Changes In Workload Research Articles

Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists’ outcomes

ObjectivesArtificial intelligence (AI) is expected to alleviate the negative consequences of rising case numbers for radiologists. Currently, systematic evaluations of the impact of AI solutions in real-world radiological practice are missing. Our study addresses this gap by investigating the impact of the clinical implementation of an AI-based computer-aided detection system (CAD) for prostate MRI reading on clinicians' workflow, workflow throughput times, workload, and stress. Materials and methodsCAD was newly implemented into radiology workflow and accompanied by a prospective pre-post study design. We assessed prostate MRI case readings using standardized work observations and questionnaires. The observation period was three months each in a single department. Workflow throughput times, PI-RADS score, CAD usage and radiologists’ self-reported workload and stress were recorded. Linear mixed models were employed for effect identification. ResultsIn data analyses, 91 observed case readings (pre: 50, post: 41) were included. Variation of routine workflow was observed following CAD implementation. A non-significant increase in overall workflow throughput time was associated with CAD implementation (mean 16.99 ± 6.21 vs 18.77 ± 9.69 min, p = .51), along with an increase in diagnostic reading time for high suspicion cases (mean 15.73 ± 4.99 vs 23.07 ± 8.75 min, p = .02). Changes in radiologists' self-reported workload or stress were not found. ConclusionImplementation of an AI-based detection aid was associated with lower standardization and no effects over time on radiologists’ workload or stress. Expectations of AI decreasing the workload of radiologists were not confirmed by our real-world study. Pre-RegistrationGerman register for clinical trials https://drks.de/; DRKS00027391.

Overview on Efficient Resource Provisioning Approach Using Cloud Computing Environment

Abstract: The goal of this study is to examine the importance of effective resource management across different fields and the critical role that cloud computing apps have in accomplishing this effectiveness. To show how these various approaches and strategies can be used in real-world scenarios, the article looks at case studies and application usage. Optimizing resource utilization and performance in cloud computing requires effective scheduling, resource allocation, and other algorithms Using Cloud Simulation (cloud sim) Tool and other cloud environment tools. By adjusting to changing workloads, these algorithms improve fault tolerance, cost effectiveness, and load balancing. Furthermore, maintaining cloud service integrity depends heavily on algorithms for data processing, network optimization, and fault tolerance. Sufficient research in this area is essential to fully utilize cloud computing.

Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network

With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.

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.

The workload change and depression among emergency medical staff after the open policy during COVID-19: a cross-sectional survey in Shandong, China.

In the middle of December 2022, the Chinese government adjusted the lockdown policy on coronavirus disease 2019 (COVID-19), a large number of infected patients flooded into the emergency department. The emergency medical staff encountered significant working and mental stress while fighting the COVID-19 pandemic. We aimed to investigate the workload change, and the prevalence and associated factors for depression symptoms among emergency medical staff after the policy adjustment. We conducted a cross-sectional online survey of emergency medical staff who fought against COVID-19 in Shandong Province during January 16 to 31, 2023. The respondents' sociodemographic and work information were collected, and they were asked to complete the 9-item Patient Health Questionnaire (PHQ-9) then. Univariate and multivariate logistic regression analyses were applied to identify the potential associated factors for major depression. Nine hundred and sixteen emergency medical personnel from 108 hospitals responded to this survey. The respondents' weekly working hours (53.65 ± 17.36 vs 49.68 ± 14.84) and monthly night shifts (7.25 ± 3.85 vs 6.80 ± 3.77) increased after the open policy. About 54.3% of the respondents scored more than 10 points on the PHQ-9 standardized test, which is associated with depressive symptoms. In univariate analysis, being doctors, living with family members aged ≤16 or ≥ 65 years old, COVID-19 infection and increased weekly working hours after the open policy were significantly associated with a PHQ-9 score ≥ 10 points. In the multivariate analysis, only increased weekly working hours showed significant association with scoring ≥10 points. Emergency medical staff' workload had increased after the open policy announcement, which was strongly associated with a higher PHQ-9 scores, indicating a very high risk for major depression. Emergency medical staff working as doctors or with an intermediate title from grade-A tertiary hospitals had higher PHQ-9 scores, while COVID-19 infection and weekly working hours of 60 or more after the open policy were associated with higher PHQ-9 scores for those from grade-B tertiary hospitals. Hospital administrators should reinforce the importance of targeted emergency medical staff support during future outbreaks.

