Demanuele Charmaine, Lokker Cynthia, Jhaveri Krishna, Georgiev Pirinka, Sezgin Emre, Geoghegan Cindy, Zou Kelly H, Izmailova Elena, McCarthy Marie
Pfizer Inc, Cambridge, Massachusetts, USA.
McMaster University, Hamilton, Ontario, Canada.
Digit Biomark. 2022 Jul 4;6(2):47-60. doi: 10.1159/000525080. eCollection 2022 May-Aug.
Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own "device" (BYOD) manner where participants use their technologies to generate study data.
The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving.
We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.
数字健康技术作为临床研究中数据收集的新型工具正受到关注。与临床数据收集相比,它们提供了替代方法,临床数据收集往往只能获取参与者生理、行为和功能的快照,这些快照可能容易出现偏差和伪像,例如白大衣高血压,且不能代表自由生活条件下的数据。配备多模态传感器的现代数字健康技术结合了不同的数据流,以得出对研究参与者很重要且具有临床意义的综合终点。用于临床试验中的数据收集时,它们可以作为预配置产品进行部署,即在研究开始时提供技术,或者以自带“设备”(BYOD)的方式,让参与者使用自己的技术来生成研究数据。
BYOD选项有可能更加用户友好,允许参与者使用他们熟悉的技术,确保更好的参与者依从性,并有可能减少引入新技术带来的偏差。然而,这种方法给研究团队带来了不同的技术、操作、监管和伦理挑战。例如,BYOD数据可能更加多样化,招募那些历来代表性不足且技术和互联网接入有限的人群可能具有挑战性。尽管用于临床和医疗保健研究的数字健康技术迅速增加,但BYOD在临床试验中的应用仍然有限,监管指南仍在不断发展。
我们为学术研究人员、药物开发商和患者权益倡导组织提供了关于在临床研究中设计和部署BYOD模型的注意事项。这些注意事项包括:(1)尽早识别并与内部和外部利益相关者互动;(2)研究设计,包括知情同意和招募策略;(3)结果、终点和技术选择;(4)数据管理,包括合规性和数据监测;(5)满足监管要求的统计考量。我们认为本文可作为入门指南,为确保BYOD临床研究的成功实施提供研究设计和操作要求方面的见解。