Purpose Snoring is closely related to obstructive sleep apnea in adults. The increasing abundance and availability of smartphone technology has facilitated the examination and monitoring of snoring at home through snoring apps. However, the accuracy of snoring detection by snoring apps is unclear. This study explored the snoring detection accuracy of Snore Clock—a paid snoring detection app for smartphones. Methods Snoring rates were detected by smartphones that had been installed with the paid app Snore Clock. The app provides information on the following variables: sleep duration, snoring duration, snoring loudness (in dB), maximum snoring loudness (in dB), and snoring duration rate (%). In brief, we first reviewed the snoring rates detected by Snore Clock; thereafter, an ear, nose, and throat specialist reviewed the actual snoring rates by using the playback of the app recordings. Results In total, the 201 snoring records of 11 patients were analyzed. Snoring rates measured by Snore Clock and those measured manually were closely correlated (r = 0.907). The mean snoring detection accuracy rate of Snore Clock was 95%, with a positive predictive value, negative predictive value, sensitivity, and specificity of 65% ± 35%, 97% ± 4%, 78% ± 25%, and 97% ± 4%, respectively. However, the higher the snoring rates, the higher were the false-negative rates for the app. Conclusion Snore Clock is compatible with various brands of smartphones and has a high predictive value for snoring. Based on the strong correlation between Snore Clock and manual approaches for snoring detection, these findings have validated that Snore Clock has the capacity for at-home snoring detection.

Background and Objectives: Heart rate variability (HRV) analysis is a noninvasive method used to examine autonomic system function, and the clinical applications of HRV analysis have been well documented. The aim of this study is to investigate the association between HRV and the apnea–hypopnea index (AHI) in patients referred for polysomnography (PSG) for obstructive sleep apnea (OSA) diagnosis. Materials and Methods: Patients underwent whole-night PSG. Data on nocturnal HRV and AHI were analyzed. We determined the correlation of time- and frequency-domain parameters of HRV with the AHI. Results: A total of 62 participants (50 men and 12 women) were enrolled. The mean age, body mass index (BMI), neck circumference, and AHI score of the patients were 44.4 ± 11.5 years, 28.7 ± 5.2, 40.2 ± 4.8 cm, and 32.1 ± 27.0, respectively. The log root mean square of successive differences between normal heartbeats (RMSSD) were negatively correlated with BMI (p = 0.034) and neck circumference (p = 0.003). The log absolute power of the low-frequency band over high-frequency band (LF/HF) ratio was positively correlated with the AHI (p = 0.006). A higher log LF/HF power ratio (β = 5.01, p = 0.029) and BMI (β = 2.20, p < 0.001) were associated with a higher AHI value in multiple linear regression analysis. Conclusions: A higher log LF/HF power ratio and BMI were positively and significantly associated with the AHI during whole-night PSG in adult patients.

Background. Snoring is the cardinal symptom of obstructive sleep apnea (OSA). The acoustic features of snoring sounds include intra-snore (including snoring index [SI]) and inter-snore features. However, the correlation between snoring sounds and the severity of OSA according to the apnea–hypopnea index (AHI) is still unclear. We aimed to use the snoring index (SI) and the Epworth Sleepiness Scale (ESS) to predict OSA and its severity according to the AHI among middle-aged participants referred for polysomnography (PSG). Methods. In total, 50 participants (mean age, 47.5 ± 12.6 years; BMI: 29.2 ± 5.6 kg/m2) who reported snoring and were referred for a diagnosis of OSA and who underwent a whole night of PSG were recruited. Results. The mean AHI was 30.2 ± 27.2, and the mean SI was 87.9 ± 56.3 events/hour. Overall, 11 participants had daytime sleepiness (ESS > 10). The correlation between SI and AHI (r = 0.33, p = 0.021) was significant. Univariate linear regression analysis showed that male gender, body mass index, neck circumference, ESS, and SI were associated with AHI. SI (β = 0.18, p = 0.004) and neck circumference (β = 2.40, p < 0.001) remained significantly associated with AHI by the multivariate linear regression model. Conclusion. The total number of snores per hour of sleep and neck circumference were positively associated with OSA among adults referred for PSG.

Background: Snoring constitutes a worldwide public health concern that may be associated with daytime fatigue, endothelial dysfunction, vascular injury, stroke, cardiovascular diseases, and diabetes among female patients. This study explored the effects of the so-called Lin Oral Appliance (LOA) on Taiwanese adults’ snoring rates.Methods: A time series analysis was conducted to examine the associations between LOAs’ tongue compressors of different lengths, and snoring rates were calculated using the SnoreClock app. The LOA comprises 2 components: custom-made dental braces and tongue compressors of adjustable lengths; different versions had different-length compressors.Results: Our multiple linear regression time-series model revealed the effects of the LOA on snoring rates. The results indicated the following: i) LOA tongue compressor lengths of 1 and 2.5 cm (LOA-1 and LOA-2.5, respectively) were associated with reduced snoring rates; ii) sleep durations of 5.5-7.5 h and daytime sleepiness were associated with increased snoring rates; and iii) among participants with snoring rates above 10%, the snoring rates observed 1-7 days before a given day constituted a significant factor influencing snoring rates on the given day.Conclusions: We discovered that the LOA could reduce snoring rates and that the 2.5-cm compressor length in the LOA produced the best results.

Snoring is a nuisance for the bed partners of people who snore and is also associated with chronic diseases. Estimating the snoring duration from a whole-night sleep period is challenging. The authors present a dependable algorithm for visualizing snoring durations through acoustic analysis. Both instruments (Sony digital recorder and smartphone’s SnoreClock app) were placed within 30 cm from the examinee’s head during the sleep period. Subsequently, spectrograms were plotted based on audio files recorded from Sony recorders. The authors thereby developed an algorithm to validate snoring durations through visualization of typical snoring segments. In total, 37 snoring recordings obtained from 6 individuals were analyzed. The mean age of the participants was 44.6 ± 9.9 years. Every recorded file was tailored to a regular 600-second segment and plotted. Visualization revealed that the typical features of the clustered snores in the amplitude domains were near-isometric spikes (most had an ascending–descending trend). The recorded snores exhibited 1 or more visibly fixed frequency bands. Intervals were noted between the snoring clusters and were incorporated into the whole-night snoring calculation. The correlative coefficients of snoring rates from digitally recorded files examined between Examiners A and B were higher (0.865, P < .001) than those with SnoreClock app and Examiners (0.757, P < .001; 0.787, P < .001, respectively). A dependable algorithm with high reproducibility was developed for visualizing snoring durations.