110 Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A45-A45
Author(s):  
Renske Lok ◽  
Jamie Zeitzer
Keyword(s):  
Daytime Sleepiness ◽  
Epworth Sleepiness Scale ◽  
Machine Learning Algorithms ◽  
Important Predictor ◽  
Health Study ◽  
Sleep Physiology ◽  
Cross Correlation Analysis ◽  
Explained Variance ◽  
Sleep Length ◽  
The Relationship

Abstract Introduction The Epworth Sleepiness Scale (ESS) is used as a clinical tool for determining excessive daytime sleepiness. However, the behavior and biology that underlie ESS scores remain to be elucidated. The main objective of this analysis is to determine objective behavioral and physiologic correlates of the ESS. Secondarily, we examine the relationship of the ESS to parallel subjective and objective endpoints that could represent measures of daytime sleepiness. Methods Using two separate machine learning algorithms, Random Forest and Lasso, we determined the association between ESS scores and 55 sleep and medical variables in individuals who participated in the Sleep Heart Health Study (N=2105). These variables include self-reported sleep characteristics (e.g., habitual sleep length and latency, frequency of not getting enough sleep), polysomnographic sleep measures from a single night, medication use, and mental and physical health status. Additional analyses were conducted on data stratified by age and gender. To investigate the relationship between ESS and other measures of daytime sleepiness, cross-correlation analysis was conducted on the ESS and five variables that could analog daytime sleepiness (feeling unrested, nap duration and frequency, sleep latency, frequency of not getting enough sleep). Results Analysis of the main dataset resulted in low explained variance (7.15 - 10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3–13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Habitual sleep length was not an important predictor in any model. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to ESS score. Conclusion Data analyses indicate that ESS scores are not well explained by habitual or polysomnographic sleep values, or a variety of other biomedical characteristics. This suggests that there are different, potentially orthogonal dimensions of the concept of “daytime sleepiness” that may be driven by different aspects of sleep physiology. Caution should be used when considering the ESS as a clinical measure given that the physiologic correlates still remain to be elucidated. Support (if any):

scholarly journals Physiological correlates of the Epworth sleepiness scale reveal different dimensions of daytime sleepiness

2021 ◽  
Author(s):  
Renske Lok ◽  
Jamie M Zeitzer
Keyword(s):  
Daytime Sleepiness ◽  
Epworth Sleepiness Scale ◽  
Machine Learning Algorithms ◽  
Health Study ◽  
Sleep Physiology ◽  
Scale Scores ◽  
Secondary Analyses ◽  
Clinical Meaning ◽  
Explained Variance ◽  
And Gender

Abstract The Epworth Sleepiness Scale is commonly used to examine subjective daytime sleepiness in clinical populations; the physiologic correlates of this scale, however, are not well understood. Furthermore, how well this scale correlates with parallel objective and subjective concepts of daytime sleepiness is not well described. As such, we used machine learning algorithms to examine the association between Epworth Sleepiness Scale scores and 55 sleep and medical variables in the Sleep Heart Health Study (N=2105). Secondary analyses examined data stratified by age and gender and the relationship between the Epworth and other measures of daytime sleepiness. Analyses of the main dataset resulted in low explained variance (7.15-10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3-13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to Epworth scores. We find that Epworth scores are not well explained by habitual or polysomnographic sleep values, or other biomedical characteristics. These analyses indicate that there are different, potentially orthogonal dimensions of the concept of “daytime sleepiness” that may be driven by different aspects of sleep physiology. As the physiologic correlates of the Epworth Sleepiness Scale remain to be elucidated, interpretation of the clinical meaning of these scores should be done with caution.

The relationship between weight change and daytime sleepiness: the Sleep Heart Health Study

2017 ◽  
Vol 36 ◽  
pp. 109-118 ◽  
Author(s):  
Winda L. Ng ◽  
Liliana Orellana ◽  
Jonathan E. Shaw ◽  
Evelyn Wong ◽  
Anna Peeters
Keyword(s):  
Weight Change ◽  
Daytime Sleepiness ◽  
Health Study ◽  
Heart Health ◽  
The Relationship

scholarly journals Comparison between Epworth Sleepiness Scale and Polysomnographic Parameters in Patients with Obstructive Sleep Apnea Syndrome

2017 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep Apnea Syndrome ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Epworth Sleepiness Scale ◽  
Sleep Apnea Syndrome ◽  
Obstructive Sleep ◽  
Apnea Syndrome ◽  
The Relationship

