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Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study

INTRODUCTION: Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the...

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Autores principales: Gabaldon-Figueira, Juan Carlos, Brew, Joe, Doré, Dominique Hélène, Umashankar, Nita, Chaccour, Juliane, Orrillo, Virginia, Tsang, Lai Yu, Blavia, Isabel, Fernández-Montero, Alejandro, Bartolomé, Javier, Grandjean Lapierre, Simon, Chaccour, C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257291/
https://www.ncbi.nlm.nih.gov/pubmed/34215614
http://dx.doi.org/10.1136/bmjopen-2021-051278
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author Gabaldon-Figueira, Juan Carlos
Brew, Joe
Doré, Dominique Hélène
Umashankar, Nita
Chaccour, Juliane
Orrillo, Virginia
Tsang, Lai Yu
Blavia, Isabel
Fernández-Montero, Alejandro
Bartolomé, Javier
Grandjean Lapierre, Simon
Chaccour, C
author_facet Gabaldon-Figueira, Juan Carlos
Brew, Joe
Doré, Dominique Hélène
Umashankar, Nita
Chaccour, Juliane
Orrillo, Virginia
Tsang, Lai Yu
Blavia, Isabel
Fernández-Montero, Alejandro
Bartolomé, Javier
Grandjean Lapierre, Simon
Chaccour, C
author_sort Gabaldon-Figueira, Juan Carlos
collection PubMed
description INTRODUCTION: Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms. METHODS AND ANALYSIS: Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions. ETHICS AND DISSEMINATION: Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l’Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences. TRIAL REGISTRATION NUMBER: NCT04762693.
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spelling pubmed-82572912021-07-09 Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study Gabaldon-Figueira, Juan Carlos Brew, Joe Doré, Dominique Hélène Umashankar, Nita Chaccour, Juliane Orrillo, Virginia Tsang, Lai Yu Blavia, Isabel Fernández-Montero, Alejandro Bartolomé, Javier Grandjean Lapierre, Simon Chaccour, C BMJ Open Respiratory Medicine INTRODUCTION: Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms. METHODS AND ANALYSIS: Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions. ETHICS AND DISSEMINATION: Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l’Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences. TRIAL REGISTRATION NUMBER: NCT04762693. BMJ Publishing Group 2021-07-02 /pmc/articles/PMC8257291/ /pubmed/34215614 http://dx.doi.org/10.1136/bmjopen-2021-051278 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Respiratory Medicine
Gabaldon-Figueira, Juan Carlos
Brew, Joe
Doré, Dominique Hélène
Umashankar, Nita
Chaccour, Juliane
Orrillo, Virginia
Tsang, Lai Yu
Blavia, Isabel
Fernández-Montero, Alejandro
Bartolomé, Javier
Grandjean Lapierre, Simon
Chaccour, C
Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
title Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
title_full Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
title_fullStr Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
title_full_unstemmed Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
title_short Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
title_sort digital acoustic surveillance for early detection of respiratory disease outbreaks in spain: a protocol for an observational study
topic Respiratory Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257291/
https://www.ncbi.nlm.nih.gov/pubmed/34215614
http://dx.doi.org/10.1136/bmjopen-2021-051278
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