Open research questions in Sleep and Work-Related Fatigue

What the literature leaves open

• The study only included non-shift nurses with a history of night shift work - The study was conducted at a single hospital - The sample size was limited to 15 participants

  A qualitative study on the delayed manifestation of night shift-related intrusive memories in non-shift nurses with a history of night shift work · 2026 · DOI

• Investigate the long-term effects of night shift work on nurses' mental health - Explore the impact of night shift work on nurses' job performance and daily functioning - Examine the effectiveness of interventions to reduce the re-emergence of negative experiences associated with night shift work

  A qualitative study on the delayed manifestation of night shift-related intrusive memories in non-shift nurses with a history of night shift work · 2026 · DOI

• The FS-14 has an inability to effectively differentiate between chronic fatigue and depression, - The FAI's complexity poses challenges in terms of the time and effort required to complete the assessment, - The application of the MFI-20 to general nursing populations in China is limited

  Research progress on assessment tools related to occupational fatigue in nurses: a traditional review · 2024 · DOI

• Future research could focus on refining the FS-14 or developing complementary instruments to better distinguish between chronic fatigue and depression, - Ongoing studies should explore the generalizability of the FS-14 across diverse nursing populations and different work environments, - Future researchers are encouraged to simplify and revise the FAI while preserving its core attributes and reliability

  Research progress on assessment tools related to occupational fatigue in nurses: a traditional review · 2024 · DOI

• However, there is limited research exploring the impact of QI-pathological constitution on mental fatigue in university students.

  QI-pathological constitution is associated with mental fatigue in class among university students: a cross-sectional study · 2025 · DOI

• Aside from occupation-specific studies, little is known about the effects of these atypical temporal work patterns on sleep among workers in the UK, even though poor sleep has been linked to adverse health problems, lower workplace productivity, and economic costs.

  Work hours, weekend working, nonstandard work schedules and sleep quantity and quality: findings from the UK household longitudinal study · 2024 · DOI

• Resource limitations, alarm burden, and deficiencies in safety culture constrain the implementation of effective interventions. Data standardization remains challenging, and the clinical application of emerging technologies is limited by challenges related to data accuracy, interoperability, user acceptance, and ecological validity.

  Injury prevention in fatigue-prone nursing environments: a comprehensive review of strategies centered on work design, human factors engineering, and safety culture · 2026 · DOI

• Implementation is constrained by resource limitations, alarm burden, and deficiencies in safety culture. Data standardization remains challenging, and evaluation commonly relies on injury incidence, near-miss reporting, sleep quality, and workforce retention.

  Injury prevention in fatigue-prone nursing environments: a comprehensive review of strategies centered on work design, human factors engineering, and safety culture · 2026 · DOI

• There is a need for reliable, real-time driver monitoring solutions that can detect drowsiness at an early stage. Traditional driver drowsiness detection methods have limitations.

  An Explainable Real-Time Driver Drowsiness Detection System Using Vision Transformers and Behavioral Fatigue Indicators · 2026 · DOI

• Future research can focus on improving the accuracy of the system in certain lighting conditions. Future research can focus on developing more advanced deep learning techniques for detecting distraction and drowsiness.

  Visionguard: an AI-Driven Real-Time Driver Attention and Distraction Monitoring System using Deep Learning · 2026 · DOI

• Current driver monitoring solutions have limitations in terms of accuracy and scalability. There is a need for a non-intrusive, real-time Driver Attention and Distraction Monitoring System.

  Visionguard: an AI-Driven Real-Time Driver Attention and Distraction Monitoring System using Deep Learning · 2026 · DOI

• Current methods have limitations, such as inadequate system robustness and difficulty adapting to individual differences - There is a need for more effective and robust driver fatigue detection systems

  Progress in Driver Fatigue Detection Technology Based on Image and State Fusion · 2026 · DOI

• Drowsiness can cause drivers to become less aware and reduce their capacity to react rapidly and correctly to road conditions, increasing the likelihood of an accident. There is limited data available on drowsy driving accidents in India. The selection of hyperparameters can affect the performance of SVM.

  Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver Using Machine Learning Approach · 2026 · DOI

• There is limited data available on drowsy driving accidents in India. There is a need for a real-time driver drowsiness detection system based on behavioral characteristics using a machine learning approach.

  Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver Using Machine Learning Approach · 2026 · DOI

• Further studies should investigate the long-term effects of sleep deprivation on laparoscopic performance. Studies should explore strategies to reduce sleep deprivation among surgeons and medical students.

