Levels Of Sleepiness Research Articles

Brain structural correlates of subjective sleepiness and insomnia symptoms in shift workers.

Studies on the brain structures of shift workers are limited; thus, this cross-sectional study aimed to compare the brain structures and the brain structural correlates of subjective sleepiness and insomnia symptoms between shift workers and non-shift workers. Shift workers (n = 63) and non-shift workers (n = 58) completed questionnaires assessing subjective sleepiness and insomnia symptoms. Cortical thickness, cortical surface area, and subcortical volumes were measured by magnetic resonance imaging. The brain morphometric measures were compared between the groups, and interaction analyses using the brain morphometric measures as the dependent variable were performed to test the interactions between the study group and measures of sleep disturbance (i.e., subjective sleepiness and insomnia symptoms). No differences in cortical thickness, cortical surface area, or subcortical volumes were detected between shift workers and non-shift workers. A single cluster in the left motor cortex showed a significant interaction between the study group and subjective sleepiness in the cortical surface area. The correlation between the left motor cortex surface area and the subjective sleepiness level was negative in shift workers and positive in non-shift workers. Significant interaction between the study group and insomnia symptoms was present for the left/right putamen volumes. The correlation between the left/right putamen volumes and insomnia symptom levels was positive in shift workers and negative in non-shift workers. Left motor cortex surface area and bilateral putamen volumes were unique structural correlates of subjective sleepiness and insomnia symptoms in shift workers, respectively.

A case-control study to investigate the prevalence of obstructive sleep apnea and the utility of the Sleep-Related Breathing Disorder scale of the Pediatric Sleep Questionnaire in children and young people with epilepsy.

Epilepsy and obstructive sleep apnea syndrome (OSAS) are each relatively common in children. OSAS may affect cognition, such that recognition of OSAS is important for children and young people with epilepsy (CYPWE). Published pilot data reported 55% of CYPWE had symptoms suggestive of OSAS, compared with 7% of typically developing controls. The primary aim of this study was to ascertain OSAS prevalence by polysomnography in CYPWE, with secondary aims being to evaluate the utility of sleep questionnaires in CYPWE. CYPWE and age- and sex-matched typically developing controls were studied. A single night of level I attended polysomnography was undertaken, along with questionnaires (Sleep-Related Breathing Disorder scale of the Pediatric Sleep Questionnaire, Pittsburgh Sleep Quality Index, and the childhood and adolescent Epworth Sleepiness Scale). OSAS was defined as obstructive apnea-hypopnea index of ≥ 1 event/h. Polysomnography was performed in 72 children including 48 CYPWE (60% male) and 24 controls (54% male). Mean age (11 years) was similar for CYPWE and controls (P = .42), with slightly higher body mass index z scores (0.7 vs 0.1, P = .03) noted in CYPWE. Mean obstructive apnea-hypopnea index was 0.61 in CYPWE vs 0.42 in controls (P = .62). Despite higher Sleep-Related Breathing Disorder scale of the Pediatric Sleep Questionnaire scores in CYPWE (0.38 vs 0.12, P < .001), no difference in OSAS prevalence (10% vs 4%, P = .78) was found. CYPWE had higher childhood and adolescent Epworth Sleepiness Scale (6 vs 3.5, P = .01) and Pittsburgh Sleep Quality Index (5 vs 3.3, P = .02) scores, indicating greater levels of daytime sleepiness and poorer sleep quality. The study found no evidence for increased OSAS prevalence in CYPWE, and the utility of the Sleep-Related Breathing Disorder scale of the Pediatric Sleep Questionnaire in predicting OSAS appears limited for CYPWE. CYPWE are, however, demonstrably sleepier and have poorer sleep quality. The cause for these findings remains unclear. Registry: ClinicalTrials.gov; Name: Investigation of Sleep Quality and Prevalence of Sleep-disordered Breathing in Children and Young People With Epilepsy; URL: https://www.clinicaltrials.gov/study/NCT03103841; Identifier: NCT03103841. Urquhart DS, McLellan AE, Hill LE, etal. A case-control study to investigate the prevalence of obstructive sleep apnea and the utility of the Sleep-Related Breathing Disorder scale of the Pediatric Sleep Questionnaire in children and young people with epilepsy. J Clin Sleep Med. 2024;20(7):1039-1047.

