作者: Molina, Gary Garcia ; Siyahjani, Farzad ; Gorski, Brandon ; Mills, Rajasi ; McGhee, Leah ; DeFranco, Susan ; Babaeizadeh, Saeed ; Aloia, Mark ; Chellamuthu, Vidhya ; Mushtaq, Faisal

AbstractIntroductionSnoring and obstructive sleep apnea (OSA) are bidirectionally associated. The majority of OSA patients snore and a substantial percentage of those who snore have OSA. The intensity and the acoustic properties of snoring in apnea patients have been shown to correlate with OSA presence. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring, sleep metrics, demographics, and OSA.MethodsAn IRB-approved survey, which included demographics and questions about sleep and its associated disorders, was presented to a cohort of Sleep Number (SN) customers during the period between June 12 to 26, 2023. Objective data collected by the SN platform included sleep duration, restful sleep, sleep latency, sleep quality, mean respiration rate, mean heart rate, and mean heart rate variability. Data were collected May 1st - June 30-2023, and were augmented by survey responses to examine associations between apnea presence, snoring properties, and objective data.ResultsOut of 22048 (12476F/9537M) respondents with mean age 55.8 (SD: 14.1) years, 3163 reported a diagnosis of apnea and 10558 reported no sleep disorder. Snoring was reported by 89.9% and 65.2% of the apnea and healthy sleepers, respectively. Among the respondents who snored, 29.2% had an apnea diagnosis (apnea and snoring are associated with R2=0.02). A mixed model considering age, gender, reported breathing interruption, snore intensity and frequency as predictors and apnea as the dependent variable resulted in a higher R2 = 0.19. The addition of objective data collected by the smart bed increased the R2 = 0.53. A logistic regression model using demographics, snoring properties, and objective metrics as independent and apnea presence as dependent variables, was trained and tested with the data from 80% and 20% of the individuals respectively. This model resulted in 0.91 area under the receiving operator curve, and 84% sensitivity and 90% specificity to detect apnea.ConclusionObjective data from a smart bed combined with demographic and snore properties can be used to screen apnea risk. This research is limited by the absence of information about apnea severity.Support (if any)

2024-04-20·SLEEP

0295 Influence of Sleep Regularity, Chronotype, and Sleep Duration on Daytime Sleepiness Caused by Sleep Disorders

作者: Chellamuthu, Vidhya ; McGhee, Leah ; Aloia, Mark ; Mills, Rajasi ; DeFranco, Susan ; Gorski, Brandon ; Molina, Gary Garcia

AbstractIntroductionExcessive daytime sleepiness is characterized by the difficulty of staying awake during daytime. The goal of this research is to examine the role of regularity, sleep duration, and chronotype on the association between sleep disorders and daytime sleepiness. To do this, we leveraged the Sleep Number platform to acquire longitudinal objective sleep with survey data from a single point in time.MethodsAn IRB-approved survey, which included questions about diagnosed sleep disorders and daytime sleepiness, was presented to a cohort of Sleep Number customers in the June 12 to 26, 2023 period. Objective data including sleep duration, bedtime, restful sleep duration, mean breathing rate, and heart rate collected between May 1 and June 30, 2023 were used along with survey responses. Individual sleep regularity and chronotype were quantified using objective data. Regularity was categorized into regular or irregular if bedtime variability was lower or greater than 60 minutes. Chronotype was categorized into early, intermediate, or late if mean sleep onset time was before 10 pm, between 10 and 11:59 pm, or after 12 am. Odd-ratios (OR) were used to quantify the influence of regularity, chronotype, and sleep duration on daytime sleepiness moderated by sleep disorders.ResultsThe responder count was 22,082 (9530M/12479F). Men were 56.6 (SD 13.9) and Women 54.9 (SD 13.8) years-old on average. Daytime sleepiness was significantly associated with the presence of untreated sleep disorders (insomnia, apnea, and RLS). Individuals being treated for apnea and insomnia demonstrated significantly less daytime sleepiness. Healthy individuals showed a significant association between early chronotype and longer sleep duration with reduced daytime sleepiness. Individuals with apnea who receive treatment showed a significant association between longer sleep and reduced daytime sleepiness. In the case of insomnia, regularity, chronotype or sleep duration do not moderate any significant relationship with daytime sleepiness. For RLS longer sleep duration leads to lower daytime sleepiness.ConclusionDaytime sleepiness is significantly associated with any untreated sleep disorders. Regularity, chronotype and sleep duration had limited influence on daytime sleepiness caused by sleep disorders. Healthy individuals had lower daytime sleepiness if they had an earlier chronotype and longer sleep duration.Support (if any)

2024-04-20·SLEEP

0323 Is Snoring Associated with Lower Sleep Quality? If Yes, Does the Association Depend on Sleep Apnea?

作者: Mills, Rajasi ; Molina, Gary Garcia ; Gorski, Brandon ; Aloia, Mark ; DeFranco, Susan ; McGhee, Leah ; Chellamuthu, Vidhya

AbstractIntroductionSnoring, as a common manifestation of sleep apnea, has often been used as a surrogate parameter for apnea. Significant associations were found between snoring and daytime sleepiness and cardiovascular conditions. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring and sleep quality depending on the presence of treated or untreated apnea.MethodsAn IRB-approved survey, which included demographics and questions about sleep and its associated disorders, was presented to a cohort of Sleep Number (SN) customers during the period between June 12 to 26, 2023. Objective data collected by the SN platform included sleep duration, restful sleep, sleep latency, mean respiration rate, mean heart rate, mean heart rate variability, and sleep quality. The latter considers a demographic dependent aggregation of sleep duration, restful sleep and cardiorespiratory activity. Data were collected May 1st - June 30-2023, and were augmented by survey responses. Four groups were defined for analysis HNS: non-snorers reporting no sleep disorder, HES: snorers reporting no sleep disorders, TAS: snorers with treated apnea, and UAS: snorers with untreated apnea. Analysis of variance (ANOVA) was performed to identify any significant objective data differences between the groups. A second analysis focused on identifying the specific metric showing significant differences.ResultsThe demographic responder composition per group was HNS 2454 (1717F/734M) individuals aged 53.9 (SD: 15.0) years-old, HES: 4702 (2626F/2069M) individuals aged 55.1 (SD: 13.5) years-old, TAS: 1784 (679F/1103M) individuals aged 59.7 (SD: 12.0) years-old, and UAS: 134 (59F/75M) individuals aged 59.4 (SD: 10.9) years-old. The ANOVA revealed significant objective data differences between groups. Only sleep quality showed significant group differences. Quality decreased significantly with respect to the HNS reference. Healthy snorers had a 0.96% lower quality, snorers with treated apnea had 1.8% lower quality, and snorers with untreated apnea had 3.9% lower quality.ConclusionCompared to healthy non-snorers, sleep quality quantification by a smart bed suggests a gradual decrease for healthy snorers, snorers with treated apnea, and snorers with untreated apnea.Support (if any)

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100 项与 Sleep Number Labs 相关的转化医学

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