| Title: | Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers |
| Journal: | Scientific Reports |
| Published: | 10 Oct 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36217013/ |
| DOI: | https://doi.org/10.1038/s41598-022-20327-z |
| URL: | https://www.nature.com/articles/s41598-022-20327-z.pdf |
| Citations: | 10 (10 in last 2 years) as of 8 Aug 2024 |
Publication 8130
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Abstract
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.4 Authors
- Lloyd L. Y. Chan
- Tiffany C. M. Choi
- Stephen R. Lord
- Matthew A. Brodie
1 Application
| Application ID | Title |
|---|---|
| 56109 | Prognostic value of movement quality parameters derived from wrist sensor data in a large population |
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