Clinical trials

Newly graduated nurses often experience high levels of psychological stress, sleep disturbance, fatigue, and burnout during the early transition into clinical practice. Early identification of burnout and retention risk may help improve mental well-being, workforce stability, and quality of patient care. This longitudinal observational study aims to develop a non-invasive sleep-based prediction platform for assessing burnout and retention risk among postgraduate nurses. Participants will undergo repeated psychological assessments and non-contact sleep monitoring during the study period. Sleep-related physiological parameters, including sleep efficiency, sleep structure, heart rate variability, and respiratory variability, will be collected together with validated psychological questionnaires. The study will further apply machine learning and artificial intelligence approaches to integrate longitudinal physiological and psychological data for risk prediction and early identification of burnout-related conditions. The findings may support future development of precision mental health monitoring and supportive management strategies for high-stress healthcare workers.

This study aims to investigate the relationship between hypertension and obstructive sleep apnea (OSA) using heart rate variability (HRV) as a non-invasive biomarker of autonomic nervous system function. Hypertensive patients at high risk for OSA will undergo 24-hour Holter electrocardiogram monitoring to assess HRV parameters, along with overnight polysomnography (PSG) to determine OSA severity. The study will analyze the association between HRV indices and the apnea-hypopnea index (AHI), and develop predictive models using machine learning techniques. In addition, patients diagnosed with OSA will be followed after treatment, and changes in HRV and blood pressure will be evaluated to assess treatment effects and autonomic function recovery. The results of this study may provide a cost-effective and clinically applicable approach for early detection, risk stratification, and management of OSA in patients with hypertension.