Smart Education for MACE Prevention and Early Detection

NCT ID: NCT06816238

Last Updated: 2025-02-10

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 87376 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-03-01
• Study Completion Date: 2027-02-28

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.

Detailed Description

With its high incidence, recurrence, disability and mortality, stroke has become an important health challenge facing the society at present. However, "stroke can be prevented and treated," and high-risk groups of stroke are the main beneficiaries of stroke prevention and control and the intervention targets of "health education" measures. Yet, their awareness of correct stroke prevention and treatment knowledge is low. The reasons may be related to the limited coverage, lack of accessibility, lack of pertinence, and uneven content quality of traditional health education measures.

Therefore, we need to adopt innovative intervention strategies to overcome these constraints and explore effective ways of health education. The occurrence of Major Adverse Cardiovascular Events (MACE), such as stroke, can be reduced by improving the knowledge level and practical ability of scientific stroke prevention and treatment in high-risk populations. In this study, the intervention is based on a large medical model using a medical intelligent agent for interactive Q&A. This approach is designed to enhance popularization, accessibility, pertinence, and content quality.

Popularization: The use of smartphone-based information software can break through the limitations of time and space, conveying health education knowledge to a wider audience.

Accessibility: Key knowledge points can be repeatedly delivered to the target audience through one-way pushes of health education content, complemented by interactive Q&A with intelligent medical agents.

Pertinence: Health education content is personalized based on the individual's age, education level, risk factors, and other personal characteristics, ensuring relevance to each educatee.

Content Quality: The educational content is authoritative, scientifically accurate, easy to understand, and practical, drawing from the latest scientific research and professional medical practices, reviewed by authoritative experts and institutions. The intervention ensures that the educated's needs for self-health management are fully considered.

This model leverages the large medical knowledge base and intelligent medical agents to provide personalized, scalable, and cost-effective education, which eliminates the need for additional manpower in education and Q&A. This approach helps reduce costs while enabling large-scale implementation.

The purpose of this study was to explore whether the incidence of MACE in high-risk stroke populations receiving interactive education based on a medical knowledge model and intelligent Q&A is lower than that in a group without such education.

Conditions

High-Risk Stroke Population

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

Intervention Group

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.

Group Type: EXPERIMENTAL

Interactive education

Intervention Type: OTHER

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

Control Group

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

Group Type: ACTIVE_COMPARATOR

Traditional health education

Intervention Type: OTHER

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

Interventions

Interactive education

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

Intervention Type: OTHER

Traditional health education

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

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

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.

Exclusion Criteria

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

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Second Affiliated Hospital, School of Medicine, Zhejiang University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Other Identifiers

SEED

Identifier Type: -

Identifier Source: org_study_id