[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100567599":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":25,"centralContacts":37,"locations":47,"responsibleParty":67,"collaborators":70,"id":73,"slug":74,"hasResults":75,"nctId":76,"briefTitle":77,"officialTitle":78,"acronym":79,"eligibilityCriteria":80,"healthyVolunteers":81,"sex":82,"minAge":83,"maxAge":25,"enrollmentInfo":84,"targetDuration":25,"studyType":87,"phases":88,"briefSummary":90,"conditions":91,"keywords":94,"overallStatus":50,"whyStopped":25,"lastUpdateSubmitDate":99,"lastUpdatePostDateStruct":100,"startDateStruct":103,"completionDateStruct":105,"leadSponsor":107,"locationsCount":108},{"fullName":5,"class":6},"Henry Ford Health System","OTHER",[8,15],{"label":9,"type":10,"description":11,"interventionNames":12},"Single vs Multi-Sensor Sleep Tracking In-Lab","EXPERIMENTAL","In Part 1 of the study, all participants' data will undergo two separate methods for analyzing sleep.\n\nThe legacy actigraphy algorithm methods will use only raw accelerometer data from a single sensor collected and processed using legacy actigraphy algorithms. The legacy algorithm is comprised first of reducing accelerometer data into activity counts per epoch, which will then be categorized into sleep or wake in accordance with the Cole-Kripke algorithm.\n\nThe multi-sensor machine learning (ML) method will use raw accelerometer data in addition to data from additional sensors from the watch, phone, and other smart sensors in the sleeping environment. These data will be processed using a machine learning algorithm.",[13,14],"Other: Single-Sensor Tracking (In-Lab)","Other: Multi-Sensor Sleep Tracking (In-Lab)",{"label":16,"type":6,"description":17,"interventionNames":18},"Multi-Sensor Sleep Tracking At-Home","This condition includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Daily sleep diaries will also be collected to enable data quality check. Once collected, all data will be processed with the same machine learning algorithm used in the in-lab experimental condition.",[19],"Other: Multi-Sensor Sleep Tracking (At-Home)",[21,26,33],{"type":6,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"Single-Sensor Tracking (In-Lab)","In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms.",[9],null,{"type":6,"name":27,"description":28,"armGroupLabels":29,"otherNames":30},"Multi-Sensor Sleep Tracking (In-Lab)","In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.",[9],[31,32],"Multi-sensor ML","Multi-Sensor Machine Learning",{"type":6,"name":34,"description":35,"armGroupLabels":36,"otherNames":25},"Multi-Sensor Sleep Tracking (At-Home)","At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.",[16],[38,43],{"name":39,"role":40,"phone":41,"phoneExt":25,"email":42},"Philip Cheng, PhD","CONTACT","248-344-7361","pcheng1@hfhs.org",{"name":44,"role":40,"phone":45,"phoneExt":25,"email":46},"Elle M Wernette, PhD","2483442409","ewernet1@hfhs.org",[48],{"facility":49,"status":50,"city":51,"state":52,"zip":53,"country":54,"countryCode":55,"cosmosGeoPoint":56,"geoPoint":61,"contacts":62},"Henry Ford Columbus Medical Center","RECRUITING","Novi","Michigan","48377","United States","US",{"type":57,"coordinates":58},"Point",[59,60],-83.47549,42.48059,{"lat":60,"lon":59},[63,64,65],{"name":39,"role":40,"phone":41,"phoneExt":25,"email":42},{"name":44,"role":40,"phone":45,"phoneExt":25,"email":46},{"name":39,"role":66,"phone":25,"phoneExt":25,"email":25},"PRINCIPAL_INVESTIGATOR",{"type":66,"investigatorFullName":68,"investigatorTitle":69,"investigatorAffiliation":5,"oldNameTitle":25,"oldOrganization":25},"Philip Cheng","Principal Investigator",[71],{"name":72,"class":6},"Michigan State University","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":85,"type":86},100,"ESTIMATED","INTERVENTIONAL",[89],"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.",[92,93],"Sleep","Nightshift Work",[95,96,97,92,98],"Sleep tracking","Actigraphy","Nightshift work","machine learning","2026-03-17",{"date":101,"type":102},"2026-03-18","ACTUAL",{"date":104,"type":102},"2026-02-23",{"date":106,"type":86},"2031-06-30",{"name":5,"class":6},1]