Ignite Creation Date:
2025-12-24 @ 2:52 PM
Ignite Modification Date:
2026-01-21 @ 3:11 AM
Study NCT ID:
NCT07033559
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-09-29
First Post:
2025-06-03
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Detecting Fatigue From Voice in Generalised Myasthenia Gravis
Sponsor:
Thymia Limited
Organization:
Study Overview
Official Title:
Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2025-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this observational study is to learn if computer analysis of voice recordings can detect a type of exhaustion called "central fatigue" in adults with generalised myasthenia gravis.
The main questions it aims to answer are:
1. Can advanced voice analysis accurately tell when participants are experiencing deep exhaustion based on how they speak?
2. How easy and acceptable is voice-based fatigue monitoring for people with myasthenia gravis?
Participants will:
1. Record themselves reading short passages and answering questions out loud twice daily (morning and evening), twice a week, for 4 weeks.
2. Answer brief questionnaires about their energy levels, mood, and myasthenia gravis symptoms during each session.
3. Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.
The main questions it aims to answer are:
1. Can advanced voice analysis accurately tell when participants are experiencing deep exhaustion based on how they speak?
2. How easy and acceptable is voice-based fatigue monitoring for people with myasthenia gravis?
Participants will:
1. Record themselves reading short passages and answering questions out loud twice daily (morning and evening), twice a week, for 4 weeks.
2. Answer brief questionnaires about their energy levels, mood, and myasthenia gravis symptoms during each session.
3. Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.
Detailed Description:
This study addresses a significant gap in understanding and measuring central fatigue in generalised myasthenia gravis (gMG), a debilitating symptom that differs from the characteristic muscle weakness fluctuations of the condition. Central fatigue encompasses mental and physical exhaustion originating in the central nervous system and remains poorly characterised with limited validated assessment tools.
Study Rationale and Innovation:
Recent developments in artificial intelligence and digital biomarkers have demonstrated potential for detecting fatigue-related changes in voice characteristics. This approach offers advantages over traditional assessment methods by providing objective, standardised measurements that can be collected remotely with minimal participant burden. Voice-based biomarkers may capture subtle physiological changes associated with central fatigue that are not readily apparent through conventional questionnaire-based assessments.
Study Design and Methodology:
This single-cohort observational study employs an intensive longitudinal monitoring design to capture the dynamic nature of fatigue fluctuations characteristic of gMG. The twice-daily assessment schedule (morning and evening sessions two days a week) over four weeks is designed to account for diurnal variation in fatigue symptoms commonly reported by MG patients.
Each assessment session lasts approximately 10-15 minutes and includes standardised voice recording tasks alongside validated fatigue questionnaires. Voice recording activities consist of structured reading tasks and answering questions out loud, designed to elicit natural speech patterns while maintaining consistency across sessions and participants.
Technical Approach:
Voice data will be analysed using machine learning algorithms to identify acoustic features potentially associated with central fatigue states. \[Note: Specific algorithmic approaches and feature extraction methods are proprietary and not detailed here\]. The study uses triangulated participant self-reported fatigue assessments as ground truth labels for model training and validation.
Data Collection and Management:
All data collection occurs remotely through a secure web-based platform accessible via standard internet browsers. Participants use their personal devices (computers, tablets, or smartphones) equipped with microphone capabilities. The platform captures voice recordings, questionnaire responses, and relevant metadata including device specifications and environmental conditions that may affect recording quality.
Sample Size Considerations:
The target enrolment of 240 participants is designed to generate sufficient data points for robust machine learning model development while accounting for expected attrition and technical issues.
Study Rationale and Innovation:
Recent developments in artificial intelligence and digital biomarkers have demonstrated potential for detecting fatigue-related changes in voice characteristics. This approach offers advantages over traditional assessment methods by providing objective, standardised measurements that can be collected remotely with minimal participant burden. Voice-based biomarkers may capture subtle physiological changes associated with central fatigue that are not readily apparent through conventional questionnaire-based assessments.
Study Design and Methodology:
This single-cohort observational study employs an intensive longitudinal monitoring design to capture the dynamic nature of fatigue fluctuations characteristic of gMG. The twice-daily assessment schedule (morning and evening sessions two days a week) over four weeks is designed to account for diurnal variation in fatigue symptoms commonly reported by MG patients.
Each assessment session lasts approximately 10-15 minutes and includes standardised voice recording tasks alongside validated fatigue questionnaires. Voice recording activities consist of structured reading tasks and answering questions out loud, designed to elicit natural speech patterns while maintaining consistency across sessions and participants.
Technical Approach:
Voice data will be analysed using machine learning algorithms to identify acoustic features potentially associated with central fatigue states. \[Note: Specific algorithmic approaches and feature extraction methods are proprietary and not detailed here\]. The study uses triangulated participant self-reported fatigue assessments as ground truth labels for model training and validation.
Data Collection and Management:
All data collection occurs remotely through a secure web-based platform accessible via standard internet browsers. Participants use their personal devices (computers, tablets, or smartphones) equipped with microphone capabilities. The platform captures voice recordings, questionnaire responses, and relevant metadata including device specifications and environmental conditions that may affect recording quality.
Sample Size Considerations:
The target enrolment of 240 participants is designed to generate sufficient data points for robust machine learning model development while accounting for expected attrition and technical issues.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: