State of science: mental workload in ergonomics

A Dual-Adversarial Model for Cross-Time and Cross-Subject Cognitive Workload Decoding

Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.

Relationship between mental workload and musculoskeletal disorders with intention to leave service among nurses working at neonatal and pediatric departments: a cross-sectional study in Iran

BACKGROUND

Despite the challenge of nursing shortage in the world and its subsequent impact on care quality as well as aggravation of the situation by intention to leave service, this issue has not been properly addressed, especially among neonatal and pediatric nurses. The present study aims to identify the relationship between mental workload and musculoskeletal disorders with intention to leave the service among nurses working at neonatal and pediatric departments.

METHODS

This descriptive-analytical study was conducted on 145 nurses working at neonatal and pediatric departments in six hospitals in Bushehr Province using full-census method. The data were collected using national aeronautics and space administration-task load index (NASA-TLX), Cornell musculoskeletal discomfort questionnaire(CMDQ) and Mobley and Horner's voluntary turnover questionnaire. The data were analyzed using descriptive statistics, independent t-test, Mann-Whitney U test, one-way analysis of variance (ANOVA), Kruskal-Wallis test, Pearson's and Spearman correlation tests and hierarchical linear regression in simultaneous model in SPSS 19.0.

RESULTS

The mean score of intention to leave the service was 9.57 ± 3.20 (higher than the moderate level) and the mean mental workload was 71.65 ± 15.14 (high level). Pain in at least one of the legs (100%), back (77.3%) and knees (76.6%) was highly prevalent. However, no statistically significant correlation was found between musculoskeletal disorder categories and intention to leave the service (p > 0.05). The regression analysis results revealed among mental workload domains, only effort-induced workload was negatively and significantly correlated with intention to leave the service (p = 0.003; β=-0.078). However, the number of night shifts per month was positively and significantly correlated with intention to leave the service (p = 0.001; β = 0.176).

CONCLUSIONS

Planning for appropriate allocation of night shifts, investigating the etiology of musculoskeletal disorders and providing solutions for reducing mental workload should be prioritized by policymakers, while maintaining pediatric nurses' motivation for making efforts.

The mediating effect of coping styles between emergency capacity and mental workload among clinical nurses: A cross-sectional study

AIMS

This study aimed to examine the relationship between emergency capacity, coping styles, and mental workload among nurses.

BACKGROUND

Emergency capacity, coping styles, and mental workload are all variables associated with work. Identifying the relationship between these variables can facilitate administrators to implement tailored and effective intervention strategies to improve individual performance, quality of care, and medical safety.

METHODS

A quantitative cross-sectional study was carried out to investigate 605 Chinese clinical nurses in seven tertiary hospitals by using personal information form, emergency capacity scale for nurses, simplified coping skill questionnaire, and the NASA-Task Load Index.

RESULTS

Emergency capacity and mental workload were found at moderate levels. The multiple linear regression model suggested that spinsterhood, no children, high workload, always anxiety or nervousness, and lower monthly income were the influencing factors of mental workload. Positive coping style was positively correlated with emergency capacity and negatively correlated with mental workload. Negative coping style was negatively related to emergency capacity and positively related to mental workload. Additionally, coping styles played a partial mediating role in the relationship between emergency capacity and mental workload through constructing a structural equation model, but the effects of positive coping style and negative coping style are opposite.

CONCLUSION

Our results showed that coping styles played a mediating role in the relationship between emergency capacity and mental workload. Managers can alleviate the mental workload of nurses by cultivating positive coping styles and improving emergency capacity.

IMPLICATIONS FOR NURSING AND NURSING POLICY

Mental workload of nurses deserves more attention in medical institutions. The results of our study provide evidence for improving employee health, promoting positive behaviors, and optimizing organizational management. Nursing managers should take feasible measures to fulfill nurses' needs for emergency capacity and coping strategies to alleviate nurses' mental workload, so as to stimulate their intrinsic motivation and positive organizational behavior.

Measuring aviator workload using EEG: an individualized approach to workload manipulation

Introduction

Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience.

Methods

Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance.

Results

Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload.

Discussion

The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

Measuring cognitive workload in the nuclear control room: a review

Despite the substantial literature and human factors guidance, evaluators report challenges in selecting cognitive workload measures for the evaluation of complex human-technology systems. A review of 32 articles found that self-report measures and secondary tasks were systematically sensitive to human-system interface conditions and correlated with physiological measures. Therefore, including a self-report measure of cognitive workload is recommended when evaluating human-system interfaces. Physiological measures were mainly used in method studies, and future research must demonstrate the utility of these measures for human-system evaluation in complex work settings. However, indexes of physiological measures showed promise for cognitive workload assessment. The review revealed a limited focus on the measurement of excessive cognitive workload, although this is a key topic in nuclear process control. To support human-system evaluation of adequate cognitive workload, future research on behavioural measures may be useful in the identification and analysis of underload and overload.

Physiological measures of operators' mental state in supervisory process control tasks: a scoping review

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research.

Comparison of two pupillometric measures as indicators of cognitive strain and under the influence of screen luminance

The pupil diameter has been shown to provide insight to a person's experienced cognitive strain. Pupillary light responses, however, make this measure unreliable in uncontrolled settings. Two derived indicators-Index of Cognitive Activity (ICA) and Index of Pupillary Activity (IPA)-aim to 'eliminate' lighting influences, changing based only on the perceived cognitive strain. The IPA potentially offers a valuable alternative to the ICA through its fully transparent calculation, which lifts the restrictions to proprietary software and supported eye trackers. The measures are examined and compared based on two experimental studies; (i) as indicators of cognitive strain during mental arithmetic tasks and (ii) under different conditions of computer screen luminance. Results indicate that neither indicator differentiates between the increasing levels of cognitive strain. Differences in screen luminance are reflected in both indicators, although differently between the conditions. Both results contradict the claims of the indicators and further investigations are thus required.

