The Use of Multiple Sensors to Track Sleep in Nightshift Workers (SENSE)

March 17, 2026 updated by: Philip Cheng, Henry Ford Health System

A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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). For each participant, sensor data will be processed using two separate methods. For the legacy actigraphy algorithm method, only raw accelerometer data will be processed. For the multi-sensor machine learning method, accelerometer data from the watch along with additional sensors will be processed using a machine learning algorithm. 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.

Study Type

Interventional

Enrollment (Estimated)

100

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • United States
    • Michigan
      • Novi, Michigan, United States, 48377
        • Recruiting
        • Henry Ford Columbus Medical Center
        • Contact:
        • Principal Investigator:
          • Philip Cheng, PhD
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Non-Randomized
  • Interventional Model: Sequential Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
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: Single-Sensor Tracking (In-Lab)
In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms.
Other: Multi-Sensor Sleep Tracking (In-Lab)
In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Other Names:
  • Multi-sensor ML
  • Multi-Sensor Machine Learning
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.
Other: Multi-Sensor Sleep Tracking (At-Home)
At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sleep Continuity- Time in Bed
Time Frame: Throughout 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.
Throughout study completion, up to 6 weeks
Sleep Continuity- Sleep Onset Latency
Time Frame: Throughout 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.
Throughout study completion, up to 6 weeks
Sleep Continuity- Wake After Sleep Onset
Time Frame: Throughout 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.
Throughout study completion, up to 6 weeks
Sleep Continuity- Sleep Efficiency
Time Frame: Throughout 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).
Throughout study completion, up to 6 weeks
Wake
Time Frame: Throughout 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.
Throughout study completion, up to 6 weeks
Detection of Daytime Sleep Periods
Time Frame: Throughout 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.
Throughout study completion, up to 6 weeks
User experience
Time Frame: Within 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).
Within two days of the at-home intervention

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Interviews
Time Frame: Within one month of the at-home intervention
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.
Within one month of the at-home intervention
Digital health technology literacy
Time Frame: During screening before the in-lab intervention
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.
During screening before the in-lab intervention

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Henry Ford Health System

Collaborators

Michigan State University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 23, 2026

Primary Completion (Estimated)

November 30, 2029

Study Completion (Estimated)

June 30, 2031

Study Registration Dates

First Submitted

October 15, 2024

First Submitted That Met QC Criteria

October 30, 2024

First Posted (Actual)

November 1, 2024

Study Record Updates

Last Update Posted (Actual)

March 18, 2026

Last Update Submitted That Met QC Criteria

March 17, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 17505 (Other Identifier: City of Hope Medical Center)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Request for data sharing will be evaluated on a case-by-case basis

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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