Predictors Of Daytime Sleepiness Research Articles

Heart Rate Variability as a Predictor of Daytime Sleepiness during Prolonged Isolation

Heart Rate Variability as a Predictor of Daytime Sleepiness during Prolonged Isolation

Heart Rate Variability as a Predictor of Daytime Sleepiness During Prolonged Isolation

The 120-day isolation experiment (SIRIUS-19 program) within the SIRIUS project (http://sirius.imbp.ru/) involved 6 volunteers aged 28 to 44 years (three men and three women). Research methods included daily ECG registration to assess heart rate variability, blood pressure measurement and self-assessment of sleep quality over the past night using a visual analogue scale. The studies were carried out in the morning after waking up. Once a week in the evening (17.00–19.00), volunteers filled out a scale of clinical daytime sleepiness self-assessment, which allows evaluate the subjective condition over the past day. During the regression analysis, the possibility of predicting the appearance of daytime sleepiness in terms of cardiac activity was assessed. It was found, that the heart rate, blood pressure and heart rate variability indicators recorded in the morning time make it possible to measure the quality of past sleep and predict the level of subsequent daytime sleepiness. We assume that the increased activity of the parasympathetic branch of the autonomic nervous system in the morning characterizes both the previous lack of sleep and the risk of developing sleep inertia and daytime sleepiness in general.

0493 Daytime Sleepiness in Children with Asthma: Is it really the lungs?

Abstract Introduction Insufficient nocturnal sleep is the most common reason of excessive daytime sleepiness. Children with asthma are at increased risk for sleep disruption secondary to nocturnal respiratory symptoms and medication effects, as well as more common behavioral pediatric sleep challenges. Not yet clear is the relative contribution of asthma severity versus behavioral factors to daytime sleepiness in a sample of children (8-17 years) with asthma. Methods This survey-based study was conducted in the outpatient pulmonology clinic at Nemours Children’s Hospital, Delaware between 2018-2021. Children with asthma were eligible if they had a diagnosis of asthma, spoke English, and did not have a developmental disorder. Participants completed four surveys: Pediatric Daytime Sleepiness Scale (PDSS, higher scores=more sleepiness), Pediatric Quality of Life Inventory (PEDS-QL, higher scores=better quality of life), PROMIS Pediatric Anxiety short form (higher scores=more anxiety symptoms), and an investigator-designed survey assessing sleep-related technology use. Asthma factors (e.g., asthma severity, lung function [FEV1], number of prescribed asthma medications) were obtained from the electronic medical record. Descriptives and multiple regression analyses were conducted to identify asthma severity and behavioral predictors of daytime sleepiness, controlling for demographics. Results Study participants (N=100) were mean age 12.1 years [SD=2.6], 54% male, and 45% Black. Persistent asthma was common (87%), with a mean of 4.2 (SD=2.5) prescribed asthma medications, and mean FEV1 of 97% (SD=17.8). On behavioral surveys patients scored: PDSS (M=14.13; range=0-31; SD=6.7), PEDS-QL (M=77.38; range=15.6-100; SD=18.6), and PROMIS (M=14.92; range=8-35; SD=7.2). 74% reported cell phone usage within 1 hour of bedtime. Multiple regression models showed that an increased number of prescribed medications (beta=0.168, p=0.042), lower patient-reported quality of life (beta=-0.310, p=0.004), increased anxiety symptoms (beta=0.203, p=0.05) and bedtime cellphone use (beta=0.290, p=0.003) were significantly associated with daytime sleepiness in children with asthma. Conclusion A greater number of behavioral than asthma severity factors related to daytime sleepiness in children with asthma. Strategies to reduce anxiety and bedtime cellphone use may be plausible behavioral targets to improve sleep in children with asthma. Further research to examine these associations longitudinally, and in children with more severe asthma, is warranted. Support (If Any) None

Sleep Patterns and Predictors of Daytime Sleepiness in High School Students

Geung Jung Sohn, MD, Hyun Ju Kim, MD, PhD, Suk-Hoon Kang, MD, Myung Ho Lim, MD, PhD, Hae Gyung Chung, MD, Jin Hee Choi, MD, Tae Yong Kim, MD, PhD, and Hyungseok So, MD Department of Psychiatry, Korean Veteran Health Service Medical Center, Seoul, Korea 2 Department of Occupational & Environmental Medicine, Mokdong Hospital, Ewha Womans University, Seoul, Korea 3 Department of Psychology, College of Public Human Resources, Dankook University, Cheonan, Korea

Sleep fragmentation indices as predictors of daytime sleepiness and nCPAP response in obstructive sleep apnea.

Sleep fragmentation and respiratory disturbance measures are used in the assessment of obstructive sleep apnea (OSA) but have proved to be disappointingly poor correlates of daytime sleepiness. This study investigates the ability of electroencephalograph (EEG) and non-EEG sleep fragmentation indices to predict both presenting sleepiness and the improvement in sleepiness with subsequent nasal continuous positive airway pressure (nCPAP) therapy (nCPAP responsive sleepiness). Forty-one patients (36 men, 5 women), ranging from nonsnorers to severe OSA (> 4% O2 dip rate, median 11.1, range 0.4 to 76.5), had polysomnography with microarousal scoring, computerized EEG analysis, autonomic arousal detection, and body movement analysis. All patients received a trial of nCPAP regardless of sleep study outcome. Spearman's correlation analysis showed significant and similar associations between all sleep fragmentation indices with both pretreatment and nCPAP responsive sleepiness. There was no deterioration in sleepiness on nCPAP in the nonsnorers. Using stepwise multiple regression analysis, the best predictor of nCPAP responsive subjective and objective sleepiness was body movement index, explaining 38% and 43% of the variance, respectively. Variability in EEG sleep depth, quantified from computerized EEG analysis, was the only other index to contribute to these models. Together these indices explained 44% and 51% of the subjective and objective response to nCPAP, respectively. These results suggest that sleep fragmentation indices are useful for identifying OSA patients with sleepiness likely to respond to nCPAP.