Study Objectives: Recent studies have shown that extended shifts worked by hospital staff nurses are associated with significantly higher risk of errors, yet little information is available about the ability to remain alert during the nurses' commutes following the completion of an extended work shift. The purpose of this study is to describe the prevalence of drowsy driving episodes and the relationship between drowsy driving and nurse work hours, alertness on duty, working at night, and sleep duration.
Participants: Data were collected from 2 national random samples of registered nurses (n=895).
Measurements And Results: Full-time hospital staff nurses (n=895) completed logbooks on a daily basis for 4 weeks providing information concerning work hours, sleep duration, drowsy and sleep episodes at work, and drowsy driving occurrences. Almost 600 of the nurses (596/895) reported at least 1 episode of drowsy driving, and 30 nurses reported experiencing drowsy driving following every shift worked. Shorter sleep durations, working at night, and difficulties remaining awake at work significantly increased the likelihood of drowsy driving episodes.
Conclusions: Given the large numbers of nurses who reported struggling to stay awake when driving home from work and the frequency with which nurses reported drowsy driving, greater attention should be paid to increasing nurse awareness of the risks and to implementing strategies to prevent drowsy driving episodes to ensure public safety. Without mitigation, fatigued nurses will continue to put the public and themselves at risk.
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http://dx.doi.org/10.1093/sleep/30.12.1801 | DOI Listing |
Sensors (Basel)
January 2025
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management.
View Article and Find Full Text PDFNat Sci Sleep
January 2025
Department of Insect Genetics, Institute of Cytology and Genetics of the Siberian Branch, the Russian Academy of Sciences, Novosibirsk, 630090, Russia.
Purpose: Two previously proposed modelling approaches to explain the bimodal pattern of activity and/or sleep in are based on 1) the concept of morning and evening oscillators underlying the peaks of activity in the morning and evening, respectively, and 2) the concept of two cycles of buildup and decay of sleep pressure, gated only by the circadian oscillator. Previously, we simulated 24-h alertness-sleepiness curves in humans using a model postulating the circadian modulation of the buildup and decay phases of two (wake and sleep) homeostatic processes. Here, we tested whether a similar model could be applied to simulate the bimodal 24-h rhythm of fly locomotor activity and sleep.
View Article and Find Full Text PDFFront Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFChronobiol Int
January 2025
Laboratory of Braintime, Graduate Institute of Mind, Brain and Consciousness (GIMBC), Taipei Medical University, Taipei, Taiwan.
The intricate relationship between circadian rhythms and mood is well-established. Disturbances in circadian rhythms and sleep often precede the development of mood disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and seasonal affective disorder (SAD). Two primary factors, intrinsic circadian clocks and light, drive the natural fluctuations in mood throughout the day, mirroring the patterns of sleepiness and wakefulness.
View Article and Find Full Text PDFJ Thorac Dis
December 2024
Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
Background: Excessive daytime sleepiness (EDS) is considered to be one of the main clinical manifestations of obstructive sleep apnea (OSA) and is a treatment target for patients with OSA. The prevalence of EDS in patients with OSA remains unclear and there is a lack of studies on the associations of EDS with quality of life among patients with OSA in China. This study aimed to evaluate the prevalence of EDS and its association with quality of life in patients with OSA in Shenzhen, China.
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