  Impact of acute sleep deprivation on laparoscopic performance: a prospective, randomized crossover trial · 2026 · DOI

• A key strength of this trial was the inclusion of a substantial number of participants, comprising students (n = 55) with comparable levels of proficiency and surgeons (n = 20). However, the observed differences between these groups may have been influenced by the methodological setup of the study. While students followed a standardized sleep-depriva- tion schedule designed to simulate the stress of a surgeon’s night shift, this curriculum may not have fully replicated the complex origin of the stress experienced during actual night shifts. On the other hand, this experimental setup made it possible to study the effects of sleep deprivation in relative isolation from other potential sources of stress, at least in the subgroup of students. In contrast, surgeons were examined during their actual night shifts, which were not standardized, potentially introducing variability in the extent of sleep deprivation. It must be acknowledged that the relatively small sample size of surgeons, as well as the wide range of experience levels, may affect the stability of the results derived from this subcohort. While it is plausible that experienced surgeons cope better with sleep deprivation than junior residents, no significant differences were observed in our data. However, the inclusion of proficiency trained students combined with a standardized simulated night shift provided high-quality data, at least for this subcohort. Still, future studies should aim to include a more homogeneous group of participants to allow for clearer conclusions. Moreover, this study focused on analyzing motor aspects of surgical performance, including motion and force param- eters. Cognitive processes such as decision-making, atten- tion and executive function were not assessed. Therefore, no conclusions can be drawn regarding their contribution to the observed effects. It is likely that sleep deprivation causes cognitive changes that may influence psychomotor performance, as demonstrated in a previous work [6]. Conse- quently, future studies should combine cognitive and motor assessments to further elucidate these effects. For practical reasons, the assessment of sleep quality and quantity was deliberately kept as simple as possible in order to minimize disruption to the night shift, especially for the surgeon group. These simple, subjective tests are therefore also suitable for follow-up studies in a clinical setting, as they require relatively little effort and can be carried out independently. Nevertheless, there is a possibility that focus- ing on this type of assessment could lead to subjective bias of the results.

  Impact of acute sleep deprivation on laparoscopic performance: a prospective, randomized crossover trial · 2026 · DOI

• Developing more robust and efficient models for drowsiness detection - Improving the system's ability to handle varying lighting and partial face occlusion - Integrating the system with other safety features, such as lane departure warning or forward collision warning

  Vision-Based Driver Drowsiness Detection: From Deep Learning Models to Real-Time Mobile Deployment · 2026 · DOI

• The absence of mobile and embedded deployment of drowsiness detection systems - Limited real-world validation of existing systems - Sensitivity to environmental conditions and occlusions

  Vision-Based Driver Drowsiness Detection: From Deep Learning Models to Real-Time Mobile Deployment · 2026 · DOI

• The occupational health outcomes of pediatric nurses in Northern Ghana remain underexplored. There is a need to assess the effect of night shift work on daytime sleepiness and physiological health among pediatric nurses.

  The effect of night shift work on daytime sleepiness and physiological health among pediatric nurses in Northern Ghana: a cross-sectional survey · 2026 · DOI

• The findings of this study have important implications for hospital management, occupational health practice, and workforce planning. ARTICLE IN PRESS ARTICLE IN PRESS ACCEPTED MANUSCRIPT Healthcare institutions should review and optimize night shift scheduling practices by limiting consecutive night duties, ensuring adequate recovery time between shifts, and incorporating nurses’ shift preferences where operationally feasible. Given that nurses who did not prefer night shifts reported poorer outcomes, flexible and participatory scheduling approaches may help reduce sleep disturbances and physical strain. Routine occupational health monitoring should be strengthened for nurses engaged in night shift work. Periodic screening for sleep problems, fatigue, blood pressure changes, and early indicators of cardiovascular strain can facilitate timely intervention and prevent long-term complications. In addition, fatigue management and sleep hygiene education programs should be implemented to equip nurses with practical strategies to improve daytime sleep quality and manage excessive sleepiness. Providing structured rest breaks and designated quiet spaces during night shifts may further enhance recovery and alertness. Nutritional support initiatives are also necessary to address gastrointestinal and dietary disruptions. Hospitals should improve access to healthy meal options during night shifts and provide guidance on balanced eating patterns to reduce reliance on irregular meals, junk food, and excessive caffeine intake. Lastly, broader staff wellness programs, including stress management interventions, ergonomic improvements to reduce physical strain, and supportive supervision, should be integrated into institutional policies. Such measures will not only protect nurses’ health and well-being but also enhance patient safety, productivity, and the overall quality of pediatric healthcare services.