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey

AIMS

To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL.

DESIGN

A cross-sectional questionnaire study.

METHODS

Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used.

RESULTS

ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0-100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks.

CONCLUSION

ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL.

IMPACT

This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Nurses' mental workload and public health emergency response capacity in COVID-19 pandemic: A cross-sectional study

AIMS

The aim of this study was to assess the level of mental workload of Chinese nurses through a latent profile analysis and to explore its relationship with public health emergency response capacity.

DESIGN

A cross-sectional design with a convenience sample.

METHODS

A convenience sample of nurses from five tertiary hospitals in Chengdu between May and December 2022. Demographic, work-related information, Nurse's version of NASA's Task Load Index Scale and Nurse's Public Health Emergency Response Capacity Scale were used in this study.

RESULTS

The mean scores for mental workload and emergency response capacity for nurses were (57.19 ± 15.67) and (3.58 ± 0.77) respectively. We found that the mental workload of nurses fell into three potential categories. In addition, there were differences in psychological training and supply of epidemic prevention materials in the department among nurses with different mental workload subtypes. There was a moderate negative correlation between nurses' mental workload and public health emergency response capacity.

CONCLUSION

Our results show that there is still a strong mental workload on a proportion of nurses, and enhanced psychological training and material supply support are beneficial in relieving nurses' mental workload. The better the nurses' capacity to cope with public health emergencies, the lower their mental workload.

IMPACT

Nursing managers should pay ongoing attention to the mental workload status of nurses in the latter stages of a pandemic and individual differences in nurses' mental workload. In addition, nursing managers should be aware of the impact of public health emergency response capacity on nurses' mental workload. They can intervene in nurses mental workload from a new perspective.

PATIENT OR PUBLIC CONTRIBUTION

560 registered nurses participated in this study.

Research on interaction and trust theory model for cockpit human-machine fusion intelligence

Based on Boyd's "Observation Orientation-Decision-Action (OODA)" aerial combat theory and the principles of operational success, an analysis of the operational division patterns for cross-generational human-machine collaboration was conducted. The research proposed three stages in the development of aerial combat human-machine fusion intelligence: "Human-Machine Separation, Functional Coordination," "Human-Machine Trust, Task Coordination," and "Human-Machine Integration, Deep Fusion." Currently, the transition from the first stage to the second stage is underway, posing challenges primarily related to the lack of effective methods guiding experimental research on human-machine fusion interaction and trust. Building upon the principles of decision neuroscience and the theory of supply and demand relationships, the study analyzed the decision-making patterns of human-machine fusion intelligence under different states. By investigating the correlations among aerial combat mission demands, dynamic operational limits of human-machine tasks, and aerial combat mission performance, a theoretical model of human-machine fusion interaction and trust was proposed. This model revealed the mechanistic coupling of human-machine interactions in aerial tasks, aiming to optimize the decision-making processes of human-machine systems to enhance mission performance. It provides methodological support for the design and application of intelligent collaborative interaction modes in aviation equipment.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking

OBJECTIVE

(1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU.

BACKGROUND

Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data.

METHODS

Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes.

RESULTS

While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter.

CONCLUSION

Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.

Granular estimation of user cognitive workload using multi-modal physiological sensors

Mental workload (MWL) is a crucial area of study due to its significant influence on task performance and potential for significant operator error. However, measuring MWL presents challenges, as it is a multi-dimensional construct. Previous research on MWL models has focused on differentiating between two to three levels. Nonetheless, tasks can vary widely in their complexity, and little is known about how subtle variations in task difficulty influence workload indicators. To address this, we conducted an experiment inducing MWL in up to 5 levels, hypothesizing that our multi-modal metrics would be able to distinguish between each MWL stage. We measured the induced workload using task performance, subjective assessment, and physiological metrics. Our simulated task was designed to induce diverse MWL degrees, including five different math and three different verbal tiers. Our findings indicate that all investigated metrics successfully differentiated between various MWL levels induced by different tiers of math problems. Notably, performance metrics emerged as the most effective assessment, being the only metric capable of distinguishing all the levels. Some limitations were observed in the granularity of subjective and physiological metrics. Specifically, the subjective overall mental workload couldn't distinguish lower levels of workload, while all physiological metrics could detect a shift from lower to higher levels, but did not distinguish between workload tiers at the higher or lower ends of the scale (e.g., between the easy and the easy-medium tiers). Despite these limitations, each pair of levels was effectively differentiated by one or more metrics. This suggests a promising avenue for future research, exploring the integration or combination of multiple metrics. The findings suggest that subtle differences in workload levels may be distinguishable using combinations of subjective and physiological metrics.

The effects of increased mental workload of air traffic controllers on time perception: Behavioral and physiological evidence

Research has shown that timing is modulated by mental workload, making duration judgments a measure of cognitive demand, alongside subjective assessments, and physiological measurements. Yet, it is unclear whether such findings can be extended in less controlled setups. By employing air traffic controllers in a real aviation environment, we tested whether tasks with different levels of cognitive load can affect their timing behavior. Participants completed temporal production, verbal estimation, and passage of time judgments, while actively engaging in real flight control sessions. Subjective assessments of task demands, as well as physiological responses (cardiac and electrodermal activity) were also measured. Accuracy of the produced intervals was measured at two distinct phases of the flight (during low-load cruising vs. high-load landing) and under two different task load manipulations (controlling one vs. two helicopters and speaking in native vs. non-native language). Analysis of interval production accuracy showed that during the high-load landing phase significant overproductions were made, compared to the low-load cruising phase, and landing two helicopters led to greater overproductions compared to landing only one. The duration of the two-helicopter sessions was significantly overestimated compared to the single-helicopter ones, and the passage of time was felt significantly faster. Subjective assessments of workload were positively correlated with the temporal estimations and passage of time judgments, and skin responses were positively correlated with the produced intervals. Overall, our results are consistent with past research, suggesting that mental workload modulates time perception in complex, real-world environments, thus making timing behavior a reliable index of the workload changes.

