The Use of Multiple Sensors to Track Sleep in Nightshift Workers

Official title

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

Overview

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.

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.

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.

Primary outcome measures

• 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]

Secondary outcome measures (2)

• Interviews [Time frame: Within one month of the at-home intervention]
• Digital health technology literacy [Time frame: During screening before the in-lab intervention]

Eligibility 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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Non-randomized

Model

Sequential

Masking

Double blind

Primary purpose

Other

Study locations

United States · 1 center

• Henry Ford Columbus Medical Center — Novi

Identifiers

NCT: NCT06670287 · 17505

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