SLEEP

Balba, Nadir; Reynolds, Christina; Modarres, Mo; McBride, Alisha; Yildiz, Selda; Heinricher, Mary; Lim, Miranda

doi: 10.1093/sleep/zsab072.068pmid: N/A

Abstract Introduction Chronic pain and sleep disturbances are intricately linked to one another, especially in individuals with a history of traumatic brain injury (TBI) who are at greater risk for both symptoms. Although prior studies have analyzed differences in sleep electroencephalogram (EEG) in these clinical populations, the association between sleep EEG slow wave coherence and pain complaints is not fully examined or known. Our novel slow wave coherence approach may provide new insights into the relationship between TBI, chronic pain, and sleep Methods Ninety-six veterans were recruited and enrolled under a VA IRB-approved protocol. Participants completed a semi-structured clinical interview to determine their history of TBI, Symptom Impact Questionnaire Revised (SIQR), a measure of chronic pain complaints, and underwent an attended overnight in-lab polysomnogram (PSG). We developed a novel computational signal processing algorithm to identify and quantify EEG slow waves within 100 ms bins across the 6 standard PSG EEG channels. When a slow wave was simultaneously observed in 4 or more of the 6 leads, slow wave coherence was inferred, and a percentage of slow wave coherence across each of the sleep stages was then calculated for each subject. Results In our sample, 65 participants (67.7%) endorsed experiencing chronic pain lasting 3 months or longer, and 54 had a history of TBI (56.3%). Participants endorsing chronic pain had a significantly lowered percent of EEG slow wave coherence during NREM sleep than subjects without chronic pain (p = 0.01). NREM EEG slow wave coherence did not correlate with SIQR scores in subjects without TBI (r = -0.03, p = 0.90), but was significantly negatively correlated in subjects with TBI (r = -0.32, p = 0.02). Conclusion EEG slow wave coherence during NREM sleep is correlated with chronic pain complaints in Veterans with a history of TBI, and could be indicative of neuronal dysfunction during sleep. Further research on slow wave coherence is warranted to understand the underlying mechanisms for the association between chronic pain and poor sleep following TBI. Support (if any) D01 W81XWH-17-1-0423 This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Caron, Jean-François; Godin, Roxanne; Gaudreault, Pascale; Roy, Jonathan; Forest, Geneviève

doi: 10.1093/sleep/zsab072.299pmid: N/A

Abstract Introduction Studies have shown that sleep in adolescents is characterized by sleep disturbances. Many teens resort to prescribed or nonprescribed medication to alleviate their sleep difficulties. Research suggests that sport and physical activity may be protective factors regarding sleep. The aims of the present study were to investigate the use of sleep aids among young athletes and non-athletes, and to identify possible factors associated with prescribed and nonprescribed sleep aids Methods 35 young athletes (14.6±0.7 years old; 54.3 % males) and 30 young non-athletes (15.1±0.7 years old; 16.7% males) completed questions on sleep aids, the Academic Motivation Scale, the anxiety and depression scales of the Beck Youth Inventory-II, and the Multidimensional Self-Esteem Questionnaire, at the beginning, middle, and end of the school year. Mean scores for the school year were computed for amotivation in school, intrinsic academic motivation of accomplishment, self-esteem, anxiety symptoms, and depressive symptoms. Teens were each categorized as user or non-user if they had or had not used sleep aids during the school year. First, comparison of sleep aids usage between groups were done using a Chi-square test. Then, both groups of athletes and non-athletes were combined. Amotivation, intrinsic motivation, self-esteem, and anxiety and depressive symptoms were compared between users and non-users using paired t-tests Results Results show that young non-athletes report using sleep aids more often than young athletes (X2(1,N=65)=5.205, p=.023). Indeed, 65.2% non-athletes compared to 34.8% athletes reported using sleep aids during school year. Users represent 35.4% of the total sample. T-tests showed that users have a significantly higher amotivation score (t(65)=-2.010, p=.049), more anxiety symptoms (t(65)=-2,480, p=.016), and more depressive symptoms (t(65)=-2,126, p=.037) than non-users. Conclusion These results show a high prevalence of prescribed and nonprescribed sleep aids usage in teens. Our results also suggest that sleep aids in young adolescents is associated with mental health problems and academic motivation issues. On the other hand, our results support that sport and physical activity may have a protective role regarding sleep. This highlights the importance to promote sport participation among adolescents. Support (if any) n/a This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

