CoughNCT05042063
Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease
An observational study of Cough and COPD, run by Clinica Universidad de Navarra, Universidad de Navarra at 1 site in Spain.
Trial summary
This study pretends to evaluate the potential use of Hyfe Cough Tracker (Hyfe) to screen for, diagnose, and support the clinical management of patients with respiratory diseases, while enriching a dataset of disease-specific annotated coughs, for further refinement of similar systems. Read more about this study
- Who is it for?
- Ages 5 to 100 yearsHealthy volunteers can take part
- What is being tested?
- Hyfe Cough Tracker and Hyfe Air
- Type of treatment
- Medical device
- How many people
- 616 participants
- Where
- 1 siteSpain
- Study dates
- 15 Sep 2021to 15 Sep 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
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 5 to 100 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: For the main study group, the population are patients with respiratory disease treated in the Clínica Universidad de Navarra (Pamplona and Madrid campuses). Since the validation sub-study 1 only requires elicited sounds, a group of participants from a previous study will be directly invited to participate. For the validation sub-study 2, participants will include both, inpatients admitted to the Clínica Universidad de Navarra and presenting cough, and healthy individuals directly invited to participate by the study team.
Who may be able to join
- For participants in the main study group
- Outpatient or inpatients at the Clinical Universidad de Navarra with a complaint of cough.
- The patient or his/her legal representative, have given consent to participate in the study.
- For participants in the sub-study groups:
- Being 18 years or older.
- Providing consent for the sub-study
Who cannot join
- Inability to accept the privacy policy and terms of use of Hyfe.
- Lack of access to a Wi-Fi network at the site of residence (for the main study group).
- Unwillingness to regularly use the cough-surveillance system throughout the monitoring period
Trial locations and contacts
1 site in 1 country.
About this study
This study pretends to evaluate the potential use of Hyfe Cough Tracker (Hyfe) to screen for, diagnose, and support the clinical management of patients with respiratory diseases, while enriching a dataset of disease-specific annotated coughs, for further refinement of similar systems.
Read the detailed description
This is an observational study that will take place in the two campuses of the Clínica Universidad de Navarra, located in Pamplona and Madrid (Spain).
An Artificial-Intelligence system (AI) that detects and records explosive putative cough sounds and identifies human cough based on acoustic characteristics will be used to automatically monitor cough. Potential participants either attending the outpatient clinic or hospitalised with a complaint of cough will be invited by their treating physician, or a member of the research team, and included in the study by part of the research team. A researcher will instruct participants on how to install and use Hyfe Cough Tracker in their smartphones. Participants will be monitored for 30 days (outpatients) or until discharged from the hospital (inpatients). Participants will be asked to complete a daily, online, standardised 100 mm visual analogue scale (VAS) to register changes in the subjective intensity of their cough, while using Hyfe to objectively monitor changes in its frequency.
In parallel, a dataset of annotated cough sounds will be constructed and retrospectively used to assess differences in acoustic patterns of cough, and to evaluate the performance of the system detecting them.
A first subgroup of participants will be recruited outside the clinical setting and asked to provide a series of elicited sounds, including coughs, which will then be used to determine the system's performance accurately discriminating coughs from non-cough sounds, and compared to trained human listeners.
A second subgroup of participants will be will be instructed to use Hyfe, and the related Hyfe Air wearable device continuously for a period between 6 and 24 hours, while they record themselves using a MP3 recorder connected to a lapel microphone. This group will be used to evaluate the performance of Hyfe and Hyfe Air in a real-life setting, with spontaneous coughs.
- Conditions
- Cough, COPD, GERD, Asthma, Tuberculosis, Non-Tuberculous Mycobacterial Pneumonia
- Being tested
- Hyfe Cough Tracker, Hyfe Air
- Medical subject headings
- Cough, Pulmonary Disease, Chronic Obstructive, Gastroesophageal Reflux, Asthma, Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Hyfe Cough TrackerDevice
Hyfe Cough Tracker is a digital acoustic surveillance system that uses an artificial intelligence system to discriminate cough from non-cough sounds. Hyfe is an AI-enabled mobile app that records short snippets (<0.5 seconds) of putative cough explosive sounds and then classifies them as cough or non-cough using a convolutional neural network (CNN) model. Briefly, the acoustic characteristics of recorded sounds are converted into an image file, which is then processed by an algorithm trained to identify graphical differences in images. This creates an adjustable prediction score, with values above it, resulting in a sound being classified as "cough", and those below being classified as "non-cough.