Quantifying the microstructural and biomechanical changes in the porcine ventricles during growth and remodelling.

Cardiac tissue growth and remodelling (G & R) occur in response to the changing physiological demands of the heart after birth. The early shift to pulmonary circulation produces an immediate increase in ventricular workload, causing microstructural and biomechanical changes that serve to maintain overall physiological homoeostasis. Such cardiac G & R continues throughout life. Quantifying the tissue's mechanical and microstructural changes because of G & R is of increasing interest, dovetailing with the emerging fields of personalised and precision solutions. This study aimed to determine equibiaxial, and non-equibiaxial extension, stress-relaxation, and the underlying microstructure of the passive porcine ventricles tissue at four time points spanning from neonatal to adulthood. The three-dimensional microstructure was investigated via two-photon excited fluorescence and second-harmonic generation microscopy on optically cleared tissues, describing the 3D orientation, rotation and dispersion of the cardiomyocytes and collagen fibrils. The results revealed that during biomechanical testing, myocardial ventricular tissue possessed non-linear, anisotropic, and viscoelastic behaviour. An increase in stiffness and viscoelasticity was noted for the left and right ventricular free walls from neonatal to adulthood. Microstructural analyses revealed concomitant increases in cardiomyocyte rotation and dispersion. This study provides baseline data, describing the biomechanical and microstructural changes in the left and right ventricular myocardial tissue during G & R, which should prove valuable to researchers in developing age-specific, constitutive models for more accurate computational simulations. STATEMENT OF SIGNIFICANCE: There is a dearth of experimental data describing the growth and remodelling of left and right ventricular tissue. The published literature is fragmented, with data reported via different experimental techniques using tissues harvested from a variety of animals, with different gender and ages. This prevents developing a continuum of data spanning birth to death, so limiting the potential that can be leveraged to aid computational modelling and simulations. In this study, equibiaxial, non-equibiaxial, and stress-relaxation data are presented, describing directional-dependent material responses. The biomechanical data is consolidated with equivalent microstructural data, an important element for the development of future material models. Combined, these data describe microstructural and biomechanical changes in the ventricles, spanning G &R from neonatal to adulthood.

Perceived research productivity of women in higher education: An investigation of the impact of COVID-19.

This study focuses on the predictors of women academics' perceived research productivity during the pandemic in Türkiye, by taking the changes in paid and unpaid workload alongside the felt pressure concerning productivity into consideration. Predicting the odds to report an above the mean level of decrease in perceived research productivity, unlike expected, increased housework time and administrative workload presented no statistically significant effect. On the other hand, extended care responsibilities (including but not limited to childcare) and felt pressure concerning research performance during the pandemic strongly predicted a high level of reported decrease in research productivity. Findings highlight that institutional care support mechanisms should be among the primary concerns since the pandemic has made the already existing gender inequalities in academia more visible in terms of the challenges women face in balancing paid and unpaid work. In addition, as excess pressure felt by women academics regarding research performance is linked to a decline in reported productivity, creating a compassionate environment in academia not only in unprecedented circumstances but at all times needs to be priority.

The Impact of Decreased Physical Activity Against Increased Risk Factors for Cardiovascular Disease in People Aged 40-50 Years During The PPKM Period in Kampung Melayu, East Jakarta

The implementation of Community Restrictions (PPKM) is an effort by the Indonesian government to prevent the chain of transmission of Covid-19 that occurred in 2021. PPKM makes changes in lifestyle and physical activity. The benefit of physical activity for health is to prevent an increase in risk factors for chronic diseases such as cardiovascular disease. This study used risk factors for cardiovascular disease age, sex, lifestyle, diet, BMI, current blood glucose, total blood cholesterol, and blood pressure. Knowing the impact of decreased physical activity against increased risk factors for cardiovascular disease which can be controlled and which cannot be controlled at the productive age of 40-50 years. This study used the observation method on 50 participants from RW 01 Kampung Melayu, East Jakarta who were randomly selected. The study participants measured blood glucose levels, total cholesterol, blood pressure, BMI, and physical activity measurements during the PPKM period. Testing the correlation of the impact of physical activity with increased risk factors was conducted using the Wilcoxon signed rank test. Decreased physical activity occurred predominantly in all categories of participants caused by changes in lifestyle, sleep patterns, diet, and uncontrolled workload, causing increases in blood glucose levels, total cholesterol, BMI, and blood pressure. Statistical tests prove that there is a significant correlation between decreased physical activity and increased risk factors for cardiovascular disease. This study proves that there is an increase in cardiovascular disease risk factors associated with decreased physical activity and lifestyle changes in residents of RW 01 Kampung Melayu, East Jakarta.

