Clinical Research Directory

Integrating Wearable Monitoring and Clinical Data for Stroke Early Warning

Sponsor: Affiliated Hospital of Nantong University

Summary

Stroke is a leading global cause of death and disability. In 2021 there were 11.9 million incident strokes (10.7-13.2 million) and 93.8 million prevalent stroke survivors (89.0-99.3 million) worldwide. The burden in China is particularly severe: stroke is the leading cause of disability-adjusted life years lost, at 45.9 million, and causes more than 2 million deaths each year. Recurrence is common after a first stroke, with reported recurrence rates ranging from 5.7% to 51.3%; approximately 7.7% of patients recur within 3 months and 10.4% within 12 months, and a cohort study of 500,000 Chinese adults found that 41% (95% CI 41%-42%) of stroke survivors had a recurrence at 5 years. Recurrent stroke is associated with higher mortality, greater disability and increased consumption of health care resources, making secondary prevention after stroke the core element of reducing recurrence and improving prognosis. Current stroke prevention and control systems remain largely reactive. Monitoring relies mainly on the patient's subjective perception after symptom onset, emergency response is triggered by telephone calls, and there is no mechanism for pre-symptomatic active sensing or active intervention at onset. The median onset-to-door time in China is still 180-240 minutes and reperfusion treatment rates (2.4%-7%) lag behind those of developed countries. Existing risk assessment tools such as the Framingham score are static models based on demographic characteristics; they cannot reflect the spatiotemporal evolution of physiological parameters in the 24-72 hours before onset and therefore cannot detect a 'recent high-risk state'. Digital health management is mostly one-way information push without bidirectional, agent-driven interaction, resulting in low patient adherence and warning messages that rarely translate into protective action. Wearable health-monitoring devices combined with agentic artificial intelligence offer continuous, real-time and quantifiable physiological monitoring in everyday life. However, the effectiveness, feasibility and safety of such warning and intervention systems for stroke patients, especially for secondary prevention after discharge, have not been systematically evaluated. This study therefore evaluates a stroke early-warning system based on wearable monitoring and clinical data fusion, in order to provide evidence for a digital management model for stroke prevention.

Official title: Integrating Wearable Monitoring and Clinical Data for Stroke Early Warning: The WISE Study

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