WISENCT07908303

Integrating Wearable Monitoring and Clinical Data for Stroke Early Warning

StatusNot Yet Recruiting

PhaseNot specified

SponsorAffiliated Hospital of Nantong University

Primary completionDec 1, 2028 (estimated)

At risk: Primary completion moved 2 months later, primary endpoint(s) modified.

See at-risk trials from Affiliated Hospital of Nantong University

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Record History

All 2 versions: 2 with changes below. See every version on ClinicalTrials.gov ↗

Notable Nov 2, 2026 Start date pushed from Oct 1, 2026 to Dec 1, 2026 Show 4 more changesShow less
- Primary endpoint(s) modified
- Primary completion pushed from Oct 1, 2028 to Dec 1, 2028
- Secondary endpoint revised
- Collaborator revised
Oct 27, 2026 Study First Submitted

Notable: the plan changed

Endpoints

Primary

Number of Participants with Stroke Events within 12 Months after Enrollment

Timeframe: 12 months

Secondary (1)

Number of Participants with other major cardiovascular events within 12 months after Enrollment

Arms and interventions

Wearable monitoring with AI early-warning system

All enrolled participants at high risk of stroke and patients discharged after imaging-confirmed ischemic stroke - receive: standard health education and standardized discharge instructions, a smart band (Huawei HAY20, a Class II medical device with certified sleep apnea screening and pulse-wave atrial fibrillation/premature-beat notification software) with the companion mobile application; automated intelligent medication reminders and adherence management; continuous dynamic physiological monitoring with individualized baseline thresholds and intelligent early warning; and periodic bidirectional proactive interaction with an AI agent. Participants are followed at 3, 6 and 12 months.

Devices: Huawei HAY20

Behavioral: AI agent dialogue

Sites

1 site in 1 country

Affiliated Hospital of Nantong UniversityNantong, Jiangsu, China

About this trial

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.

Trial details

Enrollment: 5,000 participants (estimated)
Started: Dec 1, 2026
Study completion: Jun 30, 2029 (estimated)
Eligibility: 18 and older, all sexes
Design: Observational
Conditions: Cerebral Hemorrhage, Ischemic Attack, Transient, Ischemic Stroke, Stroke, Subarachnoid Hemorrhage, Recurrent Stroke, Intracerebral Haemorrhage
Collaborators: Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences
Official title: Integrating Wearable Monitoring and Clinical Data for Stroke Early Warning: The WISE Study
First posted: Nov 2, 2026
Registry updated: Nov 4, 2026
Last verified: Oct 2026

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