Sleep Disturbances as a Consequence of Long COVID-19: Insights from Actigraphy and Clinimetric Examinations—An Uncontrolled Prospective Observational Pilot Study

The COVID-19 pandemic represents a global health and social challenge. However, the impact of a SARS-CoV-2 infection itself on mental health and sleep quality remains poorly understood. The purpose of the present uncontrolled prospective observational pilot study was to evaluate the impact of past COVID-19 disease on the incidence of quantitative and qualitative sleep disturbances. A group of 61 subjects (37 female, 24 male) reported sleep disturbances that had lasted for at least one month and had started after recovery from COVID-19. The study used self-reported instruments: the Epworth Sleepiness Scale (ESS) and Insomnia Severity Index (ISI), as well as an objective method—actigraphy—for quantitative analysis of sleep architecture. It was shown that sleep disturbances most commonly started after recovery (68.3%, n = 41) and lasted for more than one month (83.6%, n = 51). ESS scores of 7.8 ± 5.0 points indicate moderate daytime sleepiness, and ISI scores of 16.3 ± 5.8 points denote moderate insomnia. ESS scores were negatively correlated with total time in bed (r = −0.3780, p = 0.003), total sleep time (r = −0.2969, p = 0.020), and wakefulness after sleep onset (r = −0.2654, p = 0.039). In addition, ESS scores were correlated negatively with the respondents’ age (B = −0.17, p = 0.000) and time from COVID-19 onset. A positive correlation was found between wakefulness after sleep onset and ESS scores (B = −0.05, p = 0.039). ISI scores were positively correlated with time in bed (r = 0.3275, p = 0.010). Female gender was found to be a significant predictor of insomnia’s severity (B = 2.159, t = 3.04, p = 0.004). In conclusion, patients with a history of COVID-19 report long-lasting sleep disturbances that do not subside spontaneously. In the patient group studied, moderate levels of daytime sleepiness and insomnia were found. The most frequently reported problems included irregular sleep, frequent awakenings, and difficulty maintaining sleep, while normal sleep duration was preserved. These findings underscore the need for continued attention to the long-term consequences of COVID-19 on sleep health and emphasize the importance of targeted interventions to address these enduring sleep disturbances in affected individuals.

Mental Workload and Road Environment Complexity: Subjective Assessments

Since 2011, mental workload has been one of the top 10 causes of accidents in Malaysia, accounting for over a thousand fatalities yearly. Mental workload is defined as a comparable mediating variable to attention. The aim of this study is to identify the fundamental causes and influences that affect the difficulty of the road environment and vehicle task, as well as their impact on the driver’s health. In this study, subjective measurement is used to assess the level of mental workload of drivers which includes NASA-TLX and KSS on two different road environment condition namely urban and rural road environment. The findings show that the complexity of the road environment has a significant effect towards the mental workload of a driver. The average NASA-TLX score for urban roads is higher than the average score for rural roads while, the results for KSS level indicates higher drowsiness level among the drivers when driving on a rural road as opposed to an urban road. These results may serve as a guide for future research into the mental workloads of drivers in various types of complex road environments, as well as a practical guide for car manufacturers, agencies responsible for road safety, and researchers to improve the safety design of cars and consider external factors related to road environment complexity when designing roads.

