Workload Index Research Articles

Nursing Activities Score at Discharge from the Intensive Care Unit Is Associated with Unplanned Readmission to the Intensive Care Unit.

This study evaluated the accuracy of predicting unplanned the intensive care unit (ICU) readmission using the Nursing Activities Score (NAS) at ICU discharge based on nursing workloads, and compared it to the accuracy of the prediction made using the Stability and Workload Index for Transfer (SWIFT) score. Patients admitted to the ICU of Sapporo Medical University Hospital between April 2014 and December 2017 were included, and unplanned ICU readmissions were retrospectively evaluated using the SWIFT score and the NAS. Patient characteristics, such as age, sex, the Charlson Comorbidity Index, and sequential organ failure assessment score at ICU admission, were used as covariates, and logistic regression analysis was performed to calculate the odds ratios for the SWIFT score and NAS. Among 599 patients, 58 (9.7%) were unexpectedly readmitted to the ICU. The area under the receiver operating characteristic curve of NAS (0.78) was higher than that of the SWIFT score (0.68), and cutoff values were 21 for the SWIFT and 53 for the NAS. Multivariate analysis showed that the NAS was an independent predictor of unplanned ICU readmission. The NAS was superior to the SWIFT in predicting unplanned ICU readmission. NAS may be an adjunctive tool to predict unplanned ICU readmission.

Comparing Presence, Workload, and Performance in Desktop and Virtual Reality Flight Simulations

Virtual reality (VR) offers a training environment that promotes increased learning and performance. However, to what extent VR flight simulations offer increased performance compared to less-immersive simulators is not clear, and neither are their underlying cognitive aspects. In a within-subject experiment, we compared fight performance and subjective measures of workload, presence, and engagement in two flight training environments (Desktop and VR) on two flight tasks of equal difficulty (changing speed and performing turns). No differences were observed in the flight performance between these conditions. However, participants reported higher presence and engagement in the VR training environment. Additionally, we found a correlation between subjective workload and flight performance in the Desktop condition, but not in VR. We conclude that VR is promising for basic flight training tasks and encourage future work to explore this further by investigating neurophysiological indices of workload and engagement and establishing relationships between presence, workload, and flight performance.

Mask-wearing: effects on cognitive task performance and cardiac workload indices

Consistent with CDC recommendations and government mandates during COVID, masks are commonly required/suggested in public gathering places like workplaces and schools. Considering the implications for human factors research, the extent to which mask-wearing impacts cognitive abilities and/or workload assessment from cardiac indicators are unknown. This study investigated these effects by engaging subjects in cognitively-loading tasks (math or memory) while not wearing masks, and additionally by wearing one of three types of masks (surgical, N95, and cloth). Results suggest that mask-wearing does not have significant impact on task performance data (accuracy, total question answered) or cardiac indices (heart rate). This study can foster a deeper understanding of the influence of mask-wearing on actual and perceived cognitive workload, and how mask-wearing may mitigate common workload assessment methods. These findings are valuable for informing human subjects research during COVID, and in identifying implications of mask-wearing on domains that impose cognitive work demands.

Constructing a Model to Discriminate the Workload Level of Ship Interface Operators

Workload level has a significant impact on the human errors of ship operators. Therefore, the accurate discrimination of the operator’s workload level has an important effect on preventing human errors. Firstly, this study analyzed the differences in performance indicators, subjective workload indicators, and eye movement indicators under different workloads. Secondly, according to the correlation analysis result, the NASA Task Load Index (NASA-TLX) score has a correlation with error rate, operation time, NASA-TLX score, pupil dilation, blink rate, saccadic rate, and fixation rate. Thirdly, this study used a typical discriminant analysis method to construct a discrimination model based on these indicators. The validation result indicated that the accurate discrimination rate of the model is 100% for the low and general workload and that is 90.9% for the high workload. It indicated that the constructed model can effectively distinguish the workload level.

