Using multiple wearable sensors to track sleep in nightshift workers
Official title: A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
Run by Henry Ford Health System · NCT06670287 · Data updated 2026-11-12
This project will test whether combining wearable sensors and machine learning can more accurately detect daytime sleep in nightshift workers.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 100 (estimated) |
| Sponsor | Henry Ford Health System Academic / other |
| Locations | 1 site (Novi, Michigan) |
| Status | Recruiting |
| Started | February 23, 2026 |
| Main results expected | November 30, 2029 |
| Study ends | June 30, 2031 |
| Registry record updated | March 18, 2026 |
| Trial ID | NCT06670287 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are adults (18+) who have worked a fixed nightshift schedule for at least six months, work at least three night shifts per week with shifts starting between 6:00 PM and 2:00 AM and lasting 8–12 hours, and plan to maintain that schedule for the study.
✕Probably not a fit
People who do not work regular nightshifts (including rotating schedules), cannot provide an average 8-hour bed opportunity, are unwilling to use the study sensors at home, are pregnant, have certain neurological disorders, or have recent illicit drug use may not benefit from this intervention.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
The trial enrolls nightshift workers and compares a legacy actigraphy algorithm (using only wrist accelerometer data) with a multi-sensor approach that feeds accelerometer and additional sensor data into a machine learning model. Participants complete an in-lab validation with gold-standard polysomnography across planned sleep opportunities totaling eight hours to directly compare algorithm performance. A portion of participants then uses the multi-sensor system at home for four weeks to evaluate real-world implementation and to identify facilitators and barriers. The investigators will release an open-source machine learning algorithm and report differences in sleep detection accuracy and continuity metrics between the methods.
Why it matters
Potential benefit: If successful, the approach could give nightshift workers more accurate at-home sleep tracking that better reflects daytime sleep and supports improved diagnosis and management.
How similar studies have gone: Some prior research has applied actigraphy and machine learning, but multi-sensor machine learning specifically aimed at detecting daytime sleep in nightshift workers is relatively novel and not yet well validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
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
Where it's running
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Henry Ford Columbus Medical Center
Novi, Michigan, United States Recruiting
Study contacts
- Study coordinator: Philip Cheng, PhD
- Email: pcheng1@hfhs.org
- Phone: 248-344-7361
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.