Covid19NCT04762693
Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks
An observational study of Covid19 and Cough, run by Clinica Universidad de Navarra, Universidad de Navarra at 1 site in Spain.
Trial summary
An observational study to evaluate the accuracy of a digital cough monitoring tool to reflect the incidence of COVID-19 and other respiratory infections at the community level in the city of Pamplona, Spain. Read more about this study
- Who is it for?
- Ages 13 to 99 yearsHealthy volunteers can take part
- What is being tested?
- Hyfe cough tracker
- Type of treatment
- Medical device
- How many people
- 930 participants
- Where
- 1 siteSpain
- Study dates
- 11 Nov 2020to 24 May 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 13 to 99 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Participants will be recruited in the Cendea de Cizur, a municipality composed by a cluster of villages south of the city of Pamplona, and the neighbouring town of Cizur Mayor, in the Comunidad Foral de Navarra (Spain), as well as in the different campuses of the University of Navarra. The 4,000 people living in the Cendea de Cizur are served by a public health center which receives 45,000 outpatients visits per year. Of these, approximately 12% are associated with respiratory diseases. The University of Navarra has over 11,000 registered students, 900 professors and over 600 other employees. Both the Cendea de Cizur and the University are served by the Clínica Universidad de Navarra, the largest private health center in the region, which provides medical care to an estimated population of over 100,000 people.
Who may be able to join
- Be aged 13 or above,
- Own and regularly use a smartphone able to run the cough-tracking system,
- Be willing to install and regularly use it,
- Be current residents of Navarra, and
- Have an active relationship with the university (having interest in the study, or being a student or worker, be a patient with a cough-related diagnosis at the Clínica Universidad de Navarra, or Cizur's health centre).
Who cannot join
- Inability to accept the privacy policy and terms of use of the cough-tracking system.
- Inability to grant access to medical records.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain
An observational study to evaluate the accuracy of a digital cough monitoring tool to reflect the incidence of COVID-19 and other respiratory infections at the community level in the city of Pamplona, Spain.
Read the detailed description
This is a single-center prospective observational study that pretends to evaluate the accuracy of an acoustic surveillance mobile app to detect individual episodes of cough among a monitored population, as well as the barriers and facilitators that might affect uptake of similar platforms at a population level.
The app in question, Hyfe cough tracker, runs in the background of smartphones, and records short snippets (<0.5 seconds) of explosive, putative cough sounds. These are then classified as cough or non-cough, using a convolutional neural network (CNN) model, and matched to GPS and time data collected by the smartphone.
The night-time cough of participants will be monitored for a 30-day period, and their clinical records will be reviewed regularly, specifically looking for diagnoses of cough-producing diseases, and with special emphasis on COVID-19.
Cough data will be used to create a heatmap of cough density and geographic distribution. Aggregated cough registries will be used to calculate the coughs per person-hour registered in the cohort. These data will be used to carry out an ARIMA analysis on three parallel time series at the community level: The incidence of respiratory disease in the monitored cohort, in the entire study area (including the Universidad de Navarra, and the neighbouring Cendea de Cizur), and the cough frequency per monitored hours.
Changes in cough frequency will also be compared to other environmental variables such as temperature and pollution level registered in the study area.
- Being tested
- Hyfe cough tracker
- Keywords
- chronic cough, Covid19, Acoustic surveillance
- Medical subject headings
- COVID-19, Cough, Chronic Cough
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Hyfe cough trackerDevice
A mobile app that runs in the background of smartphones and detects putative cough sounds.
| Study group | Role | What they receive |
|---|---|---|
| Cough monitoring | All enrolled participants will be asked to install the acoustic surveillance software in their smartphones and use it to record night-time coughs for a minimum 30-day period. Receives: Device: Hyfe cough tracker |
What is being measured
The primary outcome is the main question the study is designed to answer.
Correlation between registered coughs per person-hour and incidence of respiratory diseases
The investigators will run an ARIMA analysis with three parallel time series: aggregated incidence of respiratory diseases in the observed cohort, in the entire study area, and aggregated cough data.
Measured: 1 year
Secondary outcomes (2)
Uptake of the surveillance system
The investigators will calculate the proportion of the total reached, eligible population that installs the app and regularly uses it in the requested way.
Measured: 1 year
Barriers and facilitators affecting uptake of the surveillance system
A sub-sample of 25 participants will be randomly recruited for focus group discussions. In the focus groups, researchers will ask participants the following general questions: (1) What do you like about the app, (2) what do you think of this app relative to other health apps, (3) what doesn't work well, (4) what keeps you committed (or not) to using the app, (5) what do you think the purpose of the app is, (6) do you think the app has commercial value, and (7) what advice do you have for the developers?
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2021
- Study start11 Nov 2020
- Primary completion23 May 2022
- Study completion24 May 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Covid19Studies 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
- 930 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
- Responsible party
- Sponsor
- Investigators
- Carlos Chaccour, Principal investigator, Clinica Universidad de Navarra, Universidad de Navarra
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04762693
- Sponsor's protocol ID
- DASRD
- Status last verified
- Jul 2022
- First submitted
- 16 Feb 2021
- Last updated
- 20 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb 15;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5. Epub 2020 Jan 24.