Managing the challenge of fatigue for pilots operating ultra-long range flights

IntroductionUltra-long range (ULR) flights are defined as exceeding regulatory limits: normally 16 h flight time. They pose challenges due to long duty periods that could result in extended wakefulness and sleep loss, increasing the risk of fatigue. This study describes the mitigations used to manage fatigue in these operations. Two data collection phases were conducted on the Auckland-Chicago ULR route: when the route commenced (Study 1) and when the flight crew complement was altered (Study 2). Seasonal differences were also investigated.MethodsStudy 1 involved 72 crew who completed diaries and wore an actigraph to record sleep pre-departure, throughout the trip, and on return. In-flight, fatigue, sleepiness and workload were reported, and reaction time performance was measured. Study 2 involved 75 crew and data collection in the northern summer and northern winter. Crew completed diaries throughout the trip.ResultsStudy 1 data found crew sleep longer than usual in the 24 h pre trip and post flights. On the shorter outbound flight in-flight sleep averaged 3.3 h and on the longer inbound flight, 3.3–3.8 h, with most crew taking 3 breaks. Ratings of sleepiness and fatigue increased, and reaction time performance declined across flights, with greater decrements on longer inbound flights. Pilots did not fully adjust their sleep patterns to local time during the layover and no seasonal differences were found. Comparisons between Study 1 and 2 showed no difference in ratings of fatigue and sleepiness or in-flight sleep duration with an altered crew complement. There was a trend for Captains to report greater workload and less in-flight sleep in Study 2.DiscussionMitigations that allow for preparation and recovery are well utilized by crew. In-flight sleep is relatively short and ways of increasing the amount of sleep obtained should be considered. The incomplete adaptation of sleep during the layover has implications for rest break strategies on the return flight. The altered crew complement did not result in higher levels of fatigue or sleepiness or less in-flight sleep on average, although findings suggest a need to understand the effects of changing the crew complement on workload and in-flight sleep for Captains.

A Real-Time Automotive Safety System Based on Advanced AI Facial Detection Algorithms

This research aims to design and implement a complete safety and comfort device that integrates a Driving Monitoring System (DMS) and a car Seat Electronic Control Unit (SECU). The DMS employs a combination of infrared sensors and a pre-trained Artificial Intelligence (AI) algorithm with advanced facial recognition capabilities. It aims to monitor and control the driver's distraction and drowsiness level during daytime and nighttime driving conditions by accurately detecting head pose and eye status. If the system detects the driver is drowsy or distracted from the road, a warning system will be activated by sending an electrical signal to the aforementioned car SECU. The designed SECU initiates a car seat vibration and triggers an audible signal to alert the driver of a dangerous situation by managing the seat movement process and protection features. It provides the basic drive and control of the seat motor drivers and position sensors. The most important consideration of this SECU is meeting the following requirements: real-time control of various motors, fast processing speed, low power, low heat dissipation, low quiescent current, and low Electromagnetic Interference (EMI). The two systems were integrated into a single compact device that includes a night vision camera mounted on the steering column to record data without obstructing the driver's view. The system has high precision at a maximum detection rate of 45 Frames Per Second (FPS) in real-time. Furthermore, the system is robust under actual driving conditions without being affected by the time of day, facial expressions, or eye occlusions.

Validation of the Karolinska Sleepiness Scale in Korean.

The Karolinska Sleepiness Scale (KSS) is widely used for assessing current level of sleepiness, but it has not been validated in South Korea. This study aimed to validate the KSS using the Stanford Sleepiness Scale (SSS), polysomnography (PSG), and electroencephalography (EEG). The sample consisted of 27 adult participants in this study aged 40.5±7.7 years (mean±standard deviation) and included 22 males. They completed questionnaires and underwent EEG recording and overnight PSG. The KSS was completed from 18:00 to 24:00 every 2 hours and following PSG (at 07:00). KSS scores changed over time and in particular increased with the time since waking, with the score peaking at 24:00. Convergent validity of the KSS was verified by performing a Spearman correlation analysis between the KSS and SSS (r=0.742, p<0.01). Concurrent validity of the KSS was verified by performing a Spearman correlation analysis between the KSS administered before sleep and the sleep onset latency measured using PSG (r=-0.456, p<0.05). Alpha waves were measured 5 minutes before administering the KSS, and the KSS scores were compared with these alpha waves. There were no significant correlations observed between the KSS scores and alpha waves measured in the left occipital area (O1), left frontal area (F3), or left central area (C3). In addition, Spearman correlation analyses of the difference between KSS scores and alpha waves measured at O1, F3, and C3 produced no significant results. This study verified the convergent validity and concurrent validity of the KSS, and confirmed the capabilities of this scale in assessing sleepiness changes over time.

A Novel Approach to Quantify Microsleep in Drivers With Obstructive Sleep Apnea by Concurrent Analysis of EEG Patterns and Driving Attributes.

