Predictors Of Sleepiness Research Articles

Predictors of sleepiness in a large-scale epidemiology study ESSE-RF.

To identify predictors of excessive daytime sleepiness we analyzed data from the 'Epidemiology of cardiovascular diseases in regions of Russia (ESSE-RF)' study. Data from participants of the cohort study ESSE-RF (2012-2013), aged 25-64 years, from 13 regions of Russia were analyzed (2012-2013). The participants were interviewed regarding their sleep complaints, including difficulties with initiating and maintaining sleep, sleepiness, and use of sleeping pills. Sleepiness was considered significant if it occurred at least three times a week. The examination encompassed social, demographic, and anthropometric measures, lifestyle factors, self-reported diseases, and laboratory parameters. The final analysis included 13,255 respondents. Frequent (≥3 times/week) sleepiness was reported by 5,8%, and occasional sleepiness (1-2 times/week) by 10.8% of respondents. Multivariate regression analysis identified significant predictors of frequent sleepiness. Sleep complaints (insomnia, sleep apnea, snoring) and frequent use of sleep medication were prominent factors. Additionally, age, female gender, higher education, and retirement status were associated with sleepiness. Beyond demographics and sleep, the analysis revealed predictors: abnormal anxiety levels, low high-density lipoprotein, high salt intake and following medical conditions: arrhythmia, hypertension, myocardial infarction, other heart diseases, and renal disease. This study identified a significant prevalence of EDS in Russians, aligning with global trends. However, findings suggest potential regional variations. Analysis revealed a complex interplay of factors contributing to EDS, highlighting the importance of individualized treatment approaches for improved sleep health.

P082 Neural aperiodic activity as a novel biomarker of sleepiness in patients with obstructive sleep apnoea

Abstract Background Obstructive sleep apnoea (OSA) is characterised by repetitive collapse of the upper airway causing intermittent episodes of hypoxia and apnoeas during sleep. Excessive daytime sleepiness (EDS) is the cardinal symptom of OSA and presents in between 40.5 to 58% of patients. Currently, the diagnosis of OSA requires demonstration of an elevated apnoea hypopnea index (AHI) on a polysomnogram (PSG). Higher AHI scores denote increased OSA disease severity. The correlation between objective AHI scores and subjective sleepiness reporting tools such as the Epworth Sleepiness Scale (ESS) is less clear. Electroencephalogram (EEG) readings form an important component of PSG testing. Traditionally, the periodic oscillatory waves captured on EEG are examined to accurately identify stages of sleep. EEG aperiodic activity is a measure of neural excitation/inhibition ratios and has emerged as a novel objective measure of sleepiness. Methods Intended data collection from 500 patients who have undergone ambulatory PSG testing at the Royal Adelaide Hospital between January 1st 2021 and December 31st 2022 as part of their routine care. Data collection will include patient demographics, cardiorespiratory comorbidities, medications, body mass index (BMI), Epworth Sleepiness Scale (ESS) and PSG EEG data including sleep parameters [Total sleep time (TST), stage of sleep, periodic limb movement limb index (PLMI), AHI scores]. Progress to date Research proposal is currently under review by the CALHN HREC. Intended outcome and impact In this study we will assess the utility of aperiodic neural activity as a predictor of sleepiness in patients with elevated AHI and ESS scores.

