Identification of Vocal Biomarkers to Monitor the Health of People With a Chronic Disease (CoLive Voice)

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.

Study Overview

• Status: Recruiting
• Conditions: Chronic Disease

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 Type: Observational
• Enrollment (Estimated): 50000

Contacts and Locations

• Study Contact: Name: Aurelie Fischer, MSc; Phone Number: 00352621328591; Email: aurelie.fischer@lih.lu

Study Locations

• Luxembourg
  • Luxembourg, Luxembourg
    • Recruiting
    • Luxembourg Institute of Health
    • Contact: Aurelie Fischer, MS; Phone Number: 00352 621328591; Email: colivevoice@lih.lu
    • Principal Investigator: Guy Fagherazzi, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 15 years and older (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Adult and adolescent above 15 years, regardless of their health status and their residence country.

Description

Inclusion Criteria:

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

Exclusion Criteria:

• Children < 15 years

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Stress	Patient reported outcome	At baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
Hypertension	Patient reported outcome	At baseline
Diabetes	Patient reported outcome	At baseline
Migraine	Patient reported outcome	At baseline
Covid-19	Patient reported outcome	At baseline
Overall pain	Patient reported outcome	At baseline
Respiratory problems	Patient reported outcome	At baseline
Level of quality of life	Patient reported outcome	At baseline

Collaborators and Investigators

• Sponsor: Luxembourg Institute of Health
• Investigators: Principal Investigator: Guy Fagherazzi, PhD, LIH

Publications and helpful links

General Publications

• Elbeji A, Pizzimenti M, Aguayo G, Fischer A, Ayadi H, Mauvais-Jarvis F, Riveline JP, Despotovic V, Fagherazzi G. A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. PLOS Digit Health. 2024 Dec 19;3(12):e0000679. doi: 10.1371/journal.pdig.0000679. eCollection 2024 Dec.

Study record dates

Study Major Dates

• Study Start (Actual): June 26, 2021
• Primary Completion (Estimated): May 1, 2026
• Study Completion (Estimated): May 1, 2031

Study Registration Dates

• First Submitted: April 12, 2021
• First Submitted That Met QC Criteria: April 15, 2021
• First Posted (Actual): April 19, 2021

Study Record Updates

• Last Update Posted (Actual): March 30, 2026
• Last Update Submitted That Met QC Criteria: March 24, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: artificial intelligence; digital health; telemonitoring; medical devices; vocal biomarker; digital biomarker; precision health digital biomarker
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Pathological Conditions, Signs and Symptoms; Chronic Disease
• Other Study ID Numbers: CoLive Voice

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No