Hyfe AirDevice
Hyfe Air is a wearable device with an incorporated wireless lapel microphone. The device´s recordings can be run through the same cough-detection algorithm used by Hyfe Cough Tracker, while its results are directly stored in a remote database and are not displayed to participants.
| Study group | Role | What they receive |
|---|---|---|
| Participants with cough as a symptom | This group will be composed of patients at the Clínica Universidad de Navarra that complain of having cough as a remarkable symptom. Receives: Device: Hyfe Cough Tracker | |
| Validation subgroup 1 | This subgroup will be composed by both, patients belonging to the main study group, as well as voluntaries, who will be asked to provide a series of elicited cough and non-cough sounds for validation purposes. Receives: Device: Hyfe Cough Tracker | |
| Validation subgroup 2 | This subgroup will be composed by inpatients admitted to the Clínica Universidad de Navarra with a diagnosis of respiratory disease, or presenting cough as a symptom, as well as healthy individuals. This group will be monitored with Hyfe Cough Tracker and Hyfe Air for a variable period of 6-24 hours, while they are recorded with a MP3 recorder connected to a lapel microphone. Receives: Device: Hyfe Cough Tracker; Device: Hyfe Air |
What is being measured
The primary outcome is the main question the study is designed to answer.
Correlation between subjective perception of cough and objective frequency
The daily VAS score of participants will be compared to the cough frequency registered by the cough surveillance system. These data will be used to fit a linear regression model to compare self-reported VAS scores to daily cough frequency and calculate a correlation coefficient (r).
Measured: 6 months.
Secondary outcomes (7)
Sensitivity of the system discriminating coughs
The sensitivity of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. Sensitivity will be reported as the proportion of sounds correctly identified as coughs (true positives), from the total cough sounds produced (true positives + false negatives).
Measured: 6 months.
Specificity of the system discriminating coughs
The specificity of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. Specificity will be defined as the proportion of non-cough sounds correctly identified by the system (true negatives) from the total non-cough sounds produced (true negatives + false positives)
Measured: 6 months.
Positive predictive value (PPV) of the system discriminating coughs
The PPV of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. PPV will be defined as the proportion of cough sounds correctly identified by the system (true positives) from the total sounds labelled as coughs (true positives + false positives).
Measured: 6 months.
Negative predictive value (NPV) of the system discriminating coughs
The NPV of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. NPV will be defined as the proportion of non-cough sounds correctly identified by the system (true negatives) from the total of sounds labelled as non-coughs (true negatives+ false negatives).
Measured: 6 months.
Construction of an annotated cough dataset
Cough registries of participants with an etiologic diagnosis will be annotated and stored to create a dataset that can be used for further algorithm training and refinement.
Measured: 5 years.
Sensitivity of the system differentiating coughs caused by different conditions
The records obtained from participants for which an etiologic diagnosis is reached before the end of the study will be analysed to detect differential acoustic patterns, which will in turn be used to train the system's convolutional neural network to perform respiratory disease cough classification. The performance of this system will be retrospectively evaluated by determining its sensitivity for the diagnosis of different respiratory conditions, compared to clinical diagnoses made by a physician. Sensitivity will be defined as the proportion of participants in which Hyfe reaches a correct diagnoses based on cough acoustic patterns (true positives) from the total number of participants diagnosed with a certain condition (true positives + false negatives).
Measured: 5 years.
Specificity of the system differentiating coughs caused by different conditions
The records obtained from participants for which an etiologic diagnosis is reached before the end of the study will be analysed to detect differential acoustic patterns, which will in turn be used to train the system's convolutional neural network to perform respiratory disease cough classification. The performance of this system will be retrospectively evaluated by determining its specificity for the diagnosis of different respiratory conditions, compared to clinical diagnoses made by a physician. Specificity will be defined as the proportion of participants in which Hyfe correctly identifies the absence of acoustic cough patterns associated to a certain disease (true negatives), from the total of participants without that specific condition (true negatives+ false positives).