Developing cognitive workload and performance evaluation models using functional brain network analysis

Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator. The functional brain network and Power Spectral Density features were extracted from EEG data using coherence and spectral analyses, respectively. Participants reported their perceived cognitive workload using the SURG-TLX questionnaire after each exercise, and the simulator generated actual performance scores. The extracted features, actual performance scores, and subjectively assessed cognitive workload values were used to develop linear models for evaluating performance and cognitive workload. Furthermore, the Pearson correlation was used to find the correlation between participants’ age, performance, and cognitive workload. The findings demonstrated that combined EEG features retrieved from spectral analysis and functional brain networks can be used to evaluate cognitive workload and performance. The cognitive workload in conducting only Matchboard level 3, which is more challenging than Matchboard level 2, was correlated with age (0.54, p-value = 0.01). This finding may suggest playing more challenging computer games are more helpful in identifying changes in cognitive workload caused by aging. The findings could open the door for a new era of objective evaluation and monitoring of cognitive workload and performance.

OptiDJS+: A Next-Generation Enhanced Dynamic Johnson Sequencing Algorithm for Efficient Resource Scheduling in Distributed Overloading within Cloud Computing Environment

The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments.

An exploration of preferences for soil health practices in potato production

Given the renewed interest in promoting healthy soils in agricultural production via practices such as cover crops, decreased fumigations, or biological fertilizers, this article explores U.S. potato farmers' preferences for soil health-related production practices. We conduct a discrete choice experiment (DCE) under which hypothetical practices varied based on their effect on pests, fertilizer requirements, irrigation needs, additional soil testing/education, and expected net returns. Surveys were deployed to potato growers using convenience sampling online—through practitioner journals—and in person—at industry meetings. Standard descriptive statistics of the sample are presented and the DCE experiment was analyzed using a mixed multinomial logit model. Results suggest that when considering a new practice, farmers are highly sensitive to changes in net returns (p ​< ​0.001), and somewhat responsive to changes in fertilizer requirements, irrigation, and pests (p ​< ​0.01). However, potato growers appear indifferent to changes in workload, education, or soil testing requirements. While potato growers’ responses to the survey suggest they are highly concerned with soil health, conventional practices (e.g., 2–3 years rotations, standard tillage, fumigation) are still common. Additionally, we find that complexity in implementation, uncertainty in economic benefits, and capital constraints are the most common barriers to adopting novel soil health practices.

Assessment of learning in simulator-based arthroscopy training with the diagnostic arthroscopy skill score (DASS) and neurophysiological measures.

Virtual arthroscopic training has become increasingly popular. However, there is a lack of efficiency-based tracking of the trainee, which may be critical for determining the specifics of training programs and adapting them for the needs of each trainee. This study aims to evaluate and compare the measures obtained with a non-invasive neurophysiological method with The Diagnostic Arthroscopy Skill Score (DASS), a commonly used assessment tool for evaluating arthroscopic skills. The study collected simulator performance scores, consisting of "Triangulation Right Hand", "Triangulation Left Hand", "Catch the Stars" and "Three Rings" and DASS scores from 22 participants (11 novices, 11 experts). These scores were obtained while participants underwent a structured program of exercises for the fundamentals of arthroscopic surgery training (FAST) and knee module using a simulator-based arthroscopy device. During the evaluation, data on oxy-hemoglobin and deoxy-hemoglobin levels in the prefrontal cortex were collected using the Functional Near-Infrared Spectroscopy (fNIRS) imaging system. Performance scores, DASS scores, and fNIRS data were subsequently analyzed to determine any correlation between performance and cortex activity. The simulator performance scores and the DASSPart2 scores were significantly higher in the expert group compared to the novice group (200.1 ± 28.5 vs 172.5 ± 48.9, p = 0.04 and 9.4 ± 5.6 vs. 5.4 ± 5.6 p = 0.02). In the expert group, fNIRS data showed a significantly lower prefrontal cortex activation during fundamental tasks in the FAST module, indicating significantly more efficient mental resource use. The analysis of cognitive workload changes during simulation-based arthroscopy training revealed a significant correlation between the trainees' DASS scores and fNIRS data. This correlation suggests the potential use of fNIRS data and DASS scores as additional metrics to create adaptive training protocols for each participant. By incorporating these metrics, the training process can be optimized, leading to more efficient arthroscopic training and better preparedness for clinical operations. III.