PubMed 31986264Kim GU, Kim MJ, Ra SH, Lee J, Bae S, Jung J, Kim SH. Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19. Clin Microbiol Infect. 2020 Jul;26(7):948.e1-948.e3. doi: 10.1016/j.cmi.2020.04.040. Epub 2020 May 1.
PubMed 32360780Peeling RW, Wedderburn CJ, Garcia PJ, Boeras D, Fongwen N, Nkengasong J, Sall A, Tanuri A, Heymann DL. Serology testing in the COVID-19 pandemic response. Lancet Infect Dis. 2020 Sep;20(9):e245-e249. doi: 10.1016/S1473-3099(20)30517-X. Epub 2020 Jul 17.
PubMed 32687805Chowdhury R, Luhar S, Khan N, Choudhury SR, Matin I, Franco OH. Long-term strategies to control COVID-19 in low and middle-income countries: an options overview of community-based, non-pharmacological interventions. Eur J Epidemiol. 2020 Aug;35(8):743-748. doi: 10.1007/s10654-020-00660-1. Epub 2020 Jul 13.
PubMed 32656618Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA, Draheim D. A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Solitons Fractals. 2020 Dec;141:110337. doi: 10.1016/j.chaos.2020.110337. Epub 2020 Oct 10.
PubMed 33071481Porter 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 31167662Sharan 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 30091716Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I, Brownstein JS. Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance. Sci Rep. 2016 May 11;6:25732. doi: 10.1038/srep25732.
PubMed 27165494Mukundarajan H, Hol FJH, Castillo EA, Newby C, Prakash M. Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. Elife. 2017 Oct 31;6:e27854. doi: 10.7554/eLife.27854.
PubMed 29087296Naseem M, Akhund R, Arshad H, Ibrahim MT. Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review. J Prim Care Community Health. 2020 Jan-Dec;11:2150132720963634. doi: 10.1177/2150132720963634.
PubMed 32996368Liss DT, Serrano E, Wakeman J, Nowicki C, Buchanan DR, Cesan A, Brown T. "The Doctor Needs to Know": Acceptability of Smartphone Location Tracking for Care Coordination. JMIR Mhealth Uhealth. 2018 May 4;6(5):e112. doi: 10.2196/mhealth.9726.
PubMed 29728349Galvosas M, Gabaldon-Figueira JC, Keen EM, Orrillo V, Blavia I, Chaccour J, Small PM, Gimenez G, Rudd M, Grandjean Lapierre S, Chaccour C. Performance evaluation of the smartphone-based AI cough monitoring app - Hyfe Cough Tracker against solicited respiratory sounds. F1000Res. 2023 Jun 9;11:730. doi: 10.12688/f1000research.122597.2. eCollection 2022.
PubMed 39931660Gabaldon-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 34215614
Study at a glance
From the registry record, last updated by the sponsor on 20 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain
- ClinicalTrials.gov ID
- NCT04762693
- Acronym / sponsor ID
- DASRD
- Keywords
- chronic cough, Covid19, Acoustic surveillance
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 930 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
- Study start
- 11 Nov 2020 (actual)
- Primary completion
- 23 May 2022 (actual)
- Study completion
- 24 May 2022 (actual)
- First posted
- 21 Feb 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.
An observational study to evaluate the accuracy of a digital cough monitoring tool to reflect the incidence of COVID-19 and other respiratory infections at the community level in the city of Pamplona, Spain.
Detailed description
This is a single-center prospective observational study that pretends to evaluate the accuracy of an acoustic surveillance mobile app to detect individual episodes of cough among a monitored population, as well as the barriers and facilitators that might affect uptake of similar platforms at a population level.
The app in question, Hyfe cough tracker, runs in the background of smartphones, and records short snippets (<0.5 seconds) of explosive, putative cough sounds. These are then classified as cough or non-cough, using a convolutional neural network (CNN) model, and matched to GPS and time data collected by the smartphone.
The night-time cough of participants will be monitored for a 30-day period, and their clinical records will be reviewed regularly, specifically looking for diagnoses of cough-producing diseases, and with special emphasis on COVID-19.
Cough data will be used to create a heatmap of cough density and geographic distribution. Aggregated cough registries will be used to calculate the coughs per person-hour registered in the cohort. These data will be used to carry out an ARIMA analysis on three parallel time series at the community level: The incidence of respiratory disease in the monitored cohort, in the entire study area (including the Universidad de Navarra, and the neighbouring Cendea de Cizur), and the cough frequency per monitored hours.
