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Background Shift work can disturb circadian homeostasis and result in fatigue, excessive sleepiness, and reduced quality of life. Light therapy has been shown to impart positive effects in night shift workers. We sought to determine whether or not prolonged exposure to bright light during a night shift reduces sleepiness and enhances psychomotor performance among ICU nurses. Methods This is a single-center randomized, crossover clinical trial at a surgical trauma ICU. ICU nurses working a night shift were exposed to a 10-h period of high illuminance (1500–2000 lx) white light compared to standard ambient fluorescent lighting of the hospital. They then completed the Stanford Sleepiness Scale and the Psychomotor Vigilance Test. The primary and secondary endpoints were analyzed using the paired t test. A p  value <0.05 was considered significant. Results A total of 43 matched pairs completed both lighting exposures and were analyzed. When exposed to high illuminance lighting subjects experienced reduced sleepiness scores on the Stanford Sleepiness Scale than when exposed to standard hospital lighting: mean (sem) 2.6 (0.2) vs. 3.0 (0.2), p  = 0.03. However, they committed more psychomotor errors: 2.3 (0.2) vs. 1.7 (0.2), p  = 0.03. Conclusions A bright lighting environment for ICU nurses working the night shift reduces sleepiness but increases the number of psychomotor errors. Trial registration ClinicalTrials.gov, NCT03331822 . Retrospectively registered on 6 November 2017.

Abstract Study Objectives To examine the effect of sleep timing intervention on sleep quality, attention, and sleepiness at work among night shift workers with shift work disorder. Methods We recruited 60 real-life night shift workers through advertisements to participate this cross-over clinical trial. Shift work disorder was confirmed with interview and sleep log. Participants were designated to follow evening sleep (15:00–23:00) and morning sleep (09:00–17:00) schedules in a randomized order. Chronotype was confirmed by the Munich Chronotype Questionnaire. Sleep behaviors and light exposure were recorded using actigraphy. Outcome measures were sleepiness evaluated by the Karolinska Sleepiness Scale, sleep quality evaluated by the Pittsburgh Sleep Quality Index, and attention performance assessed with psychomotor vigilance test. Differences in outcome between the morning and evening sleep schedules were compared using repeated measures ANOVA. Results The participants slept for longer durations during evening sleep schedules compared with morning sleep schedules. Lower sleepiness scores, higher sleep quality, and shorter reaction times and less lapse numbers in the psychomotor vigilance test were observed for participants during evening sleep schedules than morning sleep schedules after adjustment for light exposure and sleep duration. Significant interaction effects were observed for reaction time and lapse number between chronotype and sleep schedule, where the differences between sleep schedules were most prominent among those with late chronotypes. Conclusions It is recommended that night shift workers with shift work disorder arrange to sleep in the evening instead of the morning for better sleep and attention performance, especially those with late chronotypes. Trial registration Sleep Schedule Intervention Study Among Night Shift Workers, https://clinicaltrials.gov/ct2/show/NCT04160572, ClinicalTrials.gov Identifier: NTC04160572.

Objective Shift work and Shift Work Sleep Disorder (SWSD) are known to affect the secretion of several neurotransmitters and hormones associated with premature ejaculation (PE). However, their specific influence on the regulation of male ejaculation remains unclear. This study explores the relationship between shift work, SWSD, and PE. Methods From April to October 2023, a cross-sectional survey was conducted across five regions of China to explore the work schedules, sleep quality, and sexual function of male workers. Participants' sleep quality was evaluated using a validated SWSD questionnaire, and their erectile function and ejaculatory control were assessed with the International Inventory of Erectile Function (IIEF-5) scores and Premature Ejaculation Diagnostic Tool (PEDT) scores, respectively. Univariate and multivariate linear regression analyses were employed to identify risk factors associated with PE. Confounders were controlled using multiple regression models, and clinical prediction models were developed to predict PE onset and assess the contribution of risk factors. Results The study included 1239 eligible participants, comprising 840 non-shift workers and 399 shift workers (148 with SWSD and 251 without SWSD). Compared to non-shift working males, those involved in shift work (β 1.58, 95% CI 0.75 – 2.42, p  < 0.001) and those suffering from SWSD (β 2.86, 95% CI 1.86 – 3.85, p  < 0.001) they had significantly higher PEDT scores. Additionally, we identified daily sleep of less than six hours, depression, anxiety, diabetes, hyperlipidemia, frequent alcohol consumption (more than twice a week), and erectile dysfunction as risk factors for PE. The predictive model for PE demonstrated commendable efficacy. Conclusion Both shift work and SWSD significantly increase the risk of premature ejaculation, with the risk magnifying in tandem with the duration of shift work. This study reveals the potential impact of shift work and SWSD on PE and provides new theoretical foundations for the risk assessment and prevention of this condition.

