Smart Education for MACE Prevention and Early Detection

NCT06816238 | Not Yet Recruiting DMC

• 1 other identifier: SEED
• Study Type: interventional
• Target: 87,376
• Locations: 0 countries
• Sites: N/A

Brief Summary

With its high incidence, recurrence, disability, and mortality, stroke has become a significant health challenge in society today. However, "stroke can be prevented and treated," and high-risk groups for stroke are the primary beneficiaries of stroke prevention and control measures. Yet, these populations often have low awareness of proper stroke prevention and treatment knowledge. The reasons for this may include limited coverage, lack of accessibility, inadequate relevance, and uneven content quality in traditional health education measures. To address these issues, innovative intervention strategies are needed to explore more effective health education methods. The occurrence of Major Adverse Cardiovascular Events (MACE), such as stroke, can be reduced by improving the knowledge and practical abilities of high-risk populations regarding scientific stroke prevention and treatment. One such strategy is the use of smartphone-based information software, which can break the constraints of time and space, delivering health education knowledge to a broader audience. To enhance accessibility, key knowledge points can be repeatedly delivered to the target population through one-way push notifications and interactive Q\&A, allowing for more engaging and flexible learning. In terms of improving pertinence, it is essential to tailor health education delivery to the individual needs of the educatees, considering factors such as age, education level, and risk factors. Furthermore, the quality of health education content must be authoritative, scientifically accurate, easy to understand, and practically applicable. The content should be based on the latest scientific research and professional medical practice, reviewed by authoritative institutions or experts, and should align with the needs of the educated populations for self-health management. Additionally, human and financial costs should be considered when designing such interventions. In this study, a large-scale medical model based on a stroke prevention and treatment knowledge base, integrated with an intelligent medical system and interactive Q\&A, is employed. This approach ensures content quality while minimizing the need for additional manpower in education and Q\&A, making the intervention more cost-effective and scalable for widespread use. The purpose of this study is to explore whether the incidence of MACE in a high-risk stroke population receiving interactive medical model education-based on a stroke prevention and treatment knowledge base-is lower than in a group without such interactive education.

Conditions

High-Risk Stroke Population

Keywords

Stroke Prevention; Interactive Education; Medical Knowledge Model; Intelligent Medical Agents; MACE Reduction

Outcome Measures

Primary Outcomes (1)

• MACE Incidence

  The occurrence of major adverse cardiovascular events (MACE), including stroke (ischemic or hemorrhagic), myocardial infarction, heart failure, or cardiovascular death, in participants over the 1-year follow-up period. MACE is a composite outcome that captures the broad range of adverse cardiovascular events.

  1 year from enrollment

Secondary Outcomes (10)

• Stroke Incidence

  1 year from enrollment

• Hospitalization Due to Cardiovascular Events

  1 year from enrollment

• Stroke Prevention and Treatment Knowledge

  1 year from enrollment

• Blood Pressure Control

  1 year from enrollment

• Physical Function and Quality of Life (QoL)

  1 year from enrollment

Study Arms (2)

Intervention Group

EXPERIMENTAL

High-risk stroke population receiving interactive education based on a medical knowledge model, utilizing intelligent medical agents and interactive Q\&A. The educational content will cover stroke prevention and treatment, tailored to individual risk factors and needs.

Other: Interactive education

Control Group

ACTIVE COMPARATOR

High-risk stroke population receiving traditional health education methods without the use of the interactive medical model and intelligent Q\&A system.

Other: Traditional health education

Interventions

Interactive education OTHER

interactive education based on a medical knowledge model, utilizing intelligent medical agents and interactive Q\&A

Intervention Group

Traditional health education OTHER

traditional health education methods without the use of the interactive medical model and intelligent Q\&A system

Control Group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Individuals who meet the definition of a stroke risk group, including those with hypertension, dyslipidemia, diabetes, atrial fibrillation, valvular heart disease, a history of smoking, significant overweight or obesity, physical inactivity, a family history of stroke, or any combination of 8 or more stroke risk factors.
• Individuals with a history of one or more transient ischemic attacks (TIAs) or a previous stroke.
• Legal capacity to provide informed consent. Permanent residents of Zhejiang Province with Zhejiang Provincial household registration.
• Ownership of a smartphone and access to the Alipay app. Voluntary participation and adherence to the principles of informed consent.

You may not qualify if:

• Illiteracy or difficulty with reading/communication. Medical personnel. Inability to complete the study questionnaire due to physical or cognitive limitations.

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 4, 2025
• First Posted: February 10, 2025
• Study Start: March 1, 2025
• Primary Completion: February 28, 2026
• Study Completion (Estimated): February 28, 2027
• Last Updated: February 10, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share