1. Introduction
Obstructive Sleep Apnea Syndrome (OSAS) is a prevalent sleep disorder characterized by repeated episodes of upper airway obstruction during sleep, leading to disrupted breathing patterns and decreased oxygen levels in the blood. This condition affects individuals across all age groups, with a higher prevalence observed in males and older adults [1]. The consequences of untreated OSAS extend beyond mere sleep disturbances, encompassing a myriad of systemic complications such as hypertension, cardiovascular diseases, cognitive impairment, metabolic dysregulation, and increased risk of accidents due to daytime sleepiness [2]. Despite its serious implications, OSAS often goes undiagnosed or misdiagnosed, primarily due to its symptomatic overlap with common sleep disturbances. Notably, snoring, often dismissed as a benign habit, serves as a hallmark symptom of OSAS, indicating the need for further evaluation [3]. Early detection and intervention are imperative to mitigate the adverse health outcomes associated with OSAS, underscoring the importance of comprehensive sleep evaluations and polysomnography (PSG) in at-risk populations [4].
Recently, there has been rapid growth in the mobile applications (apps) business in the medical field. Among these, some aim to diagnose and monitor ear, nose, and throat (ENT) diseases. Subspecialties such as sleep medicine and rhinology have been particularly interested in the development of apps released on the Apple App Store and the Google Play Store. While these apps are available to patients, the quality varies, and only a few clinical trials have tested their accuracy [5]. An attempt to overcome this limitation and standardize contents has been seen in the development of the Mobile Application Rating Scale (MARS) [6], a simple tool for assessing the overall quality of health mobile apps together with aesthetics, engagement, function and information (score 1 to 5). MARS can also be used to provide a checklist for the design and development of new high-quality health apps.
With more than 13 million downloads and 10,000 reviews, SnoreLab [Reviva Softworks Ltd., London, UK; https://www.snorelab.com/ (accessed on 27 April 2024)] is featured as the number one iOS and Android app for recording and tracking snoring. SnoreLab provides measurements of snoring intensity and duration. Users can track their snoring patterns overnight, allowing for better understanding and management of their sleep health. SnoreLab offers a straightforward interface, requiring only the press of a button to initiate recording, with no need for calibration. This simplicity facilitates effortless usage. The app compares individuals’ snoring patterns longitudinally, facilitating the assessment of interventions and lifestyle modifications’ efficacy. Furthermore, it provides comprehensive information on various snoring remedies and factors influencing snoring, thereby empowering users with knowledge crucial for informed decision-making regarding their sleep health. SnoreLab does not serve as a diagnostic tool for OSAS; however, as stated on their website, “some users have discovered sounds in their recordings that indicate apnea events, and then found them useful in subsequent medical consultations”. Also, the app may be beneficial in capturing evidence of sleep apnea, such as “to identify risky periods using the tell-tale sign of silence followed by gasping or choking” [https://www.snorelab.com/sleep-apnea-screening-testing-and-treatment/ (accessed on 27 April 2024)]. These data can prove valuable when shared with an otorhinolaryngologist or trained sleep apnea specialist to optimize treatment strategies.
According to a previous systematic review published by our group [5], SnoreLab received a MARS score of 3.7, with the highest scores according to aesthetics and function. Despite its remarkable popularity, SnoreLab’s validation has not been extensively reported in the literature, and the published reports are scarce. Only two clinical trials [7,8] and one case report [9] have assessed its accuracy or utilized SnoreLab in a clinical setting.
