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Lemes LC, Triches MI, Carvalho C, Cardoso VDF, Sato TDO. Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil. Sleep Sci 2024; 17:e185-e193. [PMID: 38846586 PMCID: PMC11152620 DOI: 10.1055/s-0044-1779606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/11/2023] [Indexed: 06/09/2024] Open
Abstract
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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Affiliation(s)
- Lorena Caligiuri Lemes
- Department of Physiotherapy, Universidade Federal de São Carlos, Fisioterapia, São Carlos, SP, Brazil
| | - Maria Isabel Triches
- Department of Physiotherapy, Universidade Federal de São Carlos, Fisioterapia, São Carlos, SP, Brazil
| | - Cristiano Carvalho
- Department of Biosciences, Universidade Federal de São Paulo, Postgraduate Program in Bioproducts and Bioprocesses, Santos, SP, Brazil
| | - Viviane de Freitas Cardoso
- Department of Physiotherapy, Association of Education and Culture of Mato Grosso do Sul, Três Lagoas, MS, Brazil
| | - Tatiana de Oliveira Sato
- Department of Physiotherapy, Universidade Federal de São Carlos, Fisioterapia, São Carlos, SP, Brazil
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Kim SG, Cho SW, Rhee CS, Kim JW. How to objectively measure snoring: a systematic review. Sleep Breath 2024; 28:1-9. [PMID: 37421520 DOI: 10.1007/s11325-023-02865-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 07/10/2023]
Abstract
PURPOSE Snoring is the most common symptom of obstructive sleep apnea. Various objective 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 objective measurement has been reached. This study aimed to review the literature related to the objective 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 5, 2023. Twenty-nine articles were included in this study. Articles 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 objective methods of measuring snoring and snoring-related concepts vary across studies. Consensus in the academic and clinical communities on how to measure and define snoring is required.
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Affiliation(s)
- Su Geun Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Sung-Woo Cho
- Department of Otorhinolaryngology‑Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 173‑82 Gumi‑ro, Bundang‑gu, Seongnam, Gyeonggi‑do, 13620, South Korea
| | - Chae-Seo Rhee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
- Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Korea
| | - Jeong-Whun Kim
- Department of Otorhinolaryngology‑Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 173‑82 Gumi‑ro, Bundang‑gu, Seongnam, Gyeonggi‑do, 13620, South Korea.
- Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Korea.
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Tomaszewska JZ, Młyńczak M, Georgakis A, Chousidis C, Ładogórska M, Kukwa W. Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor. Diagnostics (Basel) 2023; 13:2914. [PMID: 37761281 PMCID: PMC10529205 DOI: 10.3390/diagnostics13182914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/30/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)-a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body-or photoplethysmography (PPG). METHODS The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. RESULTS The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. CONCLUSIONS The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process.
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Affiliation(s)
- Julia Zofia Tomaszewska
- School of Computing and Engineering, University of West London, London W5 5RF, UK; (J.Z.T.); (A.G.)
| | - Marcel Młyńczak
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland; (M.M.); (M.Ł.)
| | - Apostolos Georgakis
- School of Computing and Engineering, University of West London, London W5 5RF, UK; (J.Z.T.); (A.G.)
| | - Christos Chousidis
- Department of Music and Media, Institute of Sound Recording, University of Surrey, Guildford GU2 7XH, UK;
| | - Magdalena Ładogórska
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland; (M.M.); (M.Ł.)
| | - Wojciech Kukwa
- Department of Otorhinolaryngology, Faculty of Medicine and Dentistry, Medical University of Warsaw, 02-091 Warsaw, Poland
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Pires GN, Arnardóttir ES, Islind AS, Leppänen T, McNicholas WT. 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. J Sleep Res 2023:e13819. [PMID: 36807680 DOI: 10.1111/jsr.13819] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/16/2022] [Accepted: 12/18/2022] [Indexed: 02/20/2023]
Abstract
There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.
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Affiliation(s)
- Gabriel Natan Pires
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil.,European Sleep Research Society (ESRS), Regensburg, Germany
| | - Erna Sif Arnardóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Anna Sigridur Islind
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - Timo Leppänen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Walter T McNicholas
- Department of Respiratory and Sleep Medicine, St Vincent's Hospital Group, School of Medicine, University College Dublin, Dublin, Ireland
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Accuracy of a Smartphone Application Measuring Snoring in Adults-How Smart Is It Actually? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147326. [PMID: 34299777 PMCID: PMC8304057 DOI: 10.3390/ijerph18147326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 01/03/2023]
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, UK) 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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