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

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 100 (estimated)
• Sponsor: Henry Ford Health System · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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.

Detailed description

The first aim of this study is to establish an open-source machine learning algorithm for sleep tracking that outperforms legacy actigraphy algorithms in detecting daytime sleep periods. The second aim is to enhance tracking of sleep continuity variables by adding multiple sensors. The final aim is to identify facilitators and barriers of at-home implementation of multi-sensor sleep tracking. Our central hypothesis is that a multi-sensor ML approach will outperform legacy algorithms against gold-standard polysomnography (PSG). This study will be type I hybrid effectiveness-implementation trial that 1) validates the proposed multi-sensor ML approach using in-lab polysomnography, and 2) examines implementation of the multi-sensor ML approach in an ecologically valid setting via an at-home implementation for four weeks. A sample of nightshift workers will be enrolled in the in-lab validation portion of the study and will be hooked-up to PSG with continuous data collection for the duration of the lab visit to capture five planned sleep opportunities at varying lengths (4 hr, 2 hr, 1.5 hr, and two 30-minute naps; 8 hrs total). For each participant, sensor data will be processed using two separate methods. For the legacy actigraphy algorithm method, only raw accelerometer data will be processed. For the multi-sensor machine learning method, accelerometer data from the watch along with additional sensors will be processed using a machine learning algorithm. Some participants who complete the in-lab portion of the study will be asked to complete the at-home portion of the study, which includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Participants will receive the sensor kit and will have an at-home appointment with study staff to aid with sensor set-up, which will then be collected again at the end of the 4-week period. Daily sleep diaries will also be collected during the 4 weeks to enable data quality check.

Conditions

• Sleep
• Nightshift Work

Interventions

Type	Name	Description
OTHER	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.
OTHER	Multi-Sensor Sleep Tracking (In-Lab)	In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
OTHER	Multi-Sensor Sleep Tracking (At-Home)	At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.

Timeline

• Start date: 2026-02-23
• Primary completion: 2029-11-30
• Completion: 2031-06-30
• First posted: 2024-11-01
• Last updated: 2026-03-18

Locations

1 site across 1 country: United States