Identification of Vocal Biomarkers to Monitor the Health of People with a Chronic Disease

Recruiting

• Conditions: Chronic Disease

• Registration Number: NCT04848623
• Lead Sponsor: Luxembourg Institute of Health

Brief 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.

Detailed 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.

Study Timeline

• Study First Submitted: 2021-04-12
• Study First Submitted QC: 2021-04-15
• Study First Posted: 2021-04-19
• Study Start: 2021-06-26
• Status Verified: 2024-12
• Last Update Submitted: 2024-12-05
• Last Update Posted: 2024-12-10
• Primary Completion: 2026-05-01
• Study Completion: 2031-05-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 50000

Inclusion Criteria

• Adolescents and adults > 15 years
• With or without health conditions
• From all countries

Exclusion Criteria

• Children < 15 years

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Stress	At baseline	Patient reported outcome

Secondary Outcome Measures

Name	Time	Method
Covid-19	At baseline	Patient reported outcome
Hypertension	At baseline	Patient reported outcome
Diabetes	At baseline	Patient reported outcome
Migraine	At baseline	Patient reported outcome
Overall pain	At baseline	Patient reported outcome
Respiratory problems	At baseline	Patient reported outcome
Level of quality of life	At baseline	Patient reported outcome
Fatigue	At baseline	Patient reported outcome using the fatigue severity scale (FSS). Minimum value =1, max value = 7 ; 7 is the highest level of fatigue

Trial Locations

Locations (1)

Luxembourg Institute of Health; 🇱🇺 Luxembourg, Luxembourg