A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

TLDR

Remote health monitoring with connected sensing devices offers a proactive precision‑medicine approach that could address the challenges of an aging population, yet current digital biomarker research focuses on few mobile‑device measures that may widen the digital divide, whereas large‑scale systems biology methods suggest a broader, more inclusive strategy could be beneficial. The study introduces a zero‑interaction digital exhaust comprising 1,268 digital measures of activity, behavior, and physiology. The digital exhaust is built entirely from contactless, zero‑interaction sensing technologies to enhance inclusivity for older adults. Applying the digital exhaust to real‑world data, the authors created multiple ageing‑relevant digital clinical outcome assessments that, when combined with modern machine learning, performed on par with mobile approaches and revealed potential novel digital biomarkers.

Abstract

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.