An Analysis of Snoring Characteristics and Their Impact on Obstructive Sleep Apnea

Ng, Boon Han Kevin and Han, Seul A and Min, Jin-Young and Kim, Sung Wan and Min, Hye Kyu An Analysis of Snoring Characteristics and Their Impact on Obstructive Sleep Apnea. [Non Article]

PDF World Sleep Singapore 2025 - Copy.pdf Download (2MB)

Abstract

Introduction Snoring, a common clinical features associated with OSA, has garnered notable interest due to its potential diagnostic and predictive value. About 59% of women and 78% of men snore, making it a widespread condition. The objective of the study is to investigate the impact of snoring characteristics such as snoring intensity and snoring time on the severity of obstructive sleep apnea (OSA), exploring the dynamics and potential associations between these variables and other polysomnographic parameters. Materials and methods: Retrospective data analysis on patients who had polysomnography (PSG) between June 2019 and February 2020. The apnea-hypopnea index (AHI) was used as a measure of OSA severity. Snoring recordings were achieved by a non-contact directional microphone placed at the head of the bed, 20 cm from the vertex of the patients. Statistical analyses were performed using version 29 of the Statistical Package for the Social Sciences (SPSS) software. Pearson correlation coefficient, Mann-Whitney U test and Jonckheere Trend’s test were used for analysis. Results: A total of 248 patients underwent a PSG in the study period. Mean age was 41.9 ± 19.5 and mean body mass index was 25.4 ± 4.7. The polysomnographic parameters progressively increase as the patient’s age increases (P<0.001). Number of snoring episode and Epworth Sleepiness Scale (ESS) were higher in male patients and reached statistical significance (P < 0.05). In terms of snoring intensity, there is not much variation between male and female patients. Older patients have a progressively longer snoring time, higher snoring rate and a greater number of snoring episodes (P<0.001). The mean for snoring time, snoring rate, the number of snoring episodes, snoring intensity and ESS showed a gradual increase with increasing severity of AHI (P < 0.001). Polysomnographic parameters which included AHI, RDI, supine AHI, and REM and NREM index showed a positive correlation with snoring intensity (r= 0.341, r= 0.354, r= 0.385, r=0.322, r=0.137 respectively. P <0.001). Number of snoring episodes also showed a positive correlation with AHI, RDI, supine AHI, and REM and NREM index and (r= 0.391, r= 0.360, r= 0.309, r= 0.375, r = 0.355, respectively. P<0.001)). There was no significant correlation between the PSG parameters and snoring time, snoring rate and longest snoring episode. Conclusions: This study improves our understanding on the relationship between snoring characteristics and OSA severity. It highlights the intricate interactions between snoring and polysomnographic parameters as well as the substantial correlation between certain snoring qualities and the severity of OSA. Importantly, it suggests that snoring could indicate undiagnosed OSA, advocating for snoring to be used not only as a means of screening for OSA but as part of the diagnostic tool for OSA and as an adjunct to assess treatment outcome.

Actions (For repository members only: login required)

View Item