The Benefits of Wearable AI in Post-Discharge Management of AMI Patients
NCT ID: NCT07288229
Last Updated: 2025-12-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
200 participants
INTERVENTIONAL
2025-12-30
2026-12-30
Brief Summary
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This study aims to explore the clinical value of wearable device-based data analysis and AI-driven risk stratification models in post-discharge management of acute myocardial infarction (AMI) patients.
Detailed Description
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Participants will be randomly assigned to either the control group or the intervention group in a 1:1 ratio. All patients will be equipped with a wearable smartwatch and continuously monitored for 3 months after discharge. Data collected will include physiological signals, sleep and activity parameters. In both groups, patients will receive weekly telephone follow-ups and monthly office visits to record symptoms, medication use, and adverse events.
In the intervention group, wearable data and AI analytical results will be made available to both patients and their physicians. These insights will be discussed during follow-ups and used to support lifestyle modification, medication adjustment, and clinical decision-making. In the control group, AI data will be collected but not shared or used for clinical management during the study period.
The primary study endpoint is the time to first unplanned hospital readmission within 3 months, including readmissions due to chest pain, heart failure, arrhythmia, recurrent myocardial infarction, or death. The secondary endpoints include: Change in Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) score from baseline to 3 months; change in left ventricular ejection fraction (LVEF) measured by echocardiography between baseline and 3 months.
The investigators hypothesize that AI-assisted, wearable-based monitoring and feedback will improve early detection of adverse cardiovascular events, reduce unplanned hospitalizations, increase LVEF in patients with reduced LVEF at discharge, and enhance quality of life compared with standard post-discharge care.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
TREATMENT
NONE
Study Groups
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The guideline-guided traditional management group
As the control group, wearable data will be collected but not shared with the participant and responding physician or used for clinical management during the study period. All management in the participants is based on updated clinical guidelines.
No interventions assigned to this group
The guideline-guided and wearable-assisted management group
As the intervention group, in addition to clinical guidelines, wearable data and AI analytical results will be made available to both patients and their physicians. These insights will be discussed during follow-ups and used to support lifestyle modification, medication adjustment, and clinical decision-making.
Optimized Integrated Management Based on AI-Guided Wearable Data
The collected data will be shared with both patients and their treating physicians during follow-up visits. Based on these insights, the clinical team will offer personalized recommendations regarding medication adjustment, lifestyle modification, diet optimization, and physical activity guidance.
Interventions
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Optimized Integrated Management Based on AI-Guided Wearable Data
The collected data will be shared with both patients and their treating physicians during follow-up visits. Based on these insights, the clinical team will offer personalized recommendations regarding medication adjustment, lifestyle modification, diet optimization, and physical activity guidance.
Eligibility Criteria
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Inclusion Criteria
* Confirmed diagnosis of acute myocardial infarction (AMI), including both ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI).
* Underwent successful percutaneous coronary intervention (PCI) during index hospitalization.
* Hemodynamically stable at the time of hospital discharge.
* Willing and able to wear a smartwatch continuously for the study period.
* Compatible with the data collection application and have stable internet access.
Exclusion Criteria
* Unable to tolerate or contraindicated for wearing metal or electronic monitoring devices.
* Pregnant or breastfeeding women.
* Residence in an area without stable network connectivity or inability to use a smartphone for data upload and communication.
* Severe comorbidities that limit 3-month survival or follow-up.
18 Years
75 Years
ALL
No
Sponsors
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RenJi Hospital
OTHER
Responsible Party
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Zhiguo Zou
Doctor
Central Contacts
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Other Identifiers
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EARLY-MYO Wearable AI
Identifier Type: -
Identifier Source: org_study_id