[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"nurse-retention\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:nurse-retention":30},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"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":5},"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"]