Accuracy of a Smartphone Application Measuring Snoring in Adults—How Smart Is It Actually?

Abstract

About 40% of the adult population is affected by snoring, which is closely related to obstructive sleep apnea (OSA) and can be associated with serious health implications. Commercial smartphone applications (apps) offer the possibility of monitoring snoring at home. However, the number of validation studies addressing snoring apps is limited. The purpose of the present study was to assess the accuracy of recorded snoring using the free version of the app SnoreLab (Reviva Softworks Ltd., London, United Kingdom) in comparison to a full-night polygraphic measurement (Miniscreen plus, Löwenstein Medical GmbH & Co., KG, Bad Ems, Germany). Nineteen healthy adult volunteers (4 female, 15 male, mean age: 38.9 ± 19.4 years) underwent simultaneous polygraphic and SnoreLab app measurement for one night at home. Parameters obtained by the SnoreLab app were: starting/ending time of monitoring, time in bed, duration and percent of quiet sleep, light, loud and epic snoring, total snoring time and Snore Score, a specific score obtained by the SnoreLab app. Data obtained from polygraphy were: starting/ending time of monitoring, time in bed, total snoring time, snore index (SI), snore index obstructive (SI obstructive) and apnea-hypopnea-index (AHI). For different thresholds of percentage snoring per night, accuracy, sensitivity, specificity, positive and negative predictive values were calculated. Comparison of methods was undertaken by Spearman-Rho correlations and Bland-Altman plots. The SnoreLab app provides acceptable accuracy values measuring snoring >50% per night: 94.7% accuracy, 100% sensitivity, 94.1% specificity, 66.6% positive prediction value and 100% negative prediction value. Best agreement between both methods was achieved in comparing the sum of loud and epic snoring ratios obtained by the SnoreLab app with the total snoring ratio measured by polygraphy. Obstructive events could not be detected by the SnoreLab app. Compared to polygraphy, the SnoreLab app provides acceptable accuracy values regarding the measurement of especially heavy snoring.

Effect of myofunctional therapy on snoring in obese patients: a randomized trial

“…The SnoreLab app provided acceptable accuracy values for the measurement of snoring, with the percentage of total snoring assessed by SnoreLab being highly correlated with the proportion of snoring measured by polygraphy. The best agreement between the two methods was achieved when the sum of the loud and epic snoring rates obtained with the SnoreLab app was compared to the total snoring rate measured with the polygraph 25 .…”

Snoring Patterns During Hypoglossal Nerve Stimulation Therapy Up‐Titration

“…Studies have shown varying accuracies of SnoreLab compared to gold standard polygraphy at different percentages of total snoring. 8 Snoring data accrual was also dependent on the participant to activate the app prior to sleep and participants were able to visualize their nightly snoring metrics. Because of inconsistent app use prior to HNS activation, we used the initial 7 days of app data after implant initiation as the baseline.…”

“…Compared to measurements obtained by polysomnography (PSG) in a sample of 19 patients, SnoreLab provided accurate snoring detection ranging from 63% to 95%. 8 This pilot study aims to measure snoring changes during HNS therapy up-titration and demonstrate the use of a smartphone application, SnoreLab, to measure longitudinal snoring changes. This case series examines the feasibility of using snore sound tracking in association with HNS therapy uptitration and sleep apnea treatment.…”

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta‐analysis of diagnostic test accuracy

“…Their technology is simple, especially for the apps that have no intention to diagnose or correlate it with OSA severity, as their functions are usually restricted to the use of a microphone. The apps using a microphone as the main sensor appear to perform well in the detection of snoring and provide stable data with overall good accuracy (Camacho et al, 2015; Chiang et al, 2022; Figueras‐Alvarez et al, 2020; Klaus, Stummer, & Ruf, 2021). However, the specificity might be low in a real‐world scenario, as the apps might confound snoring from the bed partner, other respiratory sounds from the user, and background noise with actual snoring sounds from the user (Camacho et al, 2015; Stippig, Hübers, & Emerich, 2015).…”