Data Analysis and Teaching Model Construction of Smart Wearable Devices in Primary Care Monitoring

Traditional health assessment systems rely heavily on periodic institutionalized examinations, manipulation of Inconvenient and unable to realize continuous monitoring. As an emerging health technology product, smart wearable devices can provide more convenient and efficient health monitoring services. However, there is a lack of sufficient empirical research on the impact of such devices on health levels. With these devices, patients' vital sign data, such as heart rate, blood pressure, blood glucose, and body temperature, can be collected in real time, providing caregivers with detailed and accurate health monitoring information. This study delves into the data analysis methods of smart wearable devices in primary care monitoring, using data analysis techniques to pre-process, feature extraction and modeling of the collected vital signs data, and then realizing a comprehensive assessment and risk prediction of the patient's health status. Mechanism analysis shows that smart bracelets will play a role through daily life risk prevention, health monitoring screening for chronic diseases, stimulating positive health behaviors, and strengthening social support and norms. The results of the study show that the data analysis of smart wearable devices in primary care monitoring has significant advantages, which not only improves the efficiency of nursing care, but also provides an innovative teaching tool for nursing education, and provides a strong support for the future development of smart medical care.