Measured: 5 years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Sep 2021
- Study start15 Sep 2021
- Primary completion15 Sep 2022
- Study completion15 Sep 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for CoughStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 616 participants (actual)
Sponsor and investigators
- Lead sponsor
- Clinica Universidad de Navarra, Universidad de Navarra
- Sponsor type
- University, hospital or other
- Collaborators
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Hyfe Inc
- Responsible party
- Sponsor
- Investigators
- Carlos Chaccour, MD, PhD, Principal investigator, Clinica Universidad de Navarra
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05042063
- Sponsor's protocol ID
- PI_2021/72
- Status last verified
- Oct 2025
- First submitted
- 2 Sep 2021
- Last updated
- 20 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Datasets with anonymized IPD, including cough registries and VAS scores will be shared at the end of the study.
Publications
Barton A, Gaydecki P, Holt K, Smith JA. Data reduction for cough studies using distribution of audio frequency content. Cough. 2012 Dec 12;8(1):12. doi: 10.1186/1745-9974-8-12.
PubMed 23231789Boulet LP, Coeytaux RR, McCrory DC, French CT, Chang AB, Birring SS, Smith J, Diekemper RL, Rubin B, Irwin RS; CHEST Expert Cough Panel. Tools for assessing outcomes in studies of chronic cough: CHEST guideline and expert panel report. Chest. 2015 Mar;147(3):804-814. doi: 10.1378/chest.14-2506.
PubMed 25522203Bujang MA, Adnan TH. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. J Clin Diagn Res. 2016 Oct;10(10):YE01-YE06. doi: 10.7860/JCDR/2016/18129.8744. Epub 2016 Oct 1.
PubMed 27891446Decalmer SC, Webster D, Kelsall AA, McGuinness K, Woodcock AA, Smith JA. Chronic cough: how do cough reflex sensitivity and subjective assessments correlate with objective cough counts during ambulatory monitoring? Thorax. 2007 Apr;62(4):329-34. doi: 10.1136/thx.2006.067413. Epub 2006 Nov 13.
PubMed 17101736Gabaldon-Figueira JC, Brew J, Dore DH, Umashankar N, Chaccour J, Orrillo V, Tsang LY, Blavia I, Fernandez-Montero A, Bartolome J, Grandjean Lapierre S, Chaccour C. Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study. BMJ Open. 2021 Jul 2;11(7):e051278. doi: 10.1136/bmjopen-2021-051278.
PubMed 34215614Hall JI, Lozano M, Estrada-Petrocelli L, Birring S, Turner R. The present and future of cough counting tools. J Thorac Dis. 2020 Sep;12(9):5207-5223. doi: 10.21037/jtd-2020-icc-003.
PubMed 33145097Matos S, Birring SS, Pavord ID, Evans DH. An automated system for 24-h monitoring of cough frequency: the leicester cough monitor. IEEE Trans Biomed Eng. 2007 Aug;54(8):1472-9. doi: 10.1109/TBME.2007.900811.
PubMed 17694868Park SC, Kang MJ, Han CH, Lee SM, Kim CJ, Lee JM, Kang YA. Prevalence, incidence, and mortality of nontuberculous mycobacterial infection in Korea: a nationwide population-based study. BMC Pulm Med. 2019 Aug 1;19(1):140. doi: 10.1186/s12890-019-0901-z.
PubMed 31370826Porter P, Abeyratne U, Swarnkar V, Tan J, Ng TW, Brisbane JM, Speldewinde D, Choveaux J, Sharan R, Kosasih K, Della P. A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children. Respir Res. 2019 Jun 6;20(1):81. doi: 10.1186/s12931-019-1046-6.
PubMed 31167662Ragonnet R, Trauer JM, Geard N, Scott N, McBryde ES. Profiling Mycobacterium tuberculosis transmission and the resulting disease burden in the five highest tuberculosis burden countries. BMC Med. 2019 Nov 22;17(1):208. doi: 10.1186/s12916-019-1452-0.
