[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"nightshift-work\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:nightshift-work":29},{"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":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":30,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":36,"lastUpdatePostDateStruct":37,"startDateStruct":40,"completionDateStruct":42,"leadSponsor":44,"locationsCount":5},"100567599","the-use-of-multiple-sensors-to-track-sleep-in-nightshift-workers-100567599",false,"NCT06670287","The Use of Multiple Sensors to Track Sleep in Nightshift Workers","A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers","SENSE","Inclusion Criteria:\n\n* Participants must be working a fixed nightshift schedule, operationalized as: a) working at least three night shifts a week, b) shifts must begin between 18:00 and 02:00, and last between 8 to 12 hours, and c) must also plan to maintain the nightshift schedule for the duration of the study\n* Participants must have worked the nightshift for at least six months\n* Must plan to maintain the nightshift schedule for the duration of the study\n* Participants must be at least 18 years old\n\nExclusion Criteria:\n\n* Termination of nightshift schedule or planned travel during the study period\n* Does not have at least an average of 8-hour time bed opportunity per 24-hour period\n* Unwilling to integrate the study smart sensors in their bedroom environment\n* Illicit drug use via self-report and urine drug screen\n* History of neurological disorders\n* Alcohol use disorder\n* Pregnancy",true,"ALL","18 Years",{"count":21,"type":22},100,"ESTIMATED","INTERVENTIONAL",[25],"NA","Sleep is often a challenge for nightshift workers because their work and sleep schedules are inverted. Sleep is commonly measured using actigraphy, which is the standard measure of objective sleep in the general population; however, this method has substantial limitations for nightshift workers because the standard legacy algorithms only correctly identify 50.3% of daytime sleep. This significantly reduces the validity for nightshift workers. The purpose of this study is to test a novel method to expand actigraphy by using 1) a multi-sensor approach that 2) uses machine learning (ML) algorithms to increase the accuracy of detecting daytime sleep.",[28,29],"Sleep","Nightshift Work",[31,32,33,28,34],"Sleep tracking","Actigraphy","Nightshift work","machine learning","RECRUITING","2026-03-17",{"date":38,"type":39},"2026-03-18","ACTUAL",{"date":41,"type":39},"2026-02-23",{"date":43,"type":22},"2031-06-30",{"name":45,"class":46},"Henry Ford Health System","OTHER"]