Accurate quantification of microsleep (MS) in drivers is crucial for preventing real-time accidents. We propose one-to-one correlation between events of high-fidelity driving simulator (DS) and corresponding brain patterns, unlike previous studies focusing general impact of MS on driving performance. Fifty professional drivers with obstructive sleep apnea (OSA) participated in a 50-minute driving simulation, wearing six-channel Electroencephalography (EEG) electrodes. 970 out-of-road OOR (microsleep) events (wheel and boundary contact ≥1 s), and 1020 on-road OR (wakefulness) events (wheel and boundary disconnection ≥1 s), were recorded. Power spectrum density, computed using discrete wavelet transform, analyzed power in different frequency bands and theta/alpha ratios were calculated for each event. We classified OOR (microsleep) events with higher theta/alpha ratio compared to neighboring OR (wakefulness) episodes as true MS and those with lower ratio as false MS. Comparative analysis, focusing on frontal brain, matched 791 of 970 OOR (microsleep) events with true MS episodes, outperforming other brain regions, and suggested that some unmatched instances were due to driving performance, not sleepiness. Combining frontal channels F3 and F4 yielded increased sensitivity in detecting MS, achieving 83.7% combined mean identification rate (CMIR), surpassing individual channel's MIR, highlighting potential for further improvement with additional frontal channels. We quantified MS duration, with 95% of total episodes lasting between 1 to 15 seconds, and pioneered a robust correlation (r = 0.8913, p<0.001) between maximum drowsiness level and MS density. Validating simulator's signals with EEG patterns by establishing a direct correlation improves reliability of MS identification for assessing fitness-to-drive of OSA-afflicted adults.

Analysis of heart rate indices at different levels of sleepiness

The objective detection of sleepiness through physiological indices is crucial for ensuring transportation and industrial safety. As gadgets become more commonplace for measuring heart rate, it is important to identify valid indices that correlate with the level of sleepiness. Research indicates that time-domain and frequency-domain heart rate indices align with levels of alertness and sleepiness [e.g., 1, 2]. The aim of this paper is to identify heart rate indices that correlate with varying degrees of evening sleepiness in vivo, whether low or high. The data analyzed in this paper was obtained from 32 recordings from the SSDD (Subjective Sleepiness Dynamics Dataset) collected at the UNN Cyberpsychology Laboratory since 2022. The experiment was designed in the following manner: participants put on a heart rhythm sensor (Polar H10) at 7:40 PM in their homes and connected it to a smartphone app. Participants then completed a self-reported questionnaire in the electronic system. Starting at 8:00 PM, and every 30 minutes thereafter, the participant recorded data on the subjective level of sleepiness via the Karolinska (KSS) and Stanford (SSS) sleepiness scales up until the time of the participant’s bedtime. The selection of 32 recordings met specific criteria: all participants went to bed between 10:30 and 11:00 PM and completed both the KSS and SSS on time at each time point (08:00, 08:30, 09:00, 09:30, and 10:00 PM). For each time point, an integral sleepiness score was calculated according to the formula: sl=(KSS/10+SSS/7)/2. The 4-minute rhythmogram recordings corresponding to the time points of KSS and SSS filling were chosen for analysis. Data were processed in Jupyter Notebook. Heart rhythm indices, including time-domain, frequency-domain, and nonlinear indices, were computed for each time point from the rhythmogram — NN-interval sequence — using the “hrv-analysis” package. The study employed Student’s t-test to compare indices for varying levels of sleepiness based on integral scores, where “low level” was defined as sl 0.45 and “high level” was defined as sl 0.55. Additionally, the Pearson criterion was used to evaluate the correlation between sl and heart rate indices at each time interval. The indices responsible for the variability of the NN-interval sequence, namely NNi_50 (p=0.027), NNi_20 (p=0.007), and pNNi_20 (p=0.024), exhibited lower values during periods of “low” sleepiness (N=54) as compared to “high” sleepiness (N=58). Moreover, we observed that the autonomic balance index, i.e., the ratio of the power of the heart rate variability spectrum in the low-frequency band to that in the high-frequency band, was higher during ‘high’ sleepiness (p=0.015). Analysis revealed that correlations between integral sleepiness score and heart rate indices were present only for the 08:30 PM time point. Correlations of sl with the NN-interval range (R=–0.388; p=0.028), the severity of sympathetic regulatory circuit activity (R=–0.383; p=0.031), and the nonlinear cardiovagal index (R=–0.359; p=0.043) were found. Thus, it can be inferred that increased sleepiness is associated with decreased heart rate variability indices, along with decreased functioning of the autonomic nervous system overall.

