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:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009157', 'term': 'Myasthenia Gravis'}, {'id': 'D005221', 'term': 'Fatigue'}], 'ancestors': [{'id': 'D020361', 'term': 'Paraneoplastic Syndromes, Nervous System'}, {'id': 'D009423', 'term': 'Nervous System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D010257', 'term': 'Paraneoplastic Syndromes'}, {'id': 'D020274', 'term': 'Autoimmune Diseases of the Nervous System'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D019636', 'term': 'Neurodegenerative Diseases'}, {'id': 'D020511', 'term': 'Neuromuscular Junction Diseases'}, {'id': 'D009468', 'term': 'Neuromuscular Diseases'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 240}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-11', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2026-04', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-23', 'studyFirstSubmitDate': '2025-06-03', 'studyFirstSubmitQcDate': '2025-06-13', 'lastUpdatePostDateStruct': {'date': '2025-09-29', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-06-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-02', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Exploratory: Correlation Between Central and Peripheral Fatigue', 'timeFrame': '4 weeks', 'description': 'Correlation between AI-detected central fatigue severity scores (0 = low to 1 = high) and self-reported peripheral fatigue symptoms measured by subscales of the MG-Symptoms PRO, namely "physical fatigue" and "muscle weakness fatigability" (Regnault et al., 2024). The score for each scale ranges from 0 to 100, with higher values indicating more severe symptoms. Analyses will employ Spearman\'s rank correlation to account for potential non-linear relationships.'}, {'measure': 'Exploratory: Fatigue-Mood Relationship', 'timeFrame': '4 weeks', 'description': 'Correlation between central fatigue measures (both self-reported using the Modified Fatigue Impact Scale (MFIS) and AI-detected probability scores between 0 = low and 1 = high) and depression (PHQ-8) and anxiety (GAD-7) scores. The MFIS uses a 5-point scale from 0 = never to 4 = almost always with a total score range of 0-84. The PHQ-8 and GAD-7 use 4-point scales from 0 = not at all to 3 = nearly every day, with total score ranges of 0-24 for PHQ-8 and 0-21 for GAD-7.'}], 'primaryOutcomes': [{'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.'}], 'secondaryOutcomes': [{'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', 'timeFrame': 'From enrolment through completion of final assessment session at 4 weeks', 'description': 'Percentage of individual assessment sessions completed across all enrolled participants out of the total possible sessions'}, {'measure': 'Adherence to Specified Assessment Time Windows', 'timeFrame': 'From enrolment through completion of final assessment session at 4 weeks', 'description': 'Percentage of completed sessions that occur within the designated time windows out of all completed sessions'}, {'measure': 'Participant Acceptability of Voice-Based Monitoring System', 'timeFrame': 'At completion of final assessment session at 4 weeks', 'description': '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)'}, {'measure': 'Participant Withdrawal Patterns and Reasons', 'timeFrame': 'From enrolment through 4 weeks or until participant withdrawal', 'description': 'Number and percentage of participants who withdraw from the study, categorised by stated reasons for withdrawal (technical difficulties, time burden, health reasons, other)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['myasthenia gravis', 'generalised myasthenia gravis', 'fatigue', 'voice biomarkers', 'digital health', 'speech analysis', 'artificial intelligence'], 'conditions': ['Myasthenia Gravis Generalised']}, 'referencesModule': {'references': [{'pmid': '38978809', 'type': 'BACKGROUND', 'citation': '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.'}, {'type': 'BACKGROUND', 'citation': 'Fara, 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.'}]}, 'descriptionModule': {'briefSummary': '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.\n\nThe main questions it aims to answer are:\n\n1. Can advanced voice analysis accurately tell when participants are experiencing deep exhaustion based on how they speak?\n2. How easy and acceptable is voice-based fatigue monitoring for people with myasthenia gravis?\n\nParticipants will:\n\n1. Record themselves reading short passages and answering questions out loud twice daily (morning and evening), twice a week, for 4 weeks.\n2. Answer brief questionnaires about their energy levels, mood, and myasthenia gravis symptoms during each session.\n3. Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.', 'detailedDescription': '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.\n\nStudy Rationale and Innovation:\n\nRecent 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.\n\nStudy Design and Methodology:\n\nThis 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.\n\nEach 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.\n\nTechnical Approach:\n\nVoice 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.\n\nData Collection and Management:\n\nAll 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.\n\nSample Size Considerations:\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adults ≥18 years old\n* Self-reported generalised Myasthenia Gravis diagnosis confirmed by healthcare provider for ≥6 months\n* Disease stability for ≥6 months (no hospitalisations, medication changes, or significant symptom worsening)\n* English as first language\n* Residence in US or UK\n* Vision adequate for screen reading (with aid or correction if necessary)\n* Access to internet-connected device with compatible browser and microphone\n* Adequate internet connectivity (≥5 Mbps download, ≥3 Mbps upload)\n* Ability to complete twice-daily assessments during specified time windows\n* Signed electronic informed consent\n\nExclusion Criteria:\n\n* Pure ocular Myasthenia Gravis\n* Diagnosed mild cognitive impairment or dyslexia\n* Speech or hearing impairments affecting voice recording\n* Unable to provide credible diagnostic information (healthcare provider diagnosis, antibody test results, current medications)\n* Major inconsistencies in reported medical history\n* Unsigned informed consent'}, 'identificationModule': {'nctId': 'NCT07033559', 'briefTitle': 'Detecting Fatigue From Voice in Generalised Myasthenia Gravis', 'organization': {'class': 'INDUSTRY', 'fullName': 'Thymia Limited'}, 'officialTitle': 'Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study', 'orgStudyIdInfo': {'id': 'RWE1049'}}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Alexandra L Georgescu, PhD', 'role': 'CONTACT', 'email': 'alexandra@thymia.ai', 'phone': '0044 7533848443'}, {'name': 'Emilia Molimpakis, PhD', 'role': 'CONTACT', 'email': 'emilia@thymia.ai', 'phone': '0044 7541950731'}], 'overallOfficials': [{'name': 'Alexandra L Georgescu, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Thymia Limited'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': "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."}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Thymia Limited', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'UCB Pharma', 'class': 'INDUSTRY'}], 'responsibleParty': {'type': 'SPONSOR'}}}}