Zarpellon, Raquel Simone Maccarini; Vilela, Regina Maria; Radominski, Rosana Bento; de Souza Crippa, Ana Chrystina

doi: 10.1093/sleep/zsab072.551pmid: N/A

Abstract Introduction When studying the inherent aspects of sleep it is important to assess how the quality and quantity of sleep in the last two decades may be one of the reasons for the increase in childhood obesity, which has been growing rapidly worldwide. This study aims to assess the presence of sleep disorders in overweight children and adolescents. Methods An descriptive study was conducted with data collection from 43 patients between 6 and 13 years old diagnosed as overweight. They were patients of a specialized service for children and adolescents with obesity that is part of the Hospital de Clínicas of the Federal University of Paraná, located in Curitiba, Brazil. To investigate the presence of sleep disorders, the Sleep Disturbance Scale for Children (SDSC) questionnaire was administered. The factors assessed were: Disorders of Initiating and Maintaining Sleep, Sleep Breathing Disorders, Disorders of Arousallnightmures, Sleep Wake Transition Disorders, Disorders of Excessive Somnolence and Sleep Hyperhydrosis. Results The mean age of the patients that took part in the research was 10 years and 7 months (± 1.95). The mean BMI of the participants was 29.57 kg/m2 (± 4.38), the majority being diagnosed with obesity. The sum of all SDSC factors demonstrated the presence of pathological sleep in 58.1% (25) of the sample, whereas 51.2% (22) of the patients had Sleep Breathing Disorders and 58.1% (25) had the Sleep Wake Transition Disorder. Conclusion The present study demonstrated the presence of sleep disorders in overweight children and adolescents. As for Sleep Respiratory Disorder, a situation has already been advocated in the current literature for this audience. In relation to the Sleep-Wake Transition Disorder and pathological sleep, further research is needed to prove the presence of the disorder in other groups studied. Here is the suggestion that future research be done with subjective and objective data collection on sleep within a larger sample, in order to confirm the association between sleep disorders and childhood obesity. Support (if any): This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

Zhang, Xin; Lee, Shih-Yu

doi: 10.1093/sleep/zsab072.305pmid: N/A

Abstract Introduction Circadian rhythms play an important role in regulating sleep. Sleep disturbances are prevalent in shift-work nurses, particularly for those work in rapid-shift rotation, including night shifts and day shifts. This study aimed to: 1) describe the characters of sleep-wake index (total sleep time [TST], wake after sleep onset [WASO], circadian activity rhythms [CAR]), psychomotor vigilance test (PVT), salivary cortisol, fatigue, and activity level during 8- and 12-hour rapid-shift work nurses; and 2) compare the parameters between two different shifts. Methods This exploratory study used registered nurses (RNs) from nine intensive care units in Beijing area. 7-days consecutive wrist actigraphy data, including TST and WASO were collected. Cosiner analysis was used for computing the CAR, including amplitude and mesor. PVT and saliva cortisol were used to assess alertness level, which measured before and after shift. Self-reported fatigue severity was measured by Lee Fatigue Scale-Short Form and assessed before and after shift. Results A total of 152 RNs (12-hour, n=82; 8-hour, n=70) participated this study, with a mean age of 31.81 (SD= 6.09). Compared with the 8-hour shift nurses, the 12-hour shift nurses were significantly higher in TST (456 vs. 364 minutes), median saliva cortisol level (before day shift, 0.54 vs. 0.31), and median PVT reaction time (before night shift). However, CAR were 0.53 (SD=0.13) and 0.50 (SD=0.18) for 12-hour and 8-hour shift RNs, respectively, and indicates desynchronized CAR in both groups. Regardless shift rotation, almost three-quarters of the RNs had a 500 ms PVT reaction time. For the 12-hour and 8-hour nurses, the level of activity during day shift was similar. However, during night shift work it was significantly lower in 12-hour nurses compared to the 8-hour nurses. All RNs experienced clinical significant fatigue severity (ranged 3.78 to 8.14) regardless before or after shift work; however, the 12-hour group reported lower fatigue severity than 8-hour group. Conclusion In this study, findings revealed shift-work RNs experienced fatigue and desynchronized CAR. The TST was low and reaction time was prolonged before and after shift work. Sleep intervention should be mandatorily included in clinical continue education. Support (if any) This project was supported by Chinese National Natural Science Foundation (71603279). PDF This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

Taweesedt, Pahnwat; Kim, JungYoon; Park, Jaehyun; Park, Jangwoon; Sharma, Munish; Surani, Salim

doi: 10.1093/sleep/zsab072.413pmid: N/A

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any): This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

