Real-World Walking Patterns From Wrist-Worn Wearables Predict Incident Type 2 Diabetes: A UK Biobank WatchWalk Study
Authors' conclusion
In this large population-based cohort, real-world walking patterns measured using wrist-worn devices were independently associated with incident T2D and enabled more accurate identification of at-risk individuals.
Study context
- Population
- UK Biobank participants without diabetes at baseline who wore a wrist accelerometer for 7 days
- Participants
- 71,618 participants
- Exposure
- Digital gait biomarkers from wrist-worn accelerometer and incident T2D
- Outcome
- Incident T2D, ascertained from linked health records
Methodological detailspopulation-based, UK Biobank
- Population-based
- UK Biobank
Main finding
As reported in the abstract
Incident T2D · during follow-up
Developed T2D
2577 participants
3.6%
Observational data: associations, not effects.
Further findings
Risk discrimination (Harrell's C-index)
- Streamlined model with digital gait biomarkers and established risk factors, C-index
- 0.80
- Outperformed models based on traditional risk factors and step counts alone
Original publication
Real-World Walking Patterns From Wrist-Worn Wearables Predict Incident Type 2 Diabetes: A UK Biobank WatchWalk Study. J Diabetes Sci Technol. Published 27 July 2026. doi:10.
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