PubMed 31752895Sharan RV, Abeyratne UR, Swarnkar VR, Claxton S, Hukins C, Porter P. Predicting spirometry readings using cough sound features and regression. Physiol Meas. 2018 Sep 5;39(9):095001. doi: 10.1088/1361-6579/aad948.
PubMed 30091716Song WJ, Chang YS, Faruqi S, Kang MK, Kim JY, Kang MG, Kim S, Jo EJ, Lee SE, Kim MH, Plevkova J, Park HW, Cho SH, Morice AH. Defining Chronic Cough: A Systematic Review of the Epidemiological Literature. Allergy Asthma Immunol Res. 2016 Mar;8(2):146-55. doi: 10.4168/aair.2016.8.2.146. Epub 2015 Sep 18.
PubMed 26739408Turner RD. Cough in pulmonary tuberculosis: Existing knowledge and general insights. Pulm Pharmacol Ther. 2019 Apr;55:89-94. doi: 10.1016/j.pupt.2019.01.008. Epub 2019 Feb 1.
PubMed 30716411Sanchez-Olivieri I, Rudd M, Gabaldon-Figueira JC, Carmona-Torre F, Del Pozo JL, Moorsmith R, Jover L, Galvosas M, Small P, Grandjean Lapierre S, Chaccour C. Performance evaluation of human cough annotators: optimal metrics and sex differences. BMJ Open Respir Res. 2023 Nov;10(1):e001942. doi: 10.1136/bmjresp-2023-001942.
PubMed 37945314
Study at a glance
From the registry record, last updated by the sponsor on 20 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease
- ClinicalTrials.gov ID
- NCT05042063
- Acronym / sponsor ID
- PI_2021/72
- Conditions
- Cough, COPD, GERD, Asthma, Tuberculosis, Non-Tuberculous Mycobacterial Pneumonia
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 616 participants (actual)
- Lead sponsor
- Clinica Universidad de Navarra, Universidad de NavarraUniversity, hospital or other
- Collaborators
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Hyfe Inc
- Study start
- 15 Sep 2021 (actual)
- Primary completion
- 15 Sep 2022 (actual)
- Study completion
- 15 Sep 2022 (actual)
- First posted
- 13 Sep 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.
This study pretends to evaluate the potential use of Hyfe Cough Tracker (Hyfe) to screen for, diagnose, and support the clinical management of patients with respiratory diseases, while enriching a dataset of disease-specific annotated coughs, for further refinement of similar systems.
Detailed description
This is an observational study that will take place in the two campuses of the Clínica Universidad de Navarra, located in Pamplona and Madrid (Spain).
An Artificial-Intelligence system (AI) that detects and records explosive putative cough sounds and identifies human cough based on acoustic characteristics will be used to automatically monitor cough. Potential participants either attending the outpatient clinic or hospitalised with a complaint of cough will be invited by their treating physician, or a member of the research team, and included in the study by part of the research team. A researcher will instruct participants on how to install and use Hyfe Cough Tracker in their smartphones. Participants will be monitored for 30 days (outpatients) or until discharged from the hospital (inpatients). Participants will be asked to complete a daily, online, standardised 100 mm visual analogue scale (VAS) to register changes in the subjective intensity of their cough, while using Hyfe to objectively monitor changes in its frequency.
In parallel, a dataset of annotated cough sounds will be constructed and retrospectively used to assess differences in acoustic patterns of cough, and to evaluate the performance of the system detecting them.
A first subgroup of participants will be recruited outside the clinical setting and asked to provide a series of elicited sounds, including coughs, which will then be used to determine the system's performance accurately discriminating coughs from non-cough sounds, and compared to trained human listeners.
