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The Use of Multiple Sensors to Track Sleep in Nightshift Workers
Sponsor: Henry Ford Health System
Summary
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.
Official title: A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
100
Start Date
2026-02-23
Completion Date
2031-06-30
Last Updated
2026-03-18
Healthy Volunteers
Yes
Conditions
Interventions
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.
Multi-Sensor Sleep Tracking (In-Lab)
In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Multi-Sensor Sleep Tracking (At-Home)
At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Locations (1)
Henry Ford Columbus Medical Center
Novi, Michigan, United States