Study on Cardiac Output Evaluation Based on Wearable Monitoring Data

Based on the monitoring data of wearable devices, with cardiac output (CO) as the gold standard, this study intends to develop a non-invasive evaluation model of CO based on wearable data, and optimize the parameters to realize the cardiac capacity detection function in resting and exercise states on the wearable device.

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Key information

Conditions

Cardiac Output

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Over 18 years old
Left ventricular ejection fraction (Left ventricular ejection fraction, LVEF) < 50%(200 subjects)
Left ventricular ejection fraction (Left ventricular ejection fraction, LVEF) ≥50% (100 subjects)
Able to use smart phones and operate wearable devices such as wristbands/watches

Exclusion criteria

Patients with pacemaker implantation
No smartphone
Currently participating in other clinical trials
Lactating women
Pregnant Women
Unable to run and ride due to personal physical and external reasons (subjects participating in the exercise state cardiac output model study)
Physical examination results in the past year have clear cardiovascular, metabolic, bone and joint related diseases that have exercise risk, or have diseases and related potential health risks confirmed by the self-examination form of physical status before exercise (participants in the exercise state cardiac output model study)
No informed consent was obtained

Treatment and study plan

Exercise

Other

Cardiac output (CO) was measured after exercise intervention in patients with normal cardiac function

Primary outcomes

cardiac output

Time frame: From enrollment to the end of follow-up at 1 month

Taking cardiac function indicators such as cardiac output by echocardiography as the gold standard, using wearable device monitoring data（Photoplethysmographic pulse wave）, the resting state cardiac output artificial intelligence machine learning model was established, and the sensitivity, specificity, positive predictive value, negative predictive value, F1 score, diagnostic efficiency Area Under Curve (AUC), and the sensitivity, specificity, positive predictive value, negative predictive value, F1 score, diagnostic efficiency of the model were calculated. AUC), precision and precision-recall curves were used to evaluate the performance of the model.

Secondary outcomes

Heart failure

Time frame: From enrollment to the end of follow-up at 1 month

Heart failure symptoms, acute heart failure episodes, rehospitalization rates, and cardiovascular mortality

Study contacts

Contact information is provided by the study sponsor or research team.

Yutao Guo

CONTACT

[email protected]

+86 13683176151

Sponsors and collaborators

Lead sponsor

Navy General Hospital, Beijing

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Apr 22, 2025
Registry last updated: Apr 22, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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