A second subgroup of participants will be will be instructed to use Hyfe, and the related Hyfe Air wearable device continuously for a period between 6 and 24 hours, while they record themselves using a MP3 recorder connected to a lapel microphone. This group will be used to evaluate the performance of Hyfe and Hyfe Air in a real-life setting, with spontaneous coughs.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Participants with cough as a symptom | – | Device: Hyfe Cough TrackerThis group will be composed of patients at the Clínica Universidad de Navarra that complain of having cough as a remarkable symptom. |
| Validation subgroup 1 | – | Device: Hyfe Cough TrackerThis subgroup will be composed by both, patients belonging to the main study group, as well as voluntaries, who will be asked to provide a series of elicited cough and non-cough sounds for validation purposes. |
| Validation subgroup 2 | – | Device: Hyfe Cough Tracker, Device: Hyfe AirThis subgroup will be composed by inpatients admitted to the Clínica Universidad de Navarra with a diagnosis of respiratory disease, or presenting cough as a symptom, as well as healthy individuals. This group will be monitored with Hyfe Cough Tracker and Hyfe Air for a variable period of 6-24 hours, while they are recorded with a MP3 recorder connected to a lapel microphone. |
Who may qualify
- Ages
- 5 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- For the main study group, the population are patients with respiratory disease treated in the Clínica Universidad de Navarra (Pamplona and Madrid campuses). Since the validation sub-study 1 only requires elicited sounds, a group of participants from a previous study will be directly invited to participate. For the validation sub-study 2, participants will include both, inpatients admitted to the Clínica Universidad de Navarra and presenting cough, and healthy individuals directly invited to participate by the study team.
Key inclusion criteria (6 listed)
- For participants in the main study group
- Outpatient or inpatients at the Clinical Universidad de Navarra with a complaint of cough.
- The patient or his/her legal representative, have given consent to participate in the study.
- Being 18 years or older.
- Providing consent for the sub-study
Key exclusion criteria (3 listed)
- Inability to accept the privacy policy and terms of use of Hyfe.
- Lack of access to a Wi-Fi network at the site of residence (for the main study group).
- Unwillingness to regularly use the cough-surveillance system throughout the monitoring period
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Hyfe Cough TrackerHyfe Cough Tracker is a digital acoustic surveillance system that uses an artificial intelligence system to discriminate cough from non-cough sounds. Hyfe is an AI-enabled mobile app that records short snippets (<0.5 seconds) of putative cough explosive sounds and then classifies them as cough or non-cough using a convolutional neural network (CNN) model. Briefly, the acoustic characteristics of recorded sounds are converted into an image file, which is then processed by an algorithm trained to identify graphical differences in images. This creates an adjustable prediction score, with values above it, resulting in a sound being classified as "cough", and those below being classified as "non-cough. | Device | Participants with cough as a symptom, Validation subgroup 1, Validation subgroup 2 |
| Hyfe AirHyfe Air is a wearable device with an incorporated wireless lapel microphone. The device´s recordings can be run through the same cough-detection algorithm used by Hyfe Cough Tracker, while its results are directly stored in a remote database and are not displayed to participants. | Device | Validation subgroup 2 |
Primary outcome
Correlation between subjective perception of cough and objective frequency
Time frame: 6 months.
The daily VAS score of participants will be compared to the cough frequency registered by the cough surveillance system. These data will be used to fit a linear regression model to compare self-reported VAS scores to daily cough frequency and calculate a correlation coefficient (r).
7 secondary outcomes
Sensitivity of the system discriminating coughs
Time frame: 6 months.
Specificity of the system discriminating coughs
Time frame: 6 months.
Positive predictive value (PPV) of the system discriminating coughs
Time frame: 6 months.
Negative predictive value (NPV) of the system discriminating coughs
Time frame: 6 months.
Construction of an annotated cough dataset
Time frame: 5 years.
Sensitivity of the system differentiating coughs caused by different conditions
Time frame: 5 years.
Specificity of the system differentiating coughs caused by different conditions
Time frame: 5 years.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Nov 2025.
- Background: PubMed 23231789Barton A, Gaydecki P, Holt K, Smith JA. Data reduction for cough studies using distribution of audio frequency content. Cough. 2012 Dec 12;8(1):12. doi: 10.1186/1745-9974-8-12.
- Background: PubMed 25522203Boulet LP, Coeytaux RR, McCrory DC, French CT, Chang AB, Birring SS, Smith J, Diekemper RL, Rubin B, Irwin RS; CHEST Expert Cough Panel. Tools for assessing outcomes in studies of chronic cough: CHEST guideline and expert panel report. Chest. 2015 Mar;147(3):804-814. doi: 10.1378/chest.14-2506.
