Mahsa MohammadNamdar, Michael Lowery Wilson, Kari-Pekka Murtonen, Eeva Aartolahti, Michael Oduor, Katariina Korniloff
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Analyzing how AI-driven DR tools can boost this compliance is vital for creating sustainable practices and tackling future challenges.</p><p><strong>Objective: </strong>This study seeks to assess how AI-based DR can improve the end-user compliance or adherence to rehabilitation.</p><p><strong>Methods: </strong>Following the updated recommendations for the Cochrane rapid review methods guidance and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search strategy was led in PubMed, which yielded 922 records, resulting in 6 papers included in this study.</p><p><strong>Results: </strong>The reviewed studies identified 6 key ways in which AI enhances end-user compliance in rehabilitation. The most prevalent method (in 4 studies) involves motivating and engaging users through features like exercise tracking and motivational content. The second method, also noted in 4 studies, focuses on improving communication and information exchange between health care providers and users. Personalized solutions tailored to individual cognitive styles and attitudes were highlighted in 3 studies. Ease of use and system usability, affecting user acceptability, emerged in 2 studies. Additionally, daily notifications, alerts, and reminders were identified as strategies to promote compliance, also noted in 2 studies. While 5 studies looked at AI's role in improving adherence, 1 study specifically assessed AI's capability for objective compliance measurement, contrasting it with traditional subjective self-reports.</p><p><strong>Conclusions: </strong>Our results could be especially relevant and beneficial for rethinking rehabilitation practices and devising effective strategies for the integration of AI in the rehabilitation field, aimed at enhancing end-user adherence to the rehabilitation regimen.</p>","PeriodicalId":36224,"journal":{"name":"JMIR Rehabilitation and Assistive Technologies","volume":"12 ","pages":"e69763"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12352703/pdf/","citationCount":"0","resultStr":"{\"title\":\"How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review.\",\"authors\":\"Mahsa MohammadNamdar, Michael Lowery Wilson, Kari-Pekka Murtonen, Eeva Aartolahti, Michael Oduor, Katariina Korniloff\",\"doi\":\"10.2196/69763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The integration of artificial intelligence (AI) in rehabilitation technology is transforming traditional methods, focusing on personalization and improved outcomes. 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引用次数: 0
摘要
背景:人工智能(AI)与康复技术的融合正在改变传统的康复方法,注重个性化和改善结果。人工智能在数字康复(DR)领域的发展强调了最终用户遵守康复计划的关键作用。分析人工智能驱动的灾难恢复工具如何提高这种合规性,对于创建可持续实践和应对未来挑战至关重要。目的:本研究旨在评估基于人工智能的DR如何提高最终用户对康复的依从性或依从性。方法:根据Cochrane快速评价方法指南和PRISMA (Preferred Reporting Items for Systematic Reviews and meta - analysis)指南的更新建议,在PubMed中进行系统文献检索策略,获得922条记录,其中6篇论文被纳入本研究。结果:回顾的研究确定了人工智能提高最终用户康复依从性的6个关键方法。在4项研究中,最流行的方法是通过锻炼跟踪和励志内容等功能来激励和吸引用户。在4项研究中也提到了第二种方法,侧重于改善卫生保健提供者和使用者之间的沟通和信息交流。针对个人认知风格和态度量身定制的个性化解决方案在3项研究中得到了强调。影响用户接受度的易用性和系统可用性在两项研究中出现。此外,两项研究也指出,每日通知、提醒和提醒被确定为促进依从性的策略。虽然有5项研究考察了人工智能在提高依从性方面的作用,但有1项研究专门评估了人工智能在客观依从性测量方面的能力,并将其与传统的主观自我报告进行了对比。结论:我们的研究结果对于重新思考康复实践和制定有效的策略来整合人工智能在康复领域,旨在提高最终用户对康复方案的依从性,具有特别的相关性和有益意义。
How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review.
Background: The integration of artificial intelligence (AI) in rehabilitation technology is transforming traditional methods, focusing on personalization and improved outcomes. The growing area of AI in digital rehabilitation (DR) emphasizes the critical role of end-user compliance with rehabilitation programs. Analyzing how AI-driven DR tools can boost this compliance is vital for creating sustainable practices and tackling future challenges.
Objective: This study seeks to assess how AI-based DR can improve the end-user compliance or adherence to rehabilitation.
Methods: Following the updated recommendations for the Cochrane rapid review methods guidance and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search strategy was led in PubMed, which yielded 922 records, resulting in 6 papers included in this study.
Results: The reviewed studies identified 6 key ways in which AI enhances end-user compliance in rehabilitation. The most prevalent method (in 4 studies) involves motivating and engaging users through features like exercise tracking and motivational content. The second method, also noted in 4 studies, focuses on improving communication and information exchange between health care providers and users. Personalized solutions tailored to individual cognitive styles and attitudes were highlighted in 3 studies. Ease of use and system usability, affecting user acceptability, emerged in 2 studies. Additionally, daily notifications, alerts, and reminders were identified as strategies to promote compliance, also noted in 2 studies. While 5 studies looked at AI's role in improving adherence, 1 study specifically assessed AI's capability for objective compliance measurement, contrasting it with traditional subjective self-reports.
Conclusions: Our results could be especially relevant and beneficial for rethinking rehabilitation practices and devising effective strategies for the integration of AI in the rehabilitation field, aimed at enhancing end-user adherence to the rehabilitation regimen.