-
Sleep Medicine Dec 2022To examine the association between ethnicity and 90-day post-stroke subjective sleepiness, an important determinant of quality of life, as measured by the Epworth...
OBJECTIVE/BACKGROUND
To examine the association between ethnicity and 90-day post-stroke subjective sleepiness, an important determinant of quality of life, as measured by the Epworth Sleepiness Scale (ESS), among ischemic stroke survivors.
PATIENTS/METHODS
Mexican American (MA) and non-Hispanic white (NHW) recent ischemic stroke patients were identified from the population-based Brain Attack Surveillance in Corpus Christi Project (2010-2016). Subjects completed a baseline interview and 90-day outcome assessment that included the ESS. Excessive daytime sleepiness was defined as an ESS >10. Tobit regression models were used to assess associations between ethnicity and ESS unadjusted and adjusted for multiple potential confounders.
RESULTS
Among 1,181 (62.5% MA) subjects, mean ESS at 90 days was 8.9 (SD 6.0) among MA and 7.4 (SD 4.9) among NHW subjects: 1.45 (95% CI: 0.75, 2.15) points higher among MA than NHW subjects. After adjustment, mean ESS at 90 days was 1.16 (95% CI: 0.38, 1.94) points higher among MAs than NHWs. The prevalence of excessive daytime sleepiness was 39% among MA and 30% among NHW subjects (p = 0.0013).
CONCLUSIONS
Ninety days after stroke, sleepiness is worse in MAs compared to NHWs, even after accounting for potential confounding variables. Further studies should address ways to reduce this disparity.
Topics: Humans; Ischemic Stroke; Sleepiness; Quality of Life; Risk Factors; Stroke; Disorders of Excessive Somnolence
PubMed: 36115141
DOI: 10.1016/j.sleep.2022.08.017 -
Frontiers in Neuroscience 2020Pupillary unrest is an established indicator of drowsiness or sleepiness. How sympathetic and parasympathetic activity contribute to pupillary unrest is not entirely...
Pupillary unrest is an established indicator of drowsiness or sleepiness. How sympathetic and parasympathetic activity contribute to pupillary unrest is not entirely unclear. In this study, we investigated 83 young healthy volunteers to assess the relationship of pupillary unrest to other markers of the autonomic nervous system. Sample entropy (SE) and the established pupillary unrest index (PUI) were calculated to characterize pupil size variability. Autonomic indices were derived from heart rate, blood pressure, respiration, and skin conductance. Additionally, we assessed individual levels of calmness, vigilance, and mood. In an independent sample of 26 healthy participants, we stimulated the cardiovagal system by a deep breathing test. PUI was related to parasympathetic cardiac indices and sleepiness. A linear combination of vagal heart rate variability [root mean square of heart beat interval differences (RMSSD)] and skin conductance fluctuations (SCFs) was suited best to explain interindividual variance of PUI. Complexity of pupil diameter (PD) variations correlated to indices of sympathetic skin conductance. Furthermore, we found that spontaneous fluctuations of skin conductance are accompanied by increases of pupil size. In an independent sample, we were able to corroborate the relation of PUI with RMSSD and skin conductance. A slow breathing test enhanced RMSSD and PUI proportionally to each other, while complexity of PD dynamics decreased. Our data suggest that the slow PD oscillations ( < 0.15 Hz) quantified by PUI are related to the parasympathetic modulation. Sympathetic arousal as detected by SCFs is associated to transient pupil size increases that increase non-linear pupillary dynamics.
PubMed: 32218721
DOI: 10.3389/fnins.2020.00178 -
International Maritime Health 2023This study investigates how Faroese deep-sea fishers' exposure to work-related stressors affects their sleep, sleepiness, and levels of fatigue. Being constantly exposed...
BACKGROUND
This study investigates how Faroese deep-sea fishers' exposure to work-related stressors affects their sleep, sleepiness, and levels of fatigue. Being constantly exposed to the unpredictable and harsh North Atlantic Ocean, having long work hours and split sleep for up to 40 days consecutively, they will arguably suffer from fatigue.
