Identification of Vocal Biomarkers to Monitor the Health... | NCT04848623

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

NCT04848623

Sponsor

Luxembourg Institute of Health

Status

Recruiting

Last Update Posted

Mar 30, 2026Actual

Enrollment

50,000Estimated

Phase

Not provided

Conditions

Chronic Disease

Interventions

Not provided

Countries

Luxembourg

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Feb 2026

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jun 26, 2021Actual

Primary Completion Date

May 1, 2026Estimated

Completion Date

May 1, 2031Estimated

First Submitted Date

Apr 12, 2021

First Submission Date that Met QC Criteria

Apr 15, 2021

First Posted Date

Apr 19, 2021Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Mar 24, 2026

Last Update Posted Date

Mar 30, 2026Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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:

read a 30 sec prespecified text (from the Human Rights Declaration),
sustain voicing the vowel /aaaaaa/ as long and as steady as they can at a comfortable loudness
cough 3 times
breath in and out deeply 3 times
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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

50,000Estimated

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Stress	Patient reported outcome	At baseline

Secondary Outcomes

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

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

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

Exclusion Criteria:

Children < 15 years

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

15 Years

Maximum Age

Not provided

Standard Ages

ChildAdultOlder Adult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Aurelie Fischer, MSc	Contact	00352621328591		aurelie.fischer@lih.lu

Overall Officials

Name	Affiliation	Role
Guy Fagherazzi, PhD	LIH	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Luxembourg Institute of Health	Recruiting	Luxembourg			Luxembourg

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
39700066	Derived	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.

ID	Term
D020969	Disease Attributes
D010335	Pathologic Processes
D013568	Pathological Conditions, Signs and Symptoms