Investigating mental workload caused by NDRTs in highly automated driving with deep learning

OBJECTIVE

This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) signals.

METHODS

The experiment involved recruiting 28 participants who engaged in simulations within a driving simulator while exposed to 4 distinct NDRTs: (1) reading, (2) listening to radio news, (3) watching videos, and (4) texting. EEG data collected during NDRTs were categorized into 3 levels of mental workload, high, medium, and low, based on the NASA Task Load Index (NASA-TLX) scores. Two deep learning methods, namely, long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM), were employed to develop the classification model.

RESULTS

A series of correlation analyses revealed that the channels and frequency bands are linearly correlated with mental workload. The comparative analysis of classification results demonstrates that EEG data featuring significantly correlated frequency bands exhibit superior classification accuracy compared to the raw EEG data.

CONCLUSIONS

This research offers a reference for assessing mental workload resulting from NDRTs in the context of highly automated driving. Additionally, it delves into the development of deep learning classifiers for EEG signals with heightened accuracy.

Identifying interaction types and functionality for automated vehicle virtual assistants: An exploratory study using speech acts cluster analysis

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to communicate situation information to effectively support the transfer of control and responsibility of the driving task. This study explores 'interaction types' used for this complex human-machine transaction, by analysing how situation information is conveyed and reciprocated during a transfer of control scenario. Two human drivers alternated control in a bespoke, dual controlled driving simulator with the transfer of control being entirely reliant on verbal communication. Handover dialogues were coded based on speech-act classifications, and a cluster analysis was conducted. Four interaction types were identified for both virtual assistants (i.e., agent handing over control) - Supervisor, Information Desk, Interrogator and Converser, and drivers (i.e., agent taking control) - Coordinator, Perceiver, Inquirer and Silent Receiver. Each interaction type provides a framework of characteristics that can be used to define driver requirements and implemented in the design of future virtual assistants to support the driver in maintaining and rebuilding timely situation awareness, whilst ensuring a positive user experience. This study also provides additional insight into the role of dialogue turns and takeover time and provides recommendations for future virtual assistant designs in AVs.

Human Robot Collaboration for Enhancing Work Activities

OBJECTIVE

Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks.

BACKGROUND

As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners.

METHODS

A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities.

RESULTS

Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged.

CONCLUSIONS

The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered.Application: The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks.

Sharing imagery and analysis tools in a simulated submarine control room

We examined the impact of sharing periscope imagery and analysis tools in eighteen five-member teams (Sonar, Periscope, 2xTrack Motion Analysts, Track Manager) who undertook simulated submarine patrol tasks. Compared to a baseline condition, sharing imagery to team members increased perceived individual workload, with no improvement to team performance (tactical picture accuracy). When both imagery and analysis tools were shared, perceived workload increased and tactical picture compilation was more accurate. Despite this improved tactical picture for the imagery and analysis tools condition, there was no advantage to mission completion (rendezvous/close contact detection) or situation awareness. In contrast to the increased subjective workload, individuals in teams provided with shared imagery (with or without tools) had a lower physiological response (heart rate, electrodermal) to task demands compared to the baseline condition. Sharing imagery and analysis tools likely benefited tactical picture compilation by enabling dynamic task redistribution and multiple streams of concurrent data analysis.

Development and validation of a task load index for process control room operators (PCRO-TLX)

Process control room operators (PCRO) perform a range of complex cognitive safety-critical tasks. The aim of this exploratory sequential mixed methods study was to develop an occupation specific tool to measure the task load of PCRO using NASA Task Load Index (TLX) methodology. Participants were 30 human factors experts and 146 PCRO at two refinery complexes in Iran. Dimensions were developed via a cognitive task analysis, a research review, and three expert panels. Six dimensions were identified: perceptual demand, performance, mental demand, time pressure, effort, and stress. Data from 120 PCRO confirmed that the developed PCRO-TLX has acceptable psychometric properties, and a comparison with the NASA-TLX confirmed that perceptual, not physical, demand was relevant for measuring workload in PCRO. There was a positive convergence of scores of the Subjective Workload Assessment Technique and the PCRO-TLX. This reliable tool (α = 0.83) is recommended for risk assessing the task load of PCRO.Practitioner summary: There are benefits of having a specific tool to measure task load in safety critical roles. Thus, we developed and validated an easy-to-use targeted tool, the PCRO-TLX, for process control room operatives. Timely use and response will assure optimal production alongside health and safety in an organisation.Abbreviations: PCRO: process control room operator; TLX: task load index; PCRO-TLX: process control room operator task load index; NASA-TLX: National Aeronautics and Space Administration task load index; SWAT: subjective workload assessment technique; DALI: driving activity load index; SURG-TLX: surgery task load index; SIM-TLX: virtual reality simulation task load index; VACP: visual, auditory, cognitive and psychomotor; CVI: content validity index; CVR: content validity ratio; RMSEA: root mean square of error approximation; GFI: goodness of fit index; AGFI: adjusted goodness of fit index; CFI: comparative fit index; ANOVA: analysis of variance; CI: confidence interval.

