Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona, USA.
Health Sciences Library, The University of Arizona, Tucson, Arizona, USA.
J Am Med Inform Assoc. 2018 Dec 1;25(12):1685-1695. doi: 10.1093/jamia/ocy130.
This systematic review aims to analyze current capabilities, challenges, and impact of self-directed mobile health (mHealth) research applications such as those based on the ResearchKit platform.
A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. English publications were included if: 1) mobile applications were used in the context of large-scale collection of data for biomedical research, and not as medical or behavioral intervention of any kind, and 2) all activities related to participating in research and data collection methods were executed remotely without any face-to-face interaction between researchers and study participants.
Thirty-six unique ResearchKit apps were identified. The majority of the apps were used to conduct observational studies on general citizens and generate large datasets for secondary research. Nearly half of the apps were focused on chronic conditions in adults.
The ability to generate large biomedical datasets on diverse populations that can be broadly shared and re-used was identified as a promising feature of mHealth research apps. Common challenges were low participation retention, uncertainty regarding how use patterns influence data quality, need for data validation, and privacy concerns.
ResearchKit and other mHealth-based studies are well positioned to enhance development and validation of novel digital biomarkers as well as generate new biomedical knowledge through retrospective studies. However, in order to capitalize on these benefits, mHealth research studies must strive to improve retention rates, implement rigorous data validation strategies, and address emerging privacy and security challenges.
本系统评价旨在分析基于 ResearchKit 平台等自主移动医疗（mHealth）研究应用的当前能力、挑战和影响。
根据系统评价和荟萃分析的首选报告项目（PRISMA）声明进行系统评价。如果符合以下标准，则纳入英文出版物：1）移动应用程序用于大规模收集生物医学研究数据，而不是作为任何类型的医疗或行为干预，2）所有与参与研究和数据收集方法相关的活动均远程执行，研究人员和研究参与者之间没有任何面对面的互动。
确定了 36 个独特的 ResearchKit 应用程序。大多数应用程序用于对普通公民进行观察性研究，并生成用于二次研究的大型数据集。近一半的应用程序专注于成年人的慢性疾病。
能够在不同人群中生成可广泛共享和重复使用的大型生物医学数据集被认为是 mHealth 研究应用的一个有前途的特征。常见的挑战包括参与保留率低、不确定使用模式如何影响数据质量、需要数据验证以及隐私问题。
ResearchKit 和其他基于 mHealth 的研究非常适合通过回顾性研究来增强新型数字生物标志物的开发和验证，并产生新的生物医学知识。然而，为了利用这些优势，mHealth 研究必须努力提高保留率、实施严格的数据验证策略，并解决新出现的隐私和安全挑战。
