The Use of Multiple Sensors to Track Sleep in Nightshift Workers
A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
Henry Ford Health System
100 participants
Feb 23, 2026
INTERVENTIONAL
Conditions
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.
Eligibility
Inclusion Criteria4
- 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 Criteria7
- 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
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Interventions
In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms.
In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Locations(1)
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NCT06670287