The Use of Multiple Sensors to Track Sleep in Nightshift Workers

Part of paid clinical trials in Novi, Michigan.

Sponsor
Henry Ford Health System
Study ID
NCT06670287
Status
Recruiting
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Conditions

  • Nightshift Work
  • Sleep

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

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

Study Details

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.

Key Dates

Start date
Feb 23, 2026
Status verified
Mar 2026
Primary completion
Nov 30, 2029
Completion
Jun 30, 2031

Study Design

Enrollment
100 participants (estimated)
Allocation
NON_RANDOMIZED
Intervention model
SEQUENTIAL
Primary purpose
OTHER

Arms

  • Experimental: Single vs Multi-Sensor Sleep Tracking In-Lab
    In Part 1 of the study, all participants' data will undergo two separate methods for analyzing sleep. The legacy actigraphy algorithm methods will use only raw accelerometer data from a single sensor collected and processed using legacy actigraphy algorithms. The legacy algorithm is comprised first of reducing accelerometer data into activity counts per epoch, which will then be categorized into sleep or wake in accordance with the Cole-Kripke algorithm. The multi-sensor machine learning (ML) method will use raw accelerometer data in addition to data from additional sensors from the watch, phone, and other smart sensors in the sleeping environment. These data will be processed using a machine learning algorithm.
  • Other: Multi-Sensor Sleep Tracking At-Home
    This condition includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Daily sleep diaries will also be collected to enable data quality check. Once collected, all data will be processed with the same machine learning algorithm used in the in-lab experimental condition.

Primary Outcome Measure

Sleep Continuity- Time in Bed [ Time Frame: Throughout study completion, up to 6 weeks ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Henry Ford Columbus Medical CenterNoviMichigan48377
Philip Cheng, PhD
[email protected]
248-344-7361
Elle M Wernette, PhD
[email protected]
2483442409
Philip Cheng, PhD (PRINCIPAL_INVESTIGATOR)

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Henry Ford Columbus Medical Center· Novi, MI

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