Recruiting

Actigraphy with ML

Sponsor:

Henry Ford Health System

Code:

NCT06670287

Conditions

Sleep

Nightshift Work

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

Interventions

Single-Sensor Tracking (In-Lab)

Multi-Sensor Sleep Tracking (In-Lab)

Multi-Sensor Sleep Tracking (At-Home)

Study Details

Brief 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

Study ID

NCT06670287

Start date

Feb 23, 2026

Status verified date

Mar, 2026

Completion date

Jun 30, 2031

Anticipated

Primary completion date

Nov 30, 2029

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

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 Design

Enrollment

100 participants

Anticipated

Allocation

Non randomized

Intervention Model

Sequential

Primary purpose

Other

Interventions and Outcome Measures

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.

Interventions

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.

Multi-Sensor Sleep Tracking (In-Lab)

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

Multi-Sensor Sleep Tracking (At-Home)

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

Primary outcome measure

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

Central Contacts and Locations

Central contacts

Philip Cheng, PhD

248-344-7361pcheng1@hfhs.org

Elle M Wernette, PhD

2483442409ewernet1@hfhs.org

Locations

Henry Ford Columbus Medical Center

Recruiting

Novi, Michigan, United States, 48377

Contacts

Philip Cheng, PhD

248-344-7361pcheng1@hfhs.org

Elle M Wernette, PhD

2483442409ewernet1@hfhs.org

Principal Investigator:

Philip Cheng, PhD

More Information

Sponsor

Henry Ford Health System

Last update posted

Mar 18, 2026

Last verified

Mar, 2026

Keywords

  • Sleep tracking
  • Actigraphy
  • Nightshift work
  • Sleep
  • machine learning

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by Henry Ford Health System on 2026-03-18.