Identification of Vocal Biomarkers to Monitor the Health of People With a Chronic Disease
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Chronic Disease. Basic parameters: from 15 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Luxembourg
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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.
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.
Primary outcome measures
- Stress [Time frame: At baseline]
Secondary outcome measures (8)
- Fatigue [Time frame: At baseline]
- Hypertension [Time frame: At baseline]
- Diabetes [Time frame: At baseline]
- Migraine [Time frame: At baseline]
- Covid-19 [Time frame: At baseline]
- Overall pain [Time frame: At baseline]
- Respiratory problems [Time frame: At baseline]
- Level of quality of life [Time frame: At baseline]
Eligibility criteria
Inclusion criteria
- Adolescents and adults > 15 years
- With or without health conditions
- From all countries
Exclusion criteria
- Children < 15 years
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
Luxembourg · 1 center
- Luxembourg Institute of Health — Luxembourg
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. PMID 39700066
Identifiers
NCT: NCT04848623 · CoLive Voice