Nursing Personnel Needs Analysis Using the Workload Indicator Staff Need (WISN) Method in Installation Emergency Datu Beru Hospital Central Aceh

WISN is a resource management tool that analyzes staffing needs based on the workload for a certain staff category, as well as the facility and kind of health institution. This tool can be applied nationally, regionally, and to health facilities. It is simple to use on a technical, comprehensive, and practical level, and it facilitates the determination of variations in human resource requirements among various types of health services, such as health centers and hospitals. This study's objective is to examine the requirement for workload-based nursing staff utilizing the Workload Indicator Staff Need (WISN) approach at the Emergency Installation (IGD) Datu Beru Hospital, Central Aceh in 2021. A descriptive study using observational methods served as the research design. This study's population consisted of all 35 nurses at the Emergency Installation of RSUD Beru Takngon. This population was sampled using a total sample size of 35. 35 individuals. WISN analysis was used to analyze the data. After the researcher got data from completing the questionnaire based on observation, they recorded and made revisions to the documentation of the nurses' work in the emergency room. Research findings Using the WISN approach, it is determined that the Datu Beru Takengon Hospital Emergency Room has a nursing staff shortfall of three individuals. Working time, nursing staff activities, workload standards, the slack factor, and the nursing staff needs calculation have an effect on the nursing staff needs at the Emergency Installation of RSUD Datu Beru Takengon.

A Study of Driver Eye-Glancing Behavior at Two Different Types of Unsignalized Intersections Based on SHRP2 NDS Data

The study aimed to analyze driver visual workload at unsignalized intersections with wide medians on high-speed divided highways. The study focused on two types of traffic movements: direct left-turns from minor roads at conventional intersections and right-turns followed by U-turn movements at restricted crossing U-turn (RCUT) intersections. A total of 430 left-turn trips and 40 right-turn followed by U-turn trips were collected from the Second Strategic Highway Research Program Naturalistic Driving Study database. Researchers analyzed the data from driver eye-glancing videos, demographic information, and driving history. The entropy rate of each trip was calculated as an indicator of the driver's visual workload and was treated as the dependent variable for the statistical analysis. Entropy rate is the metric of the randomness associated with drivers’ scanning patterns. The higher the entropy rate, the higher the workload. A comparative study of these two movements at these two types of intersections was conducted. Statistical analyses indicated that drivers at RCUT intersections engaged in less random scanning and longer average fixation and spent more than 70% of the time looking forward during the whole movement. Younger drivers at both types of intersections had higher entropy rates. Additionally, drivers at conventional intersections with higher annual average daily traffic (≥30,000) had higher entropy rates. These results could provide a better understanding of driver workload and its safety implications at different types of unsignalized intersections.

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

Effects of the COVID-19 Pandemic on Pharmacovigilance Strategy, Systems, and Processes of Large, Medium, and Small Companies: An Industry Survey

PurposeThe COVID-19 pandemic poses an unprecedented threat to global business relationships and dynamics. The pharmacovigilance function of pharmaceutical companies is particularly susceptible to changing external pressures because of its highly structured compliance activities. We conducted an industry-wide survey to provide insights on how the pharmacovigilance function responded to the challenges posed by the pandemic. We compared smaller companies and larger companies regarding impact on portfolios and operational activity metrics. MethodsWe conducted a survey through the Navitas Life Science annual benchmark of pvnetTM, a network of large enterprise (LE) companies, and pvconnectTM, a network of small and medium enterprise (SME) companies, using an online surveying tool during the first quarter of 2021. We collected information on pharmacovigilance activities, including quantitative measures of workload, costs, and key performance indicators, and qualitative data on the effects of the pandemic on product portfolios and operations. FindingsSurvey questions were posed to LE (pvnet) network members (n = 12) and SME (pvconnect) network members (n = 18) for the period from January 1 through December 31, 2020. The date of data collection was March 26, 2021. Descriptive median values of parameter metrics included the following: revenue ($28.4 billion for LE companies and $1.6 billion for SME companies), number of products (127 for LE companies and 19 for SME companies), and volume of individual case safety reports (391,000 for LE companies and 13,000 for SME companies). SME companies reported a greater impact on 2 survey categories, remote working and employee well-being, than did LE companies. However, LE companies reported a greater impact than did SME companies on all other survey categories: effect on strategic priorities, shift in product focus, workload changes, changes in sourcing model, effect on case reporting compliance, effect on business continuity, changes in pharmacovigilance technology strategy, impact of interactions with health authorities, effect on resource capacity, and impact on recruitment. ImplicationsFour major themes emerge from this survey: (1) shift to remote working, (2) recognition of the impact on employee well-being, (3) shift in strategic priorities, and (4) newly recognized aspects of risk mitigation. The COVID-19 pandemic has had a marked effect on every aspect of pharmaceutical companies’ pharmacovigilance functions, although the effects appear to be different for LE companies than for SME companies.

