BACKGROUND:Intensive care unit (ICU) residents work in highly stressful environments characterized by clinical uncertainty and critically ill patients. During residency, physicians need to adapt to demanding workplace conditions while also navigating stressors related to their stage of life. How residents respond to stress depends on multiple personal and psychological factors, including coping mechanisms. Excessive stress may impair performance and negatively affect patient outcomes, underscoring the need to better understand its determinants. PURPOSE:This study aimed to identify baseline psychological factors and personal characteristics measured prior to ICU residency that influence stress levels across a three-week rotation. METHODS:We analyzed a secondary dataset from 54 ICU residents during a three-week clinical rotation. Before the rotation, participants completed validated measures of stress, burnout, affect, depression, anxiety, personality traits, social support, and conflict. Stress was assessed twice daily across the rotation. Associations between baseline factors and stress trajectories were estimated using generalized estimating equations (GEE). RESULTS:Compared with PGY-1 residents, PGY-2 and PGY-3 residents reported lower daily across the rotation. Higher trait anxiety and higher negative affect at baseline predicted higher stress, whereas higher agreeableness predicted lower stress, and higher conscientiousness predicted higher stress. Burnout due to exhaustion at baseline was counterintuitively associated with lower reported stress during the rotation. Other baseline factors, including burnout due to disengagement, depression, social support, and conflict, were not significantly associated with stress. CONCLUSIONS:Baseline psychological profiles meaningfully shape how residents experience stress during ICU rotations. Identifying these factors prior to clinical training may help target interventions to support residents at higher risk of stress, thereby enhancing both physician well-being and patient safety.
OCCUPATIONAL APPLICATIONSA time-motion laboratory study was conducted to assess the performance of individuals with intellectual disabilities on a warehouse pick-and-place task. Over a five-week period, following an initial one-week training session, participant performance was tracked to evaluate improvement with practice. The data were then compared to predetermined time standards established by the Modular Arrangement of Predetermined Time Standards (MODAPTS). The findings indicate that with targeted training, these participants may be able to achieve a pace comparable to that of neurotypical workers. This research provides evidence that the successful integration and employment of individuals with intellectual disabilities can be expanded.
OCCUPATIONAL APPLICATIONSClosed-loop communication (CLC) is widely used to support teamwork in high-risk settings; however, current approaches do not fully capture the complex and dynamic nature of communication in surgical practice. This study presents a practical method for coding and measuring CLC that better reflects real operating room interactions. The proposed approach captures both verbal and functionally equivalent non-verbal responses, as well as multi-party communication patterns. New breakdown and performance measures identify where coordination fails and quantify how reliably communication is completed across roles. For ergonomics and human factors practitioners, this approach provides systematic methods and metrics to assess teamwork quality in high-stakes environments. The method can be used to identify where coordination problems occur across different roles and support targeted communication training. These measures are also applicable to other high-stakes environments, such as aviation and emergency response, where reliable communication is essential for safety and performance.
Occupational ApplicationsThis study shows that time-critical automation intervention requests increase drivers' visual-motor demands, reflected in measurable changes in driving performance and posture (i.e., limb engagement and trunk positioning) during transitions of control. These responses indicate that maneuver urgency and takeover timing can place elevated biomechanical and coordination demands on drivers, particularly in work-based automated driving. For ergonomics and human factors practice, the findings underscore the need to design automated transportation systems (e.g., ridesharing services) that align request timing with operators' physical and perceptual capabilities. In occupational settings such as automated shuttles and fleet-supervised vehicles, intervention requests should provide adequate preparation time for visual-motor coordination, limit concurrent steering and braking actions, and support stable trunk and lower-limb postures during control transitions. System designers and fleet operators should treat automated driving as a work system, integrating interface timing, alerts, and control transitions to support safe and sustainable on-the-road performance.
