ArticleERJ open research2022
Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence.
Article in ERJ open research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07212439 (Continuous Cough Monitoring for Early Detection of COPD Exacerbations Using Hyfe), which is not on this map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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.
Continuous Cough Monitoring for Early Detection of COPD Exacerbations Using Hyfe
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Evaluating smartphone-based cough frequency monitoring for tuberculosis screening and triage in Uganda: A mixed-methods evaluation.PLOS global public health · 2026Article
- The value of continuous cough monitoring: a narrative review.Journal of thoracic disease · 2025Review
- A cross sectional feasibility study to evaluate the usability and efficacy of Swaasa AI platform for rapid respiratory health assessment.Scientific reports · 2025Article
- Asthma Symptom Self-Monitoring Methods for Children and Adolescents: Present and Future.Children (Basel, Switzerland) · 2025Review
- Continuous digital cough monitoring during 6-month pulmonary tuberculosis treatment.ERJ open research · 2025Article
- An accelerometry and gyroscopy-based system for detecting swallowing and coughing events.Journal of clinical monitoring and computing · 2025Article
- Validation and accuracy of the Hyfe cough monitoring system: a multicenter clinical study.Scientific reports · 2025Observational
- Patient-reported assessments of chronic cough in clinical trials: accessory or primary endpoints?Journal of thoracic disease · 2024Review
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- Performance evaluation of human cough annotators: optimal metrics and sex differences.BMJ open respiratory research · 2023Article
- Measuring cough: what really matters?Journal of thoracic disease · 2023Review
- Article
- Design of a Tool to Classify Chronic Cough According to its Severity: Cough Severity Questionnaire (CSQ).Open respiratory archivesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research question: Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods: This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results: We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs·h Interpretation: Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring.
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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.