Predictors of sleepiness in obstructive sleep-apnea syndrome

Abstract Introduction Excessive daytime sleepiness is a common complaint among patients with obstructive sleep apnea (OSA). On the contrary, OSA has been cited as the most common medical issue linked to excessive daytime sleepiness. In patients with OSA, there is considerable interindividual unpredictability in sleepiness, the determinants of this variability are obscure; also, there is disparity in data correlating subjective with objective sleepiness. Aim The aim of this research is to survey the predictors that assess sleepiness in patients newly diagnosed with and how they correlate to the degree of OSA severity. Patients and methods A cross-sectional research of 80 patients with recently diagnosed OSA using polysomnography (PSG) (apnea–hypopnea index ≥5/h). Subjective sleepiness was estimated by Epworth Sleep Scale (ESS) (≥10). However, objective sleepiness was determined by Psychomotor Vigilance Test (PVT) (mean lapse ≥2). Participants were also analyzed using the basic language Morningness scale, the STOP-BANG, and Berlin Questionnaires, and the interleukin 6 level was tested. Results There were four groups of participants (a: sleepy by ESS and PVT, b: sleepy by PVT only, c: sleepy by ESS only, and d: nonsleepy reference group). Sleepiness was found to be protected by longer habitual sleep duration, morning chronotype, and higher sleep efficacy [P=0.001 and odds ratio (OR)=0.549 and P=0.01 and OR=0.114 and P=0.001 and OR=0.025, respectively]. Sleepiness was attributed with no specific chronotype and a greater arousal index (P=0.043 and OR=9.14 and P=0.002, OR=1.14). In group 1, the level of interleukin 6 was higher. Conclusion Sleepiness in OSA was associated with short sleep duration, evening, and no specific chronotype, increased arousal index, and lower sleep efficacy. These findings will help to guide future research into the development of sleepiness in OSA.

Working conditions and sleepiness while driving among truck drivers

Objective: The objective of this study is to identify the role of working conditions as predictors of sleepiness while driving among truck drivers.Methods: This was a cross-sectional study carried out among truck drivers who transported grains to Paranaguá Port, Paraná, Brazil. The truck drivers were interviewed and completed a self-administered questionnaire to collect data on sociodemographic and behavioral variables, working conditions, consumption of illicit psychoactive substances, and sleep patterns. Drivers were considered to be sleepy while driving if they reported a medium or high probability of napping while driving at night, during the daytime, or while stopped in traffic. The statistical analysis used logistic regression models progressively adjusted for age, behavioral variables, sleep duration, and other working conditions.Results: In total, 670 male drivers, with a mean age of 41.9 (±11.1) years, were enrolled. The prevalence of sleepiness while driving was 31.5%. After model adjustments, the following working conditions were associated with sleepiness while driving: Distance from the last shipment of more than 1,000 km (odds ratio [OR] = 1.54; 95% confidence interval [CI], 1.07–2.23) and a formal labor contract with a productivity-based salary (OR = 2.65; 95% CI, 1.86–3.78). Consumption of illicit psychoactive substances (OR = 1.99; 95% CI, 1.14–3.47) was also associated with sleepiness while driving.Conclusions: Distance traveled and a formal labor contract with productivity-based earnings were the working conditions associated with sleepiness while driving, regardless of other working or behavioral characteristics, age, consumption of illicit psychoactive substances, and sleep duration.

Predictors of sleepiness in obstructive sleep apnoea at baseline and after 6 months of continuous positive airway pressure therapy.

We evaluated factors associated with subjective and objective sleepiness at baseline and after 6 months of continuous positive airway pressure (CPAP) therapy in patients with obstructive sleep apnoea (OSA).We analysed data from the Apnoea Positive Pressure Long-term Efficacy Study (APPLES), a prospective 6-month multicentre randomised controlled trial with 1105 subjects with OSA, 558 of who were randomised to active CPAP. Epworth sleepiness scale (ESS) scores and the mean sleep latency (MSL) on the maintenance of wakefulness test at baseline and after 6 months of CPAP therapy were recorded.Excessive sleepiness (ESS score >10) was present in 543 (49.1%) participants. Younger age, presence of depression and higher apnoea-hypopnoea index were all associated with higher ESS scores and lower MSL. Randomisation to the CPAP group was associated with lower odds of sleepiness at 6 months. The prevalence of sleepiness was significantly lower in those using CPAP >4 h·night-1versus using CPAP ≤4 h·night-1 Among those with good CPAP adherence, those with ESS >10 at baseline had significantly higher odds (OR 8.2, p<0.001) of persistent subjective sleepiness.Lower average nightly CPAP use and presence of sleepiness at baseline were independently associated with excessive subjective and objective sleepiness after 6 months of CPAP therapy.

