A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
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.
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 tria…
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- Yes
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 stud…
Interventions
- OtherSingle-Sensor Tracking (In-Lab)
In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms.
- OtherMulti-Sensor Sleep Tracking (In-Lab)
In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
- OtherMulti-Sensor Sleep Tracking (At-Home)
At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Location
- Henry Ford Columbus Medical CenterNovi, Michigan