Occupational ApplicationsThis study demonstrates that office task demands and virtual reality (VR) headset support devices substantially influence neck and upper body responses during VR-based office work. Tasks requiring frequent head and eye movement caused higher neck muscle activity and greater cervical range of motion, whereas more visually focused tasks led to more static postures. Two VR headset support prototypes-a spring-based counterbalance, and a 3D-printed shoulder-mounted brace-reduced neck muscle effort relative to the standard head strap, showing potential to mitigate physical strain during prolonged use of a VR headsets. However, the shoulder-mounted brace restricted head movement and increased trunk motion and neck discomfort. For ergonomics practitioners, these findings highlight the importance of balancing load reduction with natural movement of the neck. Lightweight and adjustable headset support designs that preserve a wide range of head and trunk motion can help reduce musculoskeletal risk as VR becomes more common in occupational settings such as office, remote work, and training.
This study presents a fast, lightweight computer vision framework that achieves high accuracy in verifying the correct use of goggles and N95 masks. For ergonomics practitioners, this model offers a computer vision tool for enhancing workplace safety compliance, that operates up to five times faster than benchmark models. The proposed algorithm enables automated PPE compliance checks at worksite entrances using consumer-grade computing hardware. Thus, reducing the burden of daily safety checks. This system can directly reduce preventable injuries through ensuring workers are properly protected by PPEs before starting work. BACKGROUND:Ensuring the proper usage of Personal Protective Equipment (PPE) is mandatory to minimize workplace injuries. U.S. Department of Labor reported that appropriate PPE usage reduces over 37% of occupational diseases and injuries. Previous research investigated RepVGG and ResNet50 for PPE compliance, but the highest accuracy reported is limited, about 80%. Another challenge of PPE compliance research is the tradeoff between large model size, with high inference times, and achieving satisfactory accuracy. PURPOSE:Our study presents a novel lightweight computer vision framework for automated PPE compliance verification prior to worksite entry. The objective is to achieve 90% of classification accuracy with faster inference time than standard Vision Transformer, while allowing potential deployment on consumer-level computing hardware. Another objective is to identify the incorrect PPE wearing scenario with the spatial relationship awareness between workers and their equipment. METHOD:The proposed method constructs a state space model layer for linear computation modeling and a spatial attention module to capture global spatial dependencies with positional encoding of an image. We utilized two public PPE compliance datasets, PPE-CLS and PPE-BQZEL, to evaluate the goggles and N95 mask compliance in manufacturing. RESULT:The model achieved 0.91 and 0.94 accuracy on two datasets, respectively. This outperforms a standard Vision Transformer (ViT) which achieved 0.88 and 0.90, while operating up to 5 times faster. The framework's minimal size (2.8 M parameters) and linear inference scaling enable real-time performance without sacrificing accuracy. CONCLUSIONS:This work provides a fast, memory-efficient solution for automated safety supervision, which advances the artificial intelligence and machine learning applications for occupational health.
OCCUPATIONAL APPLICATIONS This study evaluated the Swedish Occupational Fatigue Inventory (SOFI) and its revised versions among pediatric emergency department providers (e.g., attendings, fellows, advanced practice providers). Findings show that the original SOFI is a valid tool for measuring occupational fatigue, but a shortened version-excluding physical exertion items-offers better fit and usability. Adding items from the Center for Epidemiologic Studies Depression Scale (CES-D) did not enhance fatigue measurement. For practitioners, the reduced SOFI provides a practical, reliable way to assess fatigue in physicians and advanced practice providers in fast-paced clinical environments. Its brevity supports frequent use without burdening staff, enabling timely identification of fatigue-related risks. This tool can inform interventions such as shift redesign, break scheduling, and technology usability testing, ultimately supporting clinician wellness and patient safety. TECHNICAL ABSTRACT Background: Developing and evaluating interventions to mitigate the negative effects of fatigue requires an in-depth understanding of, and ability, to measure fatigue. Purpose: This study evaluates the Swedish Occupational Fatigue Inventory (SOFI) instrument and compares it to its two revised versions in a pediatric emergency department (ED). Methods: Thirty-five pediatric emergency department providers (e.g., attendings, fellows, advanced practice providers) completed repeated surveys, resulting in 827 survey responses across 425 shifts. Confirmatory factor analysis was used to investigate 16 possible fatigue structures. Results: Although the original SOFI instrument was an appropriate tool to measure occupational fatigue in a pediatric ED, the reduced version of the SOFI (after removing the physical exertion questions) model was the best fitting model demonstrating a good fit to the data. Adding two items from the Center for Epidemiologic Studies Depression Scale (CES-D) instruments did not improve fatigue measurement. Conclusions: The reduced SOFI offers a feasible, validated tool for routine fatigue monitoring in emergency care settings, with direct applications to shift scheduling, workload management, and intervention evaluation.