Absorbed in sleep: Dissociative absorption as a predictor of sleepiness following sleep deprivation in two high-functioning samples.

In recent years, a labile sleep-wake cycle has been implicated as a cause for dissociative experiences, and studies show that dissociation is elevated following sleep deprivation. Dissociative individuals may find it harder to regulate sleepiness in the face of sleep disruption. Although there is significant variability in reactions to sleep deprivation, research on trait predictors is scarce. The present study examined the ability of trait dissociation to prospectively predict sleepiness following sleep loss and recovery sleep. Two high-functioning samples, namely, Remotely Piloted Aircraft officers (N=29) and Air Force jet pilots (N=57) completed state and trait questionnaires assessing sleep and dissociation before and after full or partial sleep loss. Dissociative absorption was a consistent predictor of an increase in sleepiness following sleep loss and following recovery sleep, controlling for baseline sleepiness levels. We discuss the findings in light of a difficulty to regulate and monitor consciousness states.

Depressive symptoms and obesity as predictors of sleepiness and quality of life in patients with REM-related obstructive sleep apnea: Cross-sectional analysis of a large clinical population

The clinical significance of rapid eye-movement (REM)-related obstructive sleep apnea (OSA) is unknown. Moreover, it is unclear what the determinants of sleepiness and quality of life (QoL) are among these patients. Our aim was to identify whether the apnea-hypopnea index during REM sleep (AHI(REM)), AHI during NREM sleep (AHI(NREM)), depressive symptoms, or obesity are independent predictors of excessive daytime sleepiness and reduced QoL in patients with REM-related OSA. We also assessed if these characteristics were predictors of sleepiness and QoL in all patients with OSA (AHI ≥ 5) as well as in non-stage specific OSA. This was a cross-sectional analysis of a clinic-based population with OSA. In order to minimize the contribution of AHI(NREM), we defined REM-related OSA using the following criteria: an overall AHI ≥ 5, AHI(REM)/AHI(NREM) ≥ 2, AHI(NREM)<lowest quartile, and REM sleep in minutes > the lowest quartile of the entire cohort. We examined the predictors of subjective sleepiness using the Epworth Sleepiness Scale (ESS) and QoL using the short-form quality of life questionnaire-12 (SF-12). Of 1019 consecutive adults that were referred for their first in-laboratory polysomnogram for suspicion of OSA over a 10 month period, 931 had OSA. REM-related OSA was present in 126 patients. In adjusted linear regression models, AHI(NREM) was a significant predictor of sleepiness in the entire cohort of patients with OSA as well as non-stage specific OSA, but not in the REM-related OSA group. AHI(REM) was not a significant predictor of ESS or QoL in any of the three groups. However, greater depressive symptoms and body mass index were significant independent predictors of ESS and reduced QoL in the REM-related OSA group. Higher depression scores and obesity, rather than the severity of OSA (as measured by AHI(NREM) and AHI(REM)), were predictive of sleepiness and QoL scores in patients with REM-related OSA.