- Background: PubMed 27891446Bujang MA, Adnan TH. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. J Clin Diagn Res. 2016 Oct;10(10):YE01-YE06. doi: 10.7860/JCDR/2016/18129.8744. Epub 2016 Oct 1.
- Background: PubMed 17101736Decalmer SC, Webster D, Kelsall AA, McGuinness K, Woodcock AA, Smith JA. Chronic cough: how do cough reflex sensitivity and subjective assessments correlate with objective cough counts during ambulatory monitoring? Thorax. 2007 Apr;62(4):329-34. doi: 10.1136/thx.2006.067413. Epub 2006 Nov 13.
- Background: PubMed 34215614Gabaldon-Figueira JC, Brew J, Dore DH, Umashankar N, Chaccour J, Orrillo V, Tsang LY, Blavia I, Fernandez-Montero A, Bartolome J, Grandjean Lapierre S, Chaccour C. Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study. BMJ Open. 2021 Jul 2;11(7):e051278. doi: 10.1136/bmjopen-2021-051278.
- Background: PubMed 33145097Hall JI, Lozano M, Estrada-Petrocelli L, Birring S, Turner R. The present and future of cough counting tools. J Thorac Dis. 2020 Sep;12(9):5207-5223. doi: 10.21037/jtd-2020-icc-003.
- Background: PubMed 17694868Matos S, Birring SS, Pavord ID, Evans DH. An automated system for 24-h monitoring of cough frequency: the leicester cough monitor. IEEE Trans Biomed Eng. 2007 Aug;54(8):1472-9. doi: 10.1109/TBME.2007.900811.
- Background: PubMed 31370826Park SC, Kang MJ, Han CH, Lee SM, Kim CJ, Lee JM, Kang YA. Prevalence, incidence, and mortality of nontuberculous mycobacterial infection in Korea: a nationwide population-based study. BMC Pulm Med. 2019 Aug 1;19(1):140. doi: 10.1186/s12890-019-0901-z.
- Background: PubMed 31167662Porter P, Abeyratne U, Swarnkar V, Tan J, Ng TW, Brisbane JM, Speldewinde D, Choveaux J, Sharan R, Kosasih K, Della P. A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children. Respir Res. 2019 Jun 6;20(1):81. doi: 10.1186/s12931-019-1046-6.
- Background: PubMed 31752895Ragonnet R, Trauer JM, Geard N, Scott N, McBryde ES. Profiling Mycobacterium tuberculosis transmission and the resulting disease burden in the five highest tuberculosis burden countries. BMC Med. 2019 Nov 22;17(1):208. doi: 10.1186/s12916-019-1452-0.
- Background: PubMed 30091716Sharan RV, Abeyratne UR, Swarnkar VR, Claxton S, Hukins C, Porter P. Predicting spirometry readings using cough sound features and regression. Physiol Meas. 2018 Sep 5;39(9):095001. doi: 10.1088/1361-6579/aad948.
- Background: PubMed 26739408Song WJ, Chang YS, Faruqi S, Kang MK, Kim JY, Kang MG, Kim S, Jo EJ, Lee SE, Kim MH, Plevkova J, Park HW, Cho SH, Morice AH. Defining Chronic Cough: A Systematic Review of the Epidemiological Literature. Allergy Asthma Immunol Res. 2016 Mar;8(2):146-55. doi: 10.4168/aair.2016.8.2.146. Epub 2015 Sep 18.
- Individual participant dataSharing planned: Yes. Datasets with anonymized IPD, including cough registries and VAS scores will be shared at the end of the study.
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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. This study is not recruiting at the moment.
Carlos Chaccour, MD, PhD principal investigator
Clinica Universidad de Navarra
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 Cough and COPD, run by Clinica Universidad de Navarra, Universidad de Navarra at 1 site in Spain.
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 6 inclusion and 3 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: finished, as of the sponsor's last update on 20 Nov 2025. 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 Spain. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correlation between subjective perception of cough and objective frequency (time frame: 6 months.). 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 NCT05042063, which also lists 14 linked publications.
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 20 Nov 2025.