Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 240
- 主要终点
- Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
结局指标
主要结局
Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis
时间窗: 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.
次要结局
- Study Completion Rate Among Enrolled Participants(From enrolment through completion of final assessment session at 4 weeks)
- Individual Session Completion Rate Across All Participants(From enrolment through completion of final assessment session at 4 weeks)
- Adherence to Specified Assessment Time Windows(From enrolment through completion of final assessment session at 4 weeks)
- Participant Acceptability of Voice-Based Monitoring System(At completion of final assessment session at 4 weeks)
- Participant Withdrawal Patterns and Reasons(From enrolment through 4 weeks or until participant withdrawal)