sCAVA - Sleep Assessment Using the CAVA Device

NCT ID: NCT04583826

Last Updated: 2023-12-21

Basic Information

• Recruitment Status: SUSPENDED
• Clinical Phase: NA
• Total Enrollment: 40 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-04-02
• Study Completion Date: 2025-05-31

Brief Summary

Clinical investigation of a medical device (CAVA) for recording eye movements. Forty volunteers will sleep in a specially designed sleep lab for one or two nights, depending on whether they are enrolled onto phase 1 (two nights) or phase 2 (one night) of the study. Each volunteer will wear the CAVA device along with a Polysomnography (PSG) device, which is the gold standard for monitoring sleep. The twenty participants in phase 2 will simultaneously wear a commercially available consumer device for monitoring sleep. The eye movement data captured using CAVA will be processed by novel computer algorithms to classify the sleep stages in the data into Rapid Eye Movement (REM), non-REM and awake. The results will be compared with the ground-truth from the gold standard, and also compared to the results from the commercially available device. The aim of the study is to determine whether CAVA could be a viable and competitive home-monitoring device for analysing sleep.

Detailed Description

The Centres for Disease Control and Prevention (CDC) in the US has declared insufficient sleep a 'public health problem' [1]. Over 35% of adults in the United Kingdom (UK) sleep less than 7 hours and 16% sleep less than 6 hours per night and the proportion of people getting less than the recommended hours of sleep is rising (Liu et al., 2014). This is alarming as insufficient sleep can lead to increased mortality, accidents, and mistakes at work and is also linked with many chronic conditions-such as obesity, type 2 diabetes, heart disease, depression and neurodegeneration -that threaten the wellbeing of an increasing number of people across the globe. On an annual basis, the UK loses an estimated 207,000 working days due to insufficient sleep (mainly related to absenteeism) and the estimated economic cost of insufficient sleep in the UK is over 50 billion dollars, that is 1.86 % of the national Gross Domestic Product (GDP) [2].

Early detection of sleep problems is imperative for prevention of this process, however it is hindered by current technology, where the gold standard is represented by a laborious and expensive method called polysomnography (PSG). Therefore, the need for innovative technologies allowing the reliable monitoring of sleep behaviour in the home-environment using non-invasive, user friendly and broadly accessible methods is immense. This study aims to assess the suitability of the CAVA device (Continuous Ambulatory Vestibular Assessment) that was originally developed by Norfolk and Norwich University Hospitals (NNUH) clinicians and University of East Anglia (UEA scientists to monitor dizziness, for the purpose of diagnosing sleep conditions. The CAVA device is designed to record head and eye-movements continuously over several weeks. Such signals are known to be useful for sleep state diagnosis, and the preliminary analyses show that the data collected by the CAVA device clearly delineate sleep and wake episodes so it is therefore plausible that the CAVA device can be used for home-based sleep monitoring. The investigators term this new application sCAVA. The objective of the proposed study is to re-purpose the CAVA device as a reliable and broadly accessible sleep monitoring tool by developing, testing and validating algorithms to interpret sCAVA data for sleep monitoring purposes.

Conditions

Sleep

Keywords

eye movements; electronystagmography; electrooculogram; sleep; polysomnography; REM sleep; Non-REM sleep

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

CAVA

20 participants will wear the CAVA device, and a consumer-grade sleep monitoring device, and undergo polysomnography, for one night. 40 participants will wear the CAVA device and undergo polysomnography for two nights

Group Type: EXPERIMENTAL

CAVA

Intervention Type: DEVICE

CAVA is a medical device containing 5 face-mounted ECG electrodes which are used to record the movement of the eyes.

Interventions

CAVA

CAVA is a medical device containing 5 face-mounted ECG electrodes which are used to record the movement of the eyes.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• ≥ 18 years of age
• Conversational English and capacity to consent
• Availability to take part in the study

Exclusion Criteria

• Potential participants who have a diagnosed sleep disorder, ongoing neurological or psychiatric disorders, or any potentially life-threatening conditions.
• Potential participants who have a history of dermatological disease, fragile skin, or damage around the forehead.
• Potential participants who have an allergy to plasters and/or medical adhesives (similarly to materials used in the device).
• Pregnant or nursing mothers.

• Minimum Eligible Age: 19 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University of East Anglia

OTHER

Sponsor Role: collaborator

Norfolk and Norwich University Hospitals NHS Foundation Trust

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

John Phillips

Role: PRINCIPAL_INVESTIGATOR

Norfolk and Norwich University Hospitals NHS Foundation Trust

Locations

Norfolk and Norwich University Hospitals NHS Foundation Trust

Norwich, Norfolk, United Kingdom

Countries

United Kingdom

References

Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of Healthy Sleep Duration among Adults--United States, 2014. MMWR Morb Mortal Wkly Rep. 2016 Feb 19;65(6):137-41. doi: 10.15585/mmwr.mm6506a1.

Reference Type: BACKGROUND

PMID: 26890214 (View on PubMed)

Hafner M, Stepanek M, Taylor J, Troxel WM, van Stolk C. Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis. Rand Health Q. 2017 Jan 1;6(4):11. eCollection 2017 Jan.

Reference Type: BACKGROUND

PMID: 28983434 (View on PubMed)

Other Identifiers

272848 (209-11-19)

Identifier Type: -

Identifier Source: org_study_id