Carthy Marie Mc, Cappelleri Joseph C, Byrom Bill, Doll Helen, Di Junrui, Demanuele Charmaine, Buenconsejo Joan, Coon Cheryl D
Digital Endpoint Lead, Novartis Ireland Ltd, Merrion Rd, Dublin, D4, Ireland.
Pfizer Research & Development, Pfizer Inc., Groton, CT, 06340, USA.
Ther Innov Regul Sci. 2025 May 13. doi: 10.1007/s43441-025-00794-y.
This paper seeks to identify some of the complexities associated with determining meaningful change for endpoints derived from digital health technologies (DHTs) and propose possible methodologies for this process. Ultimately, this is a call to action to consider appropriate methods and practices required to enable digital endpoints (DEs) to achieve their full potential as Drug Development Tools.
Using the Food and Drug Administration (FDA) Patient-Focused Drug Development (PFDD) guidance documents as a framework, we explore the nuances and challenges that exist when determining meaningful change for DEs compared with traditional clinical outcome assessments (COAs).
There are unique characteristics associated with DEs that provide distinct challenges when determining meaningful change. This complexity spans the totality of meaningful change considerations, from ensuring that the DE itself is meaningful from the patient perspective to selecting appropriate anchors that enable determination of the magnitude of change that is meaningful for patients.
With increased adoption of DHTs in clinical trials, their specific use is evolving, as evidenced by their being referred to as DHT-passive monitoring COAs in the FDA drug development tool (DDT) qualification program. However, the determination of meaningful change for these DEs can be more nuanced and challenging than for traditional COAs. Merely adapting existing approaches for traditional COAs does not readily support DEs derived from continuous datasets collected over long periods. New methods and approaches are required, and this can only be realised by working together, to ensure that the value and limitations of various methodologies as they relate to DEs can be refined.
本文旨在识别与确定源自数字健康技术（DHTs）的终点指标的有意义变化相关的一些复杂性，并为此过程提出可能的方法。最终，这是一次行动呼吁，以考虑使数字终点指标（DEs）作为药物开发工具发挥其全部潜力所需的适当方法和实践。
以美国食品药品监督管理局（FDA）以患者为中心的药物开发（PFDD）指导文件为框架，我们探讨了与传统临床结局评估（COAs）相比，在确定DEs的有意义变化时存在的细微差别和挑战。
DEs具有独特的特征，在确定有意义的变化时会带来独特的挑战。这种复杂性贯穿于有意义变化考量的全过程，从确保DE本身从患者角度来看是有意义的，到选择合适的锚定指标以确定对患者有意义的变化幅度。
随着DHTs在临床试验中的应用增加，其具体用途也在不断演变，FDA药物开发工具（DDT）资格认证计划中将其称为DHT被动监测COAs就证明了这一点。然而，确定这些DEs的有意义变化可能比传统COAs更细微、更具挑战性。仅仅采用传统COAs的现有方法并不能轻易支持从长期收集的连续数据集中得出的DEs。需要新的方法和途径，而这只有通过共同努力才能实现，以确保各种方法与DEs相关的价值和局限性能够得到完善。