Objectives: This study aimed to evaluate, using wearable sensors, the impact of transitioning from an 8-hour to a 12-hour shift schedule on sleep patterns and well-being in intensive care unit (ICU) nurses with pre-existing sleep disturbances. We also examined differences in outcome based on chronotype.Methods: We conducted an observational study at a university hospital ICU between November 2020 and October 2023, before and after a hospital-wide shift schedule change. Nurses wore wearable sensors and completed daily surveys over 5 weeks under each shift system. Rotating-shift ICU nurses with a Pittsburgh Sleep Quality Index score >5 were eligible. Sleep metrics and subjective well-being were compared using linear mixed models, adjusting for age. Sleep episodes were categorized relative to shift timing, and chronotype-stratified subgroup analyses were performed.Results: Eighty nurses completed the study (12-hour shift: 37; 8-hour shift: 43). The interval between shifts was greater for the 12-hour shift group (36.12 vs 26.78 hours). Total sleep duration did not significantly differ between groups (12-hour shift: 418.5 minutes; 8-hour shift: 398 minutes); however, the 12-hour shift group had less fragmented sleep, higher subjective well-being scores, and lower reported stress and fatigue. Evening chronotypes appeared to benefit more from 12-hour shifts, with longer sleep duration and higher well-being scores, though these differences were not statistically significant.Conclusions: Transitioning to a 12-hour shift schedule was associated with reduced sleep fragmentation and improved well-being, particularly among evening chronotypes. These findings suggest that shift schedule structure and individual chronotype may influence adaptation to shift work in ICU settings.

Background Shiftwork sleep disorder is one of the most common health-related effects of Shiftwork, particularly among healthcare workers. It is a chronic condition that is directly related to a person’s work schedule. In Ethiopia, although a mental health strategy is in place, little attention is given to studies that focus on shiftwork sleep disorders among nurses. This study aimed to determine the magnitude of shiftwork sleep disorder and associated factors among nurses working at public hospitals in Harari Regional State and the Dire Dawa Administration. Methods Institutional based cross-sectional study was conducted from June 1–30, 2021 among 392 nurses selected by a simple random sampling technique. A structured interviewer-guided self-administered questionnaire was used for data collection. The International Classification of Sleep Disorders 3rd edition (ICSD-3), Bargen Insomnia Scale (BIS) and Epworth Sleepiness Scale were used to assess shift-work sleep disorder. The data were entered into EpiData and exported to SPSS for analysis. Bivariable logistic regression was used to see the association between the outcome and the explanatory variables. Bivariate and Multivariate analyses were performed, and AOR with 95% CI was used to measure the strength of the association. Those variables with a p-values of < 0.05 were considered as statistically significant. Results In this study, the magnitude of shiftwork sleep disorder among nurses was 30.4% (95% CI: 25.4–34.5). Being female (AOR = 2.4, 95% CI: 1.3, 4.2), working an average number of nights > 11 per month in the last 12 months (AOR = 2.5, 95% CI: 1.3, 3.8), and khat use in the last 12 months (AOR = 4.9, 95% CI: 2.9, 8.7) were significantly associated with the shiftwork sleep disorder. Conclusions The study revealed that about one-third of the nurses had a shiftwork sleep disorder implying a high burden of the problem among nurses in the study setting, which endangers nurses, patients, and the healthcare system. Being female, working an average number of nights > 11 per month in the last 12 months, and khat use showed statistically significantly associated with the shiftwork sleep disorder. Early detection of shiftwork sleep disorder, having a policy on khat use and considering rest/recovery while scheduling work time should be addressed to prevent shiftwork sleep disorder.

In the modern economy, shift work is prevalent in numerous occupations. However, it often disrupts workers' circadian rhythms and can result in shift work sleep disorder. Proper management of shift work sleep disorder involves comprehensive and patient-specific strategies, some of which are similar to cognitive behavioral therapy for insomnia. Our goal was to develop and evaluate machine learning algorithms that predict physicians' sleep advice using wearable and survey data. We developed a web- and app-based system to provide individualized sleep and behavior advice based on cognitive behavioral therapy for insomnia for shift workers. Data were collected for 5 weeks from shift workers (N=61) in the intensive care unit at 2 hospitals in Japan. The data comprised 3 modalities: Fitbit data, survey data, and sleep advice. After the first week of enrollment, physicians reviewed Fitbit and survey data to provide sleep advice and selected 1 to 5 messages from a list of 23 options. We handcrafted physiological and behavioral features from the raw data and identified clusters of participants with similar characteristics using hierarchical clustering. We explored 3 models (random forest, light gradient-boosting machine, and CatBoost) and 3 data-balancing approaches (no balancing, random oversampling, and synthetic minority oversampling technique) to predict selections for the 7 most frequent advice messages related to bedroom brightness, smartphone use, and nap and sleep duration. We tested our predictions under participant-dependent and participant-independent settings and analyzed the most important features for prediction using permutation importance and Shapley additive explanations. We found that the clusters were distinguished by work shifts and behavioral patterns. For example, one cluster had days with low sleep duration and the lowest sleep quality when there was a day shift on the day before and a midnight shift on the current day. Our advice prediction models achieved a higher area under the precision-recall curve than the baseline in all settings. The performance differences were statistically significant (P<.001 for 13 tests and P=.003 for 1 test). Sensitivity ranged from 0.50 to 1.00, and specificity varied between 0.44 and 0.93 across all advice messages and dataset split settings. Feature importance analysis of our models found several important features that matched the corresponding advice messages sent. For instance, for message 7 (darken the bedroom when you go to bed), the models primarily examined the average brightness of the sleep environment to make predictions. Although our current system requires physician input, an accurate machine learning algorithm shows promise for automatic advice without compromising the trustworthiness of the selected recommendations. Despite its decent performance, the algorithm is currently limited to the 7 most popular messages. Further studies are needed to enable predictions for less frequent advice labels.