Klaus et al. [7] evaluated the accuracy of the SnoreLab smartphone app in monitoring snoring compared to full-night PSG. The results show the app’s acceptable accuracy values in measuring snoring >50% per night: 94.7% accuracy, 100% sensitivity, and 94.1% specificity. However, it fails to detect obstructive events. Best agreement was observed when comparing loud and epic snoring ratios with total snoring ratio. Therefore, the SnoreLab app is deemed suitable for measuring heavy snoring, but not obstructive events. In the randomized trial by Sperger et al. [8], SnoreLab was used to assess the effect of myofunctional therapy (MT) on snoring in obese patients. The study aimed to assess the effectiveness of MT in treating habitual snoring in obese patients. Despite subjective improvements reported in the experimental group, assessment via the SnoreLab app showed no significant effect of MT on snoring variables compared to the control group. Additionally, neck circumference and sleep quality worsened after treatment, suggesting that MT may not be effective in managing habitual snoring in this population. The case report by Figueras-Alvarez et al. [9] aimed to assess the consistency of snoring measurements using the SnoreLab app across three different smartphones, intending to validate its use in studies on snoring treatment effectiveness. While overall measurements showed strong correlation, significant differences were noted among smartphones in assessing various snoring parameters. This suggests challenges in utilizing the SnoreLab app across different devices for research purposes, but highlights its potential for individual treatment monitoring.
The primary objective of this study was to conduct a comparative analysis between the results derived from the SnoreLab scoring system, which is predicated on the recording of patients’ snoring, and the parameters obtained from overnight, in-hospital PSG [10]. By juxtaposing the outcomes of SnoreLab against those of PSG, we sought to provide insights into its utility and efficacy as a diagnostic tool for evaluating sleep-related disorders. Through this comprehensive examination, we aimed to contribute valuable information to the ongoing discourse surrounding the clinical use of SnoreLab, thereby fostering a deeper understanding of its diagnostic capabilities, and facilitating informed decision-making in clinical practice. The overarching aim was to elucidate the potential strengths and limitations of this promising tool within the realm of sleep diagnostics.
2. Materials and Methods
The research was conducted prospectively from September 2020 to December 2021, involving voluntary patients enrolled at the Otorhinolaryngology (ORL) department of Foggia University Hospital. Data collection adhered to the Guideline for Good Clinical Practice and the ethical principles outlined in the Declaration of Helsinki. Institutional review board approval was secured for the completion of this study (Department of ORL—University Hospital of Foggia, n. 0010 of 31 January 2020), and written informed consent was obtained from each participating patient.
2.1. Patients’ Selection and Study Design
Male and female patients between 18 and 75 years of age, seeking medical counseling for suspected OSAS, were considered potentially eligible to participate in this study.
All patients received instructions regarding the study procedures and the functioning of SnoreLab (Premium version). Detailed medical histories, including information on comorbidities such as diabetes mellitus, dyslipidemia, high blood pressure, and obesity, as well as age and timing of OSAS diagnosis, were obtained. Each patient underwent a full-night PSG examination, with simultaneous recordings using SnoreLab. For the recordings, an iPhone SE was used in all patients. To minimize bias, the recording smartphone was positioned within one meter of the patient’s head, connected to a power source, and set to flight mode. To avoid background noise, no other patients were present in the room during completion of the exam. Exclusion criteria encompassed severe psychological or neurological disorders, substance abuse, and an inability to follow instructions or maintain focus and concentration.
2.2. SnoreLab (Reviva Softworks Ltd., London, UK)
SnoreLab, developed by Reviva Softworks Ltd., is a mobile app designed for recording, measuring, and tracking patients’ snoring, utilizing the smartphone’s microphone. While its primary function involves screening for snoring patterns, it also allows the comparison of snoring trends over time, thereby offering potential utility in follow-up assessments and the evaluation of the efficacy of remedies and lifestyle modifications. Notably, the app is classified as a medical device and is ranked among the top 50 in the Apple app store. For this study, the premium version of SnoreLab (EUR 5.99/monthly) allows multiple registrations and was used in this study.
Utilizing an algorithm, a “Snore Score” is generated, reflecting the intensity and frequency of snoring. The median Snore Score among SnoreLab users is 25, with a score ≥ 50 indicative of an advanced snoring category. As per the developer’s recommendations, individuals with scores ≥ 100 should seek medical advice, whereas a score ≤ 10 is considered a favorable target. Moreover, snoring severity is further categorized as “light” when unlikely to disturb a bed partner, “loud” when likely to disturb a bed partner, and “epic” when highly likely to disturb a bed partner.
