提高心房颤动患者依从性的智能手机应用程序(ADHERE-App 试验):随机对照试验。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Minjae Yoon, Ji Hyun Lee, In-Cheol Kim, Ju-Hee Lee, Mi-Na Kim, Hack-Lyoung Kim, Sunki Lee, In Jai Kim, Seonghoon Choi, Sung-Ji Park, Taeho Hur, Musarrat Hussain, Sungyoung Lee, Dong-Ju Choi
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引用次数: 0

摘要

背景:坚持口服抗凝疗法对预防心房颤动患者缺血性中风至关重要:坚持口服抗凝药治疗对于预防心房颤动(房颤)患者缺血性中风至关重要:本研究旨在评估基于智能手机应用的干预措施能否改善房颤患者的服药依从性:这项开放标签、多中心随机对照试验(ADHERE-App[使用自动应用反馈系统自我认识依多沙班用药依从性]研究)招募了接受依多沙班治疗以预防中风的房颤患者。他们被随机分配到应用条件反馈组(干预组,人数=248)和常规治疗组(对照组,人数=250)。干预组每天通过智能手机应用程序收到服用埃多沙班的提醒,并在特定时间测量血压和心率。对照组只接受指南推荐的标准治疗。主要终点是依多沙班的依从性,通过3个月或6个月的服药次数来衡量。对用药依从性和充分用药依从性(定义为连续用药依从性≥95%)的比例进行了评估:干预组和对照组在 3 个月或 6 个月时的用药依从性无显著差异(3 个月时中位数 98%,IQR 95%-100% vs 中位数 98%,IQR 91%-100%,P=.06;6 个月时中位数 98%,IQR 94.5%-100% vs 中位数 97.5%,IQR 92.8%-100%,P=.15)。然而,干预组患者在两个时间点的充分用药依从性(≥95%)比例均显著高于干预组(3 个月时为 76.8% vs 64.7%,P=.01;6 个月时为 73.9% vs 61%,P=.007)。在年龄大于65岁的患者中,干预组的用药依从性值更高,6个月时充分用药依从性(≥95%)的比例更高:结论:干预组和干预组在依多沙班的依从性方面没有差异。结论:干预组患者的依多沙班依从性没有差异,但干预组患者的充分依从性更高,而且老年患者比年轻患者更容易从基于智能手机应用的干预中获益。鉴于口服抗凝药的依从性较低,尤其是在老年人中,使用智能手机应用可能会改善用药依从性:国际临床试验注册平台KCT0004754;https://cris.nih.go.kr/cris/search/detailSearch.do?seq=28496&search_page=L.International 注册报告标识符(irrid):RR2-10.1136/bmjopen-2021-048777.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smartphone App for Improving Self-Awareness of Adherence to Edoxaban Treatment in Patients With Atrial Fibrillation (ADHERE-App Trial): Randomized Controlled Trial.

Background: Adherence to oral anticoagulant therapy is essential to prevent ischemic stroke in patients with atrial fibrillation (AF).

Objective: This study aimed to evaluate whether smartphone app-based interventions improve medication adherence in patients with AF.

Methods: This open-label, multicenter randomized controlled trial (ADHERE-App [Self-Awareness of Drug Adherence to Edoxaban Using an Automatic App Feedback System] study) enrolled patients with AF treated with edoxaban for stroke prevention. They were randomly assigned to app-conditioned feedback (intervention; n=248) and conventional treatment (control; n=250) groups. The intervention group received daily alerts via a smartphone app to take edoxaban and measure blood pressure and heart rate at specific times. The control group received only standard, guideline-recommended care. The primary end point was edoxaban adherence, measured by pill count at 3 or 6 months. Medication adherence and the proportion of adequate medication adherence, which was defined as ≥95% of continuous medication adherence, were evaluated.

Results: Medication adherence at 3 or 6 months was not significantly different between the intervention and control groups (median 98%, IQR 95%-100% vs median 98%, IQR 91%-100% at 3 months, P=.06; median 98%, IQR 94.5%-100% vs median 97.5%, IQR 92.8%-100% at 6 months, P=.15). However, the proportion of adequate medication adherence (≥95%) was significantly higher in the intervention group at both time points (76.8% vs 64.7% at 3 months, P=.01; 73.9% vs 61% at 6 months, P=.007). Among patients aged >65 years, the intervention group showed a higher medication adherence value and a higher proportion of adequate medication adherence (≥95%) at 6 months.

Conclusions: There was no difference in edoxaban adherence between the groups. However, the proportion of adequate medication adherence was higher in the intervention group, and the benefit of the smartphone app-based intervention on medication adherence was more pronounced among older patients than among younger patients. Given the low adherence to oral anticoagulants, especially among older adults, using a smartphone app may potentially improve medication adherence.

Trial registration: International Clinical Trials Registry Platform KCT0004754; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=28496&search_page=L.

International registered report identifier (irrid): RR2-10.1136/bmjopen-2021-048777.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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