Introduction: Obstructive sleep apnea syndrome (OSAS) is the most common sleep-related respiratory disorder. With the purpose of assisting in the screening or evaluation of OSAS patients, some scales and questionnaires were proposed. Objective: To evaluate and critically compare the relationship of excessive daytime sleepiness with the presence or absence of OSAS, in addition to other polysomnographic data. Material and method: Systematic review of the literature. Results: Six studies were included, with a total of 4258 patients; four studies demonstrate association between the Epworth sleepiness scale and the hypopnea apnea index (AIH); of those, two studies showed significant differences between ESS score and OSAS severity. Five studies (4233 patients) demonstrated lower minimum oxygen saturation in the group with excessive daytime sleepiness. There was an increase in sleep efficiency in in patients with excessive daytime sleepiness, as per the results of 3 studies (3002 patients). Discussion: The relationship between the presence of excessive daytime sleepiness and OSAS is still questioned, and its pathophysiological mechanism uncertain. Excessive daytime sleepiness secondary to OSAS seems to be an independent event, related to nocturnal hypoxemia and sleep fragmentation. Conclusion: The confidence intervals obtained from these studies do not provide clinical safety to use the Epworth sleepiness scale for screening or stratifying the risk of patients suspected of having sleep apnea.

scholarly journals Be Aware of Burnout! The Role of Changes in Academic Burnout in Problematic Facebook Usage among University Students

2021 ◽  
Vol 18 (15) ◽  
pp. 8055
Author(s):  
Katarzyna Tomaszek ◽  
Agnieszka Muchacka-Cymerman
Keyword(s):  
Regression Coefficient ◽  
Population Sample ◽  
Cross Sectional ◽  
Academic Burnout ◽  
Independent Variables ◽  
Social Motive ◽  
Explained Variance ◽  
The Impact ◽  
The Relationship

Most previous research has examined the relationship between FB addiction and burnout level by conducting cross-sectional studies. Little is known about the impact of changes in burnout on FB addiction in an educational context. Through a two-way longitudinal survey of a student population sample (N = 115), this study examined the influence of changes in academic burnout over time and FB motives and importance (measured at the beginning and the end of the semester) on FB intrusion measured at the end of the academic semester. The findings show that: (1) increases in cynicism and in FB motives and importance significantly predicted time2 FB intrusion; (2) FB importance enhanced the prediction power of changes in the academic burnout total score, exhaustion and personal inefficacy, and reduced the regression coefficient of changes in cynicism; (3) the interaction effects between FB social motive use and changes in academic burnout, as well as between FB importance and personal inefficacy and exhaustion, accounted for a significant change in the explained variance of time2 FB intrusion. About 20–30% of the variance in time2 FB intrusion was explained by all the examined variables and by the interactions between them. The results suggest that changes in academic burnout and FB motives and importance are suppressive variables, as including these variables in the regression model all together changed the significance of the relationship between independent variables and FB intrusion.

scholarly journals The impact of diet during adolescence on the neonatal health of offspring: evidence on the importance of preconception diet. The HUNT study

2020 ◽  
pp. 1-13
Author(s):  
Wendy Van Lippevelde ◽  
Frøydis N. Vik ◽  
Andrew K. Wills ◽  
Sofia T. Strömmer ◽  
Mary E. Barker ◽  
...  
Keyword(s):  
Dietary Assessment ◽  
Sociodemographic Factors ◽  
Neonatal Outcomes ◽  
Health Study ◽  
Physical And Mental Health ◽  
Birth Registry ◽  
Meal Patterns ◽  
Dietary Choices ◽  
The Impact ◽  
The Relationship

Abstract Emerging evidence suggests that parents’ nutritional status before and at the time of conception influences the lifelong physical and mental health of their child. Yet little is known about the relationship between diet in adolescence and the health of the next generation at birth. This study examined data from Norwegian cohorts to assess the relationship between dietary patterns in adolescence and neonatal outcomes. Data from adolescents who participated in the Nord-Trøndelag Health Study (Young-HUNT) were merged with birth data for their offspring through the Medical Birth Registry of Norway. Young-HUNT1 collected data from 8980 adolescents between 1995 and 1997. Linear regression was used to assess associations between adolescents’ diet and later neonatal outcomes of their offspring adjusting for sociodemographic factors. Analyses were replicated with data from the Young-HUNT3 cohort (dietary data collected from 2006 to 2008) and combined with Young-HUNT1 for pooled analyses. In Young-HUNT1, there was evidence of associations between dietary choices, meal patterns, and neonatal outcomes, these were similar in the pooled analyses but were attenuated to the point of nonsignificance in the smaller Young-HUNT3 cohort. Overall, energy-dense food products were associated with a small detrimental impact on some neonatal outcomes, whereas healthier food choices appeared protective. Our study suggests that there are causal links between consumption of healthy and unhealthy food and meal patterns in adolescence with neonatal outcomes for offspring some years later. The effects seen are small and will require even larger studies with more state-of-the-art dietary assessment to estimate these robustly.

scholarly journals Who Likes What Kind of News? The Relationship Between Characteristics of Media Consumers and News Interest

