ArticleBMJ open2021
Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain
Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease
Clinical Validation of the Hyfe Cough Monitoring System
Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.Scientific reports · 2026Article
- Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds.Scientific reports · 2026Article
- Real-Time Acoustic Detection of Critical Incidents in Smart Cities Using Artificial Intelligence and Edge Networks.Sensors (Basel, Switzerland) · 2025Article
- Continuous digital cough monitoring during 6-month pulmonary tuberculosis treatment.ERJ open research · 2025Article
- A dataset of Solicited Cough Sound for Tuberculosis Triage Testing.Scientific data · 2024Article
- Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Performance evaluation of human cough annotators: optimal metrics and sex differences.BMJ open respiratory research · 2023Article
- Article
- Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence.ERJ open research · 2022Article
- Longitudinal passive cough monitoring and its implications for detecting changes in clinical status.ERJ open research · 2022Article
- Making cough count in tuberculosis care.Communications medicine · 2022Review
- Drug resistant tuberculosis: Implications for transmission, diagnosis, and disease management.Frontiers in cellular and infection microbiology · 2022Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 9 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
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 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.