Measuring Cognitive Workload Using Multimodal Sensors.

This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning. A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at two levels of task difficulty (Easy and Hard). Four sensors were used to measure the participants' physiological change, including, Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and blood oxygen saturation (SpO2). To understand the perceived cognitive workload, NASA-TLX was used after each test and analysed using Chi-Square test. Three well-know classifiers (LDA, SVM, and DT) were trained and tested independently using the physiological data. The statistical analysis showed that participants' perceived cognitive workload was significantly different ( ) between the tests, which demonstrated the validity of the experimental conditions to induce different cognitive levels. Classification results showed that a fusion of ECG and EDA presented good discriminating power (acc = 0.74) for cognitive workload detection. This study provides preliminary results in the identification of a possible set of indicators of cognitive workload. Future work needs to be carried out to validate the indicators using more realistic scenarios and with a larger population.

Effect of long-distance load marching on physiological responses at desert environment

AIM: The present study was undertaken to examine the effect of long-distance load marching on the physiological responses, walking efficiency, and mobility of soldiers at sandy desert environment. METHODS: A total of nine physically fit soldiers (age, 30.00 [SE 0.9] years; height, 175.56 [SE 1.18] cm; and weight, 77.83 [SE 1.37] kg) volunteered for long-distance (6 km) load marching by self-selected speed when carrying 22 kg (28.27% of body weight [BW]) load and no load (NL). Heart rate (HR), respiratory frequency (RF), and core body temperature (CBT) were recorded. Relative workload (RWL) and Physiological Cost Index (PCI) were calculated to assess the work intensity and mobility. RESULTS: It was observed that HR (P = 0.01), RWL (P = 0.01), and PCI (P = 0.01) were significantly increased by 23.79, 39.71, and 77.73%, respectively, and mobility was significantly reduced (P = 0.01) by 18.56% during marching with 22 kg load as compared to NL. CONCLUSION: It may be that marching without external load at speed of 4.85 ± 0.54 km/h should not be continued for more than 2 h, whereas marching with load of 28.27% of BW at speed of 3.95 ± 0.55 km/h should be restricted to maximum 30 min. This combination of load, speed, and duration provides information about soldiers' routine load carriage tasks, which may help in optimizing the load carriage task to reduce the chances of cumulative carriage-related injuries and, therefore, may contribute to improved soldiers' operational readiness and mobility.

The Workload Pressures Experienced by Nurses at Public Sector Hospitals, Peshawar

OBJECTIVE The study's objective was to assess that nurses working in in-patient wards were under workload stress. METHODOLOGY Descriptive observational research on nurses working in the in-patient ward of a public sector hospital in Peshawar was undertaken in November 2020. Workload Indicators of Staffing Need (WISN), a tool established by the World Health Organization (WHO) to anticipate the number of health staff needed to cope with workload pressure, was used to determine nurses' workload. To ensure the successful implementation of the WISN methodology, three tiers of committees were developed, including steering, technical, and expert committees. Data were also analyzed using the tool. RESULTS Nurses in the hospital's in-patient unit work 1966 hours per year. Health service activities, support, and other activities account for 47.92%, 33.33%, and 18.75% of all nurses' time, respectively, during this time. Four nurses were working in the hospital during the research study; however, WISN estimated that three nurses were needed to cope with the ward's workload pressure, and one nurse was overstaffed at the time. The WISN ratio calculated was 1.33. CONCLUSIONS The study concluded that there was no workload pressure on nurses (negative), and the ward had an extra nurse who could be accommodated in any other department with greater demand.

