Technologies in Home-Based Digital Rehabilitation: Scoping Review.

Q2 Medicine
Angela Arntz, Franziska Weber, Marietta Handgraaf, Kaisa Lällä, Katariina Korniloff, Kari-Pekka Murtonen, Julija Chichaeva, Anita Kidritsch, Mario Heller, Evanthia Sakellari, Christina Athanasopoulou, Areti Lagiou, Ioanna Tzonichaki, Iosune Salinas-Bueno, Pau Martínez-Bueso, Olga Velasco-Roldán, Ralf-Joachim Schulz, Christian Grüneberg
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引用次数: 1

Abstract

Background: Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients' needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR.

Objective: In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR.

Methods: The scoping review followed the framework of Arksey and O'Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR.

Results: A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies.

Conclusions: Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients' needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.

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基于家庭的数字康复技术:范围审查。
背景:由于卫生保健系统的压力越来越大,将康复转移到家庭环境是必不可少的。然而,缺乏对家庭康复的有效支持。2019冠状病毒病大流行进一步加剧了这些挑战,并影响了康复期间的个人和卫生保健专业人员。数字康复(DR)可以支持家庭康复。为了开发和实施满足客户需求并缓解医疗保健系统日益增长的压力的DR解决方案,有必要概述现有的、相关的和未来的解决方案,这些解决方案塑造了不断发展的家庭DR技术市场。目的:在本范围综述中,我们旨在确定家庭DR的数字技术,预测新的或正在出现的DR趋势,并报告COVID-19大流行对DR的影响。范围审查遵循Arksey和O'Malley的框架,并由Levac等人进行了改进。在PubMed、Embase、CINAHL、PsycINFO和Cochrane图书馆进行文献检索。搜索时间从2015年1月持续到2022年1月。通过文献计量学分析对纳入的文献进行概述,共现分析确定了家庭医生的技术。对所有纳入的文献进行全文分析,过滤了家庭医生的发展趋势。灰色文献检索补充了综述分析的结果,揭示了COVID-19大流行对家庭医生发展的影响。文献计量分析共纳入2437条记录,全文分析纳入95条记录,灰色文献检索纳入40条记录。传感器、机器人设备、游戏化、虚拟和增强现实,以及数字和移动应用程序已经用于家庭DR;然而,人工智能和机器学习、外骨骼、数字和移动应用程序代表了新的趋势。展示了各种技术的优缺点。2019冠状病毒病大流行导致更多地使用数字技术作为远程方法,但并未导致新技术的开发。结论:多种工具可用于家庭DR;然而,一些技术在家庭康复的应用中面临局限性。然而,人工智能和机器学习可以在重新设计康复和应对医疗保健系统,特别是康复部门的未来挑战方面发挥重要作用。结果表明,无论出现COVID-19大流行等特殊情况,都需要采取可行和有效的方法来实施DR,以满足客户的需求并遵守框架条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.20
自引率
0.00%
发文量
31
审稿时长
12 weeks
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