Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation.

ABSTRACT:

Background

High-quality sleep is essential for both physical and mental well-being. Insufficient or poor-quality sleep is linked to numerous health issues, including cardiometabolic diseases, mental health disorders, and increased mortality. Snoring-a prevalent condition-can disrupt sleep and is associated with disease states, including coronary artery disease and obstructive sleep apnea.

Objective

The SleepWatch smartphone app (Bodymatter, Inc) aims to monitor and improve sleep quality and has snore detection capabilities that were built through a machine-learning process trained on over 60,000 acoustic events. This study evaluated the accuracy of the SleepWatch snore detection algorithm in a simulated real-world setting.

Methods

The snore detection algorithm was tes

SUBMITTER: Brown J 

PROVIDER: S-EPMC11970566 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature