[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Kaohsiung Armed Forces General Hospital\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":83},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,56],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":24,"conditions":25,"keywords":32,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":44,"lastUpdatePostDateStruct":45,"startDateStruct":48,"completionDateStruct":50,"leadSponsor":52,"locationsCount":55},"100644217","non-invasive-sleep-monitoring-for-burnout-and-retention-risk-in-postgraduate-nurses-100644217",false,"NCT07666633","Non-Invasive Sleep Monitoring for Burnout and Retention Risk in Postgraduate Nurses","Development of a Non-Invasive Sleep-Based Prediction Platform for Burnout and Retention Risk Among Postgraduate Nurses: A Psychophysiological and AI-Driven Approach for High-Stress Clinical Populations","Inclusion Criteria:\n\n* Newly employed postgraduate nurses within the first 3 months of clinical practice\n* Age 20 to 65 years\n* Full-time clinical nursing staff\n* Able to read and complete Chinese questionnaires\n* Willing to participate in repeated psychological assessments and non-invasive sleep monitoring\n* Able to provide written informed consent\n\nExclusion Criteria:\n\n* Diagnosed severe sleep disorders\n* Diagnosed severe psychiatric disorders\n* Current use of medications that significantly affect sleep or autonomic nervous system function\n* Inability to comply with longitudinal follow-up procedures\n* Inability to complete repeated sleep monitoring assessments",true,"ALL","20 Years","65 Years",{"count":21,"type":22},100,"ESTIMATED","OBSERVATIONAL","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.\n\nThis 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.\n\nThe 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.",[26,27,28,29,30,31],"Burnout","Sleep Disturbance","Occupational Stress","Mental Health","Nurse Retention","Fatigue",[33,34,35,36,37,38,39,40,41,42],"Postgraduate Nurses","Non-Invasive Sleep Monitoring","Burnout Risk","Retention Risk","Artificial Intelligence","Machine Learning","Sleep Quality","Heart Rate Variability","Shift Work","Psychological Stress","NOT_YET_RECRUITING","2026-06-18",{"date":46,"type":47},"2026-06-24","ACTUAL",{"date":49,"type":22},"2026-08-01",{"date":51,"type":22},"2027-04-23",{"name":53,"class":54},"Kaohsiung Armed Forces General Hospital","OTHER",1,{"id":57,"slug":58,"hasResults":11,"nctId":59,"briefTitle":60,"officialTitle":61,"acronym":62,"eligibilityCriteria":63,"healthyVolunteers":11,"sex":17,"minAge":64,"maxAge":19,"enrollmentInfo":65,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":66,"conditions":67,"keywords":70,"overallStatus":74,"whyStopped":4,"lastUpdateSubmitDate":75,"lastUpdatePostDateStruct":76,"startDateStruct":78,"completionDateStruct":80,"leadSponsor":82,"locationsCount":55},"100640306","assessment-of-heart-rate-variability-for-predicting-obstructive-sleep-apnea-in-patients-with-hypertension-100640306","NCT07603583","Assessment of Heart Rate Variability for Predicting Obstructive Sleep Apnea in Patients With Hypertension","The Exploration of the Relationship Between Hypertension and Sleep Apnea Through Heart Rate Variability, and the Subsequent Investigation of Non-pharmacological and Pharmacological Treatments","HRV-OSA-HTN","Inclusion Criteria:\n\n* Age 18 to 65 years\n* Diagnosed hypertension or resistant hypertension\n* STOP-Bang score ≥3 indicating high risk of obstructive sleep apnea\n* Willing and able to provide informed consent\n\nExclusion Criteria:\n\n* Refusal to undergo HRV monitoring or polysomnography\n* Unstable medical condition that may interfere with study participation\n* Inability to comply with study procedures","18 Years",{"count":21,"type":22},"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.\n\nHypertensive 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.\n\nIn 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.\n\nThe 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.",[68,69],"Obstructive Sleep Apnea","Hypertension",[40,71,72,73,38],"Polysomnography","CPAP","Sleep Apnea Screening","RECRUITING","2026-05-18",{"date":77,"type":47},"2026-05-22",{"date":79,"type":47},"2026-04-30",{"date":81,"type":22},"2026-12-31",{"name":53,"class":54},""]