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Sleep Medicine Sep 2023The Epworth Sleepiness Scale (ESS) is one of the most used self-reported instruments to assess sleepiness. Thus, several adaptations into different Languages have been... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE/BACKGROUND
The Epworth Sleepiness Scale (ESS) is one of the most used self-reported instruments to assess sleepiness. Thus, several adaptations into different Languages have been performed worldwide over the years. The scale has produced disparate psychometric properties when applied in different settings. In the current study, our aim was to perform a Reliability Generalization meta-analysis of the Cronbach᾽s alphas of all published studies on ESS, specifically with a psychometric focus.
PATIENTS/METHODS
Three reference databases (Scopus, PubMed and Web of Science) were searched since 1991 to October 2022 and all the records on psychometric or validation studies that reported Cronbach's alphas, from clinical and nonclinical groups, were included. In total, data from 46 publications (63 estimates) were extracted, comprising 92,503 participants.
RESULTS
Using a Random-Effects Model, the cumulative Cronbach's alpha for the 63 estimates was about 0.82 (CI: 0.798, 0.832) which can be considered as a good measure. However, and as expected, it was observed a high level of heterogeneity (I = 98.96%). Moderation analyses considering setting, date, continent, risk of bias, sex, age and language were performed in order to account for the heterogeneity. Even so, only the variables study setting and continent were significant, and had little importance in explaining the heterogeneity.
CONCLUSIONS
The ESS is a reliable tool to measure sleepiness; however, further studies are needed to investigate what variables might explain the observed variability. Moreover, it will be important to include empirical studies beyond psychometric ones.
Topics: Humans; Sleepiness; Reproducibility of Results; Surveys and Questionnaires; Psychometrics; Wakefulness
PubMed: 37487279
DOI: 10.1016/j.sleep.2023.07.008 -
Sleep Oct 2021Assess the validity of a subjective measure of sleepiness as an indicator of sleep drive by quantifying associations between intraindividual variation in evening...
STUDY OBJECTIVES
Assess the validity of a subjective measure of sleepiness as an indicator of sleep drive by quantifying associations between intraindividual variation in evening sleepiness and bedtime, sleep duration, and next morning and subsequent evening sleepiness, in young adults.
METHODS
Sleep timing and sleepiness were assessed in 19 students in late autumn and late spring on a total of 771 days. Karolinska Sleepiness Scales (KSS) were completed at half-hourly intervals at fixed clock times starting 4 h prior to participants' habitual bedtime, and in the morning. Associations between sleepiness and sleep timing were evaluated by mixed model and nonparametric approaches and simulated with a mathematical model for the homeostatic and circadian regulation of sleepiness.
RESULTS
Intraindividual variation in evening sleepiness was very large, covering four or five points on the 9-point KSS scale, and was significantly associated with subsequent sleep timing. On average, a one point higher KSS value was followed by 20 min earlier bedtime, which led to 11 min longer sleep, which correlated with lower sleepiness next morning and the following evening. Associations between sleepiness and sleep timing were stronger in early compared to late sleepers. Model simulations indicated that the directions of associations between sleepiness and sleep timing are in accordance with their homeostatic and circadian regulation, even though much of the variance in evening sleepiness and details of its time course remain unexplained by the model.
CONCLUSION
Subjective sleepiness is a valid indicator of the drive for sleep which, if acted upon, can reduce insufficient sleep.
Topics: Circadian Rhythm; Humans; Sleep; Sleep Deprivation; Sleepiness; Wakefulness; Young Adult
PubMed: 33991415
DOI: 10.1093/sleep/zsab123 -
BMC Public Health Aug 2023Insomnia disorder is a highly prevalent, significant public health concern associated with substantial and growing health burden. There are limited real-world data...
BACKGROUND
Insomnia disorder is a highly prevalent, significant public health concern associated with substantial and growing health burden. There are limited real-world data assessing the burden of insomnia disorder on daytime functioning and its association with comorbidities. The objective of this study was to leverage large-scale, real-world data to assess the burden of untreated insomnia disorder in terms of daytime impairment and clinical outcomes.
METHODS
This United States medical claims database study compares patients diagnosed with insomnia disorder but not receiving treatment ('untreated insomnia' cohort) to patients without an insomnia disorder diagnosis and without treatment ('non-insomnia' cohort). International Classification of Disease, Tenth Revision codes were used as a proxy to represent the three symptom domains (Sleepiness, Alert/Cognition, Mood) of the Insomnia Daytime Symptoms and Impacts Questionnaire (IDSIQ), a newly developed and validated tool used in clinical studies to assess daytime functioning in insomnia disorder. Chronic Fatigue (R53.83) and Other Fatigue (R53.83), Somnolence (R40.0) and Disorientation (R41.0) were selected as categories representing one or more IDSIQ domains. Clinical outcomes included cardiovascular events, psychiatric disorders, cognitive impairment and metabolic disorders.