2.3. Polysomnography
An overnight hospital PSG (Embletta MPR PG® Natus Medical Incorporated. Pleasanton, CA, USA) confirmed the diagnosis of OSAS. Electrocardiograph, thoracoabdominal movements, respiratory airflow, finger oxygen saturation, and body position, along with other standard indicators, were obtained. Results were classified according to the criteria of the American Academy of Sleep Medicine (AASM) for the Scoring of Sleep and Associated Events [11]. The severity of OSAS was categorized according to the Apnea Hypopnea Index (AHI) as follows: none/minimal (AHI < 5 per hour); mild (≥5 AHI < 15 per hour); moderate (≥15 AHI < 30 per hour); severe (≥30 per hour).
2.4. Statistical Analysis
Descriptive analysis was employed to define the main clinical and demographic features. Qualitative data were summarized as percentages. The main variable of study was the Snoring Score. Normality was explored by the Shapiro–Wilk test, resulting in a non-normal distribution (p-value = 0.022). Correlation to clinical and PSG parameters was investigated by Spearman’s rank correlation test (ρ). The Kruskal–Wallis test was used to investigate the differences in Snore Score among three groups of OSAS severity, as simple snoring, mild and moderate–severe.
A p-value ≤ 0.05 was considered the cut-off for statistical significance. The IBM® SPSS® Statistics version 25 was used to perform statistical analysis.
3. Results
Thirty-four patients underwent overnight hospital PSG at the ORL department of Foggia University Hospital during the study period. Out of this cohort, 3 patients were excluded due to an inability to operate a smartphone, and 12 were excluded owing to technical artifacts observed during PSG evaluation. Consequently, 55.9% of the initial sample (n = 19; 15 men, 4 women), with an average age of 50.9 ± 10.5 years, was included for analysis. None of the enrolled patients reported difficulties in adhering to the study protocol or utilizing the SnoreLab application.
According to the AHI, patients were classified as affected by simple snoring (31.6%; n = 6), mild OSAS (36.8%; n = 7), moderate OSAS (10.5%; n = 2), and severe OSAS (21.1%; n = 4). The demographic characteristics of the cohort (Table 1), stratified based on the severity of OSAS, are consistent with findings in the current literature. They demonstrate a higher prevalence of the condition among male subjects and a corresponding escalation in comorbidities associated with the severity of the disease [12].
The AHI and the percentage of sleep time with oxygen saturations lower than 90% (Tc90) obtained from PSG were compared with the app-based Snore Score (Table 2). Spearman correlation analysis revealed a positive correlation in both instances, although statistical significance was not reached (AHI: Spearman’s ρ = 0.145, p = 0.554; Tc90: Spearman’s ρ = 0.437, p = 0.07).
The overall cohort exhibited a Snore Score of 24.8 ± 22.2 alongside an AHI of 15.1 ± 17.0, indicating moderate OSAS (Table 2). Interestingly, elevated Snore Scores were observed in both the simple snoring (30.7 ± 19.2) and severe OSAS group (35.2 ± 21.4) (Kruskal–Wallis p-value = 0.176).
Although the cohort exhibited an overall AHI of 15.1 ± 17.0, indicative of moderate OSAS, the average Snore Score reported was 24.8 ± 22.2, consistent with the median score observed among SnoreLab users. Consequently, following the instructions provided in the app, this finding does not warrant immediate medical intervention. The Spearman correlation analysis between Snore Score, AHI, and Tc90 did not demonstrate statistical significance.