Improving the Utility of Interpreting Sleep Fragmentation

Recognition of the importance of EEG arousals in determining daytime function was facilitated by the development of the multiple sleep latency test (MSLT) as an objective measure of physiological sleepiness. Once the MSLT was incorporated into routine clinical practice, Roth et al1 found that of all EEG-based sleep parameters, shifts to stage one sleep were the best predictors of sleepiness, as measured by the MSLT, in patients with sleep apnea. This was an unexpected finding given the bias at the time that total sleep time, amount of slow wave sleep, or amount of REM sleep would provide a better indicator of the degree to which sleep was restorative. Carskadon et al2 reported that “brief arousals” had a significant correlation with MSLT score in older adults. At this time, each study used a unique approach to quantifying brief arousals, as there was no standard definition to allow systematic evaluation of this aspect of sleep. Early work in this area sought to establish an operational definition of arousal that provided the best prediction of daytime sleepiness.3 A consensus process ultimately led to the American Sleep Disorders Association (ASDA, now called the American Academy of Sleep Medicine) definition for quantifying EEG arousals in 1992.4 Subsequent to that important event, research on sleep fragmentation has been steady, but sparse.5 There have been attempts to refine the definition of arousal given that the ASDA measure does not uniformly provide robust correlations with daytime sleepiness. The bulk of the work exploring other approaches to quantifying arousals has occurred in the area of “autonomic” or “subcortical” arousals.6,7 These studies have primarily focused on arousals that occur in patients with sleep disordered breathing. A comprehensive review of these studies did not find that there is sufficient justification to include measurement of autonomic arousals as a routine clinical procedure in sleep study interpretation.8 A recent study reports significant correlations with sleepiness, as measured by the Epworth Sleepiness Scale, when using a measure of “long arousal,” 15–60 seconds, in patients with obstructive sleep apnea.9 Further work is needed to assess the utility of this measure in other patient groups, and to assess prediction of objective measures of sleepiness. Another innovative approach to measuring sleep fragmentation is to evaluate inter-arousal interval.10,11 The rationale for this approach is that arousals are presumably important predictors of sleepiness because they interrupt the continuity of sleep and thereby interfere with the restorative function of sleep.12 Studies producing various schedules of experimental sleep fragmentation suggest that intervals of sleep must be 5–10 minutes to provide restoration (i.e., eliminate daytime sleepiness).13,14 A recent study used survival curve analysis to analyze the duration each sleep segment and found differences in this metric between normal sleepers and patients with moderate to severe obstructive sleep apnea.15 Despite the attempts described above, in the 15 years since the ASDA definition of arousal was established, no research has demonstrated improvement in predicting objective sleepiness with a new measure of sleep fragmentation.8 In this issue of the Journal, Bonnet and Arand provide age-related norms for ASDA-defined EEG arousals in normal sleepers.16 Given routine use of this metric for guiding interpretation of clinical sleep studies, this addition to the literature seems overdue, and is quite welcome. Who has not had a sleep medicine fellow learning how to interpret a polysomnogram ask, “So what is a normal arousal index for a 60-year-old?” As described in this paper, a few studies have reported significant positive correlations between age and arousal index. These results supported the importance of age as a factor in determining the number of arousals, but normative data by decade to use as a basis of comparison has not been available until now. Of interest, number of arousals correlates significantly with nearly all sleep parameters in the direction of indicating decreased quantity and quality of sleep as arousals increase. Given the deterioration of sleep observed in older adults, it is not surprising that the arousal index is double for individuals over the age of 50 years compared with those under the age of 30 years. An additional strength of these data is that the authors report reliability data for the scoring of the arousals. Establishing reliability for scoring arousals is difficult and often overlooked. As the authors note, the samples here are small, and results as they relate to sex in particular need further validation with larger samples.

Daytime sleepiness and alertness in patients with Parkinson disease.

The goal of the study was to objectively measure daytime sleepiness and alertness in patients undergoing treatment for idiopathic Parkinson disease (PD) and assess potential contributing factors. Prospective study. An accredited sleep disorders center in a university-based medical center in a large urban area. Twenty consecutive subjects with idiopathic PD were recruited from a tertiary-care movement disorders clinic. N/A. Patients underwent polysomnography, with a Multiple Sleep Latency Test (MSLT) and a Maintenance of Wakefulness Test (MWT) on the following day. Forty-seven percent of the sample was pathologically sleepy according to the MSLT, but only 26% had abnormal MWT scores. There was also a different pattern of correlations between predictors of sleepiness and MSLT and MWT scores. Percentage of stage 1 sleep had the largest correlation with MSLT (-0.42), while pergolide equivalents had the largest correlation with MWT (-0.70). In this sample of subjects with PD, severe daytime sleepiness was common and was related to poor sleep quality. Daytime alertness as measured by the MWT was impaired as medication burden increased.