OCCUPATIONAL APPLICATIONSTo manage upper limb musculoskeletal disorders, treatments based on reducing spinal curvatures are used empirically. Spinal posture has been shown to modify upper limb functional capacities in terms of maximum voluntary forces and fatigability. The aim of the study was to determine whether spinal curvatures also influenced upper limb joint angles during tasks associated with a risk of musculoskeletal disorders. Twenty-two healthy participants were placed in a slouched or erect sitting posture, in random order. Three standardized tasks were performed three times each in both postures. In the erect posture, shoulder flexion was consistently decreased, and shoulder abduction was decreased over most or all of the tasks analyzed. Elbow extension was increased over half of the duration of a single task. Thus, slouched or erect seated spinal posture influences proximal upper limb joint angles. Ergonomists should consider spinal posture even when focusing on proximal upper limb musculoskeletal disorders.
Occupational ApplicationsThis study provides practical, field-tested predictive tools to schedule work and rest in hot agricultural settings. Using routinely measured inputs (wet-bulb globe temperature [WBGT], heart-rate metrics, age, mechanization status, and simple lifestyle indicators), practitioners and supervisors can estimate safe continuous work durations and the necessary recovery periods for individual workers. Applicable to smallholder and mechanized tillage operations, these models support on-site decision-making to reduce heat-related fatigue, minimize injury risk, and maintain productivity. The models can be integrated into supervisor checklists, simple mobile apps, or wearable-sensor dashboards to immediately improve heat-stress management policies and worker scheduling.
BACKGROUND:Effectively using digital human models (DHM) for proactive ergonomics requires estimating how workers interact with objects. One example is guessing when a worker might use one hand rather than two when transferring a box. A better understanding of the determinants for performing one-handed transfers will inform DHM users on when human-object interactions should be modeled with one hand versus two. PURPOSE:This work aimed to determine the maximum acceptable box width and mass that would permit a one-handed box transfer from three shelf heights. METHODS:Participants completed a series of box transfers from three shelf heights to a table with their dominant hand. Participants were instructed to grasp the box from the top or front, then adjust the box width (experiment one) or box mass when the box was set at their maximum acceptable grip span (experiment two) until participants reached their maximum acceptable span or mass, respectively, to transfer the box comfortably and safely. Within-subject repeated measure ANOVAs, with an alpha value of 0.05, were used to detect if shelf height or grip orientation affected the maximum acceptable box width and mass, respectively, for a one-handed transfer. RESULTS:The perceived maximum acceptable box width ranged from 13.3 cm to 13.8 cm. The perceived maximum acceptable box mass ranged from 2.05 to 2.51 kg. The maximum acceptable width and mass for one-handed transfers were lowest when grasping a box from a shoulder-height shelf at the front of the box. CONCLUSIONS:When the box width was less than 70% hand length, and required less than 36.7% wrist strength, participants were more likely to use one hand for box transfers. Findings provide insight into the one-handed lifting capacity of standard cardboard boxes with no handles and help inform DHM users on when to model one-handed or two-handed box transfers.
OCCUPATIONAL APPLICATIONThis study found that using head-mounted optical see-through augmented reality (AR) guidance in maintenance tasks led to faster completion times, especially for less experienced workers and when workers had to switch between different types of tasks. AR also made these tasks feel easier for those with less experience. For ergonomics and human factors practitioners, these results suggest that AR can be a valuable tool for improving onboarding and performance in environments where workers are new or frequently face varied tasks. Implementing AR guidance can help reduce learning curves and support more efficient work, particularly in settings with high complexity and employee turnover. However, if workers are expected to perform tasks independently without ongoing AR support, traditional, more thorough training methods may still be necessary. Practitioners should consider the nature of the work and the use of AR technology when designing onboarding and training programs to maximize both efficiency and long-term competence.
OCCUPATIONAL APPLICATIONSThis study demonstrated that driver anger can be feasibly modeled across three intensity levels using combined driving performance metrics and physiological signals. A two-stage machine learning framework, which first determines anger presence and then classifies its intensity, substantially improved accuracy and reduced neutral state misclassification compared to a single-stage four-class model. These findings have direct implications for reducing safety risks and promoting long-term health for workers whose jobs involve extensive driving (e.g., commercial drivers, bus operators), who encounter anger-eliciting situations more frequently than non-occupational drivers. Integrating anger-intensity detection into driver monitoring systems can enable adaptive, context-aware assistance systems that consider both intervention timing and emotional intensity. Aggregated emotion-intensity information may also inform operational decisions (e.g., dispatch assignments and break scheduling) by identifying periods when drivers may benefit from reduced demands or modified tasks. These implications can enhance fleet safety programs and support worker well-being by reducing exposure to emotionally demanding conditions.
Occupational ApplicationsThis study evaluated two passive back-support exoskeletons, a rigid and a soft model, during three construction tasks: shoveling, rebar tying, and welding, using workers' subjective feedback for the assessment. Both devices were found to reduce strain on the lower back and were generally perceived as supportive, although the rigid exoskeleton was perceived as more restrictive than the soft one. Workers reported task-specific differences in comfort, performance, and usability, with common concerns including thermal discomfort, device weight, and interference with tools or surroundings. These findings suggest that while back-support exoskeletons can help reduce physical demands, their effectiveness depends on proper task selection, exoskeleton fit, and user training. For ergonomics practitioners, this study highlights the importance of integrating exoskeletons with existing personal protective equipment and tailoring their use to specific construction tasks. Addressing usability challenges and providing targeted training can enhance worker comfort, safety, and long-term adoption in industrial settings.
Occupational ApplicationThis study found that vendors were involved in nearly one-third of all observed flow disruptions during orthopedic surgery, with a disproportionate share linked to coordination issues and protocol failures, including breaches of the sterile field. While vendors provide critical technical expertise on equipment and implants, their involvement can unintentionally blur role boundaries and disrupt team coordination in high-stakes environments. For ergonomics and human factors practitioners, these findings underscore the importance of designing systems that support clearer role delineation, structured integration of non-clinical participants, and improved communication protocols in surgical teams. Practical applications include developing vendor orientation programs, establishing explicit boundaries on clinical versus technical responsibilities, and training OR staff to effectively leverage vendor expertise without over-reliance. Addressing these challenges can improve team resilience, reduce safety risks, and optimize workflow efficiency in surgical and other complex, multidisciplinary work settings.
Occupational ApplicationsThis study protocol outlines a co-design approach to develop a workplace-based intervention for radiographers to prevent and manage work-related musculoskeletal disorders (WRMSDs) and enhance overall well-being. By engaging radiographers, occupational health specialists, and other relevant stakeholders in the intervention design process, the resulting interventions will be tailored specifically to the physical, cognitive, and organizational demands of imaging work. Beyond radiography, the methodology offers a transferable, step-by-step framework for identifying occupation-specific risk factors and translating these findings into tailored, feasible solutions. The protocol advances ergonomics practice by shifting from a prescriptive, one-size-fits-all approach to a collaborative, context-specific design, thereby ensuring that any resulting intervention is both evidence-informed and operationally sustainable, and aligned with real-world workplace needs.
OCCUPATIONAL APPLICATION An efficient software framework for analyzing driver behavior from videos is presented. The system automatically detects driver activities, such as phone use, identifies glance patterns, and determines the vehicle's automation state, achieving over 96% accuracy for glance and mode detection. For transportation and safety researchers, vehicle engineers, and fleet safety managers, this tool drastically reduces the time and cost of manual video analysis by over 90%. This efficiency enables large-scale naturalistic driving studies, the objective quantification of driver distraction and inattention in commercial and passenger fleets, and the safety evaluation of new in-vehicle information systems and advanced driver-assistance systems. The framework provides an accessible method for understanding real-world driver behavior, ultimately helping to design safer vehicle systems and inform evidence-based road safety policies. Background: The increasing prevalence of Level 2 (L2) automated driving systems introduces complex human-machine interactions, making the analysis of driver behavior critical for safety. Traditional research methods rely on manual video annotation, a time-consuming and resource-intensive process that limits the scale of naturalistic driving studies and creates a bottleneck for research. Purpose: This study aimed to develop and validate a robust, open-source, and integrated framework for the efficient, multi-faceted analysis of driver behavior using standard in-vehicle video recordings, thereby making large-scale analysis more accessible. Methods: The framework integrates several pre-trained computer vision models. It uses YOLOEv8 for object detection and MediaPipe for skeletal and facial landmark tracking to classify physical secondary tasks (e.g., phone use, consuming). Driver attentional state is determined by classifying glance zones based on head pose and eye gaze estimation. Driving mode (Manual vs. Automated) is detected using GPU-accelerated OpenCV to analyze dashboard iconography. The entire process is managed through a custom graphical user interface with comprehensive debugging tools. Results: Validation against manually annotated data confirmed the framework's high accuracy. Activity detection achieved F1-scores of 93.3% for consuming, 90.9% for browsing on a phone, and 87.5% for talking on a phone. Driving mode and glance allocation detection achieved mean accuracies of 97.4 and 96.5%, respectively. Conclusion: The proposed framework is a validated, efficient, and replicable alternative to manual coding. By significantly reducing analytical workload, it provides an accessible tool for conducting scalable research into driver distraction, attention, and human-automation interaction.
Occupational ApplicationsThe incidence of accidents, occupational diseases, and risk exposure in manufacturing is linked to the diversity of tasks and processes, often compounded by insufficient ergonomics programs. In Mexico, implementing ergonomics effectively remains challenging. This research identifies critical success factors (CSFs) from front-line employees' perspectives, including communication of ergonomic risks, health promotion and training, and accident prevention. Strengthening these factors can improve organizations' economic outcomes by reducing absenteeism, production errors, and lost workdays. Multidisciplinary workplace assessments and validated risk assessment tools further enhance ergonomics practice by identifying at-risk groups and guiding targeted interventions. Integrating these approaches supports safer, more productive manufacturing environments and the achievement of ergonomics program objectives.
OCCUPATIONAL APPLICATIONSThis preliminary flight-simulation study explored the drivers of risk-taking behavior among trainee pilots. We found that trainee pilots' risk-taking behavior increased with higher sensation seeking (particularly the experience seeking and disinhibition subscales), and decreased with elevated physiological arousal. Meanwhile, resting physiological emotion regulation was also negatively correlated with such risk-taking behavior. These initial findings clarify the associations between trait-level psychological characteristics, physiological state indicators, and trainee pilots' risk-taking in the simulated flight context. This work provides preliminary foundational reference evidence to support future large-scale in-flight validation research, which can inform the development of standardized, evidence-based guidelines for pilot personnel evaluation, targeted risk-mitigation training, and physiological arousal monitoring protocols in civil aviation.
OCCUPATIONAL APPLICATIONSThis pre-post intervention case study found that team-based job rotation (JR) has the potential to increase job-level, within-worker variance bilaterally in trapezius and forearm muscle activity compared with baseline by a mean of 35%, and that an actual increase of 27% was achieved. Most workers experienced an increase in within-worker variance at all four muscle sites. At follow-up, over 45% of JR workers reported lower work demands, less fatigue after work, and feeling more rested and recovered when starting a workday compared to baseline; in contrast, some workers (0-4, depending on body part) reported higher musculoskeletal symptoms at follow-up. These results provide evidence from an occupational setting that a re-distribution of existing tasks can increase the average within-worker exposure variation and suggest that JR can lead to lower demands, lower fatigue, and better recovery; further consideration of the longer-term JR effects on musculoskeletal health is required.
