Levels Of Sleepiness Research Articles

Winter-summer difference in post-awakening salivary α-amylase and sleepiness depending on sleep and melatonin

Winter and summer seasons are contrasted by light/dark conditions at temperate latitudes, and the negative influence of this contrast on circadian health is yet to be quantified. This field study (performed in Novosibirsk, 55°N, no daylight saving time transitions) aimed to compare post-awakening arousal state in summer and winter in subjects (N=45) on a fixed 5-workday schedule (waken up by alarm at either ∼6 am or ∼7 am). Their circadian status (by 24-h melatonin profiles) and sleep (by log data) have been previously reported. Salivary α-amylase levels (a biomarker of the sympathetic nervous system activity, or stress) and subjective sleepiness were measured immediately after awakening on Friday, at minute 0 (supine), 10, 20, and 30 (not supine). α-Amylase levels were found to be higher in winter, along with a blunted α-amylase awakening response (AAR; a decline from minute 0 to minute 10 value). Both effects were attributable mainly to the 7am group. Sleepiness levels also increased in winter, mainly due to the seasonally dependent subjects, and predictably associated with shorter, later sleep, and later melatonin circadian phase. The sleepiness and α-amylase changes did not correlate. The seasonal change in α-amylase was positively associated with the change in the amount of melatonin secreted, probably reflecting the parallelism in the noradrenergic neural control of both α-amylase and melatonin secretion. Together, higher post-awakening salivary α-amylase levels (indicating stress) and subjective sleepiness levels (indicating greater sleep need) in winter compared to summer point to a less healthy state in winter.

Real-Time Detection for Drowsy Driving via Acoustic Sensing on Smartphones

Drowsy driving is one of the biggest threats to driving safety, which has drawn much public attention in recent years. Thus, a simple but robust system that can remind drivers of drowsiness levels with off-the-shelf devices (e.g., smartphones) is very necessary. With this motivation, we explore the feasibility of using acoustic sensors on smartphones to detect drowsy driving. Through analyzing real driving data to study characteristics of drowsy driving, we find some unique patterns of Doppler shift caused by three typical drowsy behaviours (i.e., nodding, yawning and operating steering wheel), among which operating steering wheels is also related to drowsiness levels. Then, a real-time Drowsy Driving Detection system named D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -Guard is proposed based on the acoustic sensing abilities of smartphones. We adopt several effective feature extraction methods, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Besides, measures to distinguish drowsiness levels are also introduced in the system by analyzing the data of operating steering wheel. Through extensive experiments with five drivers in real driving environments, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -Guard detects drowsy driving actions with an average accuracy of 93.31%, as well as classifies drowsiness levels with an average accuracy of 86%.

Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip.

Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.

Acute sleep deprivation immediately increases serum GDNF, BDNF and VEGF levels in healthy subjects.

Acute sleep deprivation upregulates hippocampal neurogenesis. Neurotrophic factors such as glial cell line-derived neurotrophic factor (GDNF), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor (VEGF) are mediators of neuronal plasticity and neurogenesis. These neurotrophins are involved in sleep and sleep disorders and are associated with sleep deprivation. In this study, it is aimed to investigate the changes of neurotrophin levels with total sleep deprivation in healthy individuals. Seventeen healthy young adults with a mean age of 19.8 (SD = 1.0) years underwent an experimental protocol consisting of 36h of total sleep deprivation. Venous blood samples were obtained on Day1 at 09.00, on Day2 at 09.00, and at 21.00. Serum levels of neurotrophins were detected using the ELISA method. The participants were asked to mark the scores corresponding to their subjective energy, happiness, depression, tension levels on the visual analog scale; and sleepiness level on the Epworth Sleepiness Scale; during the course of the study. As a result of 36h of sleep deprivation, serum GDNF, BDNF, and VEGF levels showed a statistically significant increase compared to the baseline values in the participants included in the study (P < 0.0001). While this increase was evident in 24h, it continued after 36h. In parallel, sleepiness levels, subjective depression, and tension levels increased, on the other hand, subjective energy and happiness scores decreased at a statistically significant level at the end of the study compared to basal values (P < 0.0001). The results show that acute sleep deprivation significantly affects and increases serum levels of neurotrophic factors, and it seems that these effects are likely to occur as an immediate response to the stress and disruption caused by sleep deprivation.

