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

NCT ID: NCT04848623

Last Updated: 2024-12-10

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

50000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-06-26

Study Completion Date

2031-05-01

Brief Summary

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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

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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.

Conditions

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Chronic Disease

Keywords

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vocal biomarker digital biomarker digital health artificial intelligence telemonitoring medical devices precision health digital biomarker

Study Design

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Observational Model Type

OTHER

Study Time Perspective

CROSS_SECTIONAL

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

* Children \< 15 years
Minimum Eligible Age

15 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Luxembourg Institute of Health

OTHER_GOV

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Guy Fagherazzi, PhD

Role: PRINCIPAL_INVESTIGATOR

LIH

Locations

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Luxembourg Institute of Health

Luxembourg, , Luxembourg

Site Status RECRUITING

Countries

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Luxembourg

Central Contacts

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Aurelie Fischer, MSc

Role: CONTACT

Phone: 00352621328591

Email: [email protected]

Facility Contacts

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Aurelie Fischer, MS

Role: primary

Guy Fagherazzi, PhD

Role: backup

References

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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.

Reference Type DERIVED
PMID: 39700066 (View on PubMed)

Other Identifiers

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CoLive Voice

Identifier Type: -

Identifier Source: org_study_id