The aim of this study is to construct a digital precision intervention system for cardiometabolism disorders based on the characteristics of the Chinese population, focusing on the core components of metabolic syndrome such as hypertension, hyperglycemia, and obesity. Through dynamic monitoring of wearable devices, the individualized risk factors (including genetic predisposition, lifestyle, and psychological factors) of metabolic disorders were systematically analyzed, and dynamic intervention strategies were formulated accordingly. The closed-loop management of "monitor-feedback-optimization" based on the digital platform will provide patients with one-stop metabolic risk assessment and panoramic health management plan, realizing a paradigm shift from "generalized intervention" to "precise regulation". By reducing the abnormal fluctuations of key metabolic indicators such as blood pressure, blood glucose, and blood lipids, the risk of cardiovascular events will eventually be reduced, the long-term quality of life of patients will be improved, and a practical path of "digital therapy" can be promoted for the prevention and control of chronic diseases in China

Inclusion Criteria:1. Age 18-65 years old; 2. Meet one of the following three conditions:

1. Diastolic hypertension (systolic blood pressure < 140 MMHG and diastolic blood pressure ≥90mmHg)
2. Glycosylated hemoglobin (6.0-6.5)
3. Obesity (BMI≥28kg/m²)
4. Mild hyperlipemia: total cholesterol (TC) ≥5.2 mmol/L and < 6.2 mmol/L, or low-density lipoprotein cholesterol (LDL-C) ≥3.4 mmol/L and < 4.1 mmol/L 3. Antihypertensive or lipid-lowering medications have not been started 4. Use a smartphone and volunteer

Exclusion Criteria:

1. Complicated with chronic liver and kidney disease, coronary heart disease, heart failure and severe valvular disease
2. Previous cardiovascular and cerebrovascular events
3. Diastolic blood pressure ≥99mmHg
4. Glycated hemoglobin ≥6.5
5. Other life expectancy such as cancer is less than 5 years
6. Participate in other clinical trials