[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100573223":3},{"organization":4,"outcomesModule":7,"designInfo":33,"detailedDescription":36,"studyPopulation":37,"armGroups":38,"interventions":32,"overallOfficials":45,"centralContacts":49,"locations":59,"responsibleParty":90,"collaborators":93,"id":97,"slug":32,"hasResults":98,"nctId":99,"briefTitle":100,"officialTitle":101,"acronym":102,"eligibilityCriteria":103,"healthyVolunteers":104,"sex":105,"minAge":106,"maxAge":32,"enrollmentInfo":107,"targetDuration":32,"studyType":110,"phases":32,"briefSummary":111,"conditions":112,"keywords":115,"overallStatus":83,"whyStopped":32,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":126,"completionDateStruct":128,"leadSponsor":130,"locationsCount":131},{"fullName":5,"class":6},"Leiden University Medical Center","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":32},[9],{"measure":10,"description":11,"timeFrame":12},"Differentiating between MG-patients and non-MG participants using digital features of dysarhtria, dysphonia, proximal arm fatigue and ptosis.","Using machine-learning algorithms.","Assessed at a single time point during outpatient visit",[14,17,20,23,26,29],{"measure":15,"description":16,"timeFrame":12},"Correlating digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis in MG patients with disease severity as measured by the MGC score.","Myasthenia Gravis Composite (MGC) is scored on an ordinal scale with four possible categories and weighted and consists of 10 items with a total score of 0-50; a higher score indicating more severe disease.",{"measure":18,"description":19,"timeFrame":12},"Correlating digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis in MG patients with the impact of MG on daily activities as measured by the MG-ADL.","MG Activities of Daily Living (MG-ADL) consists of 8 items with a total score of 0-24; a higher score indicating more severe disease.\n\n.",{"measure":21,"description":22,"timeFrame":12},"The performance of automated signal processing of speech recordings collected through smartphone microphone for detection of dysarthria and dysphonia compared to clinical assessment.","Clinical assessment by two independent trained speech and language pathologists (SLP) using the Therapy Outcome Measure scale (TOMS) for dysarthria and GRBAS scale (Grade, Roughness, Breathiness, Asthenia and Strain) for dysphonia.",{"measure":24,"description":25,"timeFrame":12},"The performance of automated measurement of proximal arm-fatiguing exercises through computer vision techniques applied to smartphone camera recordings for detection of proximal arm muscle weakness and fatigability compared to clinical assessment.","Clinical assessment by accredited clinicans.",{"measure":27,"description":28,"timeFrame":12},"The performance of automated measurement of ptosis-provoking exercises through computer vision techniques applied to smartphone camera recordings for detection of ptosis compared to clinical assessment.","Clinical assessment by accredited clinicians.",{"measure":30,"description":31,"timeFrame":12},"Correlating digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis in MG patients with their level of fatigue as measured by the CIS-fatigue subscale.","Checklist Individual Strength (CIS-) fatigue subscale consists of 8 questions on fatigue experienced during the previous 2 weeks. Each question is scored on a 7-point Likert scale and a score ≥ 35 indicates severe fatigue. A higher score means more severe fatigue. The total score of the fatigue subscale ranges from 8-56 (the total score of the CIS questionnaire ranges from 20-140).",null,{"allocation":32,"interventionModel":32,"interventionModelDescription":32,"primaryPurpose":32,"observationalModel":34,"timePerspective":35,"maskingInfo":32},"CASE_CONTROL","CROSS_SECTIONAL","Due to the cross-sectional design, participants will only have to visit Leiden University Medical Center (LUMC) once. For patients already treated in the LUMC, we will try to align this visit with a standard clinical appointment.\n\nAfter inclusion, all baseline data, consisting of demographics, clinical history and a number of questionnaires (four for MG participants, three for non-MG participants), will be collected. The symptom-specific assessments are performed in a standard order, with the most fatiguing task (i.e. proximal arm fatigue static assessment) last. We estimate the visit will take a total of 60 minutes.\n\nThis study is considered to be low risk. Withholding pyridostigmine for a limited period is part of standard care of MG (before investigations or clinical assessments) and does not affect long term clinical outcome. MG participants will consent to withhold pyridostigmine for 12 hours prior to the study visit if they are on this treatment and restart it after the visit. As this is a non-interventional, observational study where only questionnaire-based and non-contact digital data are being collected, the only source of marginal risk relates to data protection and confidentiality, including arrangements for the transfer and storage of data. Given it would not be possible to deidentify the digital audio or video data while maintaining the requisite integrity for data analysis, we will seek explicit consent for the sharing of this information in this identifiable format.","We aim to include 150 patients with Myasthenia Gravis, recruited from the national Dutch-Belgia MG registry, the patient organisation 'Spierziekten Nederland' or identified by the clinical team of Leiden University Medical Center (LUMC).