Multimodal AI-Driven Out-of-Hospital Management for Hypertension and Cardiometabolic Risk (no acronym)
Effects of Multimodal Artificial Intelligence-Driven Out-of-Hospital Management on Patients With Hypertension and Cardiometabolic Risk Factors
High blood pressure often occurs together with abnormal blood glucose, abnormal blood lipids, or abdominal obesity. Managing these conditions outside the hospital can be difficult. This study will test whether a digital health program that combines artificial intelligence (AI) with review and support by doctors and nurses improves health outcomes compared with standard out-of-hospital guidance.
The study will enroll 150 adults aged 18 to 70 years who have hypertension and at least one additional cardiometabolic risk factor that is not at target. Participants will be randomly assigned in a 1:1 ratio to either the hybrid AI-assisted management group or the standard-guidance group. The intervention group will use a patient mini-program and connected devices to monitor blood pressure, glucose, and lifestyle information. The AI system will propose personalized medication, diet, exercise, and monitoring plans, which the healthcare team will review and confirm before delivery. The control group will receive routine printed health education and usual clinical follow-up.
All participants will be followed for 6 months. The main outcome is the proportion of participants achieving blood pressure control together with control of at least one glycemic or lipid indicator. Other assessments include treatment adherence, cardiovascular events, hospitalizations, medical costs, quality of life, and system usability.
研究概览
地位
详细说明
This single-center, prospective, randomized, open-label, parallel-group controlled trial evaluates a multimodal AI-driven, human-AI collaborative out-of-hospital health management system for adults with hypertension and cardiometabolic risk factors. A central randomization system will allocate 150 participants in a 1:1 ratio to hybrid AI-assisted management or standard out-of-hospital guidance. Follow-up lasts 6 months.
The intervention integrates clinical data, patient-generated data, and device data from Huawei wearable bands/watches, blood pressure monitors, and glucose meters. An AI agent informed by the COM-B and Fogg behavior models generates personalized recommendations, monitoring tasks, reminders, adherence support, and risk alerts. Healthcare professionals review, modify, and approve key recommendations, respond to medical questions, and manage alerts. The control group receives a printed health education booklet, instructions on medication use and follow-up, and usual clinical care without the study digital system or additional proactive follow-up.
Study visits occur at screening, baseline, Month 3, and Month 6, with additional telephone follow-up. Blood pressure is measured using a HUAWEI WATCH D2. Laboratory assessments include glucose, lipids, insulin, HbA1c, and free fatty acids after an 8-hour fast. The primary endpoint is a participant-level composite of blood pressure target attainment and target attainment for at least one glycemic or lipid indicator at Month 6. Secondary and exploratory assessments cover multidimensional adherence, major adverse cardiovascular events, all-cause hospitalization, medical costs related to hypertension, hyperlipidemia, hyperglycemia, and hyperuricemia, SF-36 quality of life, and System Usability Scale ratings.
The planned analysis uses intention-to-treat principles, with chi-square tests and logistic regression for the primary endpoint, analysis of covariance for continuous changes, Kaplan-Meier methods for cardiovascular events, and multiple imputation for missing primary and secondary outcome data.
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名：Yifan Wu, master
- 电话号码：01088398069
- 邮箱：wuyifan1127@126.com
参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
描述
Inclusion Criteria:
- Aged 18-70 years.
- Confirmed hypertension plus at least one of dyslipidemia, hyperglycemia, or abdominal obesity, with at least one condition not at target. Hypertension: BP ≥130/85 mmHg and/or diagnosed hypertension under treatment. Hyperglycemia: fasting glucose ≥6.1 mmol/L or 2-hour post-load glucose ≥7.8 mmol/L and/or diagnosed diabetes under treatment. Abdominal obesity: waist circumference ≥90 cm in men or ≥85 cm in women. Dyslipidemia: fasting triglycerides ≥1.7 mmol/L, LDL-C ≥4.1 mmol/L in the general population, or failure to meet the ASCVD risk-stratified LDL-C target (<2.6 mmol/L for general low-risk adults; <1.8 mmol/L for specified higher-risk groups; <1.4 mmol/L for specified very-high-risk ASCVD groups).
- Able to use a smartphone and smart wearable devices.
- Relatively stable work/life circumstances and residence for the next 6 months.
