Wearable ECG for AF Screening and Stroke Risk Assessment

March 26, 2025 updated by: Beijing Tsinghua Chang Gung Hospital

Application of Wearable ECG Garments in Atrial Fibrillation Screening and Stroke Risk Assessment

This study aims to evaluate the application of wearable ECG garments in atrial fibrillation (AF) screening and stroke risk assessment. Using a prospective, multicenter, observational design, the study will recruit high-risk stroke patients aged 40 and above to undergo 24-hour continuous ECG monitoring with wearable ECG garments. The study will assess the detection rate of AF and explore the correlation between heart rate variability (HRV) parameters and stroke risk. Additionally, the study will analyze the association between P-wave indices and AF, and evaluate the acceptability of the device among patients and healthcare providers. The primary goal is to validate the accuracy of wearable ECG garments in AF detection and explore their predictive value for stroke risk in high-risk populations.

Study Overview

Detailed Description

This study is a prospective, multicenter, observational study designed to evaluate the application of wearable ECG garments in atrial fibrillation (AF) screening and stroke risk assessment. The study will be conducted at two centers: the Tsinghua Community under the jurisdiction of Tsinghua University Hospital and the Pinggu District under the jurisdiction of Pinggu District Hospital. The study design includes the following key components:

Study Population:

The study population consists of individuals aged 40 and above who are at high risk of stroke, as determined by the "8+2" stroke risk score.

Participants must be able to operate the wearable ECG garment independently or with the assistance of family members and must provide informed consent.

Sample Size:

Based on preliminary data, the AF detection rate in the local community population aged 40 and above is 3.68%. Using a two-sided test with a significance level of α=0.05 and an allowable error of d=2.5%, the calculated sample size is 218. Considering a 10% dropout rate, the final sample size is 243.

Intervention Methods:

Baseline Assessment: Collect demographic information (e.g., age, gender, height, weight) and clinical information (e.g., hypertension, diabetes, smoking history) from participants.

Device Wear and Monitoring: Participants will wear the wearable ECG garment for 24-hour continuous ECG monitoring. The device will record ECG signals in real time, including heart rate, rhythm, HRV parameters (e.g., SDNN, RMSSD, LF/HF), and P-wave indices.

Data Processing: Daily review of uploaded ECG data to analyze AF events. For participants with suspected AF, further evaluation with a 12-lead ECG or 24-hour Holter monitoring is recommended. HRV parameters will be extracted and analyzed for their correlation with stroke risk.

Acceptability Assessment: The acceptability of the device among participants and healthcare providers will be assessed through quantitative and qualitative methods. Quantitative data include device wear time and interruption rates, while qualitative data are collected through questionnaires and semi-structured interviews.

Outcome Measures:

Primary Outcomes: AF detection rate of the wearable ECG garment; correlation between HRV parameters (e.g., SDNN, RMSSD, LF/HF) and stroke risk.

Secondary Outcomes: Correlation between P-wave indices and AF; acceptability of the device among patients and healthcare providers; incidence of stroke and composite vascular events (e.g., myocardial infarction, heart failure, vascular death) during long-term follow-up.

Follow-up Plan:

Participants will be followed up at 6 and 12 months after enrollment to record the occurrence of stroke, AF, and other cardiovascular events.

Statistical Analysis:

Data will be analyzed using SPSS 25.0. Normally distributed continuous variables will be expressed as mean ± standard deviation, while non-normally distributed variables will be expressed as median and interquartile range. Group comparisons will be made using t-tests or chi-square tests, and correlation analyses will be performed using Pearson or Spearman rank correlation. Predictive factors for AF events and other outcomes will be determined through multivariate competing risk analysis.

Data Management:

All data will be entered into an electronic case report form (eCRF) and uploaded to a cloud database in real time. The research team will regularly review the completeness and accuracy of the data to ensure data quality.

Safety Assessment:

The safety of device use will be assessed, including the comfort of long-term wear and the incidence of adverse reactions (e.g., skin allergies or local irritation). The impact of false positives or false negatives on medical decision-making will also be evaluated.

Study Type

Observational

Enrollment (Estimated)

243

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

      • Beijing, China, 101200
        • Pinggu District Hospital
        • Contact:
    • Beijing
      • Beijing, Beijing, China, 102218
        • Beijing Tsinghua Changgung Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of individuals aged 40 years and above who are identified as high-risk for stroke based on the "8+2" stroke risk score. This population includes individuals with elevated stroke risk factors such as hypertension, diabetes, smoking history, and family history of stroke. Participants must be able to operate the wearable ECG garment independently or with assistance from family members and must provide informed consent to participate in the study. The study aims to enroll 243 participants to ensure sufficient statistical power for evaluating the accuracy of the wearable ECG garment in detecting atrial fibrillation (AF) and assessing stroke risk.

Description

Inclusion Criteria:

  1. Age ≥ 40 years.
  2. High-risk stroke population identified by the "8+2" risk score in stroke screening.
  3. Ability to operate the device independently or with assistance from family members.
  4. Willingness to participate in the study and provide signed informed consent.