McCauley, Mark; McCauley, Peter; Van Dongen, Hans

doi: 10.1093/sleep/zsab072.136pmid: N/A

Abstract Introduction In commercial aviation and other operational settings where biomathematical models of fatigue are used for fatigue risk management, accurate prediction of recovery during rest periods following duty periods with sleep loss and/or circadian misalignment is critical. The recuperative potential of recovery sleep is influenced by a variety of factors, including long-term, allostatic effects of prior sleep/wake history. For example, recovery tends to be slower after sustained sleep restriction versus acute total sleep deprivation. Capturing such dynamics has proven to be challenging. Methods Here we focus on the dynamic biomathematical model of McCauley et al. (2013). In addition to a circadian process, this model features differential equations for sleep/wake regulation including a short-term sleep homeostatic process capturing change in the order of hours/days and a long-term allostatic process capturing change in the order of days/weeks. The allostatic process modulates the dynamics of the homeostatic process by shifting its equilibrium setpoint, which addresses recently observed phenomena such as reduced vulnerability to sleep loss after banking sleep. It also differentiates the build-up and recovery rates of fatigue under conditions of chronic sleep restriction versus acute total sleep deprivation; nonetheless, it does not accurately predict the disproportionately rapid recovery seen after total sleep deprivation. To improve the model, we hypothesized that the homeostatic process may also modulate the allostatic process, with the magnitude of this effect scaling as a function of time awake. Results To test our hypothesis, we added a parameter to the model to capture modulation by the homeostatic process of the allostatic process build-up during wakefulness and dissipation during sleep. Parameter estimation using previously published laboratory datasets of fatigue showed this parameter as significantly different from zero (p<0.05) and yielding a 10%–20% improvement in goodness-of-fit for recovery without adversely affecting goodness-of-fit for pre-recovery days. Conclusion Inclusion of a modulation effect of the allostatic process by the homeostatic process improved prediction accuracy in a variety of sleep loss and circadian misalignment scenarios. In addition to operational relevance for duty/rest scheduling, this finding has implications for understanding mechanisms underlying the homeostatic and allostatic processes of sleep/wake regulation. Support (if any) Federal Express Corporation This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Debian, Ahmad; Arentson-Lantz, Emily; Kokanda, Manasa; Shaib, Fidaa; Nowakowski, Sara

doi: 10.1093/sleep/zsab072.709pmid: N/A

Abstract Introduction Patients may be experiencing increased stress and sleep disturbance due to healthcare changes during the COVID-19 pandemic. Healthcare changes may include telemedicine visits, delayed or canceled appointments and sleep studies. The purpose of this study was to assess the association between changes in healthcare and sleep medication use on sleep disturbance and insomnia severity. Methods Between June-November 2020, 81 sleep medicine clinic patients (54.8 ± 15.9 y, 44% male, 69% Caucasian) completed an online survey that included questions about COVID-19 (tested for coronavirus, test results, willingness to be vaccinated for COVID-19, changes in health care visits and sleep medications during the pandemic), PROMIS measures (Sleep Disturbance, Sleep-Related Impairments), and Insomnia Severity Index (ISI). Stepwise linear regression was performed using SAS to determine if changes in healthcare and sleep medications predicted poorer sleep. Results Among participants, 32% were tested for coronavirus, out of those 8% tested positive for COVID-19. 74% were willing to get vaccinated and 65% were willing to get their children vaccinated. 35% changed their healthcare office appointments to telephone visits, 54% changed to video visits; whereas 26% cancelled and 32% rescheduled their healthcare appointments. Changes in health care visits during the pandemic had a significant increase on ISI score (3.98 ± 1.66, p=0.019). Changes in sleep medication during the pandemic had significant effect on Sleep Disturbance (7.15 ± 2.51, p=0.005), Sleep-Related Impairments (8.69 ± 2.68, p=0.001) and ISI (6.04 ± 1.66, p=0.001) measures. Conclusion Sleep medicine patients who reported changes in sleep medication reported higher insomnia severity, sleep disturbance, and sleep-related impairments. Patients who reported changes in healthcare visits during the pandemic reported higher insomnia severity. Assessing sleep medication changes and preference for healthcare visit format is advised when treating sleep medicine patients during the pandemic. Support (if any) This work is supported by National Institutes of Health (NIH) Grant # R01NR018342 (PI: Nowakowski) and by the Department of Veteran Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

Riehm, Joseph; Arora, Vineet; Tatineni, Swetha; Erondu, Amarachi; Mozer, Christine; Cook, David; Byron, Maxx; Mordell, Lisa; Ye, Fanheng; Orlov, Nicola

doi: 10.1093/sleep/zsab072.644pmid: N/A

Abstract Introduction Sleep is critical to children’s health and recovery, but pediatric inpatient sleep is often disrupted by nonessential overnight interruptions. The COVID-19 pandemic necessitated social distancing policies which minimized contact with low-risk patients. These policies have the potential to decrease overnight disruptions and improve sleep for hospitalized patients. Methods This cohort study compared sleep disruptions for pediatric inpatients admitted prior to (Sep 2018 – Feb 2020) and during (Apr 2020 – Aug 2020) the COVID-19 pandemic at a single site, urban academic medical center. Objective disruptions were measured as room entries detected by hand hygiene sensors for occupied rooms pre-pandemic (n_average=56) and during the pandemic (n_average=48) for 69 and 154 nights, respectively. Subjective reports of overnight disruptions, sleep quantity, and caregiver mood were measured by surveys adopted from validated tools: the Karolinska Sleep Log, Potential Hospital Sleep Disruptions and Noises Questionnaire, and Visual Analog Mood Scale. Caregivers of a convenience sample of pediatric general medicine inpatients completed surveys. Caregivers pre-pandemic were surveyed in person, and during the pandemic, surveys were conducted over the phone. Results 293 pre-pandemic (age_patients=4.1±4.4 years) and 154 pandemic (age_patients=8.7±5.6 years) surveys were collected from caregivers. The majority (71% pre-pandemic and 52% pandemic) of the study population identified as Black/African American. Nighttime room entries initially decreased 36% (95% CI: 30%, 42%, p<0.001), then returned towards pre-pandemic levels as the COVID-19 hospital caseload decreased. Despite this, caregivers reported more disrupted patient sleep (p<0.001) due to tests (21% vs. 38%) as well as stress (30% vs. 49%), anxiety (23% vs. 41%), and pain (23% vs. 48%). Caregivers also reported children slept 61 minutes less (95% CI: 12 min, 110 min, p<0.001) and had more awakenings. Caregivers self-reported feeling more sad and weary, less calm, and worse overall (p<0.001 for all). Conclusion Despite fewer objective room entries, caregivers reported increased sleep disruptions and an hour less nighttime sleep with more awakenings during the pandemic for pediatric patients. Caregivers also self-reported worse mood. This highlights the importance of addressing subjective perceptions and experiences of hospitalized children and their caregivers during hospitalization. Support (if any): This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

O’Brien, Louise; Levine, Rivkah; Dunietz, Galit Levi

doi: 10.1093/sleep/zsab072.417pmid: N/A

Abstract Introduction Obstructive sleep apnea (OSA) is common in pregnant women and is a risk factor for poor perinatal outcomes. The Berlin Questionnaire (BQ) is a validated OSA screening tool that is often used in pregnancy. However, it performs poorly in this population, likely attributed to the scoring paradigm that primarily identifies obesity. Moreover, the associations between the BQ and pregnancy outcomes are often those same outcomes that are obesity-related. Therefore, this study examined associations between each of the three BQ domains, independently and jointly, in relation to gestational diabetes (GDM) and hypertensive disorders of pregnancy (HDP). Methods Pregnant third-trimester women aged at least 18 years with a single fetus were recruited from a tertiary medical center. All women completed the BQ, which includes three domains: snoring; sleepiness; and obesity/high blood pressure (BMI/BP). The latter domain was further examined as two separate sub-domains: obesity or chronic hypertension. A positive response in 2-of-3 domains identifies high OSA risk. Medical records were accessed for diagnoses of GDM and HDP. Results Of 1,588 women, 44% had a positive BQ. Women with positive domains of snoring exclusively, sleepiness exclusively, or their combination did not have an increased risk of GDM or HDP. However, women without snoring or sleepiness, but with a positive score on the BMI/BP domain had increased odds of GDM (OR 2.0, 95%CI 1.3–3.3) and HDP (OR 2.9, 95%CI 1.6–5.5). Any positive score in domain combinations that included BMI/BP had increased odds of GDM and HDP compared with negative scores in all domains. A positive score in BMI/BP-alone, BMI/BP-and-sleepiness, BMI/BP-and-snoring, and an intersection of all three domains, had increased HDP odds compared with controls: OR 2.9 (95%CI 1.6–5.5), OR 2.2 (95%CI 1.1–4.4), OR 2.9 (95%CI 1.5–5.7), and OR 4.6 (95%CI 2.6–8.6), respectively. Women absent of positive BMI/BP domain but with a positive score in the other two domains (or their combination) had similar odds of GDM and HDP as controls. Conclusion The poor performance of the BQ in screening for OSA risk in pregnant women may be attributed to its predominant reliance on identification of obesity. Support (if any) NIH NHLBIHL089918 This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].

Caetano, Gabriela; Kervezee, Laura; Gonzales-Aste, Fernando; Boudreau, Philippe; Boivin, Diane

doi: 10.1093/sleep/zsab072.295pmid: N/A

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. This content is only available as a PDF. © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected].