[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100427639":3},{"organization":4,"outcomesModule":7,"designInfo":32,"detailedDescription":35,"studyPopulation":36,"armGroups":31,"interventions":31,"overallOfficials":37,"centralContacts":42,"locations":48,"responsibleParty":65,"collaborators":31,"id":67,"slug":68,"hasResults":69,"nctId":70,"briefTitle":71,"officialTitle":71,"acronym":72,"eligibilityCriteria":73,"healthyVolunteers":74,"sex":75,"minAge":76,"maxAge":31,"enrollmentInfo":77,"targetDuration":31,"studyType":80,"phases":31,"briefSummary":81,"conditions":82,"keywords":84,"overallStatus":50,"whyStopped":31,"lastUpdateSubmitDate":92,"lastUpdatePostDateStruct":93,"startDateStruct":96,"completionDateStruct":98,"leadSponsor":100,"locationsCount":101},{"fullName":5,"class":6},"Luxembourg Institute of Health","OTHER_GOV",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":31},[9],{"measure":10,"description":11,"timeFrame":12},"Stress","Patient reported outcome","At baseline",[14,17,19,21,23,25,27,29],{"measure":15,"description":16,"timeFrame":12},"Fatigue","Patient reported outcome using the fatigue severity scale (FSS). Minimum value =1, max value = 7 ; 7 is the highest level of fatigue",{"measure":18,"description":11,"timeFrame":12},"Hypertension",{"measure":20,"description":11,"timeFrame":12},"Diabetes",{"measure":22,"description":11,"timeFrame":12},"Migraine",{"measure":24,"description":11,"timeFrame":12},"Covid-19",{"measure":26,"description":11,"timeFrame":12},"Overall pain",{"measure":28,"description":11,"timeFrame":12},"Respiratory problems",{"measure":30,"description":11,"timeFrame":12},"Level of quality of life",null,{"allocation":31,"interventionModel":31,"interventionModelDescription":31,"primaryPurpose":31,"observationalModel":33,"timePerspective":34,"maskingInfo":31},"OTHER","CROSS_SECTIONAL","With the objective of using vocal biomarkers for diagnosis, risk prediction\u002Fstratification and remote monitoring of various clinical outcomes and symptoms, there is a major need to develop surveys where audio data and clinical, epidemiological and patient-reported outcomes data are collected simultaneously.\n\nThe objectives of CoLive Voice are:\n\n* To launch an international anonymized survey where vocal recordings are associated with large validated clinical and epidemiological data, in the context of various chronic diseases or frequent health symptoms in the general population\n* To extract audio features and train supervised machine learning models to identify key candidate vocal biomarkers of the aforementioned chronic conditions or related symptoms.\n\nParticipants will be recruited online and will complete the survey using a web application.\n\nThey will first answer a detailed questionnaire on their health status and then do 5 different voice records:\n\n1. read a 30 sec prespecified text (from the Human Rights Declaration),\n2. sustain voicing the vowel \u002Faaaaaa\u002F as long and as steady as they can at a comfortable loudness\n3. cough 3 times\n4. breath in and out deeply 3 times\n5. Count from 1 to 20 at a normal speed\n\nVocal records will be pre-processed and converted into features, meaning the most dominating and discriminating characteristics of a vocal signal. Following the selection of features, machine or deep learning algorithms will be trained to automatically predict or classify the clinical, medical or epidemiological outcomes of interest, from vocal features alone or in combination with other health-related data.","Adult and adolescent above 15 years, regardless of their health status and their residence country.",[38],{"name":39,"affiliation":40,"role":41},"Guy Fagherazzi, PhD","LIH","PRINCIPAL_INVESTIGATOR",[43],{"name":44,"role":45,"phone":46,"phoneExt":31,"email":47},"Aurelie Fischer, MSc","CONTACT","00352621328591","aurelie.fischer@lih.lu",[49],{"facility":5,"status":50,"city":51,"state":31,"zip":31,"country":51,"countryCode":52,"cosmosGeoPoint":53,"geoPoint":58,"contacts":59},"RECRUITING","Luxembourg","LU",{"type":54,"coordinates":55},"Point",[56,57],6.13268,49.60982,{"lat":57,"lon":56},[60,64],{"name":61,"role":45,"phone":62,"phoneExt":31,"email":63},"Aurelie Fischer, MS","00352 621328591","colivevoice@lih.lu",{"name":39,"role":41,"phone":31,"phoneExt":31,"email":31},{"type":66,"investigatorFullName":31,"investigatorTitle":31,"investigatorAffiliation":31,"oldNameTitle":31,"oldOrganization":31},"SPONSOR","100427639","identification-of-vocal-biomarkers-to-monitor-the-health-of-people-with-a-chronic-disease-100427639",false,"NCT04848623","Identification of Vocal Biomarkers to Monitor the Health of People With a Chronic Disease","CoLive Voice","Inclusion Criteria:\n\n* Adolescents and adults \\> 15 years\n* With or without health conditions\n* From all countries\n\nExclusion Criteria:\n\n* Children \\\u003C 15 years",true,"ALL","15 Years",{"count":78,"type":79},50000,"ESTIMATED","OBSERVATIONAL","The CoLive Voice research project aims to identify vocal biomarkers of severe conditions and frequent health symptoms. The project is based on digital technologies and statistical algorithms. This is an international anonymous survey where vocal recordings are collected simultaneously with large validated clinical and epidemiological data, in the context of various chronic diseases or frequent health symptoms in the general population.",[83],"Chronic Disease",[85,86,87,88,89,90,91],"vocal biomarker","digital biomarker","digital health","artificial intelligence","telemonitoring","medical devices","precision health digital biomarker","2026-03-24",{"date":94,"type":95},"2026-03-30","ACTUAL",{"date":97,"type":95},"2021-06-26",{"date":99,"type":79},"2031-05-01",{"name":5,"class":6},1]