Chronic DiseaseCoLive VoiceNCT04848623
Identification of Vocal Biomarkers to Monitor the Health of People With a Chronic Disease
An observational study of Chronic Disease, run by Luxembourg Institute of Health at 1 site in Luxembourg.
Trial 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. Read more about this study
- Who is it for?
- Ages 15 years and olderHealthy volunteers can take part
- Conditions the study is for
- Chronic Disease
- How many people
- 50,000 participants
- Where
- 1 siteLuxembourg
- Study dates
- 26 Jun 2021to 1 May 2031
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Adult and adolescent above 15 years, regardless of their health status and their residence country.
Who may be able to join
- Adolescents and adults > 15 years
- With or without health conditions
- From all countries
Who cannot join
- Children < 15 years
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Luxembourg1 site, 1 recruiting
- Recruiting
Luxembourg Institute of Health
Luxembourg
Aurelie Fischer, MS, 00352 621328591, colivevoice@lih.lu
Guy Fagherazzi, PhD
About this study
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.
Read the 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.
- Conditions
- Chronic Disease
- Keywords
- vocal biomarker, digital biomarker, digital health, artificial intelligence, telemonitoring, medical devices, precision health digital biomarker
- Medical subject headings
- Chronic Disease
What is being measured
The primary outcome is the main question the study is designed to answer.
Stress
Patient reported outcome
Measured: At baseline
Secondary outcomes (8)
Fatigue
Patient reported outcome using the fatigue severity scale (FSS). Minimum value =1, max value = 7 ; 7 is the highest level of fatigue
Measured: At baseline
Hypertension
Patient reported outcome
Measured: At baseline
Diabetes
Patient reported outcome
Measured: At baseline
Migraine
Patient reported outcome
Measured: At baseline
Covid-19
Patient reported outcome
Measured: At baseline
Overall pain
Patient reported outcome
Measured: At baseline
Respiratory problems
Patient reported outcome
Measured: At baseline
Level of quality of life
Patient reported outcome
Measured: At baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Apr 2021
- Study start26 Jun 2021
- Primary completion1 May 2031 (estimated)
- Study completion1 May 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Chronic DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 50,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Luxembourg Institute of Health
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Guy Fagherazzi, PhD, Principal investigator, LIH
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04848623
- Sponsor's protocol ID
- CoLive Voice
- Acronym
- CoLive Voice
- Status last verified
- Feb 2026
- First submitted
- 12 Apr 2021
- Last updated
- 15 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
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.
PubMed 39700066
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Identification of Vocal Biomarkers to Monitor the Health of People With a Chronic Disease
- ClinicalTrials.gov ID
- NCT04848623
- Acronym / sponsor ID
- CoLive Voice / CoLive Voice
- Conditions
- Chronic Disease
- Keywords
- vocal biomarker, digital biomarker, digital health, artificial intelligence, telemonitoring, medical devices, precision health digital biomarker
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- Luxembourg Institute of HealthOther government
- Study start
- 26 Jun 2021 (actual)
- Primary completion
- 1 May 2031 (estimated)
- Study completion
- 1 May 2031 (estimated)
- First posted
- 19 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Adult and adolescent above 15 years, regardless of their health status and their residence country.
Key inclusion criteria (3 listed)
- Adolescents and adults > 15 years
- With or without health conditions
- From all countries
Key exclusion criteria (1 listed)
- Children < 15 years
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Stress
Time frame: At baseline
Patient reported outcome
8 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Sep 2026.
- Derived from this study: PubMed 39700066Elbeji 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Guy Fagherazzi, PhD principal investigator
LIH
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Disease, run by Luxembourg Institute of Health at 1 site in Luxembourg.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Luxembourg. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Aurelie Fischer, MSc (00352621328591, aurelie.fischer@lih.lu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Stress (time frame: At baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04848623, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Sep 2026.