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Electronic health record (EHR) usability and physician task load both contribute to physician professional burnout. The association between perceived EHR usability and workload has not previously been studied at a national level. Better understanding these interactions could give further information as to the drivers of extraneous task load. This study aimed to determine the relationship between physician-perceived EHR usability and workload by specialty and evaluate for associations with professional burnout. A secondary analysis of a cross-sectional survey of US physicians from all specialties was conducted from October 2017 to March 2018. Among the 1250 physicians invited to respond to the subsurvey analyzed here, 848 (67.8%) completed it. EHR usability was assessed with the System Usability Scale (SUS; range: 0-100). Provider task load (PTL) was assessed using the mental demand, physical demand, temporal demand, and effort required subscales of the National Aeronautics and Space Administration Task Load Index (range: 0-400). Burnout was measured using the Maslach Burnout Inventory. The mean scores were 46.1 (SD 22.1) for SUS and 262.5 (SD 71.7) for PTL. On multivariable analysis adjusting for age, gender, relationship status, medical specialty, practice setting, hours worked per week, and number of nights on call per week, physician-rated EHR usability was associated with PTL, with each 1-point increase in SUS score (indicating more favorable) associated with a 0.57-point decrease in PTL score (P<.001). On mediation analysis, higher SUS score was associated with lower PTL score, which was associated with lower odds of burnout. A strong association was observed between EHR usability and workload among US physicians, with more favorable usability associated with less workload. Both outcomes were associated with the odds of burnout, with task load acting as a mediator between EHR usability and burnout. Improving EHR usability while decreasing task load has the potential to allow practicing physicians more working memory for medical decision making and patient communication.

Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency (LF) components, their sum (LF+HF), and their ratio (LF/HF), were measured in a three-behavioral-state paradigm composed of initial rest (Rest), task load (Task), and post-task rest (After) periods to examine autonomic regulation. It was found that HF was low at Rest in both disorders, but was lower in MDD than in CFS. LF and LF+HF at Rest were low only in MDD. Attenuated responses of LF, HF, LF+HF, and LF/HF to task load and an excessive increase in HF at After were found in both disorders. The results indicate that an overall HRV reduction at Rest may support a diagnosis of MDD. HF reduction was found in CFS, but with a lesser severity. Response disturbances of HRV to Task were observed in both disorders, and would suggest the presence of CFS when the baseline HRV has not been reduced. Linear discriminant analysis using HRV indices was able to differentiate MDD from CFS, with a sensitivity and specificity of 91.8% and 100%, respectively. HRV indices in MDD and CFS show both common and different profiles, and can be useful for the differential diagnosis.

In human cerebral hemodynamics, low‐frequency oscillations (LFOs) occur due to the sympathetic nervous system and blood pressure regulation. LFOs have recently been reported to be associated with cognitive‐behavioral performance, though their functional relevance to cognitive load remains unclear. Here, we employed functional near‐infrared spectroscopy to record changes in oxygenated (Δ[oxy‐Hb]) and deoxygenated (Δ[deoxy‐Hb]) hemoglobin concentrations in the prefrontal cortex (PFC) of 47 healthy young adults (aged 18–23) during N‐back working memory tasks and extracted LFOs from the cerebral hemodynamic data to study their relationship with working memory load. Increasing the task load led to a marked decrease in both LFO power and LFO peak amplitude of Δ[oxy‐Hb], alongside an increase in LFO peak frequency and PFC activation, revealing a load‐dependent feature of cognitive engagement. Correlations between LFO power and behavioral performance, including accuracy and response time, were observed. LFOs and their characteristic parameters exhibited a strong effect on working memory load, indicating the potential of LFOs in cerebral hemodynamics as a sensitive marker for quantifying the cognitive load effect of brain activity. This study focuses on the effect of low‐frequency oscillations (LFOs) on human cerebral hemodynamics and the use of functional near‐infrared spectroscopy to explore variations in the LFO parameters and their correlations with behavioral parameters under classic N‐back working memory tasks. This study contributes to our understanding of the effect of LFOs on human cerebral hemodynamics. Key points What is already known about this topic? Low‐frequency oscillations (LFOs) in cerebral hemodynamics occur in the human cerebral vascular system and are believed to be related to sympathetic nerve activity, blood pressure regulation, and neurovascular coupling. There are limited reports of the dynamic changes in LFO‐related parameters during cognitive load processes. What does this study add? This study used functional near‐infrared spectroscopy to analyze the variation in LFO parameters of the prefrontal cortex in healthy young participants under different working memory task loads. The dynamic variations in LFO parameters represent the transition characteristics of the brain between different cognitive activation states, providing physiological markers for noninvasive cognitive load assessment and brain function monitoring.