MATERIALS AND METHODS
One hundred and fifty seven fishers participated in this study, and data was gathered throughout 202 days at sea. Subjective data was collected at the start and end of trips via questionnaires, sleep and sleepiness diaries and supplemented by objective sleep data through actigraphs. Ship movements were logged with a gyroscope connected to a laptop. A noise metre measured each work station and resting area, and noise exposure profiles were calculated based on each participant's activity and location. Linear mixed-effect models investigated the effects of work exposure variables on sleep efficiency, and cumulative link mixed models measured effects on the Karolinska Sleepiness Scale and physical fatigue scale.
RESULTS
Time of day followed by ship movement were the exposure variables with the highest impact on the outcome variables of sleep efficiency, sleepiness and physical fatigue. The number of days at sea revealed correlations to outcome variables either by itself or interacting with the sleep periods per day. Crew size, shift system or noise did not impact outcome variables when in the model with other variables. Larger catches improved sleep efficiency but did not affect sleepiness and physical fatigue ratings.
CONCLUSIONS
The findings indicate a chronically fatigued fisher population, and recommends urgent attention being paid to improving the structure of vessels and installing stabilators for greater stability at sea; work schedules being evaluated for protection of health; and work environments being designed that fulfill human physiological requirements in order to ensure the wellbeing and safety of those at sea.
Topics: Humans; Work Schedule Tolerance; Working Conditions; Sleepiness; Sleep; Fatigue
PubMed: 36974488
DOI: 10.5603/IMH.2023.0001 -
PloS One 2023To investigate whether pain, sleep duration, insomnia, sleepiness, work-related factors, anxiety, and depression associate with excessive fatigue in nurses.
AIM
To investigate whether pain, sleep duration, insomnia, sleepiness, work-related factors, anxiety, and depression associate with excessive fatigue in nurses.
BACKGROUND
Fatigue among nurses is a problem in the context of ongoing nursing shortages. While myriad factors are associated with fatigue not all relationships are understood. Prior studies have not examined excessive fatigue in the context of pain, sleep, mental health, and work factors in a working population to determine if associations between excessive fatigue and each of these factors remain when adjusting for each other.
METHODS
A cross-sectional questionnaire study among 1,335 Norwegian nurses. The questionnaire included measures for fatigue (Chalder Fatigue Questionnaire, score ≥4 categorized as excessive fatigue), pain, sleep duration, insomnia (Bergen Insomnia Scale), daytime sleepiness (Epworth Sleepiness Scale), anxiety and depression (Hospital Anxiety and Depression Scale), and work-related factors. Associations between the exposure variables and excessive fatigue were analyzed using chi-square tests and logistic regression analyses.
RESULTS
In the fully adjusted model, significant associations were found between excessive fatigue and pain severity scores for arms/wrists/hands (adjusted OR (aOR) = 1.09, CI = 1.02-1.17), hips/legs/knees/feet (aOR = 1.11, CI = 1.05-1.18), and headaches/migraines (aOR = 1.16, CI = 1.07-1.27), sleep duration of <6 hours (aOR = 2.02, CI = 1.08-3.77), and total symptom scores for insomnia (aOR = 1.05, CI = 1.03-1.08), sleepiness (aOR = 1.11, CI = 1.06-1.17), anxiety (aOR = 1.09, CI = 1.03-1.16), and depression (aOR = 1.24, CI = 1.16-1.33). The musculoskeletal complaint-severity index score (aOR = 1.27, CI = 1.13-1.42) was associated with excessive fatigue in a separate model adjusted for all variables and demographics. Excessive fatigue was also associated with shift work disorder (OR = 2.25, CI = 1.76-2.89) in a model adjusted for demographics. We found no associations with shift work, number of night shifts and number of quick returns (<11 hours between shifts) in the fully adjusted model.
CONCLUSION
Excessive fatigue was associated with pain, sleep- and mental health-factors in a fully adjusted model.
Topics: Humans; Sleep Initiation and Maintenance Disorders; Sleepiness; Cross-Sectional Studies; Mental Health; Work Schedule Tolerance; Sleep; Fatigue; Surveys and Questionnaires; Pain
PubMed: 37014834
DOI: 10.1371/journal.pone.0282734 -
Entropy (Basel, Switzerland) Jan 2021The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness...
