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.
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
243
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.
Beijing Tsinghua Changgung Hospital
Beijing, Beijing Municipality, China
Pinggu District Hospital
Beijing, China
Atrial Fibrillation Detection Rate in Stroke High-Risk Populations Using Wearable ECG Garment
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.
Time frame: 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
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.
Time frame: Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess stroke.
Development of an AF Risk Prediction Model Using P-Wave Indices in High-Risk Stroke Populations
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.
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.
Patient and Healthcare Provider Acceptability of Wearable ECG Garment
Evaluation of patient and healthcare provider satisfaction, usability, and comfort with the wearable ECG garment through questionnaires and semi-structured interviews.
Time frame: 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
Occurrence of stroke、AF and Composite vascular events, including myocardial infarction, heart failure, and vascular death during the 12-month follow-up period.
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.
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.