Clinical trial NCT06670287: The Use of Multiple Sensors to Track Sleep in Nightshift Workers
Official title: A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Sleep
Nightshift Work
Keywords: Sleep tracking
Actigraphy
Nightshift work
Sleep
machine learning
Lead sponsor: Henry Ford Health System
Sponsor class: OTHER
Collaborators: name: Michigan State University
First posted: 2024-11-01
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: SEQUENTIAL
Intervention model description: Type I hybrid effectiveness-implementation trial with two steps. Step 1: In-lab trial (with a sample of participants balanced on degree of technological literacy) comparing data processing using legacy actigraphy to multi-sensor machine learning. Step 2: At-home implementation of multi-sensor machine learning approach (balanced by technological literacy).
Primary purpose: OTHER

Brief 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). Participants' sleep data will be processed using a legacy actigraphy algorithm condition or the multi-sensor ML. For the legacy actigraphy algorithm approach, only raw accelerometer data will be processed, while data from additional sensors will be processed in addition to raw accelerometer data in the multi-sensor ML condition. 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.

Eligibility criteria: 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

Primary outcomes:
measure: Sleep Continuity- Time in Bed
timeFrame: Throughout study completion, up to 6 weeks
measure: Sleep Continuity- Sleep Onset Latency
timeFrame: Throughout study completion, up to 6 weeks
measure: Sleep Continuity- Wake After Sleep Onset
timeFrame: Throughout study completion, up to 6 weeks
measure: Sleep Continuity- Sleep Efficiency
timeFrame: Throughout study completion, up to 6 weeks
measure: Wake
timeFrame: Throughout study completion, up to 6 weeks
measure: Detection of Daytime Sleep Periods
timeFrame: Throughout study completion, up to 6 weeks
measure: User experience
timeFrame: Within two days of the at-home intervention
Secondary outcomes:
measure: Interviews
timeFrame: Within one month of the at-home intervention
measure: Digital health technology literacy
timeFrame: During screening before the in-lab intervention
Other outcomes:
Locations:
city: Novi
country: United States
facility: Henry Ford Columbus Medical Center
state: Michigan
Study officials:
References: