Snoring intensity assessment with three different smartphones using the SnoreLab application in one participant

Figueras-Álvarez, Óscar; Cantó-Navés, Oriol; Cabratosa-Termes, Josep; Roig-Cayón, Miguel; Felipe-Spada, Natalia; Tomàs-Aliberas, Jordi

doi:10.5664/jcsm.8676

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…were tested and had partially positive results in scientific studies that were not randomized controlled clinical trials. 17 18 19 Table 3 offers a more detailed description of the main objectives of the applications.…”

Section: Resultsmentioning

confidence: 99%

Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil

Lemes,

Triches,

Carvalho

et al. 2024

Sleep Sci

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Introduction Decreased sleep time is detrimental to physical and mental health, exerting negative impacts on daily functioning and quality of life. Mobile health (mHealth) applications can be useful for improving sleep quality. Objective To perform a systematic search of mHealth applications for monitoring sleep time at Brazilian online stores and evaluate the selected applications in terms of engagement, functionality, aesthetics, and quality of information. Methods A systematic search was performed at the Google Play Store and the App Store developed or updated between 2020 and 2022. The mobile app rating scale (MARS) was used to assess their quality. The characteristics and information of these applications were presented. Data were analyzed descriptively. Results Among the 2,264 relevant mHealth apps identified, 11 were eligible for inclusion. There was one app that offered only sleep time recordings, three offered sleep recordings and educational content, three monitored sleep time with audiovisual resources, two offered sleep time recordings with a focus on monitoring snoring, and two offered sleep time monitoring with individual content customizations. The apps scored fair on MARS, with a mean of 3.5 points (standard deviation: 0.8) on a scale of 1 to 5. Most apps scored low due to poor evidence-based information, aesthetics, and engagement. Conclusion Although the use of mHealth technologies for monitoring sleep time has considerable potential for investigation, it is a relatively new, underexplored topic. Collaboration between researchers, clinical professionals, patients, and application developers is necessary for the creation of new evidence-based mHealth applications focused on Brazilian users.

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Section: Resultsmentioning

confidence: 99%

Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil

Lemes,

Triches,

Carvalho

et al. 2024

Sleep Sci

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show abstract

“…All smartphone snoring apps are audio-based and use the incorporated microphone. Given the large variability of mobile devices, differences caused by the different smartphone hardware components or the operating systems cannot be avoided [ 17 , 23 , 39 ]. For research purposes, one can argue that all participants should have used an identical smartphone, but regarding the commercial availability of the tested app, reliability of the measurements should be required.…”

Section: Discussionmentioning

confidence: 99%

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

Klaus

Stummer

Ruf

2021

IJERPH

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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.

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“…In a study published by Figueras-Alvarez et al in 2020, snoring was measured using a Samsung smartphone application. 31 The snoring intensity, snoring percentage, and snore score were measured using the snoring sound.…”

Section: Measuring With a Smartphonementioning

confidence: 99%

How to measure snoring: A systematic review

Kim

Cho

Rhee

et al. 2023

Preprint

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Objective Snoring is the most common and main symptom of obstructive sleep apnea. therefore, it should be managed effectively. Various methods of measuring snoring are available, and even if the measurement is performed the same way, communication is difficult because there are no common reference values between the researcher and clinician with regard to intensity and frequency, among other variables. In other words, no consensus regarding measurement has been reached. Therefore, this study aimed to review the literature related to the measurement of snoring, such as measurement devices, definitions, and device locations. Methods A literature search based on the PubMed, Cochrane, and Embase databases was conducted from the date of inception to April 30, 2022. Twenty-seven articles were included in this study. Papers that mentioned only the equipment used for measurement and did not include individual details were excluded from the study. Results Three representative methods for measuring snoring emerged. These include (1) a microphone, which measures snoring sound; (2) piezoelectric sensor, which measures snoring vibration; and (3) nasal transducer, which measures airflow. In addition, recent attempts have been made to measure snoring using smartphones and applications. Conclusion Numerous studies have investigated both obstructive sleep apnea and snoring. However, the methods of measuring snoring and snoring-related concepts vary across studies. To understand the pathophysiology underlying obstructive sleep apnea and address sleep-related diseases, consensus in the academic community on how to measure and define snoring is required.

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Snoring intensity assessment with three different smartphones using the SnoreLab application in one participant

Cited by 8 publications

References 11 publications

Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil

Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil

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

How to measure snoring: A systematic review

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