Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks

Covid19 Clinical Trial

Official title:

Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04762693
• Other study ID #: DASRD
• Status: Completed
• Start date: November 11, 2020
• Est. completion date: May 24, 2022

Study information

• Verified date: July 2022
• Source: Clinica Universidad de Navarra, Universidad de Navarra
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 930
• Est. completion date: May 24, 2022
• Est. primary completion date: May 23, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 13 Years to 99 Years

Eligibility

Inclusion Criteria:

• 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).

Exclusion Criteria:

• Inability to accept the privacy policy and terms of use of the cough-tracking system.

• Inability to grant access to medical records.

Related Conditions & MeSH terms

• Cough
• COVID-19
• Covid19
• Respiration Disorders
• Respiratory Tract Diseases

Intervention

Device:
• Hyfe cough tracker
A mobile app that runs in the background of smartphones and detects putative cough sounds.

Locations

Country	Name	City	State
Spain	Universidad de Navarra	Pamplona	Navarra

Sponsors (2)

Lead Sponsor	Collaborator
Clinica Universidad de Navarra, Universidad de Navarra	Centre de Recherche du Centre Hospitalier de l'Université de Montréal

Country where clinical trial is conducted

Spain, 

References & Publications (11)

Chowdhury 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. — View Citation

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. Erratum in: Lancet. 2020 Jan 30;:. — View Citation

Kim 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. — View Citation

Liss 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. — View Citation

Mukundarajan 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. pii: e27854. doi: 10.7554/eLife.27854. — View Citation

Naseem 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. Review. — View Citation

Peeling 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. Review. — View Citation

Porter 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. — View Citation

Rasheed 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. Review. — View Citation

Santillana 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. — View Citation

Sharan 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. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	1 year
Secondary	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.	1 year
Secondary	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?	1 month