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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04848623
Other study ID # CoLive Voice
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date June 26, 2021
Est. completion date May 1, 2031

Study information

Verified date August 2023
Source Luxembourg Institute of Health
Contact Aurelie Fischer, MSc
Phone 00352621328591
Email aurelie.fischer@lih.lu
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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.


Description:

With the objective of using vocal biomarkers for diagnosis, risk prediction/stratification 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. The objectives of CoLive Voice are: - 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 - To extract audio features and train supervised machine learning models to identify key candidate vocal biomarkers of the aforementioned chronic conditions or related symptoms. Participants will be recruited online and will complete the survey using a web application. They will first answer a detailed questionnaire on their health status and then do 5 different voice records: 1. read a 30 sec prespecified text (from the Human Rights Declaration), 2. sustain voicing the vowel /aaaaaa/ as long and as steady as they can at a comfortable loudness 3. cough 3 times 4. breath in and out deeply 3 times 5. Count from 1 to 20 at a normal speed Vocal 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.


Recruitment information / eligibility

Status Recruiting
Enrollment 50000
Est. completion date May 1, 2031
Est. primary completion date May 1, 2026
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 15 Years and older
Eligibility Inclusion Criteria: - Adolescents and adults > 15 years - With or without health conditions - From all countries Exclusion Criteria: - Children < 15 years

Study Design


Related Conditions & MeSH terms


Locations

Country Name City State
Luxembourg Luxembourg Institute of Health Luxembourg

Sponsors (1)

Lead Sponsor Collaborator
Luxembourg Institute of Health

Country where clinical trial is conducted

Luxembourg, 

Outcome

Type Measure Description Time frame Safety issue
Primary Stress Patient reported outcome At baseline
Secondary Fatigue Patient reported outcome using the fatigue severity scale (FSS). Minimum value =1, max value = 7 ; 7 is the highest level of fatigue At baseline
Secondary Hypertension Patient reported outcome At baseline
Secondary Diabetes Patient reported outcome At baseline
Secondary Migraine Patient reported outcome At baseline
Secondary Covid-19 Patient reported outcome At baseline
Secondary Overall pain Patient reported outcome At baseline
Secondary Respiratory problems Patient reported outcome At baseline
Secondary Level of quality of life Patient reported outcome At baseline
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