Chronic cough is a debilitating condition that affects quality of life and often signals underlying respiratory disease. Clinical studies currently rely on audio recordings reviewed by human analysists to objectively quantify cough frequency, but this process is time-consuming and limits long-term monitoring. Emerging automated cough measurement tools enable multi-day measurements and therefore offer a more accurate assessment of a patient’s actual cough burden. At the same time, they are expected to show varying performance across patients. The tradeoff between algorithmic accuracy and the number of measurement days required to obtain a valid estimate of an individual’s objective cough burden has not previously been explored. In this study, we aimed to validate the performance of our proprietary cough detection algorithm, part of an automated cough monitoring system, in patients with chronic cough. We also investigated how algorithm performance and daily variability in cough frequency affect the optimal number of monitoring days. The algorithm was evaluated under real-life conditions in 51 patients with chronic cough, achieving a median sensitivity of 0.93 and a median precision of 0.94. By using a confidence score to identify unreliable data and applying bootstrapping simulations to model variability, we found that the largest gains in measuring true cough burden were achieved with three days of monitoring, with diminishing returns beyond seven days. In conclusion, because cough frequency fluctuates daily, automated cough counting tools can enhance human-annotated data by enabling multi-day monitoring to more accurately capture true cough burden.

MA, LK, AD, and MH were employees of SIVA Health AG during the planning, execution, and analysis of the study. MA, AD, and MH hold equity in SIVA Health AG. AM and SB hold stock options in SIVA Health AG. DE declares no competing interests.

This study was funded by SIVA Health AG, which also contributed to the study design and data analysis. The sponsor had no role in patient recruitment and clinical data collection.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of Advarra, Inc. gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data are not publicly available because they contain sensitive patient information. Individual, de-identified participant cough data gathered in the clinical trial reported in this article may be made available to qualified researchers upon reasonable request. Data requestors will be required to sign a data-sharing agreement prior to access.