Towards constrained optimization of cloud applications: A hybrid approach

A database system is one of the most common cloud applications where elasticity allows to dynamically allocate resources in response to changing workload demands. In such systems, users usually configure resources based on empirical decisions, for example by boosting computational resources with the expectation of improving database throughput. The latter also suggests that users manually scale resources and tune the database configurations offline to meet the application demands. However, this approach could rapidly increase infrastructure expenditures while this is also time consuming. In this paper, we propose SONA, a framework to support constrained performance optimization of cloud applications using a hybrid approach of artificial neural networks and genetic algorithms. The proposed framework monitors the source system to identify the optimal configurations that maximize application performance based on genuine workload executions. As a result, the experimental analysis presents a novel dataset collected from TPC-C runs on MySQL server. The optimization process is being constrained to satisfy user and application requirements including the cloud infrastructure expenditures, the cost-performance ratio, the baseline performance and the average percentage of idle CPU resources. Furthermore, SONA uses a cloned containerized environment that replicates the main application to avoid system overhead during the optimization process. Our results demonstrate the effectiveness of SONA framework to optimize the performance of OLTP applications deployed on cloud.

Explainable real-time predictive analytics on employee workload in digital railway control rooms

Both workload peaks and lows contribute to lower employee well-being. Predictive employee workload analytics can empower management to undertake proactive prevention. For this purpose, we develop a real-time machine learning framework to predict and explain future workload in a challenging environment with variable and imbalanced workload: the digital control rooms for railway traffic management of Infrabel, Belgium’s railway infrastructure company. The proposed two-stage methodology leverages granular data of workload categories that are very different in nature and separates the effects of workload presence and magnitude. In this way, the set-up addresses the changing workload mix over 15-minute intervals. We extensively benchmark machine learning and deep learning models within this context, leading to LightGBM (Light Gradient Boosting Machine) as the best-performing model. SHAP (SHapley Additive exPlanations) values highlight the benefits of disentangling presence and magnitude and reveal associations with human-machine interaction and team exposure. As a proof of concept, our implemented predictive model offers tailored decision support to the traffic supervisor in an explainable way. In particular, the tool depicts overloaded and/or underloaded workstations and provides in-depth insights through local SHAP values.

Dynamic Hybrid Fragmentation Method for Multimedia Databases

Multimedia databases store high-volume data, which causes problems in efficient information retrieval, and increases execution costs and response times of the queries. To solve this problem, data fragmentation techniques exist to improve query performance, increase information availability, and efficiently execute more operations accessing less irrelevant data. This article presents a comprehensive review of 34 methods related to hybrid fragmentation and subsequently proposes the design of a hybrid fragmentation method that adapts the scheme according to workload changes to maintain efficient retrieval of multimedia data. The proposed technologies are Java as a programming language, Java Server Faces (JSF) as a framework, MySQL and MongoDB database management systems, and NetBeans as an Integrated Development Environment (IDE), following the UWE methodology (Unified Modeling Language-based Web Engineering).

Workload-Aware Performance Tuning for Multimodel Databases Based on Deep Reinforcement Learning

Currently, multimodel databases are widely used in modern applications, but the default configuration often fails to achieve the best performance. How to efficiently manage and tune the performance of multimodel databases is still a problem. Therefore, in this study, we present a configuration parameter tuning tool MMDTune+ for ArangoDB. First, the selection of configuration parameters is based on the random forest algorithm for feature selection. Second, a workload-aware mechanism is based on k-means++ and the Pearson correlation coefficient to detect workload changes and match the empirical knowledge of historically similar workloads. Finally, the ArangoDB configuration parameters are optimized based on the improved TD3 algorithm. The experimental results show that MMDTune+ can recommend higher-quality configuration parameters for ArangoDB compared to OtterTune and CDBTune in different scenarios.