4. Discussion
“Sleep medicine” is one of the ENT subspecialties with the highest number of medical apps for patients; however, the number of articles that validated these applications is much lower [5]. Most applications in this category primarily focus on recording and analyzing sleep patterns, and providing graphical representations of various sleep phases and snoring intensity. Previous studies have shown that smartphones can conduct pre-screening examinations with comparable quality to traditional PSG. Conducting PSG via smartphones necessitates additional hardware implementation, such as a pulse oximeter, accelerometer, and external microphone [13,14]. However, research assessing whether these applications not only diagnose or characterize but also improve OSA remains limited [15,16,17]. Additionally, the growing prevalence of consumer sleep technology, including snoring monitoring apps, has sparked privacy concerns. The AASM has addressed this issue by issuing a statement and guidelines [18].
There remain significant doubts about the effectiveness of these apps, which can register only snoring without simultaneously monitoring other vital parameters. The SnoreLab app has been developed to record snoring patterns, measuring both the frequency and intensity of snoring throughout the night. It enables users to document their snoring over time, helping to track the effectiveness of various interventions. According to SnoreLab’s website, these interventions include lifestyle adjustments such as eating earlier, engaging in mouth exercises, adopting side-sleeping positions, taking short walks, or showering before bed. Health interventions such as weight loss and maintaining hydration are also supported. Due to its simplicity and intuitiveness, SnoreLab has become increasingly popular and demonstrated potential utility as a “sleep diary” to enhance patient awareness and compliance with treatments.
Based on the authors’ experiences, it has been observed that some patients use the app to record and bring audio files of their snoring to clinical consultations as evidence. Additionally, an important consideration is the common misconception among many patients who often confound snoring with OSAS. There is a significant clinical difference between the two, and this misunderstanding can lead to misdiagnoses and the inappropriate management of the conditions. Therefore, the app can play a critical role in educating users about these differences, emphasizing the importance of proper diagnosis and treatment.
Currently, computer software programmers are developing smartphone snoring apps, often with minimal involvement of sleep medicine practitioners in the development process. Camacho et al. demonstrated that select smartphone apps are user-friendly for recording and playing back snoring sounds. A preliminary comparison of over 1500 individual snores indicated potential clinical utility; however, further validation testing is recommended [19]. A systematic review of smartphone applications and devices for OSAS identified 10 relevant smartphone apps that have been utilized in clinical settings for the diagnosis or treatment of sleep-disordered breathing. However, these apps, including SnoreLab, demonstrate lower accuracy compared to traditional options [20]. Indeed, particularly regarding SnoreLab, in comparison to other snore recording apps such as SnoreMonitorSleepLab, Quit Snoring, and Snore Spectrum, the so-called “Snore Score” is notably non-transparent. The application does not provide information regarding the methodology used for calculating this score. Consequently, for scientific endeavors, it is considered unnecessary [21].
Although in this study the app was tested in a hospital setting to avoid any recording bias, use at home can be quite different, and many environmental biases can interfere. In fact, other criticisms of SnoreLab and similar apps may arise due to recording interferences caused by the presence of other individuals sleeping nearby the patient [17]. While the app’s algorithm can effectively filter out ambient noise and speech, it currently lacks the ability to differentiate between the breathing and snoring of others, presenting a confounding factor.
Based on our data, the results do not support the efficacy of SnoreLab in detecting obstructive events. In alignment with the current literature, this suggests the limited reliability of OSAS assessments that lack the integration of snoring recordings with data pertaining to respiratory airflow and thoracoabdominal movements [13,14]. In fact, Spearman correlation analysis did not reveal a statistically significant correlation between objective measures of PSG, such as AHI and Tc90 (AHI—Spearman’s ρ = 0.145, p = 0.554; Tc90—Spearman’s ρ = 0.437, p = 0.07).
Additionally, reporting the Snore Score as an average of about 25 may be misleading for patients, posing the risk that many individuals in need of immediate treatment may underestimate the severity of the condition. Moreover, the information section does not adequately explain when to seek medical counseling. In fact, according to SnoreLab’s website, “A score above 50 puts you in the ’bad snoring’ category, and if you’re above 100 you definitely need to find some solutions!”
