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

房颤干预后人群持续使用心电图移动医疗技术的相关因素

基本信息

批准号：
9391407
负责人：
Meghan Reading Turchioe
金额：
$ 4.4万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9391407
关键词：
Address Adherence Adult Affect Age Ambulatory Monitoring Area Arrhythmia Atrial Fibrillation Cellular Phone Cessation of life Characteristics Clinical Clinical Management Communities Computer software Data Detection Development Devices Disease Disease Management Doctor of Philosophy Electrocardiogram Emergency department visit Enrollment Evaluation Focus Groups Frequencies Future Gender Geographic Locations Goals Growth Health Health Technology Hospitalization Individual Informatics Intervention Knowledge Measures Medical Methods Minority Modeling National Institute of Nursing Research Nurses Nursing Research Outcome Participant Play Population Protocols documentation Public Health Quality of life Randomized Randomized Controlled Trials Reporting Research Research Support Resources Role Scientist Self Management Sinus Specific qualifier value Stroke Surveys Technology Testing Time United States Variant Visit biomedical informatics burden of illness cardiovascular nursing community setting experience group intervention handheld mobile device high risk improved improved outcome insight mHealth mobile computing monitoring device personalized approach portability post intervention response restoration secondary analysis skills socioeconomics symptom management

项目摘要

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.

项目摘要心房颤动（AF）是实践中最常见的心律失常。这种心律不齐可以零星的发生，并且个人之间的频率和持续时间有所不同。但是，AF 仍然未被发现且未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健康（MHealth）方法来检测社区中的AF 检测和治疗旨在更好的症状和临床管理，并最终改善结果。 MHealth是一个有前途的平台，用于自我管理像AF这样的疾病，因为广泛使用移动技术，尤其是少数族裔以及移动心电图的复杂性（ECG）允许用户记录和传输ECG的软件以区分正常的节奏和AF。但是，纵向数据表明MHealth的使用随着时间的推移而下降。关于因素知之甚少与与MHealth的持续参与有关，但要了解这些因素以优化很重要 MHealth通过依从性提高的功效。这有可能改善那些生活的人。初步研究表明，年龄和疾病等个体用户特征地位，可能在持续参与中发挥作用；但是，这个概念尚未得到彻底探讨。我们旨在采用个性化的方法来理解与持续的因素参与ECG MHealth技术。在这项混合方法研究中，我们计划使用参与者在IHEART审判中，AF患者正在进行的一项持续的忍者随机对照试验。我们将专注于随机进行IHEART干预的参与者，因为他们将使用ECG MHealth技术六个月。这项研究的持续参与是自愿使用ECG MHealth技术 IHEART协议（至少每天两次六个月）。 IHEART研究协调员促使参与者忘记一周时传递ECG。初步数据表明大约一半的iHeart 参与者不参与，因为他们需要每周提示。我们将在六个月内测试ECG MHealth技术使用轨迹的差异使用个人增长模型在参与和未接触的用户之间。预测指标和主持人持续参与将来自适用于技术接受和使用的模型，该模型的使用和说明独特的用户特征，可以使用在 IHEART审判。我们将与Iheart MHealth ECG参与者（干预小组）进行焦点小组从用户的角度了解这些因素。该项目有可能提供对与MHealth参与相关的因素的宝贵见解。