The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). : changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver's condition using real-time control. : the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. : the present study confirms the possibility of continuously monitoring the driver's status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.
PubMed: 33494447
DOI: 10.3390/e23020135 -
Neuromuscular Disorders : NMD May 2022Abnormalities of sleep are common in myotonic dystrophy type 1 (DM1), but few previous studies have combined polysomnography with detailed clinical measures and brain...
Abnormalities of sleep are common in myotonic dystrophy type 1 (DM1), but few previous studies have combined polysomnography with detailed clinical measures and brain imaging. In the present study, domiciliary polysomnography, symptom questionnaires and cognitive evaluation were undertaken in 39 DM1-affected individuals. Structural brain MRI was completed in those without contra-indication (n = 32). Polysomnograms were adequate for analysis in 36 participants. Sleep efficiency was reduced, and sleep architecture altered in keeping with previous studies. Twenty participants (56%) had moderate or severe sleep-disordered breathing (apnoea-hypopnoea index [AHI] ≥ 15). In linear modelling, apnoeas were positively associated with increasing age and male sex. AHI ≥ 15 was further associated with greater daytime pCO and self-reported physical impairment, somnolence and fatigue. Percentage REM sleep was inversely associated with cerebral grey matter volume, stage 1 sleep was positively associated with occipital lobe volume and stage 2 sleep with amygdala volume. Hippocampus volume was positively correlated with self-reported fatigue and somnolence. Linear relationships were also observed between measures of sleep architecture and cognitive performance. Findings broadly support the hypothesis that changes in sleep architecture and excessive somnolence in DM1 reflect the primary disease process in the central nervous system.
Topics: Disorders of Excessive Somnolence; Fatigue; Humans; Male; Myotonic Dystrophy; Sleep; Sleepiness
PubMed: 35361525
DOI: 10.1016/j.nmd.2022.02.003 -
Inflammopharmacology Dec 2023Acute infections with SARS-CoV-2 variants of concerns (VOCs) differ in clinical presentation. Discrepancies in their long-term sequelae, commonly referred to as long... (Review)
Review
Acute infections with SARS-CoV-2 variants of concerns (VOCs) differ in clinical presentation. Discrepancies in their long-term sequelae, commonly referred to as long COVID, however, remain to be explored. We retrospectively analyzed data of 287 patients presented at the post-COVID care of the Pulmonology Department, Semmelweis University, Budapest, Hungary, and infected with SARS-CoV-2 during a period of 3 major epidemic waves in Hungary (February-July 2021, VOC: B.1.1.7, Alpha, N = 135; August-December 2021, VOC: B.1.617.2, Delta, N = 89; and January-June 2022, VOC: B.1.1.529, Omicron; N = 63), > 4 weeks after acute COVID-19. Overall, the ratio of long COVID symptomatic (LC) and asymptomatic (NS) patients was 2:1. Self-reported questionnaires on fatigue (Fatigue Severity Scale, FSS), sleepiness (Epworth Sleepiness Scale, ESS) and sleep quality (Pittsburgh Sleep Quality Index, PSQI) showed higher scores for LC (4.79 ± 0.12, 7.45 ± 0.33 and 7.46 ± 0.27, respectively) than NS patients (2.85 ± 0.16, 5.23 ± 0.32 and 4.26 ± 0.29, respectively; p < 0.05 for all vs. LC). By comparing data of the three waves, mean FSS and PSQI scores of LC patients, but not ESS scores, exceeded the normal range in all, with no significant inter-wave differences. Considering FSS ≥ 4 and PSQI > 5 cutoff values, LC patients commonly exhibited problematic fatigue (≥ 70%) and poor sleep quality (> 60%) in all three waves. Comparative analysis of PSQI component scores of LC patients identified no significant differences between the three waves. Our findings highlight the importance of concerted efforts to manage both fatigue and sleep disturbances in long COVID patient care. This multifaceted approach should be followed in all cases infected with either VOCs of SARS-CoV-2.
