Clinical trial · Observational
Detecting Fatigue From Voice in Generalised Myasthenia Gravis
Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Myasthenia Gravis Generalised | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis
- timeFrame
- Across 16 assessment sessions over 4 weeks from enrolment
- description
- 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.
Secondary outcomes (5)
- measure
- Study Completion Rate Among Enrolled Participants
- timeFrame
- From enrolment through completion of final assessment session at 4 weeks
- description
- Percentage of enrolled participants who complete all 16 required assessment sessions out of the total number of participants who begin the study
- measure
- Individual Session Completion Rate Across All Participants
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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) * 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 Exclusion Criteria: * 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
References
Publications (2)
- BACKGROUNDRegnault 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. PMID 38978809
- 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.