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

Overview

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 Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Cross-Sectional
  • Study Primary Completion Date: May 1, 2026

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.

Clinical Trial Outcome Measures

Primary Measures

  • Stress
    • Time Frame: At baseline
    • Patient reported outcome

Secondary Measures

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

Participating in This Clinical Trial

Inclusion Criteria

  • Adolescents and adults > 15 years – With or without health conditions – From all countries Exclusion Criteria:

  • Children < 15 years

Gender Eligibility: All

Minimum Age: 15 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • Luxembourg Institute of Health
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Guy Fagherazzi, PhD, Principal Investigator, LIH
  • Overall Contact(s)
    • Aurelie Fischer, MSc, 00352621328591, aurelie.fischer@lih.lu

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