Myasthenia Gravis GeneralisedNCT07033559
Detecting Fatigue From Voice in Generalised Myasthenia Gravis
An observational study of Myasthenia Gravis Generalised, run by Thymia Limited.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Myasthenia Gravis Generalised
- How many people
- 240 participants
- Where
- No sites listed yet
- Study dates
- Feb 2026to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of a convenience sample of adults with generalised Myasthenia Gravis recruited through a decentralised, multi-channel approach across the United States and United Kingdom. This remote recruitment strategy leverages established myasthenia gravis patient networks, charities and digital platforms like social media groups to reach geographically dispersed participants. The decentralised methodology is particularly appropriate for studying a rare condition where patients may have limited mobility due to fatigue and muscle weakness.
Who may be able to join
- Adults ≥18 years old
- Self-reported generalised Myasthenia Gravis diagnosis confirmed by healthcare provider for ≥6 months
- Disease stability for ≥6 months (no hospitalisations, medication changes, or significant symptom worsening)
- English as first language
- Residence in US or UK
- Vision adequate for screen reading (with aid or correction if necessary)
Show all 10Show fewer
- Access to internet-connected device with compatible browser and microphone
- Adequate internet connectivity (≥5 Mbps download, ≥3 Mbps upload)
- Ability to complete twice-daily assessments during specified time windows
- Signed electronic informed consent
Who cannot join
- Pure ocular Myasthenia Gravis
- Diagnosed mild cognitive impairment or dyslexia
- Speech or hearing impairments affecting voice recording
- Unable to provide credible diagnostic information (healthcare provider diagnosis, antibody test results, current medications)
- Major inconsistencies in reported medical history
- Unsigned informed consent
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study
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:
Read more
- Can advanced voice analysis accurately tell when participants are experiencing deep exhaustion based on how they speak?
- How easy and acceptable is voice-based fatigue monitoring for people with myasthenia gravis?
Participants will:
- Record themselves reading short passages and answering questions out loud twice daily (morning and evening), twice a week, for 4 weeks.
- Answer brief questionnaires about their energy levels, mood, and myasthenia gravis symptoms during each session.
- Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.
Read the 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.
- Conditions
- Myasthenia Gravis Generalised
- Keywords
- myasthenia gravis, generalised myasthenia gravis, fatigue, voice biomarkers, digital health, speech analysis, artificial intelligence
- Medical subject headings
- Myasthenia Gravis, Fatigue
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis
Binary classification performance (presence vs. absence of central fatigue) of the artificial intelligence-based system using voice biomarker analysis, with the subjective fatigue scale serving as ground truth. Performance will be measured using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics through cross-validation methods.
Measured: Across 16 assessment sessions over 4 weeks from enrolment
Secondary outcomes (5)
Study Completion Rate Among Enrolled Participants
Percentage of enrolled participants who complete all 16 required assessment sessions out of the total number of participants who begin the study
Measured: From enrolment through completion of final assessment session at 4 weeks
Individual Session Completion Rate Across All Participants
Percentage of individual assessment sessions completed across all enrolled participants out of the total possible sessions
Measured: From enrolment through completion of final assessment session at 4 weeks
Adherence to Specified Assessment Time Windows
Percentage of completed sessions that occur within the designated time windows out of all completed sessions
Measured: From enrolment through completion of final assessment session at 4 weeks
Participant Acceptability of Voice-Based Monitoring System
Self-reported acceptability scores including ease of use, satisfaction, and willingness for future use of the voice-based fatigue monitoring approach, assessed through three researcher developed 7-point Likert scales (1=lowest, 7=highest)
Measured: At completion of final assessment session at 4 weeks
Participant Withdrawal Patterns and Reasons
Number and percentage of participants who withdraw from the study, categorised by stated reasons for withdrawal (technical difficulties, time burden, health reasons, other)
Measured: From enrolment through 4 weeks or until participant withdrawal
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jun 2025
- Study startFeb 2026 (estimated)
- Primary completionJun 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Myasthenia Gravis GeneralisedStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 240 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Thymia Limited
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- UCB Pharma
- Responsible party
- Sponsor
- Investigators
- Alexandra L Georgescu, PhD, Principal investigator, Thymia Limited
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07033559
- Sponsor's protocol ID
- RWE1049
- Status last verified
- Jan 2026
- First submitted
- 3 Jun 2025
- Last updated
- 28 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets generated during and/or analysed during the current study are not expected to be made generally publicly available due to licensing and IP considerations. The project's findings will be shared through publications.
