Technical Evaluation of Earswitch Phase A

NCT05784727 | Unknown DMC

• 2 other identifiers: 317435NIHR203387
• Study Type: interventional
• Target: 110
• Locations: 0 countries
• Sites: N/A

Brief Summary

The goal of this study is to test the Earswitch device in both participants with and without assistive technology (AT) needs. The main aim is to determine how robust the Earswitch is in detecting voluntary eardrum movements (known as ear rumbling) and see how well the Earswitch fits with other assistive technology. To do this, participants will be asked to wear the Earswitch device and voluntarily contract their TT to complete a series of tasks shown on screen and/or explained audibly. Participants will be asked to complete questionnaires to understand their opinions about the effectiveness, usability, and comfort of both their current device (if applicable) and the Earswitch. This feedback will be invaluable in developing the Earswitch.

Conditions

Motor Neuron Disease

Keywords

Assistive Technology; Ear rumble; Tensor Tympani

Outcome Measures

Primary Outcomes (1)

• How robust is the Earswitch technology in detecting voluntary eardrum movements? This will be measured by the F-score, Matthews correlation coefficient (MCC), the receiver operating characteristics (ROC) curve, and the Area Under the ROC curve (AUC).

  The F-score represents the robustness of the Earswitch in detecting TT contraction and is the harmonic mean of the precision and recall of the binary classifier used to assess whether an ear rumble has taken place. The MCC measures the quality of binary classifications, calculated from the confusion matrix which includes true positives, true negatives, false positives and false negatives. A ROC curve shows the performance of the classification model at all classification thresholds, from which the AUC metric will be calculated providing an aggregate measure of performance across all classification thresholds. AUC ranges from 0-1, with greater values indicating more correct. This gives an overall metric of the model that is invariant to the classification-threshold and can be used to assess different classification models. However, for practical applications of the Earswitch one would want to minimise false positives and hence the individual F-scores and MCC will be calculated.

  6 months

Secondary Outcomes (4)

• Q2: How accurately can people with assistive technology needs perform voluntary TT movements?

  6 months

• : Is there a difference in the accuracy of TT movements between those who require assistive technology and those who do?

  6 months

• How comfortable and easy is for people to perform voluntary eardrum movements with the EarSwitch?

  6 months

• How physically comfortable is the Earswitch?

  6 months

Study Arms (1)

Earswitch Robustness

OTHER

Participants will wear the Earswitch device and voluntarily contract their TT to complete a series of tasks shown on screen and/or explained audibly. We will also ask participants to complete questionnaires to understand participant opinions about the effectiveness, usability, and comfort of both their current device (if applicable) and the Earswitch.

Device: Earswitch device

Interventions

Earswitch device DEVICE

The Earswitch™ device consists of an earpiece made from silicon or similar bio-compatible material and incorporates a miniature camera. The earpiece sits within the ear canal and is not in direct contact with the eardrum and the camera is not in contact with the ear-canal. The camera detects voluntary movement from the tensor tympani muscle as presented at the eardrum and the Earswitch system translates this movement to an external virtual keypad or similar to allow the patient to communicate nonverbally.

Also known as: Earswitch Ltd

Earswitch Robustness

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Able to voluntarily contract the tensor tympani muscle
• We will be recruiting participants who require assistive technology, as well as people who don't. For the former there will be the requirement that:
• Individual requires the use of an assistive device in everyday life

You may not qualify if:

• Those who do not have the capacity to understand the study and consent (e.g., severe learning disabilities).
• Inability to communicate either directly to the researcher or through a carer or communication partner and therefore inability to provide consent.

Sponsors & Collaborators

• University of Bath: lead
• National Institute for Health Research, United Kingdom: collaborator
• Earswitch Ltd.: collaborator

Related Publications (2)

• Judge S, Nasr N, Hawley M. A User-Centred Approach Exploring the Potential of a Novel EMG Switch for Control of Assistive Technology. Stud Health Technol Inform. 2017;242:381-384.

  PMID: 28873827 BACKGROUND

• Turner MR, Al-Chalabi A. REM sleep physiology and selective neuronal vulnerability in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2020 Jul;91(7):789-790. doi: 10.1136/jnnp-2020-323100. Epub 2020 Apr 30. No abstract available.

  PMID: 32354769 BACKGROUND

Related Links

• Examining the Need for and Provision of AAC Methods in the UK

MeSH Terms

Conditions

Motor Neuron Disease

Condition Hierarchy (Ancestors)

Neurodegenerative Diseases; Nervous System Diseases; Neuromuscular Diseases

Study Officials

• Dario Cazzola, PhD, FHEA

  University of Bath

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Dario Cazzola, PhD, FHEA

CONTACT

+44 1225 383569; dc547@bath.ac.uk

Christopher Clarke, MSc, PhD

CONTACT

+44 1225 388993; cjc234@bath.ac.uk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: The main research question about the robustness of the Earswitch will be calculated based on the whole participant pool. There is no evidence to suggest that the tensor tympani contraction will physically differ between participants with or without assistive technology needs, and therefore the data is equally valid in determining whether the algorithms accurately detect the tensor tympani contraction.

Study Record Dates

• First Submitted: January 24, 2023
• First Posted: March 27, 2023
• Study Start: June 1, 2023
• Primary Completion: November 1, 2023
• Study Completion: September 1, 2024
• Last Updated: May 11, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share