Efficacy of eHealth Technologies on Medication Adherence in Patients With Acute Coronary Syndrome: Systematic Review and Meta-Analysis.

IF 2.2 Q2 Medicine JMIR Cardio Pub Date : 2023-12-19 DOI:10.2196/52697
Akshaya Srikanth Bhagavathula, Wafa Ali Aldhaleei, Tesfay Mehari Atey, Solomon Assefa, Wubshet Tesfaye
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Abstract

Background: Suboptimal adherence to cardiac pharmacotherapy, recommended by the guidelines after acute coronary syndrome (ACS) has been recognized and is associated with adverse outcomes. Several randomized controlled trials (RCTs) have shown that eHealth technologies are useful in reducing cardiovascular risk factors. However, little is known about the effect of eHealth interventions on medication adherence in patients following ACS.

Objective: The aim of this study is to examine the efficacy of the eHealth interventions on medication adherence to selected 5 cardioprotective medication classes in patients with ACS.

Methods: A systematic literature search of PubMed, Embase, Scopus, and Web of Science was conducted between May and October 2022, with an update in October 2023 to identify RCTs that evaluated the effectiveness of eHealth technologies, including texting, smartphone apps, or web-based apps, to improve medication adherence in patients after ACS. The risk of bias was evaluated using the modified Cochrane risk-of-bias tool for RCTs. A pooled meta-analysis was performed using a fixed-effect Mantel-Haenszel model and assessed the medication adherence to the medications of statins, aspirin, P2Y12 inhibitors, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and β-blockers.

Results: We identified 5 RCTs, applicable to 4100 participants (2093 intervention vs 2007 control), for inclusion in the meta-analysis. In patients who recently had an ACS, compared to the control group, the use of eHealth intervention was not associated with improved adherence to statins at different time points (risk difference [RD] -0.01, 95% CI -0.03 to 0.03 at 6 months and RD -0.02, 95% CI -0.05 to 0.02 at 12 months), P2Y12 inhibitors (RD -0.01, 95% CI -0.04 to 0.02 and RD -0.01, 95% CI -0.03 to 0.02), aspirin (RD 0.00, 95% CI -0.06 to 0.07 and RD -0.00, 95% CI -0.07 to 0.06), angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (RD -0.01, 95% CI -0.04 to 0.02 and RD 0.01, 95% CI -0.04 to 0.05), and β-blockers (RD 0.00, 95% CI -0.03 to 0.03 and RD -0.01, 95% CI -0.05 to 0.03). The intervention was also not associated with improved adherence irrespective of the adherence assessment method used (self-report or objective).

Conclusions: This review identified limited evidence on the effectiveness of eHealth interventions on adherence to guideline-recommended medications after ACS. While the pooled analyses suggested a lack of effectiveness of such interventions on adherence improvement, further studies are warranted to better understand the role of different eHealth approaches in the post-ACS context.

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电子健康技术对急性冠状动脉综合征患者坚持服药的效果:系统回顾与元分析》。
背景:人们已经认识到,急性冠状动脉综合征(ACS)后指南推荐的心脏药物治疗的依从性不佳与不良预后有关。多项随机对照试验(RCT)表明,电子健康技术有助于减少心血管风险因素。然而,人们对电子健康干预对 ACS 患者坚持服药的影响知之甚少:本研究旨在探讨电子健康干预对 ACS 患者坚持服用选定的 5 类心脏保护药物的效果:在2022年5月至10月期间对PubMed、Embase、Scopus和Web of Science进行了系统性文献检索,并于2023年10月进行了更新,以确定评估电子健康技术(包括短信、智能手机应用或基于网络的应用)对改善ACS患者用药依从性的有效性的RCT。采用修改后的 Cochrane RCT 偏倚风险工具对偏倚风险进行了评估。采用固定效应曼特尔-海恩泽尔模型进行了汇总荟萃分析,评估了他汀类药物、阿司匹林、P2Y12抑制剂、血管紧张素转换酶抑制剂或血管紧张素受体阻滞剂和β-受体阻滞剂的用药依从性:我们在荟萃分析中确定了 5 项 RCT,涉及 4100 名参与者(2093 名干预者与 2007 名对照者)。与对照组相比,在最近发生 ACS 的患者中,使用电子健康干预与不同时间点他汀类药物(6 个月时的风险差异 [RD] -0.01,95% CI -0.03 至 0.03;12 个月时的风险差异 [RD] -0.02,95% CI -0.05 至 0.02)、P2Y12 抑制剂(RD -0.01,95% CI -0.04 至 0.02和RD -0.01,95% CI -0.03至0.02)、阿司匹林(RD 0.00,95% CI -0.06至0.07和RD -0.00,95% CI -0.07至0.06)、血管紧张素转换酶抑制剂或血管紧张素受体阻滞剂(RD -0.01,95% CI -0.04 至 0.02 和 RD 0.01,95% CI -0.04 至 0.05),以及 β 受体阻滞剂(RD 0.00,95% CI -0.03 至 0.03 和 RD -0.01,95% CI -0.05 至 0.03)。无论采用哪种依从性评估方法(自我报告还是客观评估),干预措施也与依从性的改善无关:本综述发现,电子健康干预对 ACS 后遵守指南推荐药物治疗的有效性证据有限。虽然汇总分析表明此类干预措施对改善依从性缺乏有效性,但仍有必要开展进一步的研究,以更好地了解不同的电子健康方法在 ACS 后环境中的作用。
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来源期刊
JMIR Cardio
JMIR Cardio Computer Science-Computer Science Applications
CiteScore
3.50
自引率
0.00%
发文量
25
审稿时长
12 weeks
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