- Voluntary participation and signed informed consent.
Exclusion Criteria:
- Secondary hypertension, acute hypertension, subacute hypertension, or hypertensive encephalopathy.
- Severe primary hypertension (SBP ≥180 mmHg and/or DBP ≥110 mmHg) or poorly controlled primary hypertension.
- Acute myocardial infarction, unstable angina, or coronary revascularization (PCI or CABG) within 6 months.
- Cerebral infarction or cerebral hemorrhage within 6 months.
- Severe heart failure (NYHA class III-IV).
- Uncontrolled severe arrhythmia, such as symptomatic ventricular tachycardia or high-grade atrioventricular block.
- Chronic kidney disease.
- Severe psychiatric illness preventing cooperation.
- Life expectancy <12 months.
- ALT or AST ≥3 × ULN; for patients receiving lipid-lowering therapy, ALT or AST ≥2 × ULN that does not normalize after drug discontinuation.
- Other severe conditions preventing digital management, including severe visual/hearing impairment or cognitive dysfunction.
- Any other condition considered by the investigator to make participation unsuitable.
- Participation in another clinical study within 3 months.
- Refusal to sign informed consent.
学习计划
研究是如何设计的？
设计细节
- 主要用途：治疗
- 分配：随机化
- 介入模型：并行分配
- 屏蔽：无（打开标签）
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
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实验性的：Hybrid AI-Assisted Out-of-Hospital Management
Participants use a patient mini-program with automatically synchronized wearable-device, blood pressure, and glucose data plus manual lifestyle entries. An AI agent generates personalized medication reminders, dietary advice, exercise plans, monitoring tasks, education, and risk alerts. Healthcare professionals review, modify, and approve recommendations and manage medical questions and alerts for 6 months. |
A 6-month personalized digital health intervention integrating multimodal clinical, patient-generated, and device data. AI informed by COM-B and Fogg behavior models provides lifestyle and medication-adherence support, monitoring tasks, reminders, and risk alerts; healthcare professionals review and confirm key recommendations and provide clinical oversight. |
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有源比较器：Control
Participants receive routine discharge health education and a printed booklet, with instructions to take medications as prescribed and attend regular follow-up visits. They do not receive the study digital system or additional proactive follow-up beyond usual clinical care. Follow-up lasts 6 months. |
Routine health education at discharge, including a printed booklet and advice on medication adherence and regular follow-up, with usual clinical follow-up for 6 months and no study digital system or additional proactive follow-up. |
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Proportion of Participants Achieving Blood Pressure Control and Control of at Least One Glycemic or Lipid Indicator
大体时间：At 6 months after randomization
|
Proportion of participants achieving both: (1) blood pressure <140/90 mmHg for general hypertension, or <130/80 mmHg for participants with metabolic syndrome; and (2) at least one glycemic or lipid target. Glycemic target: fasting plasma glucose <7.0 mmol/L and HbA1c <7.0%. LDL-C target: <2.6 mmol/L for intermediate ASCVD risk or <1.8 mmol/L for high/very high ASCVD risk. |
At 6 months after randomization
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Composite Treatment and Health-Management Adherence
大体时间：At 6 months after randomization
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Composite adherence assessed using MMAS-8 (score ≥6 indicates good medication adherence) and system data. Good monitoring adherence: ≥4 valid blood pressure measurements/week. Good home-monitoring adherence: ≥80% of AI-recommended tasks completed/week. Dietary/physical-activity adherence is defined in the protocol using achievement of at least 4 of 5 domains (medication, monitoring, diet, physical activity, follow-up), with >80% of participants meeting this threshold. |
At 6 months after randomization
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Incidence of Major Adverse Cardiovascular Events (MACE)
大体时间：From randomization through 6 months
|
Composite of cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke. |
From randomization through 6 months
|
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Health-Related Quality of Life (SF-36 Score)
大体时间：Baseline and 6 months after randomization
|
Assessed using the 36-Item Short Form Health Survey (SF-36). |
Baseline and 6 months after randomization
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System Satisfaction and Usability (System Usability Scale Score)
大体时间：At 6 months after randomization
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Patient and healthcare-professional satisfaction and usability assessed using the System Usability Scale (SUS). |
At 6 months after randomization
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (估计的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
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