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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.
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High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
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Cognição , Carga de Trabalho , Humanos , Cognição/fisiologia , Carga de Trabalho/psicologia , Eletroencefalografia/métodos , MemóriaRESUMO
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.
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Frequência Cardíaca , Aprendizado de Máquina , Pilotos , Carga de Trabalho , Humanos , Frequência Cardíaca/fisiologia , AviaçãoRESUMO
Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.
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Conscientização , Pilotos , Carga de Trabalho , Humanos , Carga de Trabalho/psicologia , Pilotos/psicologia , Masculino , Conscientização/fisiologia , Adulto , Aeronaves , Aviação , Eletroencefalografia/métodos , Feminino , Adulto JovemRESUMO
Air traffic controllers' mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, ß + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability.
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Algoritmos , Aviação , Eletroencefalografia , Carga de Trabalho , Humanos , Eletroencefalografia/métodos , Masculino , Adulto , Processamento de Sinais Assistido por Computador , Feminino , Eletrocardiografia/métodosRESUMO
Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.
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Atenção , Condução de Veículo , Atenção/fisiologia , Carga de Trabalho , Análise e Desempenho de Tarefas , Movimentos Oculares , Acidentes de TrânsitoRESUMO
OBJECTIVES: The purpose of this study was to investigate fatigue, mental workload, and burnout among health care workers (HCWs) and explore the possible underlying factors. MATERIALS AND METHODS: An online cross-sectional survey design was used to collect data from HCWs in Chongqing, China. The online survey included the Fatigue Severity Scale, NASA Task Load Index, and Chinese version of the Maslach Burnout Inventory-General Survey to assess fatigue, mental workload, and burnout, respectively, and was conducted from February 1 to March 1, 2023. RESULTS: In this study, the incidence of fatigue and burnout among HCWs was 76.40% and 89.14%, respectively, and the incidence of moderate to intolerable mental workloads was 90.26%. Work-family conflict, current symptoms, number of days of COVID-19 positivity, mental workload, burnout and reduced personal accomplishment were significantly associated with fatigue. Mental workload was affected by fatigue and reduced personal accomplishment. Furthermore, burnout was influenced by marital status and fatigue. Moreover, there was a correlation among mental workload, fatigue, and burnout. CONCLUSIONS: Fatigue, mental workload and burnout had a high incidence and were influenced by multiple factors during COVID-19 public emergencies in China.
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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; ß=-0.078). However, the number of night shifts per month was positively and significantly correlated with intention to leave the service (p = 0.001; ß = 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.
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Head-up displays (HUDs) have the potential to change work in operation environments by providing hands-free information to wearers. However, these benefits may be accompanied by trade-offs, primarily by increasing cognitive load due to dividing attention. Previous studies have attempted to understand the trade-offs of HUD usage; however, all of which were focused on land-based tasks. A gap in understanding exists when examining HUD use in aquatic environments as immersion introduces unique environmental and physiological factors that could affect multitasking. In this study, we investigated multitasking performance associated with swimming with a HUD. Eighteen participants completed three tasks: swimming only, a HUD-administered word recall task, and a dual-task combining both tasks. Results revealed significant dual-task interference in both tasks, though possibly less pronounced than in land-based tasks. These findings enhance not only help characterise dual-task performance, but also offer valuable insights for HUD design for aquatic settings.
HUDs have become an increasingly popular tool to present information to users in complex working environments. However, past research examining HUD task performance has been restricted to land-based contexts. The current study examines HUD use while swimming and provides characterisation of multitasking performance within aquatic environments.
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The visual approach is the most accident-prone phase of a flight, especially in low-visibility conditions. This preliminary study aimed to examine the effects of flight experience on pilots' decision-making and visual scanning pattern in low-visibility approaches. Twenty pilots were separated into two groups based on their flight experience and completed the high- and low-visibility approaches in balanced order using a high-fidelity flight simulator. Pilots' mental workload and visual scanning patterns were recorded via an eye tracker. The results showed that, compared to less flight-experienced pilots (20%, 3/15), experienced pilots (80%, 4/5) were more likely to make go-around decisions in the low-visibility approaches. Furthermore, they exhibited a more flexible and adaptable visual scanning pattern by quickly shifting their attention, as evidenced by decreased fixations and increased saccades. These findings suggest that the integration of visual scanning strategy and training solution with a marginally meteorological approach may enhance decision-making safety for novice pilots.
This study investigates the 'expertise effects' in visual scanning pattern, mental workload and decision-making among pilots with different levels of flight experience in a modern flight simulator. For safer decision-making, less flight-experienced pilots should enlarge their visual scanning span in a more unburdened manner, especially in low-visibility approaches.
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Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected. Results demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.
This study aimed to estimate the mental workload of human drivers using physiological signals and Functional Data Analysis (FDA). By comparing models using raw data and extracted features, the results show that the FDA with raw data achieved a high accuracy of 90%, outperforming the model with extracted features (73%).
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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.
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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.
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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.
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Condução de Veículo , Aprendizado Profundo , Humanos , Análise e Desempenho de Tarefas , Acidentes de Trânsito , Carga de Trabalho , EletroencefalografiaRESUMO
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).
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Encéfalo , Eletroencefalografia , Aprendizado de Máquina , Carga de Trabalho , Humanos , Eletroencefalografia/métodos , Encéfalo/fisiologia , Masculino , Máquina de Vetores de Suporte , Feminino , Adulto , Algoritmos , Adulto Jovem , Cognição/fisiologiaRESUMO
This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.