RESULTS
Approximately 1 million patients were included (untreated insomnia: n = 139,959; non-insomnia: n = 836,975). Compared with the 'non-insomnia' cohort, the 'untreated insomnia' cohort was more likely to experience daytime impairments, with mean differences in occurrences per 100 patient-years for: (a) fatigue, at 27.35 (95% confidence interval [CI] 26.81, 27.77, p < 0.01); (b) dizziness, at 4.66 (95% CI 4.40, 4.90, p < 0.01); (c) somnolence, at 4.18 (95% CI 3.94, 4.43, p < 0.01); and (d) disorientation, at 0.92 (95% CI 0.77, 1.06, p < 0.01). During the 1-year look-back period, patients in the 'untreated insomnia' cohort were also more likely to have been diagnosed with arterial hypertension (40.9% vs. 26.3%), psychiatric comorbidities (40.1% vs. 13.2%), anxiety (29.2% vs. 8.5%), depression (26.1% vs. 8.1%) or obesity (21.3% vs. 11.1%) compared with those in the 'non-insomnia' cohort.
CONCLUSIONS
This large-scale study confirms the substantial burden of insomnia disorder on patients in a real-world setting, with significant daytime impairment and numerous comorbidities. This reinforces the need for timely insomnia disorder diagnosis and treatments that improve both sleep, as well as daytime functioning.
Topics: Humans; Adult; Sleep Initiation and Maintenance Disorders; Sleepiness; Cohort Studies; Wakefulness; Sleep
PubMed: 37537544
DOI: 10.1186/s12889-023-16329-9 -
Brain Stimulation 2024No study on neurostimulation in narcolepsy is available until now. Arousal- and wake-promoting effects of vagus nerve stimulation (VNS) have been demonstrated in animal... (Comparative Study)
Comparative Study
BACKGROUND AND OBJECTIVE
No study on neurostimulation in narcolepsy is available until now. Arousal- and wake-promoting effects of vagus nerve stimulation (VNS) have been demonstrated in animal experiments and are well-known as side effects of VNS therapy in epilepsy and depression. The objective was to evaluate the therapeutic effect of VNS on daily sleepiness and cataplexies in narcolepsy.
METHODS
In our open-label prospective comparative study, we included narcolepsy patients who were treated with VNS because of depression or epilepsy and compared them to controls without narcolepsy treated with VNS for depression or epilepsy (18 patients in each group, aged 31.5 ± 8.2 years). We evaluated daily sleepiness (Epworth Sleepiness Scale, ESS) and the number of cataplexies per week before the implantation of VNS and at three and six month follow-ups.
RESULTS
Compared to baseline (ESS: 15.9 ± 2.5) patients with narcolepsy showed a significant improvement on ESS after three months (11.2 ± 3.3, p < 0.05) and six months (9.6 ± 2.8, p < 0.001) and a trend to reduction of cataplexies. No significant ESS-improvement was observed in patients without narcolepsy (14.9 ± 3.9, 13.6 ± 3.7, 13.2 ± 3.5, p = 0.2 at baseline, three and six months, correspondingly). Side effects did not differ between the study groups.
CONCLUSION
In this first evaluation of VNS in narcolepsy, we found a significant improvement of daily sleepiness due to this type of neurostimulation. VNS could be a promising non-medical treatment in narcolepsy.
Topics: Humans; Cataplexy; Epilepsy; Narcolepsy; Prospective Studies; Sleepiness; Treatment Outcome; Vagus Nerve; Vagus Nerve Stimulation; Adult
PubMed: 38184192
DOI: 10.1016/j.brs.2024.01.002 -
Sensors (Basel, Switzerland) Jun 2022Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features...
Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.
Topics: Algorithms; Electroencephalography; Humans; Machine Learning; Signal Processing, Computer-Assisted; Support Vector Machine
PubMed: 35808261
DOI: 10.3390/s22134764 -
Computer Methods and Programs in... Jan 2024Drowsiness behind the wheel is a major road safety issue with efforts focused on developing drowsy driving detection systems. However, most drowsy driving detection...