However, as previously mentioned, the cohort exhibited an overall AHI of 15.1 ± 17.0, indicative of moderate OSAS, while the average Snore Score reported was 24.8 ± 22.2. Additionally, the severe OSA group (AHI 44.9 ± 12.0) reached a Snore Score of 35.2 ± 21.4, not indicating an advanced snoring category nor the need for immediate medical counseling, according to the app. Conversely, the simple snoring group with AHI <5 reported a Snore Score of 30.7 ± 19.2. It is important to note that not all snorers have OSAS and require treatment [22], but there are inadequate explanations about these two conditions in the information provided, and also, how the Snore Score is calculated.
Unfortunately, the number of validation studies addressing SnoreLab and snoring apps, in general, is limited. Klaus et al. (2021) [7] were pioneers in studying the SnoreLab app beyond a case report level [9], evaluating a cohort of 19 patients undergoing overnight PSG to assess the accuracy of recorded snoring. Similarly, they found that the Snore Score provided by the app averaged 29.1 ± 35.2, consistent with median user scores and our findings. However, compared to PSG-measured AHI, all snoring indices showed only a low to moderate correlation (r = 0.495–0.645). Thus, we concur with their conclusion that, while SnoreLab may not be suitable for OSA screening in clinical practice, it could serve as a valuable tool for snoring monitoring alongside different treatment evaluations. While the SnoreLab website disclaims any intention to diagnose OSAS, users may still confuse obstructive apnea events with snoring. Integrating clearer explanations within the app and associated scientific material could mitigate this risk and potentially expedite OSAS diagnosis.
Also, it is important to consider that although SnoreLab is a cost-effective solution, additional features such as night recording, trends, and backup require a SnoreLab Premium subscription (EUR 5.99/monthly). A cost analysis is necessary to understand if this small annual expenditure for patients can prevent major costs due to OSAS comorbidities, absence from work, and travel to reach clinics, especially for patients coming from rural or underserved areas [23].
Limitations
Certainly, the small sample size (n = 19) limits the generalizability of these findings. In this pilot study, the primary focus remains the potential use of SnoreLab in detecting obstructive events. Despite the limited number of cases, it may be intriguing to conduct a gender analysis in subsequent research with a larger cohort. Furthermore, future investigations could explore using SnoreLab as a sleep diary, as suggested by Klaus et al. [7], to validate or refute their findings. Also, the proportion of snoring determined by PSG was not compared with other measures like p-flow, warranting further analysis. Correlating snoring patterns with varying degrees of OSA severity in a larger, more diverse sample would provide valuable insights for comparing severity groups effectively.
In addition to the variables already considered, including factors such as the presence of sinus disease, allergies, the use of allergy medications or nasal sprays, and neck circumference would have provided a more comprehensive comparison between the PSG and the application. These additional variables could potentially influence the outcomes, and their absence may limit the scope of our findings. Future studies should consider incorporating these factors to enhance the robustness of the analysis.
Additionally, the use of concomitant environmental noise trackers would be beneficial to further validate this technology. Incorporating such trackers could provide more comprehensive data on external noise influences, thereby enhancing the robustness and accuracy of our findings.
5. Conclusions
The efficacy of SnoreLab in reliably detecting snoring has yet to be demonstrated, and its capability to detect OSAS remains unsubstantiated. While SnoreLab records snoring, it is not designed for screening sleep apnea, as explicitly stated on their website. However, this distinction must be clear to users, as snoring and OSAS are often confounded and the multisystemic effects of OSAS still ignored in most of the population. Building on the pioneering work of Klaus et al. [7], our study represents a second attempt to investigate the utility of the SnoreLab app in assessing OSA. We examined correlations between SnoreLab metrics and PSG-derived parameters, specifically the AHI and Tc90. The absence of a significant correlation observed between SnoreLab indices and PSG-measured AHI emphasizes the need for caution when using the app for OSA screening. Therefore, our data do not support the efficacy of SnoreLab in detecting obstructive events. Nonetheless, due to its simplicity and user-friendly interface, SnoreLab holds potential as a “sleep diary” to enhance patient awareness and treatment compliance. Our findings contribute to the growing body of evidence suggesting that while SnoreLab may not replace PSG for diagnosis, it could serve as a complementary tool for monitoring snoring, and to improve the interaction between patients and clinicians when integrated into a suitable clinical assessment [18]. Further research, including multicenter international studies, is crucial to fully elucidate the app’s clinical utility. Moreover, integrating clearer explanations within the app and associated scientific material may help users better understand the distinction between snoring and OSA, facilitating timely diagnosis and intervention, especially in rural underserved areas.
