Denver/Seattle Center of Innovation for Veteran-centered and Value Driven Care, Rocky Mountain Regional VA Medical Center, Aurora, CO, United States.
Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
JMIR Res Protoc. 2024 Oct 24;13:e60496. doi: 10.2196/60496.
Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations.
This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions.
The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness.
We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024.
Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes.
Open Science Framework jys53; https://osf.io/jys53.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60496.
连接传感器技术可以捕获原始数据，并使用先进的统计方法（如机器学习或人工智能）对其进行分析，从而生成可解释的行为或生理结果。先前关于连接传感器技术的研究侧重于设计、开发和验证。已发表的综述研究要么总结了针对特定行为（如身体活动）的通用技术解决方案，要么侧重于特定患者群体的远程监测解决方案。
本研究旨在通过告知护理决策，绘制使用连接传感器技术来增强康复服务的研究。
将使用人群、概念和背景框架来定义纳入标准。如果（1）研究纳入成年人（人群），（2）干预措施使用了至少一种连接传感器技术，并涉及将数据传输给临床医生，以便数据可用于指导干预（概念），以及（3）干预措施在康复范围内（背景），则在 2008 年至现在发表的相关文章将被纳入。将在 Embase 中构建初始搜索策略；同行评审；然后翻译成 Ovid MEDLINE ALL、Web of Science 核心合集和 CINAHL。在筛选纳入文章之前，将删除重复项。两名独立评审员将分两个阶段筛选文章：标题/摘要和全文。如有分歧，将通过小组讨论解决。将从符合条件的文章中提取与人群、概念和背景相关的数据。将使用描述性统计数据报告研究结果，相关结果将包括使用的连接传感器的类型和频率以及数据共享的方法。其他详细信息将以叙述性方式总结并显示在表格和图表中。关键合作伙伴将审查结果，以增强解释和可信度。
我们于 2024 年 2 月进行了初步搜索，以完善搜索策略。预计 2024 年 10 月将得出本次范围综述的结果。
范围综述的结果将确定推进技术增强康复领域的关键研究领域。结果还将支持开发一个支持长期健康结果的纵向模型。
开放科学框架 jys53；https://osf.io/jys53。
国际注册报告标识符（IRRID）：DERR1-10.2196/60496。
