Multi-Sensor Sleep Tracking for Nightshift Work

(SENSE Trial)

Overseen By Philip Cheng, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Henry Ford Health System

Disqualifiers: Neurological disorders, Alcohol use disorder, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 3

100 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment Multi-Sensor Sleep Tracking for Nightshift Work?

Research shows that consumer-grade multi-sensor trackers, like the Fitbit Charge 2, can effectively estimate sleep patterns and behaviors in shift workers, which can help manage sleep-wake cycles better. This suggests that using such devices may help nightshift workers improve their sleep quality by aligning their rest with their natural body rhythms. ^{1 2 3 4 5}

Is Multi-Sensor Sleep Tracking safe for humans?

The research on multi-sensor sleep tracking devices, like the Fitbit and Zulu watch, shows they are generally safe for use in humans, as they are non-invasive and have been tested in various studies to track sleep patterns without causing harm. ^{4 5 6 7 8}

How does Multi-Sensor Sleep Tracking differ from other treatments for nightshift work?

Multi-Sensor Sleep Tracking is unique because it uses multiple sensors to provide a detailed analysis of sleep patterns, which can help optimize rest-activity management for nightshift workers. Unlike traditional methods, this approach can capture variations in sleep behavior and structure, offering insights tailored to individual circadian preferences. ^{4 5 6 9 10}

What is the purpose of this trial?

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.

Eligibility Criteria

This trial is for nightshift workers who struggle with sleep due to their inverted schedules. It's designed to test new methods of tracking sleep more accurately during the day, which traditional actigraphy fails to do.

Inclusion Criteria

Participants must have worked the nightshift for at least six months

Must plan to maintain the nightshift schedule for the duration of the study

I work at least three night shifts a week, starting between 6 PM and 2 AM, for 8-12 hours and will continue to do so.

Exclusion Criteria

Illicit drug use via self-report and urine drug screen

Alcohol use disorder

Termination of nightshift schedule or planned travel during the study period

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

In-Lab Validation

Participants undergo in-lab validation using polysomnography to test the multi-sensor ML approach against legacy algorithms

1 day

1 visit (in-person)

At-Home Implementation

Participants use the multi-sensor approach for sleep tracking at home for four weeks

4 weeks

1 visit (in-person) for setup, daily virtual check-ins

Follow-up

Participants are monitored for data quality and user experience feedback after the at-home implementation

2 weeks

1 visit (virtual)

Treatment Details

Interventions

• Multi-Sensor Sleep Tracking
• Single-Sensor Tracking

Trial Overview The study is comparing three ways of monitoring sleep: using multiple sensors at home, a single sensor in a lab setting, and multiple sensors in a lab. The goal is to improve accuracy with machine learning algorithms.

Participant Groups

Group I: Multi-Sensor Tracking At-Home Experimental Treatment 1 Intervention

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.

Group II: Multi-Sensor Sleep Tracking In-Lab Experimental Treatment 1 Intervention

This condition will also use raw accelerometer data collected but include raw data from additional sensors. All data will be processed with the machine learning algorithms. Signals will be time-aligned using epoch time to ensure time synchronization, and the quality and validity of the collected dataset will be ensured via visual inspection.

Group III: Single Sensor Sleep Tracking In-Lab Active Control 1 Intervention

This condition will use only raw accelerometer data from a single sensor collected and processed using legacy actigraphy algorithms, comprising 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.

Who Is Running the Clinical Trial?

Henry Ford Health System

Lead Sponsor

Trials

334

Recruited

2,197,000+

Michigan State University

Collaborator

Trials

202

Recruited

687,000+

Findings from Research

Shift workers who align their sleep-wake patterns with their circadian rhythm experience less daytime sleepiness, even if their total sleep time is shorter, indicating the importance of circadian alignment over just sleep duration.

A new computational package can help individuals optimize their sleep-wake patterns in real-time, potentially reducing daytime sleepiness by adjusting sleep durations based on varying bedtimes, which could be integrated with wearable technology.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Personalized sleep-wake patterns aligned with circadian rhythm relieve daytime sleepiness. Hong, J., Choi, SJ., Park, SH., et al.[2021]

Commercially available sleep tracking technology, such as wearables and smartphone apps, is becoming increasingly popular for monitoring sleep patterns, which are essential for health and well-being.

The systematic review included 842 studies, focusing on those that provided sleep data for at least 4 nights, highlighting the growing interest and research into the effectiveness and limitations of these sleep trackers.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Sleep tracking: A systematic review of the research using commercially available technology. Robbins, R., Seixas, A., Masters, LW., et al.[2022]

References

1. Japan pubmed.ncbi.nlm.nih.gov

[Objective and subjective measures of sleep of shift-working nurses]. [2019]

2. Finland pubmed.ncbi.nlm.nih.gov

Shift work, sleep, and sleepiness - differences between shift schedules and systems. [2019]

3. France pubmed.ncbi.nlm.nih.gov

[Comparative study of actigraphy and ambulatory polysomnography in the assessment of adaptation to night shift work in nurses]. [2006]

4. Switzerland pubmed.ncbi.nlm.nih.gov

Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation. [2021]

5. England pubmed.ncbi.nlm.nih.gov

Diurnal variations in multi-sensor wearable-derived sleep characteristics in morning- and evening-type shift workers under naturalistic conditions. [2021]

6. Switzerland pubmed.ncbi.nlm.nih.gov

Objective Assessment of Sleep Patterns among Night-Shift Workers: A Scoping Review. [2021]

7. United States pubmed.ncbi.nlm.nih.gov

Personalized sleep-wake patterns aligned with circadian rhythm relieve daytime sleepiness. [2021]

8. England pubmed.ncbi.nlm.nih.gov

Sleep complaints and polysomnographic findings: a study of nuclear power plant shift workers. [2015]

9. Switzerland pubmed.ncbi.nlm.nih.gov

Sleep tracking: A systematic review of the research using commercially available technology. [2022]

10. United States pubmed.ncbi.nlm.nih.gov

Validation of a nonwearable device in healthy adults with normal and short sleep durations. [2023]