Comparison of sleep characteristics between Parkinson's disease with and without freezing of gait: A systematic review

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. Among the motor complaints, freezing of gait (FOG) is a common and disabling phenomenon that episodically hinders patients' ability to produce efficient steps. Concurrently, sleep disorders are prevalent in PD and significantly impact the quality of life of affected individuals. Numerous studies have suggested a bidirectional relationship between FOG and sleep disorders. Therefore, our objective was to systematically review the literature and compare sleep outcomes in PD patients with FOG (PD + FOG) and those without FOG (PD-FOG). By conducting a comprehensive search of the PubMed and Web of Science databases, we identified 20 eligible studies for inclusion in our analysis. Our review revealed that compared to PD-FOG, PD + FOG patients exhibited more severe symptoms of rapid eye movement sleep behavior disorder in nine studies, increased daytime sleepiness in eight studies, decreased sleep quality in four studies, and more frequent and severe sleep disturbances in four studies. These findings indicate that PD + FOG patients generally experience worse sleep quality, higher levels of daytime sleepiness, and more disruptive sleep disturbances compared to those without FOG (PD-FOG). The association between sleep disturbances and FOG highlights the importance of evaluating and monitoring these symptoms in PD patients and open the possibility for future studies to assess the impact of managing sleep disturbances on the severity and occurrence of FOG, and vice versa.

Development and Validation of a Digital Photographic Atlas of Argentine Foods

Introduction. Before the Second National Health and Nutrition Survey in Argentina, it was necessary to create a digital visual tool to help participants in the quantification of intake. This study describes the development of a Digital Photographic Atlas of Argentinean Foods (AFDAA) and evaluates its accuracy in visually estimating the amounts of foods consumed in Argentina. Methods. A total of 292 photographs of food/dishes were taken in standardized conditions and classified into 103 series according to food group. Thirty series were selected for validation. Adults ≥ 18 years of age were invited to participate in three validation sessions that were conducted at “blinded” between 2016 and 2018. During each session, the participant´s ability to visually relate a real amount of food presented on a plate to an amount depicted in a photograph series was assessed. The difference between the participant’s perception of the weight of foods/dishes in photographs and the real weight was expressed as a percentage. The average percentage difference was estimated, and 95% confidence intervals were used. When ≥ 50% of the differences were outside the ±30% range, the set of pictures was removed from the final version. Results. This free-to-use digital Atlas is a valuable tool that can be employed in future dietary surveys to quantify the consumption of foods similar to those depicted in the images. Conclusion. The degree of BMI tends to increase the level of sleepiness. Funding: This research received financial support from UNICEF and the Universidad Nacional de La Matanza (Grant C2SAL012).

EEG-fest: few-shot based attention network for driver's drowsiness estimation with EEG signals

The leading factor behind most vehicular accidents is the driver’s inattentiveness. To accurately determine driver’s drowsiness, Electroencephalography (EEG) has been proven to be a reliable and effective method. Even though previous studies have developed accurate driver’s drowsiness detection algorithms, certain challenges still persist, such as (a) limited training sample sizes, (b) detecting anomalous signals, and (c) achieving subject-independent classification. In this paper we propose a novel solution, names as EEG-Fest, which is a generalized few-shot model aimed at addressing the aforementioned limitations. The EEG-Fest has the ability to (a) classify a query sample’s level of drowsiness with only a few support sample inputs (b) identify whether a query sample is anomalous signals or not, and (c) perform subject-independent classification. During the evaluation, our proposed EEG-Fest algorithm demonstrates better performance compared to other two conventional EEG algorithms in cross-subject validation.

Validation and interpretation of a multimodal drowsiness detection system using explainable machine learning

Background and objectiveDrowsiness 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. MethodsDriving 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. ResultsThe 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. ConclusionsThe 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.

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.

Detection of driver drowsiness level using a hybrid learning model based on ECG signals.

Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability. An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database. The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %. Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

The Sleepiness-Depression Link in Obstructive Sleep Apnea: Preliminary Results on the Mediation of Impulsivity.

Emotional impulsivity has been found to be relevant in explaining the association between sleep problems and depressive symptoms, suggesting the potential role of impulsivity as a key underlying mechanism of this link. The objective of this study was to take a preliminary step in understanding the mediating role of impulsivity in the relation between excessive daytime sleepiness (EDS) and depression in patients with obstructive sleep apnea syndrome (OSAS) and to compare psychological and demographic characteristics between different levels of daytime sleepiness. A total of 138 patients with OSAS underwent polygraphic cardiorespiratory monitoring and completed a series of questionnaires investigating perceived sleepiness, depression, impulsivity, and other psychological characteristics. A mediational model was tested in order to assess whether impulsivity mediated the relation between sleepiness and depressive symptoms while controlling for the effects of age, sex, BMI, and oxygen saturation parameters. the mediation model showed that there was a significant indirect effect of impulsivity in the sleepiness-depression link (αβ = 0.084 [0.0243-0.1617]). The here-presented results showed that the sleepiness-depression link is not direct as previous studies asserted, but instead it may be better explained by impulsivity. Research and practical implications are discussed.

Sertraline versus escitalopram in South Asians with moderate to severe major depressive disorder: (SOUTH-DEP) a double-blind, parallel, randomized controlled trial.

The study design included the double-blind, parallel, randomized controlled trial. The aim of this randomized controlled trial was to compare the efficacy and safety of sertraline and escitalopram in participants with moderate to severe major depressive disorder (MDD). The study was conducted in South Asian participants. A total of 744 participants with moderate to severe MDD were randomly assigned to receive either sertraline or escitalopram for 8 weeks. Drug dosages and titration schedules were based on the recommendations of the prescribing information for each product and according to the judgment of the clinicians involved in the study. The primary outcome measures were changes from baseline on the Montgomery-Åsberg Depression Rating Scale (MADRS) and the clinical global impression (CGI) scale as well as the frequency of adverse events in both groups. Baseline MADRS scores in the escitalopram and sertraline groups were 28.2±0.47 (mean±SD) and 29.70±0.46 (mean±SD) respectively, and was no variability in the baseline assessments. Changes in MADRS as well as CGI scales at the end of the study were significant only for the sertraline group whereas they remained statistically nonsignificant for the escitalopram group. Results: The results of the study showed that sertraline was more efficacious than escitalopram in reducing depression rating scales such as MADRS and CGI, and that participants subjectively felt better regarding their symptoms in the sertraline group. Sertraline displays enhanced safety or tolerability than other groups of antidepressants, which frequently cause high levels of drowsiness, dizziness, blurred vision, and other undesirable effects. Adverse events were seen in both groups, but delayed ejaculation was the most frequent adverse event seen in both groups. However, a greater number of participants reported having nausea and insomnia in the sertraline group compared to the escitalopram group. Our study clearly highlights that there is a statistically significant difference in efficacy between sertraline and escitalopram at the doses used in our study. Sertraline was able to significantly lower the depression rating scales like MADRS and CGI in participants with moderate to severe MDD. Participants subjectively felt better regarding their symptoms in the sertraline group. The most frequent adverse event in both groups was delayed ejaculation. From an efficacy standpoint, sertraline was more efficacious than escitalopram. The study indicates that the prevalence of depressive disorders in South Asia is comparable to the global estimate, and Bangladesh and India has higher proportions of people with depressive disorders in South Asia. Additionally, females and older adults (75-79 years) have the highest burden of depressive disorders across all countries in the region. This study's limitation included the absence of a placebo arm. An additional limitation of the current study was the lack of an evaluation of inter-rater reliability and the research sample could not have been uniform in terms of the kind of depressive disorders and bipolarity.

Assessing ‘readiness’ by tracking fluctuations in daily sleep duration and their effects on daily mood, motivation, and sleepiness