  The effect of night shift work on daytime sleepiness and physiological health among pediatric nurses in Northern Ghana: a cross-sectional survey · 2026 · DOI

• Detecting hazards in real-time. Predicting risks and preventing accidents. Integrating multiple sensors and AI models for proactive safety monitoring. Addressing the research gap in industrial safety.

  Smart Industrial Safety Wearable Device Using Artificial Intelligence For Proactive Risk Prevention And Worker Protection · 2026 · DOI

• The study was conducted under controlled conditions simulating industrial environments. The dataset used was limited to 1200 sensor observations.

  Smart Industrial Safety Wearable Device Using Artificial Intelligence For Proactive Risk Prevention And Worker Protection · 2026 · DOI

• Existing driver monitoring systems have limitations such as high cost and poor user acceptance. There is a need for a reliable and non-invasive monitoring system.

  A DEEP LEARNING APPROACH FOR DRIVER FATIGUE DETECTION · 2026 · DOI

• The lack of intelligent systems capable of real-time monitoring and automated alert generation. The need for a system that can detect driver fatigue and distraction.

  An approach for identification of driver drowsiness, emotion state, and behavior patterns based on vision transformers, CNN and multi-modal sensor fusion using RAG-based interpretability · 2026 · DOI

• Future work will focus on formal real-time latency benchmarking on edge deployment hard- ware, occlusion-aware handling of spectacles and face masks, visible light versus infra- red performance comparison for night-time driving, temporal driver state modelling incorporating physiological signals such as EEG and heart rate, and validation on large- scale naturalistic driving datasets to establish real-world deployment readiness. Additionally, future work will focus on restructuring knowledge base chunks to cover composite multi-signal risk patterns rather than individual features, which would further improve RAGAS Context Recall beyond the current value of 0. Validation on large-scale naturalistic driving datasets, such as those collected through instrumented vehicles under real on-road conditions, remains the primary open question for establishing the system’s external validity.

  An approach for identification of driver drowsiness, emotion state, and behavior patterns based on vision transformers, CNN and multi-modal sensor fusion using RAG-based interpretability · 2026 · DOI

Most-cited papers in Sleep and Work-Related Fatigue

• Vigilance Requires Hard Mental Work and Is Stressful · Human Factors The Journal of the Human Factors and Ergonomics Society · 2008 · 997 citations
• Psychometric qualities of a brief self-rated fatigue measure · Journal of Psychosomatic Research · 2003 · 714 citations
• The assessment of fatigue · Journal of Psychosomatic Research · 2004 · 667 citations
• The link between fatigue and safety · Accident Analysis & Prevention · 2010 · 645 citations
• Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep · Organizational Behavior and Human Decision Processes · 2014 · 592 citations
• Monotony of road environment and driver fatigue: a simulator study · Accident Analysis & Prevention · 2003 · 543 citations
• Driver fatigue: Electroencephalography and psychological assessment · Psychophysiology · 2002 · 447 citations
• Fatigue: a concept analysis · International Journal of Nursing Studies · 1996 · 415 citations
• Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator · Accident Analysis & Prevention · 2011 · 408 citations
• Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? · Ergonomics · 2008 · 398 citations

Most recent work

• High Prevalence of Disorders of Irritable Bowel Syndrome and Functional Dyspepsia in Night Shift Workers: A Cross-Sectional Study in Australia and the United Kingdom · Clinical Gastroenterology and Hepatology · 2026
• Multi-modal driving fatigue detection with causal-aware fusion and Kolmogorov-Arnold network · Information Fusion · 2026
• DrowsyDG-Phys: Generalizable driver drowsiness estimation in conditional automated vehicles using physiological signals · Accident Analysis & Prevention · 2026
• Vision-Based Driver Drowsiness Detection: From Deep Learning Models to Real-Time Mobile Deployment · International Journal for Research in Applied Science and Engineering Technology · 2026
• Predicting driver fatigue using HRV measures and machine learning · Frontiers in Sports and Active Living · 2026
• Development of a probability model for high-resolution drowsiness detection using electroencephalogram · Sleep Medicine · 2026
• Insomnia, fatigue, and risky driving behaviors among professional drivers: A cross-sectional study in Tunisia · Journal of Safety Research · 2026
• Injury prevention in fatigue-prone nursing environments: a comprehensive review of strategies centered on work design, human factors engineering, and safety culture · Frontiers in Public Health · 2026
• An Explainable Real-Time Driver Drowsiness Detection System Using Vision Transformers and Behavioral Fatigue Indicators · International Journal of Advanced Research in Science Communication and Technology · 2026
• Research on Driver Drowsiness and Distraction Detection System · International Journal for Research in Applied Science and Engineering Technology · 2026

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