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The Use of Multiple Sensors to Track Sleep in Nightshift Workers

NCT06670287 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 100

Last updated 2026-03-18

No results posted yet for this study

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.

Conditions

  • Sleep
  • Nightshift Work

Interventions

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

Multi-Sensor Sleep Tracking (At-Home)

At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.

Sponsors & Collaborators

Study Design

Allocation
NON_RANDOMIZED
Purpose
OTHER
Masking
DOUBLE
Model
SEQUENTIAL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-02-23
Primary Completion
2029-11-30
Completion
2031-06-30

Countries

  • United States

Study Locations

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Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06670287 on ClinicalTrials.gov