Exclusion Criteria:

  1. Patients with severe diseases that limit device wear (e.g., advanced malignant tumors, severe infections, Class IV heart failure) or those receiving hospice care.
  2. Patients unable to operate the device or understand the study procedures due to cognitive impairment, mental illness, or language communication barriers.
  3. Patients who may experience severe discomfort or allergic reactions from wearing the device.
  4. Patients already using implanted cardiac monitoring devices.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
High-Risk Stroke Cohort with Wearable ECG Garment
This cohort consists of individuals aged 40 and above who are at high risk of stroke, as determined by the "8+2" stroke risk score. Participants will wear a wearable ECG garment for 24-hour continuous ECG monitoring to detect atrial fibrillation (AF) and assess stroke risk. The device will record heart rate, rhythm, HRV parameters (e.g., SDNN, RMSSD, LF/HF), and P-wave indices. The study aims to evaluate the accuracy of the wearable ECG garment in AF detection, explore the correlation between HRV parameters and stroke risk, and assess the acceptability of the device among patients and healthcare providers. Participants will undergo follow-up at 6 and 12 months to monitor the occurrence of stroke, AF, and other cardiovascular events.
This intervention utilizes a wearable ECG garment, a non-invasive, textile-based device for continuous 24-hour ECG monitoring. The garment features embedded electrodes to capture heart rate, rhythm, HRV parameters (e.g., SDNN, RMSSD, LF/HF), and P-wave indices (e.g., P-wave duration, PtfV1), enabling comprehensive assessment of atrial fibrillation (AF) and stroke risk. The device is lightweight, comfortable, and supports wireless data transmission to the cloud for real-time analysis. The study incorporates machine learning algorithms to identify AF patterns and explore stroke risk predictors, targeting individuals aged 40+ at high stroke risk. It also evaluates device acceptability and usability, aiming to improve AF detection rates, enable early intervention, and reduce stroke risk.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Atrial Fibrillation Detection Rate in Stroke High-Risk Populations Using Wearable ECG Garment
Time Frame: Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess AF.
1.Atrial Fibrillation (AF) Detection Rate: The proportion of AF cases identified by the wearable ECG garment in the high-risk stroke population during 24-hour continuous monitoring.
Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess AF.
Correlation Between HRV Parameters and Stroke Risk in High-Risk Populations Using Wearable ECG Garment
Time Frame: Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess stroke.
Correlation Between HRV Parameters and Stroke Risk: Analysis of key HRV parameters (e.g., SDNN, RMSSD, LF/HF) to assess their association with AF and stroke risk in high-risk individuals.
Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess stroke.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Development of an AF Risk Prediction Model Using P-Wave Indices in High-Risk Stroke Populations
Time Frame: P-wave indices collected during 24-hour ECG monitoring at baseline, with model validation using follow-up data at 6 and 12 months.
Development and validation of an AF risk prediction model incorporating P-wave indices (e.g., P-wave duration, PtfV1) to assess its predictive ability in high-risk stroke populations.
P-wave indices collected during 24-hour ECG monitoring at baseline, with model validation using follow-up data at 6 and 12 months.
Patient and Healthcare Provider Acceptability of Wearable ECG Garment
Time Frame: Baseline to 12 months: Acceptability assessed at baseline (after initial use) and during follow-up visits at 6 and 12 months.
Evaluation of patient and healthcare provider satisfaction, usability, and comfort with the wearable ECG garment through questionnaires and semi-structured interviews.
Baseline to 12 months: Acceptability assessed at baseline (after initial use) and during follow-up visits at 6 and 12 months.
Incidence of Stroke, AF, and Composite Vascular Events During Long-Term Follow-Up
Time Frame: Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up assessments at 6 and 12 months to track clinical outcomes.
Occurrence of stroke、AF and Composite vascular events, including myocardial infarction, heart failure, and vascular death during the 12-month follow-up period.
Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up assessments at 6 and 12 months to track clinical outcomes.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Study Chair: Jian Wu, MD., BeijingTsinghua Changgung Hospital, School of Clinical Medicine,Tsinghua Medicine, Tsinghua University
  • Principal Investigator: Yating Wu, MD., BeijingTsinghua Changgung Hospital, School of Clinical Medicine,Tsinghua Medicine, Tsinghua University
  • Principal Investigator: Yifei Chen, MD., Pinggu District Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

April 1, 2025

Primary Completion (Estimated)

December 31, 2026

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

March 26, 2025

First Submitted That Met QC Criteria

March 26, 2025

First Posted (Actual)

April 2, 2025

Study Record Updates

Last Update Posted (Actual)

April 2, 2025

Last Update Submitted That Met QC Criteria

March 26, 2025

Last Verified

February 1, 2025

More Information

Terms related to this study

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data (IPD) will not be shared to protect participant privacy and confidentiality. The data contain sensitive personal health information, and sharing it publicly could compromise participant anonymity. Additionally, the informed consent process did not include provisions for public data sharing, and releasing the data could violate participant consent agreements. The data are solely intended for use by the research team to achieve the study objectives."

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Stroke

Clinical Trials on Wearable ECG Garment for Continuous Atrial Fibrillation Screening and Stroke Risk Assessment

Subscribe