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.

Study on the Rehabilitation Therapist Estimation Under Institutional Perspective by Applying the Workload Indicators of Staffing Needs in the Aging Context

BackgroundThe need for rehabilitation therapy has increased dramatically with the aging of the population, the prevalence of non-communicable diseases, and the increase in the number of disabilities. Rehabilitation therapists are crucial to provide high quality rehabilitation therapy; however, there is a significant shortage of these professionals in China. One of the effective strategies to address this challenge is using the norm of the workforce for rehabilitation therapy, which is an index for assessing the personnel required in a facility. This research aimed to create a rehabilitation therapist-required norm under institutional perspective in Shandong Province, China, based on the Workload Indicators of Staffing Needs (WISN) method, which was created by the World Health Organization (WHO) in 1998 to analyse staff utilization at various levels of the health care system.MethodsWe conducted descriptive and quantitative research from October to November 2020 in the rehabilitation department of a tertiary hospital in Weifang City, China. Focus groups, online interviews, and document reviews were conducted to gather data and calculations of the WISN method performed.ResultsAdmission assessment, pre-treatment evaluation, rehabilitation therapy, post-treatment evaluation, and health education for patients were identified as the main priority group activities. Interviews and analysis of documents summarized five factors related to rehabilitation therapists' health service activities. In this study, the annual working time of each therapist was 1,776 h per year. The WISN method calculations showed that the norm of rehabilitation therapists in this tertiary hospital was 23 therapists. As the department had 13 therapists, there was a shortage of 10 therapists based on the WISN calculation, with a ratio of 0.57, which represented the actual compared to the ideal number of therapists.ConclusionWorkload pressure was high for therapists in this tertiary hospital. This model revealed a demand for ten more therapists in the rehabilitation department. The WISN method can help hospital administrators in therapist workforce monitoring, including in regard to therapists. Therefore, the WISN method should be embraced as part of hospital human resource planning and recruitment strategies to meet increasing rehabilitation needs.

ANALISIS BEBAN KERJA DAN KEBUTUHAN TENAGA PRAMUBAKTI DENGAN METODE WORK SAMPLING DAN METODE WORKLOAD INDICATOR STAFF NEEDES

One of the important components for calculating power requirements is the workload. This means that the determination of power requirements is adjusted to the existing workload. The Workload Indicator Staff Needs (WISN) method is a method for calculating the workforce needs based on the actual workload carried out by the workforce. This study aims to analyze the workload of services using the Work Sampling technique and analyze the needs of service workers using the WISN method. This research was carried out in the Surgery and Anesthesia Room Installation, Dr. Sardjito. This type of research is descriptive qualitative by conducting observations, in-depth interviews and document review. The results showed that the use of time for productive activities was 71.11%, the use of time for non-productive activities was 9.36% and the use of time for personal activities was 19.53%. The results of the calculation of the need for energy using the WISN method show the number of service staff as many as 20 people with a WISN ratio of 0.75. From the results of this study, it is suggested to the management of Dr. Sardjito to consider adding a staff of 5 people according to the existing workload

Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study.

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.

Exploring the Association Between Nurse Workload and Nurse-Sensitive Patient Safety Outcome Indicators: Retraction.

Exploring the Association Between Nurse Workload and Nurse-Sensitive Patient Safety Outcome Indicators: Retraction.

