支持的AF患者随机对照试验。 重点关注随机分配到iHEART干预的参与者，因为他们将使用ECG mHealth技术 六个月本研究的持续参与是自愿使用ECG mHealth技术， iHEART方案（每天至少两次，持续6个月）。iHEART研究协调员提示参与者 忘记一周后发送ECG。初步数据显示，大约一半的iHEART 参与者没有参与，因为他们需要每周提示。 我们将测试在六个月期间使用ECG mHealth技术的轨迹差异 使用个人增长模型对参与和未参与的用户进行分类。预测者和主持人 持续的参与将来自于一种经过调整的技术接受和使用模式， 考虑了独特的用户特征，并且可以使用在 iHEART试验我们将与iHEART mHealth ECG参与者（干预组）进行焦点小组讨论 从用户的角度深入了解这些因素。该项目有可能提供 对与高危AF人群中的移动医疗参与相关的因素的有价值的见解。

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.