Topics: Humans; SARS-CoV-2; Post-Acute COVID-19 Syndrome; Sleep Quality; Sleepiness; Retrospective Studies; COVID-19; Fatigue
PubMed: 37020055
DOI: 10.1007/s10787-023-01190-4 -
Review of Human Factors and Ergonomics Jun 2015Sleep deficiency, which can be caused by acute sleep deprivation, chronic insufficient sleep, untreated sleep disorders, disruption of circadian timing, and other...
Sleep deficiency, which can be caused by acute sleep deprivation, chronic insufficient sleep, untreated sleep disorders, disruption of circadian timing, and other factors, is endemic in the U.S., including among professional and non-professional drivers and operators. Vigilance and attention are critical for safe transportation operations, but fatigue and sleepiness compromise vigilance and attention by slowing reaction times and impairing judgment and decision-making abilities. Research studies, polls, and accident investigations indicate that many Americans drive a motor vehicle or operate an aircraft, train or marine vessel while drowsy, putting themselves and others at risk for error and accident. In this chapter, we will outline some of the factors that contribute to sleepiness, present evidence from laboratory and field studies demonstrating how sleepiness impacts transportation safety, review how sleepiness is measured in laboratory and field settings, describe what is known about interventions for sleepiness in transportation settings, and summarize what we believe are important gaps in our knowledge of sleepiness and transportation safety.
PubMed: 26056516
DOI: 10.1177/1557234X15573949 -
BMC Pharmacology & Toxicology Apr 2023Standard doses of second-generation H-antihistamines (sgAHs) as first-line treatment are not always effective in treating chronic spontaneous urticaria (CSU), and hence... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Standard doses of second-generation H-antihistamines (sgAHs) as first-line treatment are not always effective in treating chronic spontaneous urticaria (CSU), and hence an increase in the dose of sgAHs is recommended. However, literature evaluating the efficacy and safety of this treatment remains inconclusive, highlighting the need for a systematic review and meta-analysis. The aim of this systematic review and meta-analysis was to evaluate the efficacy and safety of high-dose sgAHs compared with standard-dose sgAHs in treating CSU.
METHODS
A systematic literature search of double-blind, randomized controlled trials (RCT) utilizing multiple doses of sgAHs was performed by searching the electronic databases Medline, Embase, PsycInfo, Cochrane databases, and Web of Science. Bibliographies were also manually searched. The Cochrane Risk of Bias Tool for assessing risk of bias was used to assess the quality of randomized controlled trials (RCTs). Two reviewers screened studies, extracted data, and evaluated the risk of bias independently. The response rate, the number of adverse events, somnolence, and withdrawal due to adverse events were extracted from each article. The data were combined and analyzed to quantify the safety and efficacy of the treatment. RevMan (V5.3) software was used for data synthesis.
RESULTS
A total of 13 studies were identified, seven of which met the eligibility criteria for the meta-analysis. Our pooled meta-analyses showed that high-dose sgAHs was associated with a significantly higher response rate than standard-dose (RR 1.13, 95% CI 1.02 to 1.26; P = 0.02). Conversely, high doses of sgAHs were associated with significantly higher somnolence rates than standard dose (RD 0.05, 95% CI 0.01 to 0.09; P = 0.02). There was no significant difference in adverse events or withdrawal due to adverse events between standard- and high-dose treatments.
CONCLUSIONS
Our analyses showed that a high dose of sgAHs (up to two times the standard dose) might be more effective than a standard dose in CSU treatment. High-dose and standard-dose sgAHs showed similar adverse events, except for somnolence, where incidence was found to be dose-dependent in some studies. However, given the limited number of studies, our meta-analysis results should be interpreted with caution.
Topics: Humans; Sleepiness; Randomized Controlled Trials as Topic; Chronic Urticaria; Histamine H1 Antagonists, Non-Sedating; Histamine Antagonists
PubMed: 37024900
DOI: 10.1186/s40360-023-00665-y -
Neural Computing & Applications 2021Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using...
Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.
PubMed: 33967397
DOI: 10.1007/s00521-021-06038-y