Pattern analysis of physiological data for the assessment of mental workload

Measuring mental workload at the workplace using (psycho-) physiological measurement techniques seems desirable but is difficult to implement. Conventional analysis techniques are designed to cover longer measurement durations, neglecting the demands of modern work places: high worker flexibility and constantly fluctuating mental workload. As an alternative analysis approach, measurement (resp. analysis) duration can be shortened and event-based pattern analysis of various physiological parameters can be performed. The effects of such approaches are demonstrated by experimental examples. Furthermore, an event-timestamp independent framework is presented. Focusing on occasionally occurring peaks and longer lasting plateaus in mental workload trajectories, an automatized analysis of workload during work processes becomes possible.Practical relevance: With steadily increasing cognitive demands at work the risk of mental fatigue increases too. Mental workload is not directly observable at the workplace and the objective measurement and interpretation is complicated. Improving the overall assessment and analysis strategies for (physiological) mental workload indicators can benefit the quality of risk assessments of workplaces and processes as well as enable the possibility of demand-orientated control of (informational) assistance systems to prevent mental overload and resulting health constraints.

Satisfaction Level of Engineering Students in Face-to-Face and Online Modalities under COVID-19—Case: School of Engineering of the University of León, Spain

University education in times of COVID-19 was forced to seek alternative teaching/learning methods to the traditional ones, having to abruptly migrate to the online modality, changes that have repercussions on student satisfaction. That is why this study aims to compare the level of student satisfaction in face-to-face and “forced” online modalities under COVID-19. A quantitative, cross-sectional methodology was applied to two groups of students: Under a face-to-face modality (n = 116) and under an online modality (n = 120), to which a questionnaire was applied under a Likert scale, with four dimensions: Course design structure, content, resources, and instructor. Non-parametric statistics, specifically the Mann–Whitney U-test, were used to compare the groups. The results showed that there are significant differences in the level of satisfaction of students in the face-to-face and online “forced” modalities (p = 0.01984 < 0.05), and the dimensions of the level of satisfaction that presented significant differences were course design structure (p = 0.04523 < 0.05) and content (p = 0.00841 < 0.05). The research shows that students in the face-to-face modality express a higher level of satisfaction, which is reflected in the dimension design structure of the course, specifically in its workload indicator, as well as in the dimension content, in its indicators, overlapping with other courses and materials.

Constructing Neural Network Model to Evaluate and Predict Human Error Probability in Nuclear Power Plants Based on Eye Response, Workload Rating, and Situation Awareness

The accurate assessment of human error probability (HEP) has an important impact on the safety of nuclear power plants. Therefore, it is necessary to develop a HEP model. This study analyzes the validity, sensitivity, and relationship between HEP and the indices of eye response and the subjective rating method. The analysis result showed that there is a correlation between HEP and the indices of eye response, subjective workload, and situation awareness level. Therefore, a back propagation neural network model was developed based on these indices. The correlation coefficient is more than 0.95 between the predicted data of the developed model and the target data. Also, the root mean square error was 0.0073, 0.0083, and 0.0077, and the determination coefficient was 0.965, 0.933, and 0.931 for the training, validation, and testing data sets, respectively. Therefore, the developed back propagation neural network model has reliable prediction accuracy for HEP.

Evaluating Changes in Mental Workload in Indoor and Outdoor Ultra-Distance Cycling.

Whilst increasing mental workload has been shown to have a detrimental effect on cycling performance and more generally to increase the risk of harm, no studies have measured how mental workload changes as a function of ultra-distance cycling, indoors or outdoors. Our objective was to measure the difference in mental workload, as indicated by changes in EEG theta power, components of HRV and psychomotor vigilance and as reported using the ‘NASA Task Load Index questionnaire’, before and after a 5 h indoor ride and outdoor ride completed at 65% of functional threshold power. Results of the NASA-TLX indicated the mental demand of outdoor cycling to be significantly less than that of indoor cycling. There were significant differences in the PVT results between the pre and the post outdoor ride average and median response times. The slowest 10% PVT responses were significantly slower pre than post the indoor ride. There were significant differences in HRV between pre and post outdoor and indoor rides, specifically, in the average RR intervals, RMSSD (ms2), LFPower (ms2), NN50. There were modest changes in indicators of mental workload during an ultra-distance cycle ride. As such, mental workload during ultra-distance cycling is unlikely to be a contributory factor to decreases in performance or to an increased likelihood of accident and injury.