ArticleScientific reports2022
Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Associations Between Digital Exclusion and Physical or Cognitive Function in Middle-Aged and Older Adults: Systematic Review and Meta-Analysis.JMIR aging · 2026Pooled it
- Real-world gait changes preceding Parkinson's disease diagnosis in a population-scale UK biobank cohort.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Real-world walking patterns are associated with regional brain atrophy and white matter lesions in middle-aged and older people: a Watch Walk-UK Biobank study.GeroScience · 2026Article
- Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data.NPJ digital medicine · 2026Article
- Detection of nocturnal desaturation and hypercapnia using accelerometer-integrated pulse oximetry: a prospective observational study.Respiratory research · 2026Observational
- Are Real-World Mobility Patterns Early Indicators of COPD Onset? Insights from Wrist-Worn Sensors.International journal of chronic obstructive pulmonary disease · 2026Article
- Wearable AI for on-device frailty assessment.Nature communications · 2025Article
- Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Article
- Comparing Step-Counting Algorithms for High-Resolution Wrist Accelerometry Data in Older Adults in the ARIC Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.Scientific reports · 2024Article
- Daily-Life Walking Speed, Quality and Quantity Derived from a Wrist Motion Sensor: Large-Scale Normative Data for Middle-Aged and Older Adults.Sensors (Basel, Switzerland) · 2024Article
- Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.JMIR formative research · 2024Article
- Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet.Research square · 2024Article
- The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence detection of cognitive impairment in older adults during walking.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.