This project intends to conduct a large-sample, prospective, multicenter clinical cohort study in healthy populations. By utilizing a digital cardiopulmonary rehabilitation clinical data research platform, The investigators aim to achieve automated, standardized, and uniform collection, analysis, and AI modeling of large-scale cardiopulmonary exercise testing (CPET) data. The ultimate goals are:
To establish reference standards for cardiopulmonary exercise capacity in the Chinese healthy population.
To develop machine learning-based predictive models for key CPET variables (e.g., peak VO₂) tailored to Chinese demographics.
To compare performance differences between domestically produced and imported CPET devices.
Detailed medical history taken, with no symptoms of discomfort reported
Good physical condition, with no history of severe chronic diseases
No history of long - term medication use
Willing to undergo cardiopulmonary exercise testing, with no ST - T segment changes observed during the test
Pre - test resting blood pressure ≥160/100 mmHg (1 mmHg=0.133 kPa)
Chronic cardiovascular diseases, such as heart failure, coronary atherosclerotic heart disease, hypertension, congenital heart disease, cardiomyopathy, and severe arrhythmias
Respiratory diseases, such as chronic obstructive pulmonary disease, asthma, pulmonary artery hypertension, bronchiectasis, and respiratory failure
Digestive system diseases, such as peptic ulcer, gastrointestinal bleeding, hepatitis, liver cirrhosis, ulcerative colitis, Crohn's disease, chronic pancreatitis, and chronic cholecystitis