Research QuestionWhat is the impact of the duration of cough monitoring on its accuracy in detecting changes in the cough frequency?Materials and MethodsThis is a statistical analysis of a prospective cohort study. Participants were recruited in the city of Pamplona (Northern Spain) and their cough frequency was passively monitored using smartphone-based acoustic artificial intelligence software. Differences in cough frequency were compared using a one-tailed Mann-Whitney U test and a randomisation routine to simulate 24-h monitoring.Results616 participants were monitored for an aggregated duration of over 9 person-years and registered 62 325 coughs. This empiric analysis found that an individual's cough patterns are stochastic, following a binomial distribution. When compared to continuous monitoring, limiting observation to 24 h can lead to inaccurate estimates of change in cough frequency, particularly in persons with low or small changes in rate.InterpretationDetecting changes in an individual's rate of coughing is complicated by significant stochastic variability within and between days. Assessing change based solely on intermittent sampling, including 24-h, can be misleading. This is particularly problematic in detecting small changes in individuals who have a low rate and/or high variance in cough pattern.
Background: Emerging technologies to remotely monitor patients’ cough show promise for various clinical applications. Currently available cough detection systems all represent a trade-off between convenience and performance. The accuracy of such technologies is highly contingent on the clinical settings in which they are intended to be used. Moreover, establishing gold standards to measure this accuracy is challenging. Objectives: We present the first performance evaluation study of the Hyfe Cough Tracker app, a passive cough monitoring smartphone application. We evaluate performance for cough detection using continuous audio recordings and cough counting by trained individuals as the gold standard. We propose standard procedures to use multi-observer cough sound annotation from continuous audio recordings as the gold standard for evaluating automated cough detection devices. Methods: This study was embedded in a larger digital acoustic surveillance study (clinicaltrial.gov NCT04762693). Forty-nine participants were included and instructed to produce a diverse series of solicited sounds in 10-minute sessions. Simultaneously, continuous audio recording was performed using a MP3 recorder and two smartphones running Hyfe Cough Tracker app monitored and identified cough events. All continuous audio recordings were independently labeled by three medically-trained researchers. Results: Hyfe Cough Tracker app showed sensitivity of 91% and specificity of 98% with a very high correlation between the cough rate measured by Hyfe and that of human annotators (Pearson correlation of 0.968). A standardized approach to establish an acoustic gold standard for identifying cough sounds with multiple observers is presented. Conclusion: This is the first performance evaluation of a new smartphone-based cough monitoring system. Hyfe Cough Tracker can detect, record and count coughs from solicited cough-like explosive sounds in controlled acoustic environments with very high accuracy. Additional steps are required to validate the system in clinical and community settings.
Background: Emerging technologies to remotely monitor patients’ cough show promise for various clinical applications. Currently available cough detection systems all represent a trade-off between convenience and performance. The accuracy of such technologies is highly contingent on the clinical settings in which they are intended to be used. Moreover, establishing gold standards to measure this accuracy is challenging. Objectives: We present the first performance evaluation study of the Hyfe Cough Tracker app, a passive cough monitoring smartphone application. We evaluate performance for cough detection using continuous audio recordings obtained within a controlled environment and cough counting by trained individuals as the gold standard. We propose standard procedures to use multi-observer cough sound annotation from continuous audio recordings as the gold standard for evaluating automated cough detection devices. Methods: This study was embedded in a larger digital acoustic surveillance study (clinicaltrial.gov NCT04762693). Forty-nine participants were included and instructed to produce a diverse series of solicited sounds in 10-minute sessions. Simultaneously, continuous audio recording was performed using a MP3 recorder and two smartphones running Hyfe Cough Tracker app monitored and identified cough events. All continuous audio recordings were independently labeled by three medically-trained researchers. Results: Hyfe Cough Tracker app showed sensitivity of 91% and specificity of 98% with a very high correlation between the cough rate measured by Hyfe and that of human annotators (Pearson correlation of 0.968). A standardized approach to establish an acoustic gold standard for identifying cough sounds with multiple observers is presented. Conclusion: This is the first performance evaluation of a new smartphone-based cough monitoring system. Hyfe Cough Tracker can detect, record and count coughs from solicited cough-like explosive sounds in controlled acoustic environments with very high accuracy. Additional steps are required to validate the system in clinical and community settings.
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