Information technologies exist to enable us to either do things we have not done before or do familiar things more efficiently. Metaverse (i.e. extended reality: XR) enables novel forms of engrossing telepresence, but it also may make mundate tasks more effortless. Such technologies increasingly facilitate our work, education, healthcare, consumption and entertainment; however, at the same time, metaverse bring a host of challenges. Therefore, we pose the question whether XR technologies, specifically Augmented Reality (AR) and Virtual Reality (VR), either increase or decrease the difficulties of carrying out everyday tasks. In the current study we conducted a 2 (AR: with vs. without) × 2 (VR: with vs. without) between-subject experiment where participants faced a shopping-related task (including navigating, movement, hand-interaction, information processing, information searching, storing, decision making, and simple calculation) to examine a proposed series of hypotheses. The NASA Task Load Index (NASA-TLX) was used to measure subjective workload when using an XR-mediated information system including six sub-dimensions of frustration, performance, effort, physical, mental, and temporal demand. The findings indicate that AR was significantly associated with overall workload, especially mental demand and effort, while VR had no significant effect on any workload sub-dimensions. There was a significant interaction effect between AR and VR on physical demand, effort, and overall workload. The results imply that the resources and cost of operating XR-mediated realities are different and higher than physical reality.

Patient monitoring is indispensable in any operating room to follow the patient's current health state based on measured physiological parameters. Reducing workload helps to free cognitive resources and thus influences human performance, which ultimately improves the quality of care. Among the many methods available to assess perceived workload, the National Aeronautics and Space Administration Task Load Index (NASA-TLX) provides the most widely accepted tool. However, only few studies have investigated the validity of the NASA-TLX in the health care sector. This study aimed to validate a modified version of the raw NASA-TLX in patient monitoring tasks by investigating its correspondence with expected lower and higher workload situations and its robustness against nonworkload-related covariates. This defines criterion validity. In this pooled analysis, we evaluated raw NASA-TLX scores collected after performing patient monitoring tasks in four different investigator-initiated, computer-based, prospective, multicenter studies. All of them were conducted in three hospitals with a high standard of care in central Europe. In these already published studies, we compared conventional patient monitoring with two newly developed situation awareness-oriented monitoring technologies called Visual Patient and Visual Clot. The participants were resident and staff anesthesia and intensive care physicians, and nurse anesthetists with completed specialization qualification. We analyzed the raw NASA-TLX scores by fitting mixed linear regression models and univariate models with different covariates. We assessed a total of 1160 raw NASA-TLX questionnaires after performing specific patient monitoring tasks. Good test performance and higher self-rated diagnostic confidence correlated significantly with lower raw NASA-TLX scores and the subscores (all P<.001). Staff physicians rated significantly lower workload scores than residents (P=.001), whereas nurse anesthetists did not show any difference in the same comparison (P=.83). Standardized distraction resulted in higher rated total raw NASA-TLX scores (P<.001) and subscores. There was no gender difference regarding perceived workload (P=.26). The new visualization technologies Visual Patient and Visual Clot resulted in significantly lower total raw NASA-TLX scores and all subscores, including high self-rated performance, when compared with conventional monitoring (all P<.001). This study validated a modified raw NASA-TLX questionnaire for patient monitoring tasks. The scores obtained correctly represented the assumed influences of the examined covariates on the perceived workload. We reported high criterion validity. The NASA-TLX questionnaire appears to be a reliable tool for measuring subjective workload. Further research should focus on its applicability in a clinical setting.

Objetivo: Evaluar la fuerza de agarre de la mano como un indicador de la carga de trabajo física y mental en diferentes condiciones, con el fin de desarrollar estrategias para la promoción de la salud pública. Metodología: Utilizando un dinamómetro de fuerza de agarre de la mano, se registró la fuerza máxima de los participantes (30 personas). Durante la evaluación de esta fuerza, se ejecutó la tarea de demanda física, ejerciendo la fuerza en niveles bajo, medio y alto. Asimismo, se efectuaron tareas de demandas mentales, mediante la resolución de operaciones aritméticas en niveles bajo, medio y alto. Se definieron dos condiciones del experimento: 1) tarea llevada a cabo al evaluar la carga de trabajo física y la tarea mental combinada (primero se efectúa la demanda física y después la mental), y 2) tareas ejecutadas y evaluadas de forma simultánea (ambas demandas, física y mental, al mismo tiempo). Se utilizaron herramientas de carga mental (índice de carga de tareas de la nasa) para evaluar la carga de trabajo. Resultados: El tiempo para finalizar las tareas fue significativamente mayor de manera combinada que simultánea y el rendimiento fue significativamente mayor en las tareas combinadas que las simultáneas. Además, se observa que existen efectos considerables de la salud en la forma simultánea. Conclusiones: La forma combinada obtuvo mejores resultados que la simultánea y el índice de carga de tareas de la nasa Tradicional presentó un nivel de índice de carga de trabajo global significativamente mayor que el índice de carga de tareas de la NASA RAW.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot’s speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration.

The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The DASS addresses static symbology limitations by employing a modular Python 3.10 architecture that uses machine learning-driven threat detection to dynamically adapt symbol visualization based on threat severity and context. Empirical testing assessed the DASS against a MIL-STD-2525D baseline using active cybersecurity professionals. Results show that the DASS significantly improves threat identification rates by 30% and reduces response times by 25%, while achieving 90% accuracy in symbol interpretation. Although the current implementation focuses on virus-based scenarios, the DASS successfully prioritizes critical threats and reduces operator cognitive load.

Pupil size is a significant biosignal for human behavior monitoring and can reveal much underlying information. This study explored the effects of task load, task familiarity, and gaze position on pupil response during learning a visual tracking task. We hypothesized that pupil size would increase with task load, up to a certain level before decreasing, decrease with task familiarity, and increase more when focusing on areas preceding the target than other areas. Fifteen participants were recruited for an arrow tracking learning task with incremental task load. Pupil size data were collected using a Tobii Pro Nano eye tracker. A 2 × 3 × 5 three-way factorial repeated measures ANOVA was conducted using R (version 4.2.1) to evaluate the main and interactive effects of key variables on adjusted pupil size. The association between individuals’ cognitive load, assessed by NASA-TLX, and pupil size was further analyzed using a linear mixed-effect model. We found that task repetition resulted in a reduction in pupil size; however, this effect was found to diminish as the task load increased. The main effect of task load approached statistical significance, but different trends were observed in trial 1 and trial 2. No significant difference in pupil size was detected among the three gaze positions. The relationship between pupil size and cognitive load overall followed an inverted U curve. Our study showed how pupil size changes as a function of task load, task familiarity, and gaze scanning. This finding provides sensory evidence that could improve educational outcomes.

Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to characterize the interactions among cortical networks that are sensitive to working memory load since such interactions can also hint at the impaired dynamics in patients with poor working memory performance. Functional connectivity is a powerful tool used to investigate coordinated activity among local and distant brain regions. Here, we identified connectivity footprints that differentiate task states representing distinct working memory load levels. We employed linear support vector machines to decode working memory load from task-based functional connectivity matrices in 177 healthy adults. Using neighborhood component analysis, we also identified the most important connectivity pairs in classifying high and low working memory loads. We found that between-network coupling among frontoparietal, ventral attention and default mode networks, and within-network connectivity in ventral attention network are the most important factors in classifying low vs. high working memory load. Task-based within-network connectivity profiles at high working memory load in ventral attention and default mode networks were the most predictive of load-related increases in response times. Our findings reveal the large-scale impact of working memory load on the cerebral cortex and highlight the complex dynamics of intrinsic brain networks during active task states. •Working memory load can be reliably decoded from cortex-wide task functional connectivity matrices using machine learning.•Coupling among frontoparietal, ventral attention and default networks is the most discriminative of high vs. low load.•Coupling within ventral attention and default networks at high load best predicts load-related increases in response time.