Detecting Fatigue From Voice in Generalised Myasthenia Gravis
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Conditions studied: Myasthenia Gravis Generalised
In brief
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.
Key facts
- Study ID
- NCT07033559
- Run by
- Thymia Limited
- People needed
- 240
- Starts
- 2026-02-01
- Expected to finish
- 2026-12-01
- Last updated by the study team
- 2026-01-28
Who can join
Age: 18 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- 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
You may not qualify if…
- 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
Full record on ClinicalTrials.gov
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