[{"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":46},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":4,"hasResults":10,"nctId":11,"briefTitle":12,"officialTitle":13,"acronym":14,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":29,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":5},"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":20,"type":21},100,"ESTIMATED","INTERVENTIONAL",[24],"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.",[27,28],"Sleep","Nightshift Work",[30,31,32,27,33],"Sleep tracking","Actigraphy","Nightshift work","machine learning","RECRUITING","2026-03-17",{"date":37,"type":38},"2026-03-18","ACTUAL",{"date":40,"type":38},"2026-02-23",{"date":42,"type":21},"2031-06-30",{"name":44,"class":45},"Henry Ford Health System","OTHER",""]