Publications
Regnault A, Habib AA, Creel K, Kaminski HJ, Morel T. Clinical meaningfulness and psychometric robustness of the MG Symptoms PRO scales in clinical trials in adults with myasthenia gravis. Front Neurol. 2024 Jun 24;15:1368525. doi: 10.3389/fneur.2024.1368525. eCollection 2024.
PubMed 38978809Fara, S., Goria, S., Molimpakis, E., Cummins, N. (2022). Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression. Proc. INTERSPEECH 2022, 1911-1915.
Study at a glance
From the registry record, last updated by the sponsor on 28 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study
- ClinicalTrials.gov ID
- NCT07033559
- Acronym / sponsor ID
- RWE1049
- Conditions
- Myasthenia Gravis Generalised
- Keywords
- myasthenia gravis, generalised myasthenia gravis, fatigue, voice biomarkers, digital health, speech analysis, artificial intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 240 participants (estimated)
- Lead sponsor
- Thymia LimitedIndustry (pharma, biotech, devices)
- Collaborators
- UCB Pharma
- Study start
- Feb 2026 (estimated)
- Primary completion
- Jun 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 24 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Can advanced voice analysis accurately tell when participants are experiencing deep exhaustion based on how they speak?
- How easy and acceptable is voice-based fatigue monitoring for people with myasthenia gravis?
Participants will:
- Record themselves reading short passages and answering questions out loud twice daily (morning and evening), twice a week, for 4 weeks.
- Answer brief questionnaires about their energy levels, mood, and myasthenia gravis symptoms during each session.
- 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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of a convenience sample of adults with generalised Myasthenia Gravis recruited through a decentralised, multi-channel approach across the United States and United Kingdom. This remote recruitment strategy leverages established myasthenia gravis patient networks, charities and digital platforms like social media groups to reach geographically dispersed participants. The decentralised methodology is particularly appropriate for studying a rare condition where patients may have limited mobility due to fatigue and muscle weakness.
Key inclusion criteria (10 listed)
- Adults ≥18 years old
- Self-reported generalised Myasthenia Gravis diagnosis confirmed by healthcare provider for ≥6 months
- Disease stability for ≥6 months (no hospitalisations, medication changes, or significant symptom worsening)
- English as first language
- Residence in US or UK
Key exclusion criteria (6 listed)
- Pure ocular Myasthenia Gravis
- Diagnosed mild cognitive impairment or dyslexia
- Speech or hearing impairments affecting voice recording
- Unable to provide credible diagnostic information (healthcare provider diagnosis, antibody test results, current medications)
- Major inconsistencies in reported medical history
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis
Time frame: Across 16 assessment sessions over 4 weeks from enrolment
Binary classification performance (presence vs. absence of central fatigue) of the artificial intelligence-based system using voice biomarker analysis, with the subjective fatigue scale serving as ground truth. Performance will be measured using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics through cross-validation methods.
5 secondary outcomes
Study Completion Rate Among Enrolled Participants
Time frame: From enrolment through completion of final assessment session at 4 weeks
Individual Session Completion Rate Across All Participants
Time frame: From enrolment through completion of final assessment session at 4 weeks
Adherence to Specified Assessment Time Windows
Time frame: From enrolment through completion of final assessment session at 4 weeks
Participant Acceptability of Voice-Based Monitoring System
Time frame: At completion of final assessment session at 4 weeks
Participant Withdrawal Patterns and Reasons
Time frame: From enrolment through 4 weeks or until participant withdrawal
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Jan 2026.
- Background: PubMed 38978809Regnault A, Habib AA, Creel K, Kaminski HJ, Morel T. Clinical meaningfulness and psychometric robustness of the MG Symptoms PRO scales in clinical trials in adults with myasthenia gravis. Front Neurol. 2024 Jun 24;15:1368525. doi: 10.3389/fneur.2024.1368525. eCollection 2024.
- BackgroundFara, S., Goria, S., Molimpakis, E., Cummins, N. (2022). Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression. Proc. INTERSPEECH 2022, 1911-1915.
- Individual participant dataSharing planned: No. The datasets generated during and/or analysed during the current study are not expected to be made generally publicly available due to licensing and IP considerations. The project's findings will be shared through publications.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Alexandra L Georgescu, PhD principal investigator
Thymia Limited
Questions about this trial
What is this clinical trial studying?
An observational study of Myasthenia Gravis Generalised, run by Thymia Limited.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 28 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Alexandra L Georgescu, PhD (0044 7533848443, alexandra@thymia.ai), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis (time frame: Across 16 assessment sessions over 4 weeks from enrolment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07033559, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Jan 2026.