Evaluation of physiological responses to mental workload in n-back and arithmetic tasks

Working memory tasks, such as n-back and arithmetic tasks, are frequently used in studying mental workload. The present study investigated and compared the sensitivity of several physiological measures at three levels of difficulty of n-back and arithmetic tasks. The results showed significant differences in fixation duration and pupil diameter among three task difficulty levels for both n-back and arithmetic tasks. Pupil diameters increase with increasing mental workload, whereas fixation duration decreases. Blink duration and heart rate (HR) were significantly increased as task difficulty increased in the n-back task, while root mean square of successive differences (RMSSD) and standard deviation of R-R intervals (SDNN) were significantly decreased in the arithmetic task. On the other hand, blink rate and Galvanic Skin Response (GSR) were not sensitive enough to assess the differences in task difficulty for both tasks. All significant physiological measures yielded significant differences between low and high task difficulty except for SDNN.Practitioner summary: This study aimed to assess the sensitivity levels of several physiological measures of mental workload in n-back and arithmetic tasks. It showed that pupil diameter was the most sensitive in both tasks. This study also found that most physiological indices are sensitive to an extreme change in task difficulty levels.

Workload-dependent hemispheric asymmetries during the emotion-cognition interaction: a close-to-naturalistic fNIRS study

Introduction

We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study.

Methods

Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence.

Results

Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions.

Discussion

In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).

Royal Netherlands Marechaussee Personnel's Self-Perceived Occupational Demand Profiles: A Latent Profile Analysis Shows the "Good" Versus the "Bad"

INTRODUCTION

Research has linked high occupational demands to multiple adverse health outcomes, both physical and mental. As far as we know, researchers have not identified the profile characteristics of military police personnel based on occupational demands. The current study aims to identify profiles based on self-perceived occupational demands and work-related factors. This study is a starting point for characterizing performance and health in a military police population.

METHODS

This was a cross-sectional study in which we gathered survey data from 1,135 Royal Netherlands Marechaussee members. We used Latent Profile Analysis to identify profiles based on nine indicators of workload and work characteristics selected via focus groups and interviews with Royal Netherlands Marechaussee personnel. We determined if the profiles differed significantly across all indicators with an analysis of variance. Then, we used binominal logistic regression to determine the odds ratio (OR) for the indicators on profile membership.

RESULTS

We discovered two profiles that were distinct across all indicators. Experience (OR = 1.02, 95% CI [1.00-1.04]), autonomy (OR = 1.18, 95% CI [1.06-1.31]), task clarity (OR = 1.49, [1.32-1.69]), and work support (OR = 2.63, 95% CI [2.26-3.09]) were all predictors for a low perceived occupational demand profile. In contrast, mental (OR = 0.18, 95% CI [0.13-0.25]) and physical (OR = 0.42, 95% CI [0.32-0.54]) fatigue, and boredom (OR = 0.14, 95% CI [0.10-0.20]) were predictors for high perceived occupational demand profiles.

CONCLUSION

We established two distinct profiles that describe the characteristics reported by the Royal Netherlands Marechaussee personnel based on workload and work characteristics. High scores on autonomy, work support, and task clarity predict favorable perceived occupational demands, whereas fatigue and boredom predict unfavorable occupational demands. Remarkably, the physical workload did not predict high perceived occupational demands.

Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis

In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers' physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.

The levels and related factors of mental workload among nurses: A systematic review and meta-analysis

AIM

The aim was to determine the overall levels and related factors of mental workload assessed using the NASA-TLX tool among nurses.

BACKGROUND

Mental workload is a key element that affects nursing performance. However, there exists no review regarding mental workload assessed using the NASA-TLX tool, focusing on nurses.

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

PubMed, MEDLINE, Web of Science, EMBASE, PsycINFO, Scopus, CINAHL, CNKI, CBM, Weipu and WanFang databases were searched from 1 January 1998 to 30 February 2022.

REVIEW METHODS

Following the PRISMA statement recommendations, review methods resulted in 31 quantitative studies retained for inclusion which were evaluated with the evaluation criteria for observational studies as recommended by the Agency for Healthcare Research and Quality. The data were pooled and a random-effects meta-analysis conducted.

RESULTS

Findings showed the pooled mental workload score was 65.24, and the pooled prevalence of high mental workload was 54%. Subgroup analysis indicated nurses in developing countries and emergency departments experienced higher mental workloads, and the mental workloads of front-line nurses increased significantly during the COVID-19 pandemic.

CONCLUSION

These findings highlight that nurses experience high mental workloads as assessed using the NASA-TLX tool and there is an urgent need to explore interventions to decrease their mental workloads.

Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators' Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator

The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks.

Estimation of Subjectively Reported Trust, Mental Workload, and Situation Awareness Using Unobtrusive Measures

OBJECTIVE

We use a set of unobtrusive measures to estimate subjectively reported trust, mental workload, and situation awareness (henceforth "TWSA").

BACKGROUND

Subjective questionnaires are commonly used to assess human cognitive states. However, they are obtrusive and usually impractical to administer during operations. Measures derived from actions operators take while working (which we call "embedded measures") have been proposed as an unobtrusive way to obtain TWSA estimates. Embedded measures have not been systematically investigated for each of TWSA, which prevents their operational utility.

METHODS

Fifteen participants completed twelve trials of spaceflight-relevant tasks while using a simulated autonomous system. Embedded measures of TWSA were obtained during each trial and participants completed TWSA questionnaires after each trial. Statistical models incorporating our embedded measures were fit with various formulations, interaction effects, and levels of personalization to understand their benefits and improve model accuracy.

RESULTS

The stepwise algorithm for building statistical models usually included embedded measures, which frequently corresponded to an intuitive increase or decrease in reported TWSA. Embedded measures alone could not accurately capture an operator's cognitive state, but combining the measures with readily observable task information or information about participants' backgrounds enabled the models to achieve good descriptive fit and accurate prediction of TWSA.

CONCLUSION

Statistical models leveraging embedded measures of TWSA can be used to accurately estimate responses on subjective questionnaires that measure TWSA.

APPLICATION

Our systematic approach to investigating embedded measures and fitting models allows for cognitive state estimation without disrupting tasks when administering questionnaires would be impractical.