Author Contributions
Conceptualization, E.M.C.T. and A.L.; methodology, E.M.C.T. and M.C.; software, V.C.A.C.; validation, D.C., L.C. and M.C.; formal analysis, V.C.A.C.; investigation, A.L.; resources, D.C. and M.C.; data curation, S.P.; writing—original draft preparation, E.M.C.T. and A.L.; writing—review and editing, E.M.C.T. and S.P.; visualization, D.C.; supervision, M.C.; project administration, M.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Department of Otorhinolaryngology—University Hospital of Foggia (n. 0010 of 31 January 2020).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Data are available upon request to the corresponding author (E.M.C.T.).
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Mabry, S.; Bradshaw, J.L.; Gardner, J.J.; Wilson, E.N.; Cunningham, R.L. Sex-dependent effects of chronic intermittent hypoxia: Implication for obstructive sleep apnea. Biol. Sex Differ. 2024, 15, 38. [Google Scholar] [CrossRef]
- Somers, V.K.; White, D.P.; Amin, R.; Abraham, W.T.; Costa, F.; Culebras, A.; Daniels, S.; Floras, J.S.; Hunt, C.E.; Olson, L.J.; et al. Sleep Apnea and Cardiovascular Disease. J. Am. Coll. Cardiol. 2008, 52, 686–717. [Google Scholar] [CrossRef]
- Keropian, B.; Murphy, N. The prevalence of OSA in snorers presenting with various chief complaints: A pilot study. CRANIO® 2014, 32, 217–218. [Google Scholar] [CrossRef]
- Zasadzińska-Stempniak, K.; Zajączkiewicz, H.; Kukwa, A. Prevalence of Obstructive Sleep Apnea in the Young Adult Population: A Systematic Review. J. Clin. Med. 2024, 13, 1386. [Google Scholar] [CrossRef]
- Trecca, E.M.C.; Lonigro, A.; Gelardi, M.; Kim, B.; Cassano, M. Mobile Applications in Otolaryngology: A Systematic Review of the Literature, Apple App Store and the Google Play Store. Ann. Otol. Rhinol. Laryngol. 2021, 130, 78–91. [Google Scholar] [CrossRef]
- Stoyanov, S.R.; Hides, L.; Kavanagh, D.J.; Zelenko, O.; Tjondronegoro, D.; Mani, M. Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps. JMIR mHealth uHealth 2015, 3, e27. [Google Scholar] [CrossRef]
- Klaus, K.; Stummer, A.-L.; Ruf, S. Accuracy of a Smartphone Application Measuring Snoring in Adults—How Smart Is It Actually? Int. J. Environ. Res. Public Health 2021, 18, 7326. [Google Scholar] [CrossRef]
- Sperger, T.; Araujo, A.C.F.; Soares, C.F.d.P. Effect of myofunctional therapy on snoring in obese patients: A randomized trial. Sleep Sci. 2022, 15, 421–428. [Google Scholar] [CrossRef]
- Figueras-Alvarez, O.; Cantó-Navés, O.; Cabratosa-Termes, J.; Roig-Cayón, M.; Felipe-Spada, N.; Tomàs-Aliberas, J. Snoring intensity assessment with three different smartphones using the SnoreLab application in one participant. J. Clin. Sleep Med. 2020, 16, 1971–1974. [Google Scholar] [CrossRef]
