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The Use of Multiple Sensors to Track Sleep in Nightshift Workers

A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers

Who can join

Ages 18 and older · All sexes · Healthy volunteers welcome

Inclusion Criteria:

* 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
* Participants must have worked the nightshift for at least six months
* Must plan to maintain the nightshift schedule for the duration of the study
* Participants must be at least 18 years old

Exclusion Criteria:

* Termination of nightshift schedule or planned travel during the study period
* Does not have at least an average of 8-hour time bed opportunity per 24-hour period
* Unwilling to integrate the study smart sensors in their bedroom environment
* Illicit drug use via self-report and urine drug screen
* History of neurological disorders
* Alcohol use disorder
* Pregnancy

About the study

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.

What is being tested

• Single-Sensor Tracking (In-Lab) (other)
• Multi-Sensor Sleep Tracking (In-Lab) (other)
• Multi-Sensor Sleep Tracking (At-Home) (other)

Sponsor: Henry Ford Health System · Participants: 100 · Started: Feb 23, 2026

Contact the study team

• Philip Cheng, PhD · Phone: 248-344-7361
• Elle M Wernette, PhD · Phone: 2483442409

Official record on ClinicalTrials.gov — NCT06670287

Locations in the U.S.

From ClinicalTrials.gov, data retrieved Sep 30, 2026. Each study sets its own eligibility; the study team decides who can join.