Cognitive aging at work and in daily life-a narrative review on challenges due to age-related changes in central cognitive functions

Demographic change is leading to an increasing proportion of older employees in the labor market. At the same time, work activities are becoming more and more complex and require a high degree of flexibility, adaptability, and cognitive performance. Cognitive control mechanism, which is subject to age-related changes and is important in numerous everyday and work activities, plays a special role. Executive functions with its core functions updating, shifting, and inhibition comprises cognitive control mechanisms that serve to plan, coordinate, and achieve higher-level goals especially in inexperienced and conflicting actions. In this review, influences of age-related changes in cognitive control are demonstrated with reference to work and real-life activities, in which the selection of an information or response in the presence of competing but task-irrelevant stimuli or responses is particularly required. These activities comprise the understanding of spoken language under difficult listening conditions, dual-task walking, car driving in critical traffic situations, and coping with work interruptions. Mechanisms for compensating age-related limitations in cognitive control and their neurophysiological correlates are discussed with a focus on EEG measures. The examples illustrate how to access influences of age and cognitive control on and in everyday and work activities, focusing on its functional role for the work ability and well-being of older people.

Exploring the influence of individual factors on the perception of mental workload and body postures

Studies have revealed that physical and mental demands, psychosocial factors, and individual factors can contribute to the development of WMSDs. Yet, much is still unknown regarding the effects of individual characteristics on WMSDs susceptibility. Previous studies discovered people assumed more awkward body postures to perform an activity when the perception of mental workload is higher. This research study explored if individual characteristics such as age, sex, personality, and anxiety help explain changes or differences in the perception of mental workload and body postures assume when performing activities. The study provided evidence that these individual characteristics have a modifying role on perceived mental workload and body postures. The results suggest that perceived mental workload is influenced to a higher extent by individual characteristics such as anxiety, sex, and personality traits. Women have a higher (18.7%) mental workload perception than men. Likewise, NASA-TLX scores are 22% higher for feelers than thinkers. In general, higher perceptions of mental workload were observed in participants with higher anxiety levels. On the other hand, body postures seem to be influenced by different individual factors depending on the nature of the activity. RULA scores increased on average by 13.1% between baseline and time constraint conditions. Larger differences were observed in certain individuals (e.g. introverts (19.7%) and intuitors (13.8%)) across conditions.

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

Degree of Alarm Fatigue and Mental Workload of Hospital Nurses in Intensive Care Units

This study aimed to determine the degree of alarm fatigue and mental workload of ICU nurses, and to clarify the relationship between these two variables. A cross-sectional, descriptive research design was used. Data were collected from 90 nurses working in four ICUs in Seoul, Republic of Korea, using a questionnaire determining their degree of alarm fatigue and mental workload. Data were collected from 6 March to 26 April 2021 and were analyzed using a t-test, ANOVA, and Pearson's correlation coefficient. The average alarm-fatigue score was 28.59 out of 44. The item with the highest score was "I often hear a certain amount of noise in the ward", with a score of 3.59 out of 4. The average of the mental workload scores was 75.21 out of 100. The highest mental workload item was effort, which scored 78.72 out of 100. No significant correlation was found between alarm fatigue and mental workload. Although nurses were consistently exposed to alarm fatigue, this was not directly related to their mental workloads, perhaps owing to their professional consciousness as they strived to accomplish tasks despite alarm fatigue. However, since alarm fatigue can affect efficiency, investigations to reduce it and develop appropriate guidelines are necessary. This study was not registered.

Subjective mental workload in Spanish emergency nurses. A study on predictive factors

INTRODUCTION

Mental workload refers to the cognitive or intellectual requirements that a worker is subjected to in a workday. The objective of the present work was to discover the subjective mental workload of nursing staff at Hospital Emergency Units, and its relationship with sociodemographic, work, environmental factors at the workplace, and personality variables.

METHOD

A quantitative, descriptive, observational, and crosssectional study was conducted with 201 emergency nurses from 13 different provinces in Spain. Each participant completed 5 questionnaires (sociodemographic, work conditions, environmental conditions, personality, and subjective mental workload). Descriptive statistics were obtained, and Pearson's correlations and multivariate models (multiple linear regression) were performed.

RESULTS

The nurses had medium to high levels of mental workload. The environmental conditions had a direct relationship with the mental workload, especially with respect to noise and lighting. The participants obtained high scores in kindness, responsibility, openness/intellect, and extraversion. Positive and statistically significant relations were found between neuroticism and mental workload. Being female, older, and having stable employment or a permanent contract were associated with a greater mental workload of emergency nurses.

CONCLUSION

The domain of neuroticism personality, and the hygienic conditions in the workplace were the predictors with the most weight in the model. This study could be useful for defining aspects that need to be considered for the well-being of emergency nurses, such as lighting conditions or environmental noise in the workplace. It also invites reflection on the influence of personal factors (age, gender, personality) and work factors (type of contract, professional experience) on the mental workload of emergency nurses.

Design guidelines for limiting and eliminating virtual reality-induced symptoms and effects at work: a comprehensive, factor-oriented review

Virtual reality (VR) can induce side effects known as virtual reality-induced symptoms and effects (VRISE). To address this concern, we identify a literature-based listing of these factors thought to influence VRISE with a focus on office work use. Using those, we recommend guidelines for VRISE amelioration intended for virtual environment creators and users. We identify five VRISE risks, focusing on short-term symptoms with their short-term effects. Three overall factor categories are considered: individual, hardware, and software. Over 90 factors may influence VRISE frequency and severity. We identify guidelines for each factor to help reduce VR side effects. To better reflect our confidence in those guidelines, we graded each with a level of evidence rating. Common factors occasionally influence different forms of VRISE. This can lead to confusion in the literature. General guidelines for using VR at work involve worker adaptation, such as limiting immersion times to between 20 and 30 min. These regimens involve taking regular breaks. Extra care is required for workers with special needs, neurodiversity, and gerontechnological concerns. In addition to following our guidelines, stakeholders should be aware that current head-mounted displays and virtual environments can continue to induce VRISE. While no single existing method fully alleviates VRISE, workers' health and safety must be monitored and safeguarded when VR is used at work.