- Boulos, M.I.; Jairam, T.; Kendzerska, T.; Im, J.; Mekhael, A.; Murray, B.J. Normal polysomnography parameters in healthy adults: A systematic review and meta-analysis. Lancet Respir. Med. 2019, 7, 533–543. [Google Scholar] [CrossRef]
- Berry, R.B.; Budhiraja, R.; Gottlieb, D.J.; Gozal, D.; Iber, C.; Kapur, V.K.; Marcus, C.L.; Mehra, R.; Parthasarathy, S.; Quan, S.F.; et al. Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. J. Clin. Sleep Med. 2012, 8, 597–619. [Google Scholar] [CrossRef]
- Heinzer, R.; Vat, S.; Marques-Vidal, P.; Marti-Soler, H.; Andries, D.; Tobback, N.; Mooser, V.; Preisig, M.; Malhotra, A.; Waeber, G.; et al. Prevalence of sleep-disordered breathing in the general population: The HypnoLaus study. Lancet Respir. Med. 2015, 3, 310–318. [Google Scholar] [CrossRef]
- Ong, A.A.; Gillespie, M.B. Overview of smartphone applications for sleep analysis. World J. Otorhinolaryngol. Neck Surg. 2016, 2, 45–49. [Google Scholar] [CrossRef]
- Chaudhry, B.M. Sleeping with an Android. mHealth 2017, 3, 7. [Google Scholar] [CrossRef]
- Guul, M.K.; Jennum, P.; Sorensen, H.B.D. Portable Prescreening System for Sleep Apnea. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; pp. 4917–4920. [Google Scholar] [CrossRef]
- Gradl, S.; Leutheuser, H.; Kugler, P.; Members, S.; Biermann, T.; Kreil, S.; Kornhuber, J.; Bergner, M.; Eskofier, B. Somnography using unobtrusive motion sensors and Android-based mobile phones. In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 1182–1185. [Google Scholar] [CrossRef]
- Al-Mardini, M.; Aloul, F.; Sagahyroon, A.; Al-Husseini, L. Classifying obstructive sleep apnea using smartphones. J. Biomed. Inform. 2014, 52, 251–259. [Google Scholar] [CrossRef]
- Khosla, S.; Deak, M.C.; Gault, D.; Goldstein, C.A.; Hwang, D.; Kwon, Y. Consumer Sleep Technology: An American Academy of Sleep Medicine Position Statement. J. Clin. Sleep Med. 2018, 14, 877–880. [Google Scholar] [CrossRef]
- Camacho, M.; Robertson, M.; Abdullatif, J.; Certal, V.; Kram, Y.A.; Ruoff, C.M.; Brietzke, S.E.; Capasso, R. Smartphone apps for snoring. J. Laryngol. Otol. 2015, 129, 974–979. [Google Scholar] [CrossRef]
- Baptista, P.M.; Martin, F.; Ross, H.; O’Connor Reina, C.; Plaza, G.; Casale, M. A systematic review of smartphone applications and devices for obstructive sleep apnea. Braz. J. Otorhinolaryngol. 2022, 88, S188–S197. [Google Scholar] [CrossRef]
- Stippig, A.; Hübers, U.; Emerich, M. Apps in sleep medicine. Sleep Breath. 2015, 19, 411–417. [Google Scholar] [CrossRef]
- Yaremchuk, K. Why and When to Treat Snoring. Otolaryngol. Clin. N. Am. 2020, 53, 351–365. [Google Scholar] [CrossRef]
- Sapienza, A.; Lítlá, M.; Lehmann, S.; Alessandretti, L. Exposure to urban and rural contexts shapes smartphone usage behavior. Proc. Natl. Acad. Sci. USA Nexus 2023, 2, pgad357. [Google Scholar] [CrossRef]
Table 1.Demographic features of the patients’ cohort.
