Real-World Walking Patterns From Wrist-Worn Wearables Predict Incident Type 2 Diabetes: A UK Biobank WatchWalk Study

Area of Science:

• Biomedical Engineering
• Epidemiology
• Metabolic Health

Background:

• Type 2 diabetes (T2D) poses a significant global health challenge.
• Early identification of individuals at risk for T2D is crucial for effective prevention.
• Wearable technology offers novel opportunities for objective health monitoring.

Purpose of the Study:

• To investigate the association between digital gait biomarkers and incident T2D.
• To determine if gait characteristics improve T2D risk prediction beyond established factors.

Main Methods:

• Analysis of UK Biobank data from 71,618 participants without diabetes.
• Derivation of gait biomarkers (walking speed, quality, duration) from wrist accelerometer data.
• Cox proportional hazards models and C-index used for risk assessment and model discrimination.

Main Results:

• Slower walking speed, shorter walking bouts, and lower daily running duration were independently associated with increased T2D risk.
• A model incorporating gait biomarkers and traditional risk factors achieved a C-index of 0.80.
• This combined model outperformed models using only traditional risk factors or step counts.

Conclusions:

• Real-world gait patterns measured by wearables are independently associated with incident T2D.
• Digital gait biomarkers enhance the accuracy of identifying individuals at risk for T2D.
• Gait biomarkers may reflect early metabolic vulnerability, aiding in risk stratification and prevention.