Gaze Behavior and Mental Workload While Using a Whole-Body Powered Exoskeleton: A Pilot Study

The mental demands associated with operating complex whole-body powered exoskeletons are poorly understood. This study aimed to explore the overall workload associated with using a powered wholebody exoskeleton among expert and novice users, as well as the changes in workload resulting from novices adapting to exoskeleton-use over time. We used eye-tracking measures to quantify the differences in workload of six novices and five experts while they performed a levelwalking task, with and without wearing a whole-body powered exoskeleton. We found that only novices’ pupil dilation (PD) increased, while experts showed a greater proportion of downward-directed pathfixations (PF) compared to novices while wearing the exoskeleton. These results indicate that novices’ mental demands were higher, and that experts and novices exhibited distinct visuomotor strategies. Eyetracking measures may potentially be used to detect differences in workload and skill-level associated with using exoskeletons, and also considered as inputs for future adaptive exoskeleton control algorithms.

Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

Cross-recurrence quantification analysis (CRQA) metrics may offer a means to provide information about the quality of collaboration in real-time. The goal of the present work is to use Area of Interest (AOI) based CRQA metrics to analyze the eye-tracking data of 10 pairs who participated in a shared unmanned aerial vehicle (UAV) command and control task. We are interested in how teams respond to workload transitions and how it affects AOI-based CRQA metrics. The results showed that as workload increased, team members spent a longer time on the same task which may indicate that they are coordinating together on a task, or they are not adapting and getting “trapped” in certain tasks. The findings suggest that CRQA AOI-based metrics are sensitive to workload changes and validate these metrics in unraveling the visual puzzle of how workload impacts scanpath patterns which contribute to quantifying the adaptation process of pairs over time. This also has the potential to inform the design of real-time technology in the future.

Cognitive Workload of Novice Forklift Truck Drivers in VR-based Training

There is increasing use of Virtual Reality (VR) to train forklift truck operators but a lack of sufficient understanding of how cognitive workload changes with respect to different task demands in VR-based training. In this study, 19 novice participants completed three forklift driving lessons with varying difficulty levels (low, medium, and high) using a VR simulator. To examine the effect of repeated training on cognitive workload, two sessions were repeated by participants using the same procedures. Cognitive workload was assessed with objective (electroencephalogram [EEG] activity) and subjective (NASA-TLX) measurements. EEG theta power and NASA-TLX (mental workload) scores were significantly higher for high than low difficulty levels. However, both EEG and NASA-TLX responses were reduced with repeated training in the second session. These findings highlight the effectiveness of EEG in continuous monitoring of workload variation caused by task difficulty and implementing training programs to moderate cognitive workload for forklift operators.

Impact of Covid-19 pandemic on the workload and home office in some European countries

The spread of a new and unknown viral infectious disease named the Covid-19 pandemic paralysed world without any preparation time. It has significantly changed approaches to organizational management as well as HR policy. Governments have taken immediate measures to address the challenges caused by the COVID-19 pandemic, including protecting jobs, providing financial support to businesses and households experiencing a sharp drop in income. The present study focuses on a subfield of a previous larger research, on the change in workload due to the COVID-19 pandemic and the increase in the frequency of remote work, as well as their consequences. The increase in telecommuting is one of the innovations that can clearly be attributed to the impact of the pandemic. It has become an accepted form of work in many sectors where even the professionals did not think of using it before. The research has been conducted based on the questionnaire consisted of groups of questions related to the demographic characteristics (age, gender, place of residence, position), development of the workload, and the more complicated working conditions caused by Covid19 pandemic. The respondents from Hungary, Montenegro, Spain and the United Kingdom were selected using a respondent-driven time space sampling procedure. The method of sampling and the size of the target group can be considered as limitations of our research – the statistical conditions for evaluation are nevertheless met, given the for the number of elements and normality of the sample as a whole and its individual groups. As results, authors found that the same epidemic in each country led to different workload increases and affected subordinates and non-subordinates, and women and men to different degrees. Among the effects of the crises caused by the pandemic, the increase in workload is the most significant. That must be taken into consideration in the HRM policy. The most important finding is that the increasing frequency of remote work to a certain extend can be considered as innovation. The nature of the research conducted on basis of four European countries can contribute to the future development of scientific study related the solution of similar problems in HR management and labour organization.