Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study (Preprint)

Wu, Robert; Liaqat, Daniyal; Lara, Eyal de; Son, Tatiana; Rudzicz, Frank; Alshaer, Hisham; Abed-Esfahani, Pegah; Gershon, Andrea S.

doi:10.2196/preprints.10046

Cited by 1 publication

(1 citation statement)

References 16 publications

(20 reference statements)

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“…In the past, device monitoring in clinical trials has led to the loss of participants over privacy concerns [14]. Our efforts to mitigate these concerns will improve the user experience, yet we anticipate that there may be limits to the sense of security we instill.…”

Section: Discussionmentioning

confidence: 99%

Continuous Sound Collection Using Smartphones and Machine Learning to Measure Cough

Kvapilova¹,

Boža²,

Dubec³

et al. 2019

Digit Biomark

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Background: Despite the efforts of research groups to develop and implement at least partial automation, cough counting remains impractical. Analysis of 24-h cough frequency is an established regulatory endpoint which, if addressed in an automated manner, has the potential to ease cough symptom evaluation over multiple 24-h periods in a patient-centric way, supporting the development of novel treatments for chronic cough, an unmet clinical need. Objectives: In light of recent technological advancements, we propose a system based on the use of smartphones for objective continuous sound collection, suitable for automated cough detection and analysis. Two capabilities were identified as necessary for naturalistic cough assessment: (1) recording sound in a continuous manner (sound collection), and (2) detection of coughs from the recorded sound (cough detection). Methods: This work did not involve any human subject testing or trials. For sound collection, we designed, built, and verified technical parameters of a smartphone application for sound collection. Our cough detection work describes the development of a mathematical model for sound analysis and cough identification. Performance of the model was compared to previously published results of commercially available solutions and to human raters. The compared solutions use the following methods to automatically or semi-automatically assess cough: 24-h sound recording with an ambulatory device with multiple microphones, automatic silence removal, and manual recording review for cough count. Results: Sound collection: the application demonstrated the ability to continuously record sounds using the phone’s internal microphone; the technical verification informed the configuration of the technical and user experience parameters. Cough detection: our cough recognition sensitivity to cough as determined by human listeners was 90 at 99.5% specificity preset and 75 at 99.9% specificity preset for a dataset created from publicly available data. Conclusions: Sound collection: the application reliably collects sound data and uploads them securely to a remote server for subsequent analysis; the developed sound data collection application is a critical first step toward future incorporation in clinical trials. Cough detection: initial experiments with cough detection techniques yielded encouraging results for application to patient-collected data from future studies.

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Section: Discussionmentioning

confidence: 99%