Non-Invasive Remote Monitoring in Heart Failure: Towards Wearable Devices and Artificial Intelligence Solutions : Short Title: Remote Monitoring and Wearable Devices in Heart Failure.

IF 3.7 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Camila S Pizarro, Bas B S Schots, Mark J Schuuring, Pim van der Harst, René van Es, Marish I F J Oerlemans
{"title":"Non-Invasive Remote Monitoring in Heart Failure: Towards Wearable Devices and Artificial Intelligence Solutions : Short Title: Remote Monitoring and Wearable Devices in Heart Failure.","authors":"Camila S Pizarro, Bas B S Schots, Mark J Schuuring, Pim van der Harst, René van Es, Marish I F J Oerlemans","doi":"10.1007/s11897-025-00725-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>This review examines the potential benefits of non-invasive remote monitoring in patients with heart failure (HF), focusing on early detection of clinical deterioration and reducing hospitalizations. Key questions addressed include: Can remote monitoring prevent hospitalisations in patients with HF? Does it improve quality of life and promote self-care? Is it cost-effective? Can artificial intelligence (AI) facilitate its implementation?</p><p><strong>Recent findings: </strong>Monitoring with non-wearable and wearable devices reduces hospitalizations by detecting early signs of deterioration and enhancing self-care behaviour. While the initial investment can be high, the long-term cost-effectiveness is supported by reduced hospitalisations. AI is increasingly integrated into monitoring systems, enhancing predictive accuracy and personalized care. Remote monitoring reduces mortality and hospitalisations in patients with HF, with benefits in cost-effectiveness, and the potential to optimize care delivery by integrating AI. Future research should focus on identifying monitoring strategies for specific HF populations, such as patients with advanced HF.</p>","PeriodicalId":10830,"journal":{"name":"Current Heart Failure Reports","volume":"22 1","pages":"44"},"PeriodicalIF":3.7000,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12678519/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Heart Failure Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11897-025-00725-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose of review: This review examines the potential benefits of non-invasive remote monitoring in patients with heart failure (HF), focusing on early detection of clinical deterioration and reducing hospitalizations. Key questions addressed include: Can remote monitoring prevent hospitalisations in patients with HF? Does it improve quality of life and promote self-care? Is it cost-effective? Can artificial intelligence (AI) facilitate its implementation?

Recent findings: Monitoring with non-wearable and wearable devices reduces hospitalizations by detecting early signs of deterioration and enhancing self-care behaviour. While the initial investment can be high, the long-term cost-effectiveness is supported by reduced hospitalisations. AI is increasingly integrated into monitoring systems, enhancing predictive accuracy and personalized care. Remote monitoring reduces mortality and hospitalisations in patients with HF, with benefits in cost-effectiveness, and the potential to optimize care delivery by integrating AI. Future research should focus on identifying monitoring strategies for specific HF populations, such as patients with advanced HF.

Abstract Image

心力衰竭中的无创远程监测:走向可穿戴设备和人工智能解决方案:简短标题:心力衰竭中的远程监测和可穿戴设备。
综述目的:本综述探讨了无创远程监测对心力衰竭(HF)患者的潜在益处,重点是早期发现临床恶化和减少住院治疗。讨论的关键问题包括:远程监测能否预防心衰患者住院?它能提高生活质量和促进自我照顾吗?它是否具有成本效益?人工智能(AI)能否促进其实施?最近的发现:通过检测早期恶化迹象和加强自我保健行为,使用非穿戴式和穿戴式设备进行监测可减少住院率。虽然初期投资可能很高,但住院率的降低支持了长期成本效益。人工智能越来越多地集成到监测系统中,提高了预测的准确性和个性化护理。远程监测可降低心衰患者的死亡率和住院率,具有成本效益效益,并有可能通过整合人工智能优化护理服务。未来的研究应侧重于确定特定心衰人群(如晚期心衰患者)的监测策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Current Heart Failure Reports
Current Heart Failure Reports Medicine-Emergency Medicine
CiteScore
5.30
自引率
0.00%
发文量
44
期刊介绍: This journal intends to provide clear, insightful, balanced contributions by international experts that review the most important, recently published clinical findings related to the diagnosis, treatment, management, and prevention of heart failure. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas, such as investigative, pharmacologic, and nonpharmacologic therapies, pathophysiology, and prevention. Section Editors, in turn, select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An international Editorial Board reviews the annual table of contents, suggests articles of special interest to their country/region, and ensures that topics are current and include emerging research. Commentaries from well-known figures in the field are also provided.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书