Changes in cough frequency will also be compared to other environmental variables such as temperature and pollution level registered in the study area.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cough monitoring | – | Device: Hyfe cough trackerAll enrolled participants will be asked to install the acoustic surveillance software in their smartphones and use it to record night-time coughs for a minimum 30-day period. |
Who may qualify
- Ages
- 13 to 99 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Participants will be recruited in the Cendea de Cizur, a municipality composed by a cluster of villages south of the city of Pamplona, and the neighbouring town of Cizur Mayor, in the Comunidad Foral de Navarra (Spain), as well as in the different campuses of the University of Navarra. The 4,000 people living in the Cendea de Cizur are served by a public health center which receives 45,000 outpatients visits per year. Of these, approximately 12% are associated with respiratory diseases. The University of Navarra has over 11,000 registered students, 900 professors and over 600 other employees. Both the Cendea de Cizur and the University are served by the Clínica Universidad de Navarra, the largest private health center in the region, which provides medical care to an estimated population of over 100,000 people.
Key inclusion criteria (5 listed)
- Be aged 13 or above,
- Own and regularly use a smartphone able to run the cough-tracking system,
- Be willing to install and regularly use it,
- Be current residents of Navarra, and
- Have an active relationship with the university (having interest in the study, or being a student or worker, be a patient with a cough-related diagnosis at the Clínica Universidad de Navarra, or Cizur's health centre).
Key exclusion criteria (2 listed)
- Inability to accept the privacy policy and terms of use of the cough-tracking system.
- Inability to grant access to medical records.
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 trackerA mobile app that runs in the background of smartphones and detects putative cough sounds. | Device | Cough monitoring |
Primary outcome
Correlation between registered coughs per person-hour and incidence of respiratory diseases
Time frame: 1 year
The investigators will run an ARIMA analysis with three parallel time series: aggregated incidence of respiratory diseases in the observed cohort, in the entire study area, and aggregated cough data.
2 secondary outcomes
Uptake of the surveillance system
Time frame: 1 year
Barriers and facilitators affecting uptake of the surveillance system
Time frame: 1 month
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 Jul 2022.
- Background: PubMed 31986264Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb 15;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5. Epub 2020 Jan 24.
- Background: PubMed 32360780Kim GU, Kim MJ, Ra SH, Lee J, Bae S, Jung J, Kim SH. Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19. Clin Microbiol Infect. 2020 Jul;26(7):948.e1-948.e3. doi: 10.1016/j.cmi.2020.04.040. Epub 2020 May 1.
- Background: PubMed 32687805Peeling RW, Wedderburn CJ, Garcia PJ, Boeras D, Fongwen N, Nkengasong J, Sall A, Tanuri A, Heymann DL. Serology testing in the COVID-19 pandemic response. Lancet Infect Dis. 2020 Sep;20(9):e245-e249. doi: 10.1016/S1473-3099(20)30517-X. Epub 2020 Jul 17.
- Background: PubMed 32656618Chowdhury R, Luhar S, Khan N, Choudhury SR, Matin I, Franco OH. Long-term strategies to control COVID-19 in low and middle-income countries: an options overview of community-based, non-pharmacological interventions. Eur J Epidemiol. 2020 Aug;35(8):743-748. doi: 10.1007/s10654-020-00660-1. Epub 2020 Jul 13.
- Background: PubMed 33071481Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA, Draheim D. A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Solitons Fractals. 2020 Dec;141:110337. doi: 10.1016/j.chaos.2020.110337. Epub 2020 Oct 10.
- 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 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 27165494Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I, Brownstein JS. Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance. Sci Rep. 2016 May 11;6:25732. doi: 10.1038/srep25732.
- Background: PubMed 29087296Mukundarajan H, Hol FJH, Castillo EA, Newby C, Prakash M. Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. Elife. 2017 Oct 31;6:e27854. doi: 10.7554/eLife.27854.
- Background: PubMed 32996368Naseem M, Akhund R, Arshad H, Ibrahim MT. Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review. J Prim Care Community Health. 2020 Jan-Dec;11:2150132720963634. doi: 10.1177/2150132720963634.
- Background: PubMed 29728349Liss DT, Serrano E, Wakeman J, Nowicki C, Buchanan DR, Cesan A, Brown T. "The Doctor Needs to Know": Acceptability of Smartphone Location Tracking for Care Coordination. JMIR Mhealth Uhealth. 2018 May 4;6(5):e112. doi: 10.2196/mhealth.9726.
- Derived from this study: PubMed 39931660Galvosas M, Gabaldon-Figueira JC, Keen EM, Orrillo V, Blavia I, Chaccour J, Small PM, Gimenez G, Rudd M, Grandjean Lapierre S, Chaccour C. Performance evaluation of the smartphone-based AI cough monitoring app - Hyfe Cough Tracker against solicited respiratory sounds. F1000Res. 2023 Jun 9;11:730. doi: 10.12688/f1000research.122597.2. eCollection 2022.
- Individual participant dataSharing planned: Undecided
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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 principal investigator
Clinica Universidad de Navarra, 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 Covid19 and Cough, 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 5 inclusion and 2 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 Jul 2022. 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 registered coughs per person-hour and incidence of respiratory diseases (time frame: 1 year). 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 NCT04762693, which also lists 13 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 Jul 2022.