Cognitive tasks and combined statistical methods to evaluate, model, and predict mental workload

Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks.

Mental workload variations during different cognitive office tasks with social media interruptions

Interruption at work by social media (SM) is a pervasive phenomenon. This study investigated the impact of SM interruptions and task cognitive levels on mental workload (MWL) and physiological indexes. Each subject performed six simulated computer tasks differentiated by two factors: task cognitive level and performing condition. MWL was reflected through three categories of data: perceived mental workload, physiological indexes, and primary task performance. The results revealed significant effects of SM interruptions on heart rate, low-frequency/high-frequency (LF/HF) ratio, and skin conductance. ANOVA results showed there were main effects of task cognitive level on LF/HF and skin conductance. These effects during interrupted tasks were more profound. In addition, participants experienced higher MWL and recorded lower primary task performance in the knowledge-based task than the rule- and skill-based tasks. Our findings can guide managers and employees regarding appropriate use of SM in the workplace and better managing interruption and workload.Practitioner Summary: Office workers suffer from increased overall mental workload due to unpredictable interruptions while working. This study shows that participants' mental workload increased when receiving SM interruptions, which was more profound during complex tasks. This highlights the importance of SM interruptions management for employees' health, performance, and mobile application developers.Abbreviations: ANOVA: analysis of variance; DSSQ: dundee stress state questionnaire; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalographic; HPA: hypothalamus-pituitaryadrenocortical; HR: heart rate; HRV: heart rate variability; LF/HF: low frequency/high frequency; MSDs: musculoskeletal disorders; MWL: mental workload; NN: normal to normal; RMS: root means square; RR: time duration between two successive R peaks; RT: response time; SC: skin conductance; SDNN: standard deviation of normal to normal; SM: social media; TCL: task cognitive level; TPC: task performing condition; WMC: working memory capacity.

Workflow interruption and nurses' mental workload in electronic health record tasks: An observational study

BACKGROUND

Workflow interruptions are common in modern work systems. Electronic health record (EHR) tasks are typical tasks involving human-machine interactions in nursing care, but few studies have examined interruptions and nurses' mental workload in the tasks. Therefore, this study aims to investigate how frequent interruptions and multilevel factors affect nurses' mental workload and performance in EHR tasks.

METHODS

A prospective observational study was conducted in a tertiary hospital providing specialist and sub-specialist care from June 1^st to October 31^st, 2021. An observer documented nurses' EHR task interruptions, reactions and performance (errors and near errors) during one-shift observation sessions. Questionnaires were administered at the end of the electronic health record task observation to measure nurses' mental workload for the electronic health record tasks, task difficulty, system usability, professional experience, professional competency, and self-efficacy. Path analysis was used to test a hypothetical model.

RESULTS

In 145 shift observations, 2871 interruptions occurred, and the mean task duration was 84.69 (SD 56.68) minutes per shift. The incidence of error or near error was 158, while 68.35% of errors were self-corrected. The total mean mental workload level was 44.57 (SD 14.08). A path analysis model with adequate fit indices is presented. There was a relationship among concurrent multitasking, task switching and task time. Task time, task difficulty and system usability had direct effects on mental workload. Task performance was influenced by mental workload and professional title. Negative affect mediated the path from task performance to mental workload.

CONCLUSIONS

Nursing interruptions occur frequently in EHR tasks, come from different sources and may lead to elevated mental workload and negative outcomes. By exploring the variables related to mental workload and performance, we offer a new perspective on quality improvement strategies. Reducing harmful interruptions to decrease task time can avoid negative outcomes. Training nurses to cope with interruptions and improve competency in EHR implementation and task operation has the potential to decrease nurses' mental workload and improve task performance. Moreover, improving system usability is beneficial to nurses to mitigate mental workload.

Changes in Perceived Mental Load and Motor Performance during Practice-to-Learn and Practice-to-Maintain in Basketball

BACKGROUND

Attentional resource allocation during sports practice is associated with the players' perceived mental load. However, few ecological studies address this problem by considering the players' characteristics (e.g., practice experience, skill and cognition). Therefore, this study aimed to analyse the dose-response effect of two different types of practice, each with different learning objectives, on mental load and motor performance by using a linear mixed model analysis.

METHOD

Forty-four university students (age 20.36 ± 3.13 years) participated in this study. Two sessions were conducted, one based on a standard rules 1 × 1 basketball situation ("practice to maintain") and one with motor, temporal and spatial restrictions in 1 × 1 tasks ("practice to learn").

RESULTS

"Practice to learn" produced a higher perceived mental load (NASA-TLX scale) and a worse performance than "practice to maintain", but was moderated by experience and inhibition (p = 0.001). The same happens in the most demanding restriction (i.e., temporal, p < 0.0001).

CONCLUSION

The results showed that increasing the difficulty of 1 × 1 situations through restrictions harmed the player's performance and increased their perceived mental load. These effects were moderated by previous basketball experience and the player's inhibition capacity, so the difficulty adjustment should be based on the athletes themselves.

Being active over one's own motion: Considering predictive mechanisms in self-motion perception

Self-motion perception is a key element guiding pilots' behavior. Its importance is mostly revealed when impaired, leading in most cases to spatial disorientation which is still today a major factor of accidents occurrence. Self-motion perception is known as mainly based on visuo-vestibular integration and can be modulated by the physical properties of the environment with which humans interact. For instance, several studies have shown that the respective weight of visual and vestibular information depends on their reliability. More recently, it has been suggested that the internal state of an operator can also modulate multisensory integration. Interestingly, the systems' automation can interfere with this internal state through the loss of the intentional nature of movements (i.e., loss of agency) and the modulation of associated predictive mechanisms. In this context, one of the new challenges is to better understand the relationship between automation and self-motion perception. The present review explains how linking the concepts of agency and self-motion is a first approach to address this issue.

