Clinical trial NCT04848623: Identification of Vocal Biomarkers to Monitor the Health of People with a Chronic Disease
Official title: Identification of Vocal Biomarkers to Monitor the Health of People with a Chronic Disease
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Chronic Disease
Keywords: vocal biomarker
digital biomarker
digital health
artificial intelligence
telemonitoring
medical devices
precision health digital biomarker
Lead sponsor: Luxembourg Institute of Health
Sponsor class: OTHER_GOV
First posted: 2021-04-19
Enrollment: 50000
Sex: ALL
Minimum age (years): 15
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult and adolescent above 15 years, regardless of their health status and their residence country.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* Children \< 15 years

Primary outcomes:
measure: Stress
timeFrame: At baseline
Secondary outcomes:
measure: Fatigue
timeFrame: At baseline
measure: Hypertension
timeFrame: At baseline
measure: Diabetes
timeFrame: At baseline
measure: Migraine
timeFrame: At baseline
measure: Covid-19
timeFrame: At baseline
measure: Overall pain
timeFrame: At baseline
measure: Respiratory problems
timeFrame: At baseline
measure: Level of quality of life
timeFrame: At baseline
Other outcomes:
Locations:
city: Luxembourg
country: Luxembourg
facility: Luxembourg Institute of Health
Study officials:
affiliation: LIH
name: Guy Fagherazzi, PhD
role: PRINCIPAL_INVESTIGATOR
References: