The Use of Multiple Sensors to Track Sleep in Nightshift Workers
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorHenry Ford Health System
About this trial
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 criteria
This trial accepts healthy volunteersQualifiers
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
Disqualifiers
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
Trial design
Design model
Sequential
Treatments tested in this trial
Single-Sensor Tracking (In-Lab)
Other interventionIn-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)
Other interventionIn-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Multi-Sensor Sleep Tracking (At-Home)
Other interventionAt-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Treatment groups
100 Participants
are divided into 2 treatment groupsGroup A: Single vs Multi-Sensor Sleep Tracking In-LabExperimental treatment 2 interventions
Group B: Multi-Sensor Sleep Tracking At-HomeOther 1 intervention
Trial outcomes
Primary outcomes
1
Sleep Continuity- Time in Bed
The amount of time (in minutes) a participant spends in bed from lights out to their final awakening time. All PSG variables will use standard American Academy of Sleep Medicine (AASM) sleep scoring rules. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable.
Time frame
Throughout study completion, up to 6 weeks2
Sleep Continuity- Sleep Onset Latency
The amount of time (in minutes) a participant takes to fall asleep, from the time of lights out, or the amount of time spent awake but attempting sleep from lights out. All PSG variables will use standard AASM sleep scoring rules; indicated with "lights out" marker on a PSG, EEG scored as wake, accompanied with a prototypical sleep posture (e.g. supine) with eyes closed. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable including dim lights or darkness with lux near zero, presence in bed, rare/interspersed motion from phone and watch.
Time frame
Throughout study completion, up to 6 weeks3
Sleep Continuity- Wake After Sleep Onset
The amount of time (in minutes) a participant spends awake from the time they initially falling asleep, and excluding their final wake up. All PSG variables will use standard AASM sleep scoring rules; indicated with "lights out" marker on a PSG, electroencephalography (EEG) scored as wake, accompanied with a prototypical sleep posture (e.g. supine) with eyes closed. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable.
Time frame
Throughout study completion, up to 6 weeks4
Sleep Continuity- Sleep Efficiency
The proportion of the total amount of time a participant is asleep of the total amount of time in bed \[(Total Sleep Time in minutes) / (Time in Bed in minutes)\]. All PSG variables will use standard AASM sleep scoring rules. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable, including dim lights or darkness, presence in bed, prolonged low motion from phone and watch, breathing rate changes, and heart rate (sleep staging).
Time frame
Throughout study completion, up to 6 weeksSecondary outcomes
1
Interviews
These interviews will be semi-structured using the Consolidated Framework for Implementation Research (CFIR). The moderator guide will solicit in-depth feedback on participants' experiences, challenges, and suggestions for improvement. Key themes to be explored in the interviews include ease of use, comfort, perceived accuracy, and any barriers to regular use.
Time frame
Within one month of the at-home intervention2
Digital health technology literacy
This will be measured using the Digital Health Technology Literacy scale (DHTL). This validated scale assesses degree of experience and skills in using digital health technology and services. The DHTL has strong internal consistency (Cronbach's α = 0.95) and strong validity with completion of ten digital tasks such as connecting a device to Wi-Fi and Bluetooth, downloading and installing an app, and entering weight data into an app.
Time frame
During screening before the in-lab interventionSponsors and contacts
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Henry Ford Health System
Lead sponsor
Michigan State University
Collaborator
Source ClinicalTrials.gov