A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972-2020

During the Information Search and Retrieval (ISR) process, user-system interactions such as submitting queries, examining results, and engaging with information, impose some degree of demand on the user’s resources. Within ISR, these demands are well recognised, and numerous studies have demonstrated that the Cost, Effort, and Load (CEL) experienced during the search process are affected by a variety of factors. Despite this recognition, there is no universally accepted definition of the constructs of CEL within the field of ISR. Ultimately this has led to problems with how these constructs have been interpreted and subsequently measured. This systematic review contributes a synthesis of literature, summarising key findings relating to how researchers have been defining and measuring CEL within ISR over the past 50 years. After manually screening 1,109 articles, we detail and analyse 91 articles which examine CEL within ISR. The discussion focuses on comparing the similarities and differences between CEL definitions and measures before identifying the limitations of the current state of the nomenclature. Opportunities for future research are also identified. Going forward, we propose a CEL taxonomy that integrates the relationships between CEL and their related constructs, which will help focus and disambiguate future research in this important area.

Domain- and task-analytic workload (DTAW) method: a methodology for predicting mental workload during severe accidents in nuclear power plants

Excessive mental workload reduces operators' performance and threatens the safety of nuclear power plants (NPPs) in severe accident management (SAM). Given the lack of suitable mental workload measurement methods for SAM tasks, we proposed a Domain- and Task-Analytic Workload (DTAW) method to predict SAM workload. The DTAW method is developed in three stages: scenario construction based on work domain analysis, task analysis, and workload estimation with eight workload components scored through task-analytic and projective methods. To demonstrate its utility, we applied the method to construct two SAM scenarios and predict the mental workload demand of operators in these scenarios as compared to two design basis accident scenarios. With statistical analysis, the DTAW method can predict the overall subjective workload rated by NPP operators, be used to identify high-load tasks, cluster tasks with similar workload patterns, and provide direct implications for improving SAM strategies and supporting systems.Practitioner summary: To predict mental workload in severe accident management (SAM) scenarios in nuclear power plants, we proposed an analytic method and applied it to estimate mental workload in two SAM scenarios and two design basis accident (DBA) scenarios. We found that the workload pattern in SAM scenarios is different from that in DBA scenarios.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

BACKGROUND AND OBJECTIVE

Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs).

METHODS

Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis.

RESULTS

MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively).

CONCLUSIONS

The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

A comparative study of electronic and pen-paper safety inspections: A mixed method study design for assessing ergonomic parameters

BACKGROUND

Workplace inspections are applied to facilitate the adherence to the occupational health and safety regulations. The Iranian Ministry of Health introduced a new software system for tablets to inspect workplaces.

OBJECTIVES

The aim of this study was to take measurements of the usability, mental workload, and mood of inspectors.

METHODS

Inspectors used both pen-and-paper and tablet methods to inspect the automotive industry in a mixed-method procedure. The NASA-TLX score, QUIS score, I-PANAS (SF) situation, inspection time, and number of errors were collected throughout the procedure. The differences were investigated using a paired sample and the Wilcoxon signed ranks test.

RESULTS

In terms of efficacy, using the tablet resulted in lower error rates, but it took longer to complete the inspection task (P < 0.001). Participants perceived a lower workload when inspecting with a tablet rather than the traditional method. (Mental Demand: p < 0.002, Performance: p < 0.009, Effort: p < 0.012, TLX: p < 0.002 based on various subcomponents of NASA-TLX). The newly introduced system's usability was insufficient.

CONCLUSION

Although the use of tablets has improved safety inspections, ergonomic redesign of the system and consideration of a user-centered approach, as well as inspector training, can make the system more likely to succeed.

Cognitive Analyses for Interface Design Using Dual N-Back Tasks for Mental Workload (MWL) Evaluation

In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

Effects of mental workload on manufacturing systems employees: A mediation causal model

BACKGROUND

Although some research has been done in the Mexican manufacturing industry regarding mental workload, none has explored its association with physical fatigue, body weight gain, and human error simultaneously.

OBJECTIVE

This research examines the association between mental workload and physical fatigue, body weight gain, and human error in employees from the Mexican manufacturing systems through a mediation analysis approach.

METHODS

A survey named Mental Workload Questionnaire was developed by merging the NASA-TLX with a questionnaire containing the mental workload variables mentioned above. The Mental Workload Questionnaire was applied to 167 participants in 63 manufacturing companies. In addition, the mental workload was used as an independent variable, while physical fatigue and body weight gain were mediator variables, and human error was a dependent variable. Six hypotheses were used to measure the relationships among variables and tested using the ordinary least squares regression algorithm.

RESULTS

Findings indicated that mental workload significantly correlates with physical fatigue and human error. Also, the mental workload had a significant total association with human error. The highest direct association with body weight gain was provided by physical fatigue, and body weight gain had an insignificant direct association with human error. Finally, all indirect associations were insignificant.

CONCLUSION

Mental workload directly affects human error, which physical fatigue does not; however, it does affect body weight gain. Managers should reduce their employees' mental workload and physical fatigue to avoid further problems associated with their health.

The effect of information level of digital worker guidance systems on assembly performance, user experience and strain

Worker guidance systems provide product-specific and digital assembly information and can make an important contribution to increasing productivity and quality and relieving employees, especially in flexible variant assembly. However, a critical factor here is the successful design of the systems. While much of the research focuses on aspects of the hardware, this article is dedicated to the question of how the configuration of the level of information, i.e. the amount and depth of information in worker guidance systems, is affected. For this purpose, two studies, a laboratory study at the Technical University of Darmstadt (N = 53) and a field study at a company (N = 30), were conducted and two variants of the information level of a worker guidance system were compared. The study results show that assembly quality in particular is significantly influenced by the level of information; to the advantage of detailed information. No significant differences were found for production times. For the subjective evaluation of the system as well as for the objective and subjectively perceived strain, it can be stated that the high level of information also performs better. The results thus help to design worker guidance systems more precisely so that they fulfill the information needs of the employees as well as possible.

