Objective: Individuals with obstructive sleep apnea (OSA) are at increased risk to suffer from further somatic and sleep-related complaints. To assess OSA, demographic, anthropometric, and subjective/objective sleep parameters are taken into consideration, but often separately. Here, we entered demographic, anthropometric, subjective, and objective sleep- and breathing-related dimensions in one model. Methods: We reviewed the demographic, anthropometric, subjective and objective sleep- and breathing-related data, and polysomnographic records of 251 individuals with diagnosed OSA. OSA was considered as a continuous and as categorical variable (mild, moderate, and severe OSA). A series of correlational computations, X²-tests, F-tests, and a multiple regression model were performed to investigate which demographic, anthropometric, and subjective and objective sleep dimensions were associated with and predicted dimensions of OSA. Results: Higher apnea/hypopnea index (AHI) scores were associated with higher BMI, higher daytime sleepiness, a higher respiratory disturbance index, and higher snoring. Compared to individuals with mild to moderate OSA, individuals with severe OSA had a higher BMI, a higher respiratory disturbance index (RDI) and a higher snoring index, while subjective sleep quality and daytime sleepiness did not differ. Results from the multiple regression analysis showed that an objectively shorter sleep duration, more N2 sleep, and a higher RDI predicted AHI scores. Conclusion: The pattern of results suggests that blending demographic, anthropometric, and subjective/objective sleep- and breathing-related data enabled more effective discrimination of individuals at higher risk for OSA. The results are of practical and clinical importance: demographic, anthropometric, and breathing-related issues derived from self-rating scales provide a quick and reliable identification of individuals at risk of OSA; objective assessments provide further certainty and reliability.

Background: While there is sufficient evidence that children and adolescents who stutter reported more impaired sleep compared to children and adolescents who did not stutter, findings among adults who stutter (AWS) were scarce. Furthermore, stuttering is associated with issues related to verbal communication in a social context. As such, it was conceivable that AWS reported higher scores for social anxiety, compared to adults who do not stutter (AWNS). In the present study, we tested whether AWS reported higher sleep complaints compared to AWNS. We further tested whether scores for social anxiety and stuttering independently predicted sleep disturbances. Methods: A total of 110 AWS (mean age; 28.25 years, 27.30% females) and 162 AWNS (mean age; 29.40 years, 51.20% females) completed a series of self-rating questionnaires covering sociodemographic information, sleep disturbances and social anxiety. Adults with stuttering further completed a questionnaire on stuttering. Results: Compared to AWNS, AWS reported a shorter sleep duration, a lower sleep efficiency, higher scores for drug use in terms of sleep-promoting medications (significant p-values and medium effect sizes), and an overall higher PSQI score (significant p-values and large effect size), when controlling for age and social anxiety. Next, while p-values were always significant for subjective sleep quality, sleep disturbances, and daytime functioning, when controlling for age and social anxiety, their effect sizes were trivial or small. For sleep latency, the p-value was not significant and the effect size was trivial. Among AWS, higher scores for stuttering and older age, but not social anxiety, predicted higher sleep disturbances. The association between higher sleep disturbances and higher stuttering severity was greatest among those AWS with highest scores for social anxiety. Conclusions: When compared to AWNS, AWS self-reported higher sleep disturbances, which were associated with older age, and higher scores for stuttering severity, but not with social anxiety. Adults who stutter might be routinely asked for their sleep quality.

The data prepared here had been originally collected for a study project entitled 'Breathing pattern analysis in insomnia suffers'. This data describes the information of 82 individuals; participating 41 normal individuals and 41 insomnia suffers with tow phenotype included 30 sleep state misperception and 11 psychophysiological suffers. The data presents 8 hours of respiratory signals included flow pressure, flow temperature, Oxygen saturation, Thorax and Abdomen signal in frequency sampling 256, 32, 32, 32, 4 Hz respectively. It includes breathing features and sleep profiles in segments of 30s for each individuals. In addition, the full demographic and objective specifications was attached.