{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:25:54Z","timestamp":1776284754285,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Training Program of the Australian government"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45\u201379 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test\u2013retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m\/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test\u2013retest reliability was good to excellent (ICC \u2265 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications.<\/jats:p>","DOI":"10.3390\/s24165159","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:23:46Z","timestamp":1723461826000},"page":"5159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Daily-Life Walking Speed, Quality and Quantity Derived from a Wrist Motion Sensor: Large-Scale Normative Data for Middle-Aged and Older Adults"],"prefix":"10.3390","volume":"24","author":[{"given":"Lloyd L. Y.","family":"Chan","sequence":"first","affiliation":[{"name":"Neuroscience Research Australia, Sydney, NSW 2031, Australia"},{"name":"School of Health Sciences, University of New South Wales, Sydney, NSW 2052, Australia"}]},{"given":"Stephen R.","family":"Lord","sequence":"additional","affiliation":[{"name":"Neuroscience Research Australia, Sydney, NSW 2031, Australia"},{"name":"School of Population Health, University of New South Wales, Sydney, NSW 2052, Australia"}]},{"given":"Matthew A.","family":"Brodie","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1111\/ggi.12979","article-title":"Comparison between clinical gait and daily-life gait assessments of fall risk in older people","volume":"17","author":"Brodie","year":"2017","journal-title":"Geriatr. 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