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110030
Author(s):  
Kai Kaspar ◽  
Lisa Anna Marie Fuchs
Keyword(s):  
Big Five ◽  
Information Filtering ◽  
Personal Characteristics ◽  
Self Esteem ◽  
Big Five Personality ◽  
Regression Analyses ◽  
Uses And Gratification ◽  
German Speaking ◽  
Explained Variance ◽  
The Relationship

Stimulated by the uses-and-gratification approach, this study examined the joint relation of several consumer characteristics to news interest. In total, 1,546 German-speaking participants rated their interest in 15 major news categories and several personal characteristics, including gender, age, the Big Five personality traits, self-esteem, as well as general positive and negative affect. Regression analyses examined the amount of interindividual variance in news interest that can be explained by this set of consumer characteristics. Overall, the amount of explained variance differed remarkably across news categories, ranging from 4% for entertainment-related news to 25% for news about technology. The most powerful explaining variables were participants’ gender, age, openness to experiences, and their amount of general positive affect. The results suggest that news interest should be defined and operationalized as a concept with multiple facets covering a huge range of content. Also, the results are important for media producers and journalists with respect to the conflict between increased need gratification of consumers and information filtering via personalized news content.

scholarly journals The Relationship between Suicidality and Socio-Demographic Variables, Physical Disorders, and Psychiatric Disorders: Results from the Singapore Mental Health Study 2016

2021 ◽  
Vol 18 (8) ◽  
pp. 4365
Author(s):  
Kundadak Ganesh Kudva ◽  
Edimansyah Abdin ◽  
Janhavi Ajit Vaingankar ◽  
Boon Yiang Chua ◽  
Saleha Shafie ◽  
...  
Keyword(s):  
Mental Health ◽  
Suicidal Ideation ◽  
Psychiatric Disorders ◽  
Population Level ◽  
Risk Groups ◽  
Epidemiological Survey ◽  
Health Study ◽  
Physical Disorders ◽  
Mental Health Study ◽  
The Relationship

Suicidality encompasses suicidal ideation, plans, and attempts. This paper aims to establish associations between suicidality and sociodemographic variables, physical disorders, and psychiatric disorders. The Singapore Mental Health Study 2016 was a population-level epidemiological survey, which determined the prevalence of physical disorders, psychiatric disorders, and suicidality. Questionnaires were used to determine socio-demographic information. A total of 6216 respondents were interviewed. Lifetime prevalence of suicidal ideation, planning, and attempts were 7.8%, 1.6%, and 1.6%, respectively. All components of suicidality were more likely in those with major depressive disorder, bipolar disorder, generalized anxiety disorder, alcohol use disorder, and chronic pain. Suicidal ideation and attempts were more likely in those with diabetes. Age above 65, being male, and a monthly household income of ≥ SGD 10,000 were associated with a lower likelihood of suicidal ideation. These findings indicate that there are high-risk groups for whom suicidality is a concern, and for whom interventions may be needed.

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

scholarly journals Daily Sitting for Long Periods Increases the Odds for Subclinical Atheroma Plaques

2021 ◽  
Vol 10 (6) ◽  
pp. 1229
Author(s):  
Jose Luis Perez-Lasierra ◽  
Martin Laclaustra ◽  
Pilar Guallar-Castillón ◽  
Jose Antonio Casasnovas ◽  
Jose Antonio Casajús ◽  
...  
Keyword(s):  
Physical Activity ◽  
Subclinical Atherosclerosis ◽  
Sitting Time ◽  
Health Study ◽  
Activity Levels ◽  
Cross Sectional ◽  
Trained Personnel ◽  
Atherosclerosis Development ◽  
Sectional Analysis ◽  
The Relationship

Sedentarism is a risk factor for cardiovascular disease (CVD), but currently it is not clear how a sedentary behavior such as long sitting time can affect atherosclerosis development. This study examined the relationship between sitting time and the prevalence of carotid and femoral subclinical atherosclerosis. A cross-sectional analysis based on a subsample of 2082 participants belonging to the Aragon Workers’ Health Study was carried out. Ultrasonography was used to assess the presence of plaques in carotid and femoral territories; the validated Spanish version of the questionnaire on the frequency of engaging in physical activity used in the Nurses’ Health Study and the Health Professionals’ was used to assess physical activity and sitting time; and demographic, anthropometric, and clinical data were obtained by trained personnel during the annual medical examination. Participants were categorized into <9 h/day and ≥9 h/day sitting time groups. After adjusting for several confounders, compared with participants that remain seated <9 h/day, those participants who remain seated ≥9 h/day had, respectively, OR = 1.25 (95%CI: 1.01, 1.55, p < 0.05) and OR = 1.38 (95%CI: 1.09, 1.74, p < 0.05) for carotid and any-territory plaque presence. Remaining seated ≥9 h/day is associated with higher odds for carotid and any-territory plaque presence independently of physical activity levels and other cardiovascular risk factors.

Sign in / Sign up

Export Citation Format

Share Document