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

Abstract

IntroductionCough 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 analysisParticipants 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 disseminationEthics 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 numberNCT04762693.

Longitudinal passive cough monitoring and its implications for detecting changes in clinical status

“…Another limitation of this study is that it was conducted with a cough monitoring tool that has not been fully validated. Although Hyfe's performance was analysed in a pilot stage of the study [ 14 ], its sensitivity and specificity for detecting cough in different practical settings are only now being formally quantified (NCT05042063). It is, however, reassuring that it did detect changes in those patients who reported changes, and found a diurnal pattern consistent with prior publications [ 17 ].…”

“…Recruitment strategies included a broad information campaign carried out via social networks and through face-to-face meetings, with support from university and municipal authorities. A full protocol describing inclusion criteria has been published [ 14 ]. All participants signed informed consent, and this project was reviewed and approved by the Ethics Committee for Medical Research of the Chartered Community of Navarra (PI2020/107) and the Comité d’éthique à la recherche du Centre Hospitalier de l'Université de Montréal (2021-9247).…”

“…This is an empiric statistical analysis of a larger cohort acoustic surveillance study. Sample size calculations and the study's main results have been described elsewhere [ 13 , 14 ].…”

“…The CNN assigns a cough prediction score to each sound; if such a score lies above a predetermined value (in this study, 0.70 out of 1.0), the sound is labelled as a cough. The analytical performance of the algorithm was evaluated in a pilot sub-study that reported a sensitivity of 96.34% and a specificity of 96.54% among detected sounds [ 14 ], and is better characterised in a nested study of solicited sounds from 49 participants submitted jointly with this article. Additional clinical validation in specific diseases is underway (NCT05042063).…”

Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence

“…These patterns can be identified by a wide range of machine learning techniques, such as convolutional neural networks [ 6 , 9 ]. The widespread use of smartphones provides an opportunity to deploy these systems at the scale needed to construct a community-based surveillance network [ 10 , 11 ].…”