BACKGROUND AND OBJECTIVE
Drowsiness behind the wheel is a major road safety issue with efforts focused on developing drowsy driving detection systems. However, most drowsy driving detection studies using physiological signals have focused on developing a 'black box' machine learning classifier, with much less focus on 'robustness' and 'explainability'-two crucial properties of a trustworthy machine learning model. Therefore, this study has focused on using multiple validation techniques to evaluate the overall performance of such a system using multiple supervised machine learning-based classifiers and then unbox the black box model using explainable machine learning.
METHODS
Driving was simulated via a 30-minute psychomotor vigilance task while the participants reported their level of subjective sleepiness with their physiological signals: electroencephalogram (EEG), electrooculogram (EOG) and electrocardiogram (ECG) being recorded. Six different techniques, comprising subject-dependent and independent techniques were applied for model validation and robustness testing with three supervised machine learning classifiers, namely K-nearest neighbours (KNN), support vector machines (SVM) and random forest (RF), and two explainable methods, namely SHapley Additive exPlanation (SHAP) analysis and partial dependency analysis (PDA) were leveraged for model interpretation.
RESULTS
The study identified the leave one participant out, a subject-independent validation technique to be most useful, with the best sensitivity of 70.3 %, specificity of 82.2 %, and an accuracy of 80.1 % using the random forest classifier in addressing the autocorrelation issue due to inter-individual differences in physiological signals. Moreover, the explainable results suggest most important physiological features for drowsiness detection, with a clear cut-off in the decision boundary.
CONCLUSIONS
The implication of the study will ensure a rigorous validation for robustness testing and an explainable machine learning approach to developing a trustworthy drowsiness detection system and enhancing road safety. The explainable machine learning-based results show promise in real-life deployment of the physiological-signal based in-vehicle trustworthy drowsiness detection system, with higher reliability and explainability, along with a lower system cost.
Topics: Humans; Sleepiness; Reproducibility of Results; Wakefulness; Machine Learning; Electroencephalography; Support Vector Machine
PubMed: 38000319
DOI: 10.1016/j.cmpb.2023.107925 -
Chinese Journal of Traumatology =... Oct 2017To identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving. (Review)
Review
PURPOSE
To identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving.
METHODS
This systematic review searched the following electronic databases: Medline, Science direct, Scopus, EMBASE, PsycINFO, Transport Database, Cochrane, BIOSIS, ISI Web of Knowledge, specialist road injuries journals and the Australian Transport and Road Index database. Additional searches included websites of relevant organizations, reference lists of included studies, and issues of major injury journals published within the past 15 years. Studies were included if they investigated interventions/exposures accounting for reducing fatigue and sleepiness as the outcome, measured any potential interventions for mitigation of sleepiness and were written in English. Meta-analysis was not attempted because of the heterogeneity of the included studies.
RESULTS
Of 63 studies identified, 18 met the inclusion criteria. Based on results of our review, many interventions in the world have been used to reduce drowsiness while driving such as behavioral (talking to passengers, face washing, listening to the radio, no alcohol use, limiting the driving behavior at the time of 12 p.m. - 6 a.m. etc), educational interventions and also changes in the environment (such as rumble strips, chevrons, variable message signs, etc). Meta-analysis on the effect of all these interventions was impossible due to the high heterogeneity in methodology, effect size and interventions reported in the assessed studies.
CONCLUSION
Results of present review showed various interventions in different parts of the world have been used to decrease drowsy driving. Although these interventions can be used in countries with high incidence of road traffic accidents, precise effect of each intervention is still unknown. Further studies are required for comparison of the efficiency of each intervention and localization of each intervention according to the traffic patterns of each country.
Topics: Accidents, Traffic; Automobile Driving; Fatigue; Humans; Sleep Stages
PubMed: 28689801
DOI: 10.1016/j.cjtee.2017.03.005 -
International Journal of Environmental... Sep 2022This study identified clinical nurses' fatigue and related factors during the COVID-19 pandemic. This was a cross-sectional study. Data were collected from South Korean...
This study identified clinical nurses' fatigue and related factors during the COVID-19 pandemic. This was a cross-sectional study. Data were collected from South Korean hospitals on 234 nurses' general characteristics, fatigue, depression, occupational stress, insomnia, and perceived daytime sleepiness using a structured questionnaire. The prevalence of fatigue was 62.0%, depression 52.1%, insomnia 20.7%, and daytime sleepiness 36.1%. Insomnia, sleepiness, depression, and occupational stress were significantly associated with fatigue. Ward nurses who cared for COVID-19 patients within the past month had significantly higher occupational stress related to organizational climate than those who had not provided care, and ICU nurses who cared for COVID-19 patients had significantly higher job insecurity-related occupational stress. Nurses have a high prevalence of fatigue and depression during the pandemic. Thus, insomnia, sleepiness, depression, and occupational stress must be reduced to lower nurses' fatigue. Caring for COVID-19 patients was not significantly associated with fatigue, but there were significant differences in occupational stress between nurses who provided such care and those who did not. Work environment-specific strategies are needed to reduce nurses' occupational stress during the pandemic.
