Relations between mental workload and decision-making in an organizational setting

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Repeated assessments and predictors of nurses' shift-specific perceived workload

BACKGROUND

Workload perception is of interest to researchers and policymakers as it captures subjective assessments of nurses' workload which has implications for staffing and patient outcomes.

AIMS

We aimed to describe repeated assessments of nurses' perceived workload among registered nurses (RNs) in day and night shifts and to examine the association of perceived workload with workdays, units, and nurse-staffing.

METHODS

Repeated data on the indictors of interest were collected from 90 RNs across 91 shifts in a Lebanese acute-care hospital. Perceived workload was assessed using the NASA-Task-Load Index (NASA-TLX). Linear mixed-effect models were used for analysis.

RESULTS

Mean perceived workload was high reaching 6.63 (95% confidence interval [CI] = 6.34, 6.92) in day and 5.90 (95% CI = 5.43, 6.36) in night shifts. In mixed-effect models, perceived workload was lower on weekends/holidays as compared to weekdays in day (ß = -.32; 95% CI = -0.53, -0.12) and night (ß = -.46; 95% CI = -0.85, -0.07) shifts. Higher perceived workload (ß = .19; 95% CI = 0.04, 0.33) was associated with higher patient-to-nurse ratio in the day but not night shifts.

CONCLUSION

Repeated workload assessments support the presence of elevated perceived workload among RNs which is related to weekdays and higher patient-to-nurse ratio. Future investigations would benefit from better characterization of workload particularities to address perceived burden and improve organizational and management decisions.

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

Unlocking the Subconscious Consumer Bias: A Survey on the Past, Present, and Future of Hybrid EEG Schemes in Neuromarketing

Fueled by early success stories, the neuromarketing domain advanced rapidly during the last 10 years. As exciting new techniques were being adapted from medical research to the commercial domain, many neuroscientists and marketing practitioners have taken the chance to exploit them so as to uncover the answers of the most important marketing questions. Among the available neuroimaging technologies, electroencephalography (EEG) stands out as the less invasive and most affordable method. While not equally precise as other neuroimaging technologies in terms of spatial resolution, it can capture brain activity almost at the speed of cognition. Hence, EEG constitutes a favorable candidate for recording and subsequently decoding the consumers' brain activity. However, despite its wide use in neuromarketing, it cannot provide the complete picture alone. In order to overcome the limitations imposed by a single monitoring method, researchers focus on more holistic approaches. The exploitation of hybrid EEG schemes (e.g., combining EEG with eye-tracking, electrodermal activity, heart rate, and/or other) is ever growing and will hopefully allow neuromarketing to uncover consumers' behavior. Our survey revolves around last-decade hybrid neuromarketing schemes that involve EEG as the dominant modality. Beyond covering the relevant literature and state-of-the-art findings, we also provide future directions on the field, present the limitations that accompany each of the commonly employed monitoring methods and briefly discuss the omni-present ethical scepticizm related to neuromarketing.

Work environment-related factors and nurses' health outcomes: a cross-sectional study in Lebanese hospitals

Background

Worldwide, studies show a relationship between nurses’ health and some work environment factors; however, data on nurses’ health and self-perceived workload and nursing task allocation are lacking, particularly for Lebanese nurses. We assessed the relationship of several work environment factors: overall workload and specific temporal, physical, mental, effort, frustration, and performance demands (NASA Task Load Index), staffing resources and adequacy and leadership (Practice Environment Scale of Nursing Work Index), teamwork climate (Safety Attitudes Questionnaire), and nursing task allocation (Basel Extent of Rationing of Nursing Care)) with self-reported musculoskeletal, cardiovascular, skin, and mental health diseases (Work Ability Index) and emotional exhaustion (Maslach Burnout Inventory) among Lebanese nurses.

Methods

A cross-sectional self-report survey was distributed to all 289 registered nurses (RNs) in the medical, surgical, and pediatric units in two Lebanese university-affiliated hospitals; 170 RNs had complete data. Adjusted multivariable logistic regression models were used to estimate the association between work environment factors and health outcomes.

Results

The most prevalent outcomes were musculoskeletal disease (69%), emotional exhaustion (59%), and mental health problems (56%); 70% of RNs had ≥2 and 35.29% had ≥4 co-occurring health problems. Musculoskeletal disease was associated with higher overall (OR = 1.36 (95%CI = 1.03, 1.80)), temporal (OR = 1.30 (95%CI = 1.09, 1.55)), and physical demands (OR = 1.20 (95%CI = 1.03, 1.49)), higher task allocation to RNs (OR = 1.11 (95%CI = 1.01, 1.23)) and lower teamwork climate (OR = 0.60 (95%CI = 0.36, 0.98). Higher odds of mental/emotional problems were associated with higher overall, temporal, frustration, and effort demands, and lower teamwork climate, performance satisfaction, and resources adequacy (increased odds ranging from 18 to 88%). Work environment indicators were associated with higher co-occurrence of health problems.

Conclusions

Results show elevated health burden and co-morbidity among Lebanese RNs and highlight the value of comprehensive approaches that can simultaneously improve several work environment factors (namely self-perceived workload, teamwork,, resources, and nursing task allocation) to reduce this burden.