Table 1.Demographic features of the patients’ cohort.
| Demographic Features | Simple Snoring (n = 6) | Mild OSAS (n = 7) | Moderate OSAS (n = 2) | Severe OSAS (n = 4) | Overall Cohort (n = 19) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | n | % | |
| Sex | ||||||||||
| M | 4 | 66.7 | 5 | 71.4 | 2 | 100 | 4 | 100 | 15 | 78.9 |
| F | 2 | 33.3 | 2 | 28.6 | 0 | 0 | 0 | 0 | 4 | 21.0 |
| Comorbidities | ||||||||||
| DM | 1 | 16.7 | 1 | 14.3 | 1 | 50 | 1 | 25 | 4 | 21.0 |
| Dyslipidemia | 0 | 0.0 | 2 | 28.6 | 2 | 100 | 2 | 50 | 6 | 31.6 |
| HBP | 2 | 33.3 | 2 | 28.6 | 2 | 100 | 4 | 100 | 10 | 52.4 |
| Obesity (BMI ≥ 30) | 3 | 50.0 | 1 | 14.3 | 0 | 0 | 3 | 75 | 8 | 42.1 |
| Age and timing of diagnosis | μ | SD | μ | SD | μ | SD | Μ | SD | μ | SD |
| Age | 49.5 | 9.2 | 51.71 | 9.3 | 61 | 3 | 45.3 | 13.3 | 50.9 | 10.5 |
| Timing of symptoms (years) | 4 | 0.8 | 9 | 6.5 | 4 | 0.0 | 6 | 0.8 | 6 | 4.3 |
Abbreviations: OSAS = obstructive sleep apnea syndrome; DM = diabetes mellitus; HBP = high blood pressure; BMI = body mass index; n = number; SD = standard deviation; μ = average; % = percentage of patients for each subgroup.
Table 2.Polysomnography parameters and app-based snore score.
Table 2.Polysomnography parameters and app-based snore score.
| PSG and App-Based Parameters | Simple Snoring (n = 6) | Mild OSAS (n = 7) | Moderate OSAS (n = 2) | Severe OSAS (n = 4) | Overall Cohort (n = 19) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| μ | SD | μ | SD | μ | SD | μ | SD | μ | SD | |
| PSG parameters | ||||||||||
| AHI | 2.3 | 1.4 | 9.6 | 3.3 | 13.3 | 1.0 | 44.9 | 12.0 | 15.1 | 17.0 |
| O2 Sat. | 93.9 | 2.0 | 94.6 | 0.6 | 94.1 | 0.2 | 93.5 | 1.0 | 95.0 | 1.4 |
| Tc90 | 1.4 | 2.4 | 1.8 | 3.4 | 1.2 | 0.3 | 10.4 | 5.7 | 3.6 | 5.2 |
| NADIR | 88.6 | 3.1 | 85.3 | 4.9 | 82.0 | 3.0 | 74.5 | 5.0 | 83.7 | 6.7 |
| App-based Snore Score | 30.7 | 19.2 | 15.9 | 23.7 | 18.0 | 8.0 | 35.2 | 21.4 | 24.8 | 22.2 |
| Light snoring (%) | 25.0 | 15.2 | 11.7 | 12.4 | 16.0 | 7 | 28.7 | 11.5 | 19.9 | 14.6 |
| Loud snoring (%) | 8.0 | 6.5 | 1.9 | 4.2 | 2.5 | 0.5 | 5.5 | 5.9 | 4.6 | 5.9 |
| Epic snoring (%) | 0.2 | 0.4 | 0.6 | 1.4 | 0.0 | 0.0 | 0.5 | 0.9 | 0.4 | 1.0 |
Abbreviations: PSG = polysomnography; OSAS = obstructive sleep apnea syndrome; AHI = apnea hypopnea index; n = number; O2 Sat. = oxygen saturation; Tc90 = percentage of sleep time with oxygen saturations lower than 90%; NADIR = lowest saturation value; SD = standard deviation; μ = average.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