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Condução de Veículo , Processos Mentais , Desempenho Psicomotor , Carga de Trabalho , Condução de Veículo/psicologia , Processos Mentais/fisiologia , Análise de Dados , Humanos , Masculino , Feminino , Adulto Jovem , Adulto , Eletrodos , Envio de Mensagens de Texto , Rádio , Estimulação Acústica , Estimulação Luminosa , Matemática , Eletrocardiografia , Eletroencefalografia , Eletromiografia , Resposta Galvânica da Pele , Cognição/fisiologia , Segurança , Desempenho Psicomotor/fisiologiaRESUMO
This letter offers a constructive review of the article 'Influence of the time-task constraint on ocular metrics of semi-elite soccer' by Luis-del Campo et al. (2023), focusing on methodological considerations and avenues for future research enhancement. The study investigates the impact of task constraints on eye-tracking metrics among semi-elite soccer players, aiming to gauge mental workload during training sessions. While the study presents valuable insights, there are opportunities for methodological refinement. Suggestions include emphasizing sample size determination, randomization of the experimental conditions, and employing robust statistical analyses to mitigate potential biases. Moreover, future studies could benefit from integrating external load measures alongside heart rate monitoring to comprehensively assess training task variations. Despite these considerations, the study underscores the promising application of eye-tracking techniques in evaluating mental workload during soccer training, paving the way for further exploration and refinement of methodologies to enhance player performance assessment and training optimization in the field.
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The perception of work is closely linked to body reactions that facilitate task performance. Previous studies have shown that psychosocial work factors significantly impact employee health on both psychological and physical levels, though their cross-sectional designs limit causal interpretations. In this study, participants performed sitting and standing tasks under four different levels of mental workload. The NASA-Task Load Index (NASA-TLX) assessed mental workload perception across six dimensions, while Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA) scores evaluated body postures for standing and sitting tasks, respectively. This study examined the effects of alarms, distractions, and time constraints-common psychosocial factors in healthcare environments-on human behavior. We compared NASA-TLX scores with corresponding REBA/RULA scores to evaluate how perceived mental workload affects body postures. One-way ANOVA assessed the impact of experimental conditions on response variables, and Pearson correlation analyses explored the relationships between psychosocial factors and these variables. Results indicated that alarm conditions most negatively impacted mental workload perception and body postures. Temporal demand and effort scores were particularly affected by psychosocial factors in both tasks. Gender influenced physical demand and performance scores (higher in females) for the standing task but did not affect REBA and RULA scores. These findings suggest that organizational and psychosocial factors significantly influence healthcare workers' behavior, health, and patient safety. Further research is needed to evaluate the specific effects of psychosocial factors on both physical and mental workload to understand the relationship between overall task workload and occupational disorders.
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Postura , Carga de Trabalho , Humanos , Masculino , Feminino , Carga de Trabalho/psicologia , Adulto , Adulto Jovem , Percepção , Análise e Desempenho de TarefasRESUMO
The prevalence of mental fatigue is a noteworthy phenomenon that can affect individuals across diverse professions and working routines. This paper provides a comprehensive dataset of physiological signals obtained from 23 participants during their professional work and questionnaires to analyze mental fatigue. The questionnaires included demographic information and Chalder Fatigue Scale scores indicating mental and physical fatigue. Both physiological signal measurements and the Chalder Fatigue Scale were performed in two sessions, morning and evening. The present dataset encompasses diverse physiological signals, including electroencephalogram (EEG), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and 3-axis accelerometer (ACC) data. The NeuroSky MindWave EEG device was used for brain signals, and the Empatica E4 smart wristband was used for other signals. Measurements were carried out on individuals from four different occupational groups, such as academicians, technicians, computer engineers, and kitchen workers. The provision of comprehensive metadata supplements the dataset, thereby promoting inquiries about the neurophysiological concomitants of mental fatigue, autonomic activity patterns, and the repercussions of a cognitive burden on human proficiency in actual workplace settings. The accessibility of the aforementioned dataset serves to facilitate progress in the field of mental fatigue research while also laying the groundwork for the creation of customized fatigue evaluation techniques and interventions in diverse professional domains.
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BACKGROUND: Work-related musculoskeletal disorders (WRMSDs) is a multi-factorial disorder in most occupational setting and it has increased significantly in recent years. OBJECTIVE: This study aimed to investigate the relationship between physical, cognitive, and environmental factors of ergonomics with the prevalence of WRMSDs in a car-parts manufacturing industry. METHODS: This cross-sectional study was performed among 220 workers in a milling unit of a car parts manufacturing company in 2021-2022. The prevalence of WRMSDs was assessed using the Extended Version of the Nordic Musculoskeletal Questionnaire. Noise exposure was evaluated using dosimetry method. Mental and physical workload were evaluated by the NASA-TLX and key index methods (KIM-MHO and KIM-LHC), respectively. Data analysis was performed using SPSS version 25.0. RESULTS: The subjects' mean age and work experience were 36.3±6.5 and 8.35±6.41 years, respectively. Eighty-five percent of the subjects reported WRMSDs in at least one area of the body. The results of mental workload assessment revealed a high workload mean range (73.23±14.89) in all of the subjects. Mean score of KIM-LHC and KIM-MHO were 738.18±336.42 and 201.86±36.41, respectively with odds ratio of 1.32 for KIM-LHC in creating the WRMSDs. There was a significant relationship between the noise exposure, mental and physical workload and the prevalence of WRMSDs (p-valueâ< â0.05). CONCLUSION: The results of the present study revealed that environmental, physical and cognitive factors can simultaneously be effective in the prevalence of WRMSDs. Therefore, performing effective control measures requires comprehensive attention to physical, environmental, and cognitive ergonomics in the algorithm of ergonomics management in the workplace.