Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.

可穿戴传感器技术在健康研究中变得越来越重要，特别是在慢性病管理方面。它们生成实时健康数据，可以转化为数字生物标记，从而深入了解我们的健康和福祉。收集、解释、分析和将可穿戴设备的健康数据转化为数字生物标记的科学方法各不相同，目前缺乏指导这些过程的系统方法。本文基于一项观察性纵向队列研究 BarKA-MS，该研究收集了有关多发性硬化症 (MS) 患者身体康复的可穿戴传感器数据。根据我们在 BarKA-MS 方面的经验，我们提供并讨论了我们在关键研究阶段的数字生物标志物开发方面学到的十个经验教训。然后，我们将这些经验教训总结成一个指导框架（DACIA），旨在为未来的研究和教学使用可穿戴传感器数据进行数字生物标志物开发和慢性病管理提供信息。