Background: Shift duty disrupts the sleep-wake cycle, leading to shift work disorder, which is characterized by daytime sleepiness and insomnia. Excessive daytime sleepiness decreases work performance, increases workplace accidents, and impairs neurocognitive function. Sleep disturbance also impacts the employee-patient relationship; thereby, it is necessary to assess the prevalence of daytime sleepiness. Aim and Objective: The aim of the study is to compare the prevalence of daytime sleepiness in healthcare employees working in the daytime versus shift duty. Materials and Methods: Subjects were chosen from the Parent Institute between the ages of 24 and 38. A total of 260 health-care employees were included in the study and were divided into two equal groups, with one working on day duty and the other working on shift duty. To evaluate subjective and objective daytime sleepiness, the Epworth sleepiness scale questionnaire was given to both groups. Results: The parameters were analyzed statistically. There was a significant difference in daytime sleepiness between the two groups. There was increased daytime sleepiness in health-care employees working on shift duty as compared to those working on day duty. Conclusion: Sleep deprivation due to disruption of internal sleep regulation leading to shift work disorder is thought to be an important cause of increased daytime sleepiness in health-care employees working on shift duty.

Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284. DERR1-10.2196/24799.

To meet the demands of a 24/7 society, shift work is necessary. Shift work is outside the traditional regular 9-to-5 work schedule, is characterized by irregular working hours, and exists in various industries. However, this abnormal working time can disrupt the natural day and night rhythm, and if poorly adjusted, it can lead to shift work sleep disorder (SWSD). SWSD is associated with multiple health risks, including impaired cognitive function, increased risk of accidents, and various metabolic and cardiovascular diseases. The frontline nurses typically work shifts to provide comprehensive patient care. This article aims to discuss sleep physiology, apply existing literature to discuss the impact on nurses resulting from shift work, and further offer strategies to regulate sleep to promote physical and mental health. These strategies range from organizational interventions (e.g., optimizing shift schedules) to individual interventions (e.g., lifestyle changes) and the use of chronobiological techniques (e.

Background Flight controllers of the International Space Station (ISS) are engaged in shift work to provide 24-h coverage to support ISS systems. The purpose of this study was to investigate the prevalence and associated factors of shift work sleep disorder (SWSD) among Japanese ISS flight controllers. Methods A questionnaire study was conducted using the Standard Shiftwork Index to evaluate sleep-related problems and possible associated variables. Among 52 respondents out of 73 flight controllers, 30 subjects were identified as night shift workers who worked 3 or more night shifts per month. Those night shift workers who answered “almost always” to questions about experiencing insomnia or excessive sleepiness in any case of work shifts and days off were classified as having SWSD. Additionally, 7 night shift workers participated in supplemental wrist actigraphy data collection for 7 to 8 days including 3 to 4 days of consecutive night shifts. Results Fourteen of 30 night shift workers were classified as having SWSD. Significant group differences were observed where the SWSD group felt that night shift work was harder and reported more frequent insomniac symptoms after a night shift. However, no other variables demonstrated remarkable differences between groups. Actigraphy results characterized 5 subjects reporting better perceived adaptation as having regular daytime sleep, for 6 to 9 h in total, between consecutive night shifts. On the other hand, 2 subjects reporting perceived maladaptation revealed different sleep patterns, with longer daytime sleep and large day-to-day variation in daytime sleep between consecutive night shifts, respectively. Conclusions As the tasks for flight control require high levels of alertness and cognitive function, several characteristics, namely shift-working schedule (2 to 4 consecutive night shifts), very short break time (5 to 10 min/h) during work shifts, and cooperative work with onboard astronauts during the evening/night shift, accounted for increasing workloads especially in the case of night shifts, resulting in higher or equal prevalence of SWSD to that among other shift-working populations. Further studies are required to collect more actigraphy data and examine the possibility of interventions to improve SWSD.