P-SP002. Prevalence of daytime sleepiness among emergency medical service ambulance driver in kuala lumpur hospital

Introduction . The Emergency Medical Service (EMS) ambulance driver's prevalence of daytime sleepiness is the most important field of study because it concerns the health and well-being of a rescue team member, a survivor or patient and the public. Objective of the study was to measure the self-reported level of daytime sleepiness among EMS ambulance drivers at Kuala Lumpur Hospital, Malaysia. Methods . Cross-section and descriptive analysis using a self-filled questionnaire for the target population sample of 31 EMS ambulance drivers was conducted in Asia's largest public hospital, HKL, Malaysia, using the Epworth Sleepiness Scale (ESS). Results . Measures for the prevalence of daytime sleepiness among 31 respondents. The ESS shows that 20 (65%) respondents have excessive daytime sleepiness while 11 (35%) were free of daytime sleepiness. In comparison, the frequency of a near-miss crash was relatively high (74%) and also significant (52%) slept while driving. Conclusion . Ambulance driver at HKL working as an EMS is at risk symptoms of daytime sleepiness. This result highlights the importance of sleepiness screening, providing adequate instruction, support and providing the necessary facilities to prevent adverse health effects among EMS ambulance drivers from developing.

Sleep and daytime sleepiness in elite athletes and sedentary individuals

Summary Objectives The objectives of this study were to evaluate the duration and the quality of sleep, as well as daytime sleepiness, among different levels of Arab athletes (i.e., amateur, elite) and to compare sleep quality and duration and daytime sleepiness in athletes and sedentary individuals. Material and methods A total of 409 participants, characterized as elite athletes (n = 78), amateur athletes (n = 231), and sedentary individuals (n = 100), participated in this study. Participants were asked to complete the Pittsburgh Sleep Quality Index (PSQI) questionnaire, for the evaluation of sleep quality, and the Epworth Sleepiness Scale (ESS), for the assessment of daytime sleepiness. Results The total PSQI score was significantly higher in sedentary (total PSQI score = 5.74 ± 2.5) vs. amateur (total PSQI score = 5 ± 2.3) (P 0.05). There was no significant difference (P > 0.05) between levels of daytime sleepiness reported in amateur athletes (ESS score = 9.6 ± 4.3), elite athletes (ESS score = 10 ± 3.8), and sedentary individuals (ESS score = 10.4 ± 2.4). Conclusion Athletes experienced a better sleep quality compared to sedentary individuals. However, some athletes can suffer from poor sleep quality, suggesting an urgent need for the implementation of sleep hygiene education. Levels of daytime sleepiness, comparable in athletes and sedentary individuals, indicate higher normal daytime sleepiness. Thus, daily naps could be useful to prevent excessive daytime sleepiness, but future studies on this topic, efficacy, and practicality are needed.

Medium‐term changes in patients with epilepsy during the COVID‐19 pandemic

The novel coronavirus disease (COVID-19) pandemic has led to social distancing measures and impaired medical care of chronic neurological diseases, including epilepsy, which may have adversely affected well-being and quality of life of patients with epilepsy (PWE). The objective of this study is to evaluate the impact of the COVID-19 pandemic in the levels of anxiety, depression, somnolence, and quality of life using validated scales in PWE in real-life clinical practice. Self-administered scales of anxiety disorders (GAD-7), depression (NDDI-E), somnolence (Epworth Sleepiness Scale; ESS), and quality of life (QOLIE-31-P) in PWE treated in a Refractory Epilepsy Unit were longitudinally analyzed. Data were collected before the beginning (December 2019 - March 2020) and during the COVID-19 pandemic (September 2020-January 2021). 158 patients (85 from the first round and 73 from the second round) 45.0±17.3years of age, 43.2% women, epilepsy duration 23.0±14.9years, number of antiepileptic drugs 2.1±1.4, completed the survey. Significant longitudinal reduction of QOLIE-31-P (from 58.9±19.7 to 56.2±16.2, p=.035) and GAD-7scores (from 8.8±6.2 to 8.3±5.9, corrected p=.024) was identified. No statistically significant longitudinal changes in the number of seizures (from 0.9±1.9 to 2.5±6.2, p=.125) or NDDI-E scores (from 12.3±4.3 to 13.4±4.4, p=.065) were found. Significant longitudinal increase of ESS (from 4.9±3.7 to 7.4±4.9, p=.001) was found. During the COVID-19 pandemic, quality of life and anxiety levels were lower in PWE, and sleepiness levels were raised, without seizure change.

Vehicular Security: Drowsy Driver Detection System

Detecting the driver's drowsiness in a consistent and confident manner is a difficult job because it necessitates careful observation of facial behaviour such as eye-closure, blinking, and yawning. It's much more difficult to deal with when they're wearing sunglasses or a scarf, as seen in the data collection for this competition. A drowsy person makes a variety of facial gestures, such as quick and repetitive blinking, shaking their heads, and yawning often. Drivers' drowsiness levels are commonly determined by assessing their abnormal behaviours using computerised, nonintrusive behavioural approaches. Using computer vision techniques to track a driver's sleepiness in a non-invasive manner. The aim of this paper is to calculate the current behaviour of the driver's eyes, which is visualised by the camera, so that we can check the driver's drowsiness. We present a drowsiness detection framework that uses Python, OpenCV, and Keras to notify the driver when he feels sleepy. We will use OpenCV to gather images from a webcam and feed them into a Deep Learning model that will classify whether the person's eyes are "Open" or "Closed" in this article.

Emotion Recognition and Drowsiness Detection using Digital Image Processing and Python

We suggest a system that can detect a person's emotions as well as their level of sleepiness. The majority of our work is devoted to extracting information from the frontal face. The article goal is to create a product that is both reasonable and efficient in its operation. The system was created in Python using artificial intelligence and digital image processing technologies. Identifying eye blinking is essential in some situations, such as in the prevention of automobile accidents or the monitoring of safety vigilance.

Younger drivers executive functioning and the relationship with experiencing signs of sleepiness

Young drivers are more likely to continue driving when experiencing signs of sleepiness and are over-represented in sleep-related crashes. Adolescence and early adulthood are characterised by comparatively poor executive functioning, and while previous research has demonstrated a link between poor executive functions and several risky driving behaviours, the relationship with sleepy driving is not well understood. Accordingly, the first aim of the current study was to examine the association between executive functions and experiencing the signs of driver sleepiness in a sample of young adult drivers. Additionally, young drivers who have less experience with driving while sleepy, may attribute less importance to the signs of sleepiness as an indicator of underlying sleepiness level. To test this assumption (aim two), the impact of experiencing signs of sleepiness on perceptions of the importance of those signs was examined. Participants included 118 young adults aged between 17 and 25 years, who completed an online survey measuring experiences with the signs of sleepiness while driving, executive functions, and demographic characteristics. This sample of young adults reported having considerable experience with several signs of sleepiness (i.e., yawning, mind wandering, and difficulty keeping eyes while driving). A linear regression analysis found that the demographic variables of age and hours driven per week, as well as the executive function constructs of organization, strategic planning, and impulse control were associated with experiencing signs or sleepiness. Moreover, having experienced more signs of sleepiness was associated with an increased likelihood in rating those signs as important indicators of sleepiness. The current findings suggest both that several high-level cognitive processes as well as levels of experience with driving when experiencing signs of sleepiness contribute to young peoples’ sleepy driving.

An investigation of the effects of different shift schedules on the fatigue and sleepiness of officers on oil tankers during cargo handling operations

Cargo handling is an operation, which requires a high level of performance from the officer of the watch (OOW). This study aimed to investigate the effect of different shift schedules on sleep quality, cognitive performance, and sleepiness of 139 OOWs on oil tankers with 4on–8off shifts, during the first shift of cargo handling. Sleep quality (Pittsburgh Sleep Quality Index (PSQI)), level of sleepiness (the Karolinska Sleepiness Scale (KSS)), Psychomotor Vigilance Task (PVT), and Arrow Flanker task performance were examined. The results showed that OOWs with (00:00–04:00, 12:00–16:00) and (04:00–08:00, 16:00–20:00) shifts had impaired cognitive performance and higher sleepiness during the cargo handling operation, and they also experienced impaired sleep quality. The results demonstrated that the circadian rhythm and homeostatic sleep drive have a greater impact on cognitive performance and sleepiness than time on shifts. These results suggest that allocating rest hours immediately before the cargo handling operation may reduce the risk of fatigue. Practitioner Summary: To the best of our knowledge, this maritime field study shows for the first time the prevalence of seafarers’ sleepiness and cognitive performance while on duty during cargo handling, using a pre-post shift comparison between three different shifts. The results show the negative effects of keeping watch at night on sleep quality, sleepiness, and the impaired cognitive performance both in the day and the night shifts.

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles.

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

The impact of shift work on the wellbeing and subjective levels of alertness and sleepiness in firefighters and rescue service workers

Shift work can be associated with health and sleep problems which may lead to cognitive impairment. This study investigated effects of shift work on sleep, health behaviours and cognitive functions of polish rescue service workers. We tested 18 paramedics working 12-hour shifts, 15 firefighters working 24-hours shifts, and 17 daytime workers. We measured general sleepiness, workload during shifts, an occurrence of health behaviours. Additionally we measured attention, executive functions and subjective alertness. Paramedics showed lower average sleep duration and quality, and fewer health behaviours than firefighters and control group. However, no differences in performance in cognitive tests between the groups and between the measures were found. The results indicate that the differences in job specificity may contribute to the effects of shift-work on sleep and health of the workers.

Self-reported reasons for on-duty sleepiness among commercial airline pilots

ABSTRACT Experimental and epidemiological research has shown that human sleepiness is determined especially by the circadian and homeostatic processes. The present field study examined which work-related factors airline pilots perceive as causing on-duty sleepiness during short-haul and long-haul flights. In addition, the association between the perceived reasons for sleepiness and actual sleepiness levels was examined, as well as the association between reporting inadequate sleep causing sleepiness and actual sleep-wake history. The study sample consisted of 29 long-haul (LH) pilots, 28 short-haul (SH) pilots, and 29 mixed fleet pilots (flying both SH and LH flights), each of whom participated in a 2-month field measurement period, yielding a total of 765 SH and 494 LH flight duty periods (FDPs) for analyses (FDP, a period between the start of a duty and the end of the last flight of that duty). The self-reports of sleepiness inducers were collected at the end of each FDP by an electronic select menu. On-duty sleepiness was rated at each flight phase by the Karolinska Sleepiness Scale (KSS). The sleep-wake data was collected by a diary and actigraph. The results showed that “FDP timing” and “inadequate sleep” were the most frequently reported reasons for on-duty sleepiness out of the seven options provided, regardless of FDP type (SH, LH). Reporting these reasons significantly increased the odds of increased on-duty sleepiness (KSS ≥ 7), except for reporting “inadequate sleep” during LH FDPs. Reporting “inadequate sleep” was also associated with increased odds of a reduced sleep-wake ratio (total sleep time/amount of wakefulness ≤ 0.33). Both “FDP timing” and “inadequate sleep” were most frequently reported during early morning and night FDPs, whereas the other options showed no such phenomenon. The present study suggests that airline pilots’ perceptions of work-related factors that make them sleepy at work are in line with the previous experimental and epidemiological studies of sleepiness regulation.

Sleep Quality in Semi-Professional Male Basketball Players in Bermuda

Objectives: This study sought to determine the quality of sleep using the Pittsburgh Sleep Quality Index (PSQI), the presence of sleepiness using the Epworth Sleepiness Scale (ESS) and the association between sleep quality and sleepiness in basketball players in Bermuda. Methods: Once ethical approval was granted, players were recruited from the Bermuda Basketball Association League. All participants completed the PSQI and the ESS questionnaires based on their recollection of events as they occurred over the previous 30 days. Their responses were analysed using the IBM SPSS version 19 for Windows. Results: A total of 71 subjects, mean age 24.96 ± 3.19 years, participated in this study. The mean PSQI score was 7.8 ± 4.7 (scores of 5 or more indicate poor sleep quality). Thirty percent of players rated their sleep quality as fairly bad to very bad. The mean sleepiness score was 7.35 ± 4.17 and over 60% of persons surveyed had more than normal levels of sleepiness. There was a significant correlation between sleep quality and sleepiness; 0.61 (p &lt; 0.01), as well as a correlation between age and Global PSQI which had a score of 0.31 (p &lt; 0.01). Conclusion: Basketball players in Bermuda are experiencing less than optimal sleep. Insomnia was among the most popular self-reported cause of sleep disturbances. Further research is required in this population, exploring causal factors for poor sleep quality. Key words: Athletes, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Sleep Quality.

A six-factor structure of individual variation in the tendencies to become sleepy and to sleep at different times of the day

A multidimensional approach has been previously applied for modeling and assessment of individual differences in the ability to sleep or to stay awake at certain clock hours. More recently the 19 time-point Visuo-verbal Judgment Task (VJT) has been proposed to predict changes in sleepiness level from the morning hours to the next day afternoon. The dimensionality of the VJT has not been explored so far. We applied a structural model of individual variation in sleep-wake behavior and habits for explaining the pattern of relationship between the VJT's dimensions yielded by rotation of principal components with eigenvalue>1. The responses to 19 items of the VJT, 72 items of 6-scale Sleep Wake Pattern Assessment Questionnaire (SWPAQ) and 60 items of 6-scale Sleep-Wake Adaptability Test (SWAT) were collected from 1037 survey participants. Factor analyses yielded 4 factorial dimensions of morning (08:00–11:00), daytime (12:00–20:00), nighttime (21:00–04:00), and after 24 h sleepiness (06:00–12:00 next day). The model was found to be capable to explain the correlations among 4 constructs of the VJT as well as the correlations among previously developed scales of the SWPAQ and SWAT. The results confirmed the predictive power of the model and its applicability for multidimensional assessments in chronobiological and chronopsychological studies.

Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes.

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7-14 days in young adults with type 1 diabetes. In addition, person-level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined. In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring over 6-14 days. At baseline, participants completed the Psychomotor Vigilance Test, the Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries. Forty-six participants (mean age, 22.3 ± 3.2 years) wore a wrist actigraph and underwent continuous glucose monitoring concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences; r = .33, P = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences; r = .50, P = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for type 1 diabetes duration (P > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, P = .010, pr2 = 0.40) after controlling for type 1 diabetes duration and accounting for higher daytime sleepiness. Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with type 1 diabetes. Sleep habits in this population may explain higher glucose variability, and optimizing sleep may improve overall diabetes management. Griggs S, Hickman RL Jr, Strohl KP, Redeker NS, Crawford SL, Grey M. Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes. J Clin Sleep Med. 2021;17(9):1865-1874.

673 Did the COVID-19 pandemic increase insomnia?

IntroductionCOVID-19 has been an unprecedented health event with far-reaching health and economic consequences. There have been numerous surveys published that have suggested that insomnia has increased during the pandemic. However, there have been no comparisons of data from the pandemic with that from other years. Here we present baseline data from people signing up to an online CBTi course to investigate the impact of COVID-19 on sleep.MethodsWe investigated the difference in age; diary-reported Sleep Efficiency (SE%) and Total Sleep Time (TST); sleep quality and disturbances as measured by the Pittsburgh Sleep Quality Index (PSQI) and daytime sleepiness using the Epworth Sleepiness Scale (ESS); between the first wave of COVID-19 in the UK (1st March -31st July 2020) as compared to the same period in 2019.ResultsIn 2019 n=2231 patients were assessed as compared to n=6173 in 2020. There were no significant differences in the age of the two cohorts (47.1 years v 46.3 years, NS). SE% was significantly lower in the 2019 cohort (66% v 67.6, p <0.001) as was their total sleep time (5.71 hrs v 6.05 hrs, p<0.0001). PSQI scores were also higher in 2019 (13.13 v 12.72. p<0.0001). The level of daytime sleepiness was lower in the 2019 cohort (5.4 v 5.6 p <0.001)ConclusionOur results show that there was no evidence of an increase in the severity of sleep disturbance during the 1st wave of the COVID-19 pandemic in the UK in contrast to what numerous surveys have suggested. Indeed, we found that people signing up to Sleepstation’s online dCBTi course during the 1st wave of the pandemic had statistically significant better subjective sleep, although they had a higher level of daytime sleepiness than those in the same period a year previously. Although statistically significant, our results do not demonstrate a clinically relevant difference between the two cohorts. It is also interesting that despite the age-related impact of COVID-19, there was no significant difference in the age of the patients. Thus, in contrast to the survey data, we found no evidence for a worsening of sleep during the 1st wave of the pandemic.Support (if any):

518 Preliminary report on the efficacy of behavioral therapy for insomnia in shift work disorder: a randomized control trial

Abstract Introduction Around 33% of shift workers suffer from shift work disorder (SWD). SWD includes insomnia and sleepiness related to the atypical work schedule. Most SWD treatments rely on light therapy or pharmacology. Few studies explored CBT-I efficacy for insomnia in shift work and reported promising results. The study aims to evaluate the efficacity of a behavioral therapy for insomnia adapted for SWD (BT-SWD). Methods Forty-three night shift workers meeting SWD criteria were recruited (mean age = 34 years old; 77% women) and randomized to either BT-SWD or waiting list control group. Eight participants dropped-out while waiting. Before and after treatment, participants completed questionnaires (ESS, ISI, STAI and BDI-II) and sleep diaries throughout the experimentation. BT-SWD involves sleep restriction therapy and stimulus control for insomnia as well as fixed sleep periods in the dark for shift workers. BT-SWD has been applied to night sleep, day sleep, and naps in this order. It consists of 6 sessions of 50 minutes delivered on 8 weeks. A mixed MANOVA was conducted on questionnaires scores with group (treatment or waitlist) as the between-subject factor and time as the within subject factor. Results A multivariate interaction effect was significant, F(6,21) = 8.24, p&amp;lt;.001. A univariate interaction effect was observed for the BDI-II (p=.042), the trait scale of the STAI (p=.021) and the ISI, both for night sleep (p=.027) and day sleep (p&amp;lt;.001), indicating that the scores of participants in the treatement group lowered significantly more than those of participants on the waiting group. The treatment group had a significantly less severe insomnia, both for day sleep during night work (p&amp;lt;.001), and night sleep during days off (p&amp;lt;.001). There was no significant difference between the control and the treatment group on sleepiness levels. Conclusion BT-SWD is effective at reducing insomnia severity as well as levels of trait anxiety and depression. Results are more equivocal for sleepiness. The waiting list control group design used has led to an important attrition in the context of shift work. Further analyses are needed to determine the BT-SWD efficacy on sleep variables. Support (if any) The study was supported by a CIHR grant (#110254) awarded to the first author

296 Sex differences in sleep and wakefulness of police officers working shifts: evidence from a field study

Abstract Introduction National reports of work-related injuries found the excess risk of work injury attributed to shift work to be significantly higher among women. The Working Time Society (WTS) concluded that male sex is one of the few factors that is “consistently associated with perceived or actual shift work tolerance”. However, it is unclear if physiological parameters are involved. Laboratory-controlled studies report sex differences in circadian rhythms (body temperature, melatonin). In sleep deprivation protocols, alertness and cognitive performances were affected by sex, menstrual cycle phase and hormonal contraceptives [HC] use. Nevertheless, field studies that compare male and female shift workers are scarce. Methods An observational study including 76 police officers working on patrol: 56 males and 20 females (11 using [HC], 6 not using [non-HC] and 3 with unknown use of hormonal contraception) aged 32.0 ± 5.3 years. Participants were followed throughout a month-long work cycle (1,457 morning, evening, night, or other shifts, plus rest days). They filled out time-stamped questionnaires (Samn-Perelli, KSS, Visual Analogue Scales, ~5/day; sleep and work-related information, ~1–2/day), completed 5-min Psychomotor Vigilance Tasks (PVT, ~2/day), and wore an actigraph to collect activity data. Linear mixed-effects models were used to analyze the effects of group, time awake and time-of-day on fatigue, sleepiness, alertness, mood and PVT measures. Results Self-reported measures and psychomotor performance significantly varied with time awake and time-of-day. Fatigue and sleepiness levels were significantly higher among female compared to male police officers, both with time awake and across the 24-h day. These variations were similar between non-HC females and the other groups. Compared to males, HC females were more fatigued and less alert, both with time awake and across the 24-h day, and sleepier with time awake. Having children at home did not explain these differences. Conclusion The results of this study expand our knowledge on the sex differences in the sleep and circadian physiology and demonstrate a critical effect of HC on women fatigue, sleepiness and alertness when working shifts. Sex and hormonal parameters must be considered in occupational medicine as well as in future laboratory and field studies on shift workers and circadian rhythms. Support (if any) IRSST, FRQS.