Falls, Balance and Injury Research Centre, Neuroscience Research Australia, 139 Baker Street, Randwick, NSW, 2031, Australia.
School of Population Health, University of New South Wales, Kensington, NSW, Australia.
Sci Rep. 2022 Oct 10;12(1):16211. doi: 10.1038/s41598-022-20327-z.
Digital gait biomarkers (including walking speed) indicate functional decline and predict hospitalization and mortality. However, waist or lower-limb devices often used are not designed for continuous life-long use. While wrist devices are ubiquitous and many large research repositories include wrist-sensor data, widely accepted and validated digital gait biomarkers derived from wrist-worn accelerometers are not available yet. Here we describe the development of advanced signal processing algorithms that extract digital gait biomarkers from wrist-worn devices and validation using 1-week data from 78,822 UK Biobank participants. Our gait biomarkers demonstrate good test-retest-reliability, strong agreement with electronic walkway measurements of gait speed and self-reported pace and significantly discriminate individuals with poor self-reported health. With the almost universal uptake of smart-watches, our algorithms offer a new approach to remotely monitor life-long population level walking speed, quality, quantity and distribution, evaluate disease progression, predict risk of adverse events and provide digital gait endpoints for clinical trials.
数字步态生物标志物（包括步行速度）可提示功能下降，并预测住院和死亡。然而，常用的腰部或下肢设备并非专为长期连续使用而设计。虽然腕部设备无处不在，并且许多大型研究存储库都包含腕部传感器数据，但尚未开发出广泛接受和验证的源自腕部佩戴加速度计的数字步态生物标志物。在这里，我们描述了从腕部设备中提取数字步态生物标志物的先进信号处理算法的开发，并使用来自 78822 名英国生物库参与者的 1 周数据进行了验证。我们的步态生物标志物具有良好的重测信度，与电子步道测量的步行速度和自我报告的步速具有很强的一致性，并能显著区分自我报告健康状况不佳的个体。随着智能手表的广泛普及，我们的算法为远程监测终身人群水平的步行速度、质量、数量和分布提供了一种新方法，可评估疾病进展、预测不良事件风险，并为临床试验提供数字步态终点。