Study objectivesConsumer sleep trackers issue daily guidance on ‘readiness’ without clear empirical basis. We investigated how self-rated mood, motivation, and sleepiness (MMS) levels are affected by daily fluctuations in sleep duration, timing, and efficiency and overall sleep regularity. We also determined how temporally specific these associations are. Methods119 healthy university students (64 female, mean age = 22.54 ± 1.74 years) wore a wearable sleep tracker and undertook twice-daily smartphone-delivered ecological momentary assessment of mood, motivation, and sleepiness at post-wake and pre-bedtime timings for 2–6 weeks. Naps and their duration were reported daily. Nocturnal sleep on 2471 nights were examined using multilevel models to uncover within-subject and between-subject associations between sleep duration, timing, efficiency, and nap duration on following day MMS ratings. Time-lagged analyses examined the temporal specificity of these associations. Linear regression models investigated associations between MMS ratings and sleep variability, controlling for sleep duration. ResultsNocturnal sleep durations were short (6.03 ± 0.71 h), and bedtimes were late (1:42AM ± 1:05). Within-subjects, nocturnal sleep longer than a person's average was associated with better mood, higher motivation, and lower sleepiness after waking. Effects of such longer sleep duration lingered for mood and sleepiness till the pre-bedtime window (all Ps < .005) but did not extend to the next day. Between-subjects, higher intraindividual sleep variability, but not sleep duration, was associated with poorer mood and lower motivation after waking. Longer average sleep duration was associated with less sleepiness after waking and lower motivation pre-bedtime (all Ps < .05). Longer naps reduced post-nap sleepiness and improved mood. Controlling for nocturnal sleep duration, longer naps also associated with lower post-waking sleepiness on the following day. ConclusionsPositive connections between nocturnal sleep and nap duration with MMS are temporally circumscribed, lending credence to the construction of sleep-based, daily ‘readiness’ scores. Higher sleep duration variability lowers an individual's post waking mood and motivation. Clinical trial idClinicalTrials.gov NCT04880629.

Assessment of the Brazilian Version of the Epworth Sleepiness Scale Using Item Response Theory: A Psychometric Study.

There seems to be a consensus that adolescents worldwide are not getting enough sleep. This study aimed to evaluate the psychometric properties of the Epworth Sleepiness Scale (ESS) in adolescents using the item response theory. A psychometric study was conducted with 2206 adolescents aged 18 and 19 years in the city of São Luís, Maranhão, Brazil. The dimensionality of the ESS was assessed by principal component analysis. A Samejima's graded response model (SGRM) was fitted to it. The findings of this study showed a good internal consistency and the unidimensionality of the ESS. Considering the latent trait continuum, we obtained three levels, with anchor items. For the item 'Sitting still in a public place', the adolescents presented a small possibility of dozing in level 1, and a medium and great possibility in level 2. The item 'Sitting around talking to someone' presented small, medium, and great possibilities of dozing in level 3. The ESS with five items showed satisfactory psychometric properties. In addition, the results from the SGRM showed that adolescents with high levels of sleepiness are likely to nod off or sleep sitting up while talking to someone. This study allows us to understand excessive daytime sleepiness in adolescents.

Driver drowsiness modeling based on spatial factors and electroencephalography using machine learning methods: A simulator study

Driver drowsiness is one of the leading causes of fatal road traffic accidents (RTA). While studies have illustrated the effectiveness of spatial criteria on driver drowsiness, the effects of these factors on drowsy driver behavior have yet to be mapped or spatially modeled properly. Meanwhile, driver behavior has been known as a major factor in the RTA occurrence. This paper investigated the effect of four spatial criteria of chainage, distance traveled after a horizontal curve, horizontal curvature, and slope on drowsy driver behavior in a simulated driving task. Accordingly, the drowsiness level on a monotone 108 km virtual expressway in northeastern Iran was determined by modeling the EEG data of 20 subjects. Four machine learning (ML) algorithms, namely bagged decision trees (BDT), multi-layer perceptron (MLP), random forest (RF), and support vector regression (SVR), were used to analyze electroencephalogram (EEG) signals and to estimate driver drowsiness based on spatial criteria. The results of the receiver operating characteristic curve (ROC) showed that BDT (0.925), RF (0.919), MLP (0.807), and SVR (0.706) had the highest accuracy in estimating drowsiness level on the virtual road using EEG signals, respectively. Using the estimated drowsiness level by BDT and spatial criteria values, ML algorithms were trained to map driver drowsiness on the Tehran-Mashhad road. Based on ROC results, MLP (0.921), BDT (0.918), RF (0.917), and SVR (0.896) had the most accurate performance in mapping driver drowsiness, respectively. Generated drowsiness level maps were validated, and the estimations of BDT and RF were more consistent with real drowsy driving accident black spots in the study area. In conclusion, mapping driver drowsiness by applying ML algorithms and spatial criteria detects high-risk areas and helps to improve road safety.