Topics: COVID-19; Cross-Sectional Studies; Disorders of Excessive Somnolence; Fatigue; Humans; Nurses; Occupational Stress; Pandemics; Sleep Initiation and Maintenance Disorders; Sleepiness; Surveys and Questionnaires
PubMed: 36141652
DOI: 10.3390/ijerph191811380 -
Sleep Medicine May 2022The role of the sleep environment and presleep conditions that may influence adolescents' sleep are understudied. The aims of the current study were to examine linear...
OBJECTIVE/BACKGROUND
The role of the sleep environment and presleep conditions that may influence adolescents' sleep are understudied. The aims of the current study were to examine linear and nonlinear associations between the sleep environment and presleep conditions and adolescents' daytime sleepiness and sleep/wake problems.
METHOD
Participants included 313 adolescents (M = 17.39 years, SD = 10.38 months; 51.4% girls, 48.6% boys; 59.1% White/European American, 40.3% Black/African American) from a wide range of socioeconomic backgrounds living in the southeastern United States. Adolescents completed surveys assessing the sleep environment (eg, light, bedding), four presleep conditions (ie, general worries, family concerns, arousal, somatic complaints), and sleep (daytime sleepiness, sleep/wake problems).
RESULTS
Sleep environment disruptions and worse presleep conditions were positively associated with sleepiness and sleep/wake problems in a linear fashion. Nonlinear associations emerged such that levels of sleepiness increased rapidly between low and average levels of the sleep environment and two presleep conditions (worries, arousal); the slope leveled off between average and high levels. Moreover, linear effects of environmental disruptions, family concerns, somatic complaints, and presleep arousal on sleep/wake problems were moderated by race and/or SES, indicating that positive associations between some presleep conditions and sleep/wake problems were more pronounced for Black and lower SES youth.
CONCLUSIONS
Results support the importance of the sleep environment and multiple presleep conditions and assessments of both linear and nonlinear effects for a better understanding of factors that may contribute to sleep. Additionally, results indicate the sleep environment and some presleep conditions may be more consequential for disadvantaged youth.
Topics: Adolescent; Disorders of Excessive Somnolence; Female; Humans; Male; Sleep; Sleep Initiation and Maintenance Disorders; Sleep Wake Disorders; Sleepiness; Social Class
PubMed: 34879983
DOI: 10.1016/j.sleep.2021.11.004 -
BMC Public Health Sep 2022Sufficient sleep is important to an individual's health and well-being, but also for school achievement among adolescents. This study investigates the associations...
BACKGROUND
Sufficient sleep is important to an individual's health and well-being, but also for school achievement among adolescents. This study investigates the associations between sleepiness, sleep deficits, and school achievements among adolescents.
METHODS
This trend study involved a representative sample of Norwegian adolescents based on the "Trends in International Mathematics and Science Study" (TIMSS), N = 4499 (2015) and N = 4685 (2019) and their teachers. The students were 9th graders from a Norwegian compulsory secondary school. The survey included questions on students' sleepiness as students reported in 2019 and sleep deficits among students that limited teaching in class as their teachers reported in 2015 and 2019. Regression, triangulation, and mediation analyses were used. Mplus was used to perform the statistical analyses.
RESULTS
The results revealed significant negative associations between sleep deficits and school achievements, adjusted for gender, socioeconomic status (SES), and minority status among Norwegian 9th graders. These results were found for both mathematics and science achievements in 2015 and 2019. Sleepiness that the students reported was negatively associated with school achievements in 2019. Trend and mediation analyses showed that sleep deficits explained 18 and 11% of the decrease in mathematics and science achievements, respectively, from 2015 to 2019.
CONCLUSIONS
Sleep deficits were associated with school achievements in mathematics and science among Norwegian 9th graders. Mediation analyses revealed that sleep deficits explained a significant part of the decline in academic achievements. Insufficient sleep may have negative public health implications and influence adolescents' academic achievements and competences, and should therefore be discussed in both the educational and health systems.
Topics: Academic Success; Adolescent; Humans; Mathematics; Schools; Sleep; Sleepiness; Students
PubMed: 36131267
DOI: 10.1186/s12889-022-14161-1