Tracking Sleep in Nightshift Workers
This study is looking for nightshift workers to test new ways to track sleep. It aims to improve how we measure sleep for people who work nights, as current methods often aren't accurate for daytime sleep. Researchers are comparing standard sleep tracking (Single-Sensor Tracking) with new methods that use more sensors and machine learning (Multi-Sensor Sleep Tracking). These new methods will be tested both in a lab setting and at home. The goal is to see if the multi-sensor approach can more accurately identify when you're sleeping and how well you're sleeping, specifically focusing on how long you sleep, how long it takes to fall asleep, and how much you wake up after falling asleep. You might be able to join if you're 18 or older, work a fixed night shift schedule (at least three nights a week, 8-12 hours, starting between 6 PM and 2 AM), and have been doing so for at least six months.
- Study design
- This interventional study plans to enroll 100 participants. It will compare different sleep tracking methods, including a single-sensor approach and multi-sensor approaches using machine learning, both in a lab and at home.
- What's involved
- Participants will have sensor data collected in a lab setting, including five planned sleep opportunities. There will also be an at-home implementation phase for four weeks.
- Compensation
- Not stated in the trial record.
- Follow-up
- Sleep continuity variables will be measured throughout the study completion, up to 6 weeks.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
The Use of Multiple Sensors to Track Sleep in Nightshift Workers
At a glance
Conditions
Where it's being run
1 sites across 1 statesWho to contact
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What this trial measures
- Sleep Continuity- Time in BedThroughout study completion, up to 6 weeks
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.
- Sleep Continuity- Sleep Onset LatencyThroughout study completion, up to 6 weeks
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.
- Sleep Continuity- Wake After Sleep OnsetThroughout study completion, up to 6 weeks
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.
- Sleep Continuity- Sleep EfficiencyThroughout study completion, up to 6 weeks
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).
- WakeThroughout study completion, up to 6 weeks
The amount of time (in minutes) a participant is awake \[or the absence of any type of sleep- Stage 1 (N1), Stage 2 (N2), Stage 3 (N3), Rapid Eye Movement (REM)\]. All PSG variables will use standard AASM sleep scoring rules; represented on PSG by activities prior to "lights out" marker or video monitoring (eg, video monitoring showing scrolling on social media in bed). Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform these sleep continuity variables including motion, lights on, high heart rate.
- Detection of Daytime Sleep PeriodsThroughout study completion, up to 6 weeks
Any sleep periods between 6a and 6p will be designated as daytime sleep. A daytime sleep period from the Apple Watch will be considered successfully detected if it falls within ±30 minutes of the PSG start and end times, and is at least 50% the length of the actual sleep period.
- User experienceWithin two days of the at-home intervention
This will be indexed with the User Experience Questionnaire (UEQ) that has been validated for evaluation of new products and has clear and well-established benchmarks. The UEQ includes items along six domains: 1) Attractiveness (overall likability or appeal), 2) Perspicuity (learning curve and ease of use), 3) Efficiency (speed and efficiency of interactions), 4) Dependability (predictability of system behaviors), 5) Stimulation (how exciting and motivating the product is), 6) Novelty (innovation and creativity of the product).