Mental workload assessments of aerial photography missions performed by novice unmanned aerial vehicle operators

BACKGROUND

Mental workload is one of the important variables in understanding human performance in drone operation.

OBJECTIVE

To test the effects of gender, age group, flight route, and altitude on the flight performance and mental workload of the novice drone operators.

METHODS

Ten male and 10 female participants without prior drone operating experience joined. They were split into two age groups. After attending a training, the participants operated a drone to perform photo taking missions under flight route and altitude conditions. The weighted NASA task-load-index (TLX), modified Cooper-Harper (MCH) scale, heart rate, and interbeat interval were measured to assess the mental workload of the participants. Flight time to complete the mission was adopted to indicate flight performance.

RESULTS

The effect of age group was significant (p <  0.05) on flight time, weighted TLX score, and MCH score. Flight route and altitude were not significant on the two subjective ratings and two cardiac measures.

CONCLUSION

The flight performance of younger participants was significantly better than that of their older counterpart. The effects of both the flight route and altitude on the perceived mental workload of the drone operators were insignificant. Both the weighted NASA TLX and MCH scales were appropriate in measuring the mental workload of the novice drone operators.

Cortical theta-gamma coupling tracks the mental workload as an indicator of mental schema development during simulated quadrotor UAV operation

Objective. In the emerging field of neuroergonomics, mental workload assessment is one of the most important problems. Previous studies have made some progress on the relationship between task difficulties and mental workload, but how the mental schema, a reflection of the understanding and mastery degree of a task, affects mental workload has not been clearly discussed.Approach. There is emerging appreciation for the role of theta-gamma coupling (TGC) in high-level cognitive functions. Here, we attempt to further our understanding of how mental schema development and task difficulty had an impact on mental workload from the perspective of TGC. Specifically, the variation of TGC coupling strength and coupling pattern was estimated with different test orders and task difficulties performed by 51 students in a ten-day simulated quadrotor unmanned aerial vehicle flight training and test tasks.Main results. During the training, TGC increased with mental schema development. For the test tasks, TGC did not change with increasing task difficulty before the operator formed a mental schema but decreased with the increasing mental workload after the formation of the mental schema.Significance. Our results suggest that TGC was a robust indicator of mental schema development and could be biased by task difficulty. In conclusion, TGC can be a promising measure of mental workload, but only for experienced operators.

Exploring the Peak-End Effects in Air Traffic Controllers' Mental Workload Ratings

OBJECTIVE

This study examined whether professional air traffic controllers (ATCos) were subject to peak-end effects in reporting their mental workload after performing an air traffic control task, and in predicting their mental workload in future scenarios.

BACKGROUND

In affective experience studies, people's evaluation of a period of experience is strongly influenced by the most intense (peak) point and the endpoint. However, whether the effects exist in mental workload evaluations made by professional operators is still not known.

METHOD

In Study 1, 20 ATCos performed air traffic control scenarios on high-fidelity radar simulators and reported their mental workload. We used a 2 (high peak, low peak) × 2 (high end, low end) within-subject design. In Study 2, another group of 43 ATCos completed a survey asking them to predict their mental workload given the same air traffic control scenarios.

RESULTS

In Study 1, ATCos reported higher mental workload after completing the high-peak and the high-end scenarios. In contrast, in Study 2, ATCos predicted the peak workload effect but not the end workload effect when asked to predict their experience in dealing with the same scenarios.

CONCLUSION

Peak and end effects exist in subjective mental workload evaluation, but experts only had meta-cognitive awareness of the peak effect, and not the end effect.

APPLICATION

Researchers and practitioners that use subjective workload estimates for work design decisions need to be aware of the potential impact of peak and end task demand effects on subjective mental workload ratings provided by expert operators.

Assessing mental workload with wearable devices - Reliability and applicability of heart rate and motion measurements

Wearable devices are increasingly used for assessing physiological data. Industry 4.0 aims to achieve the real-time assessment of the workers' condition to adapt processes including the current mental workload. Mental workload can be assessed via physiological data. This paper researches the potential of wearable devices for mental workload assessment by utilizing heart rate and motion data collected with a smartwatch. A laboratory study was conducted with four levels of mental workload, ranging from none to high and during sitting and stepping activities. When sitting, a difference in the heart rate and motion data from the smartwatch was only found between no mental workload and any mental workload task. For the stepping condition, differences were found for the movement data. Based on these results, wearable devices could be useful in the future for detecting whether a mental demanding task is currently performed during low levels of physical activity.

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing a continuous brain rate, an index of cognitive activation, and does not require human declarative knowledge. The aim is to induce models automatically from data, supporting replicability, generalisability and applicability across fields and contexts. This specific method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data, aimed at fitting a novel brain rate variable. Findings demonstrate the capacity of the convolutional layers to learn meaningful high-level representations from EEG data since within-subject models had, on average, a test Mean Absolute Percentage Error of around 11%. The addition of a Long-Short Term Memory layer for handling sequences of high-level representations was not significant, although it did improve their accuracy. These findings point to the existence of quasi-stable blocks of automatically learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses. Additionally, across-subject models, induced with data from an increasing number of participants, thus trained with data containing more variability, obtained a similar accuracy to the within-subject models. This highlights the potential generalisability of the induced high-level representations across people, suggesting the existence of subject-independent cognitive activation patterns. This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable and does not rely on ad hoc human crafted models.