\n\nNon-MG participants are preferentially sampled from friends and family of enrolled MG patients. The highest preference will be same-household family members, lowest preference will be non-household non-family members. This acts as both a natural source of control participants and a natural control for any environmental confounders. Furthermore, non-MG participants will be recruited separately by placing flyers in crowded areas in the LUMC.",[39,42],{"label":40,"type":32,"description":41,"interventionNames":32},"MG patients","MG patients with at least one of the symptoms of interest (i.e. dysarthria, dysphonia, proximal arm fatigue and\u002For ptosis). We aim to include 150 patients with Myasthenia Gravis.",{"label":43,"type":32,"description":44,"interventionNames":32},"Non-MG participants","Non-MG participants that do not have a medical history of any of the symptoms of interest. We aim to include 75 controls.",[46],{"name":47,"affiliation":5,"role":48},"Martijn R. Tannemaat, MD, PhD","PRINCIPAL_INVESTIGATOR",[50,55],{"name":51,"role":52,"phone":53,"phoneExt":32,"email":54},"Martijn R Tannemaat, MD, PhD","CONTACT","+31715262197","m.r.tannemaat@lumc.nl",{"name":56,"role":52,"phone":57,"phoneExt":32,"email":58},"Yvonne JM Campman, MD","+31715262118","y.j.m.campman@lumc.nl",[60,82],{"facility":5,"status":61,"city":62,"state":63,"zip":64,"country":65,"countryCode":66,"cosmosGeoPoint":67,"geoPoint":72,"contacts":73},"NOT_YET_RECRUITING","Leiden","South Holland","2333 ZA","Netherlands","NL",{"type":68,"coordinates":69},"Point",[70,71],4.49306,52.15833,{"lat":71,"lon":70},[74,76,77,78,80],{"name":75,"role":52,"phone":53,"phoneExt":32,"email":54},"Martijn R Tannemaat, MD PhD",{"name":56,"role":52,"phone":57,"phoneExt":32,"email":58},{"name":75,"role":48,"phone":32,"phoneExt":32,"email":32},{"name":56,"role":79,"phone":32,"phoneExt":32,"email":32},"SUB_INVESTIGATOR",{"name":81,"role":79,"phone":32,"phoneExt":32,"email":32},"Jan JGM Verschuuren, MD PhD",{"facility":5,"status":83,"city":62,"state":32,"zip":32,"country":65,"countryCode":66,"cosmosGeoPoint":84,"geoPoint":86,"contacts":87},"RECRUITING",{"type":68,"coordinates":85},[70,71],{"lat":71,"lon":70},[88,89],{"name":51,"role":52,"phone":57,"phoneExt":32,"email":54},{"name":56,"role":52,"phone":57,"phoneExt":32,"email":58},{"type":48,"investigatorFullName":91,"investigatorTitle":92,"investigatorAffiliation":5,"oldNameTitle":32,"oldOrganization":32},"Martijn R. Tannemaat, MD PhD","Principal investigator",[94],{"name":95,"class":96},"Huma","INDUSTRY","100573223",false,"NCT06743490","Capturing Key MG-symptoms Using Smartphone Recordings.","Capturing Key Symptoms Using Smartphone Recordings in Patients With Myasthenia Gravis (CAPTURE-MG)","CAPTURE - MG","Inclusion Criteria:\n\n1. Age ≥ 18 years\n2. Ability to understand the requirements of the study and provide written informed consent.\n\nInclusion Criteria for MG participants only:\n\n1. A clinical diagnosis of myasthenia gravis (ocular or generalized) as defined by the Dutch national guideline (category \"definite\" or \"probable\" MG).\n2. MGFA Clinical Classification of disease severity I-IV.\n3. Subjects have at least one of the symptoms of interest (namely dysarthria, dysphonia, proximal arm fatigue and\u002For ptosis).\n\nInclusion Criteria for non-MG participants only\n\n1. Subjects are not diagnosed with and have no clinical suspicion of MG.\n2. Subjects do not have a medical history of any of the symptoms of interest (namely dysarthria, dysphonia, proximal arm fatigue and\u002For ptosis).\n\nExclusion Criteria:\n\n1. Not willing to be audio-recorded for the study assessments.\n2. Not willing to be video-recorded for the study assessments.\n3. Subjects currently taking part in a clinical trial of an Investigational Medicinal Product.\n4. Subjects who have used an immediate release pyridostigmine-based medication in the 12 hours prior to their participation and participants on prolonged release pyridostigmine.\n5. Subjects have cognitive or physical limitations that, in the opinion of the investigator, limits the subject's ability to complete study procedures\u002F\n\nExclusion Criteria for MG participants only:\n\n1. Subjects with an upper-limb amputation or who are non-verbal.\n2. Subjects with a diagnosed neurological disease resulting in muscle weakness, other than MG.\n\nExclusion Criteria for non-MG participants only:\n\n1\\. Limitation of upper limb mobility or speech impairment of any cause.",true,"ALL","18 Years",{"count":108,"type":109},225,"ESTIMATED","OBSERVATIONAL","This study will make use of a cross-sectional design of MG patients and non-MG participants to quantitatively assess key MG symptoms, and to explore the applicability of machine learning algorithms to their measurement.",[113,114],"Myasthenia Gravis","Fatigue",[113,114,116,117,118,119,120,121],"Digital features","Machine Learning Algorithms","Dysarthria","Dysphonia","Proximal arm fatigue","Ptosis","2025-09-02",{"date":124,"type":125},"2025-09-09","ACTUAL",{"date":127,"type":125},"2025-03-18",{"date":129,"type":109},"2026-04-01",{"name":5,"class":6},2]