Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

使用连接的传感设备远程监控健康是一种很有前途的方法，可以帮助将医疗保健从一种相当被动的方式转变为一种更精确的面向医学的主动方式，这在面对人口快速老龄化及其对医疗保健系统构成的挑战时尤其重要。传感器衍生的健康数字测量，例如数字生物标志物或数字临床结果评估，可用于监测健康状况或跌倒等不良事件的风险。目前围绕此类数字措施的研究主要集中在探索使用通过移动设备获得的少数个别措施。然而，特别是对于老年人的长期应用，这种技术选择可能并不理想，并可能进一步扩大数字鸿沟。此外，大规模系统生物学方法，像基因组学一样，已经证明在精准医学中是有益的，这使得远程健康监测也有可能同样适用。在这种情况下，我们介绍并描述了一种零交互数字耗尽：一组 1268 种数字测量，涵盖了一个人的大部分活动、行为和生理。为了使这种方法对老年人更具包容性，我们将这套方法完全基于非接触式、零交互传感技术。将由此产生的数字废气应用于真实世界的数据，然后我们展示了创建多个与老化相关的数字临床结果评估的可能性。与现代机器学习相结合，我们发现这些评估非常强大，而且通常与移动方法不相上下。最后，