Designing a practical fatigue detection system: A review on recent developments and challenges.

INTRODUCTION: Fatigue is considered to have a life-threatening effect on human health and it has been an active field of research in different sectors. Deploying wearable physiological sensors helps to detect the level of fatigue objectively without any concern of bias in subjective assessment and interfering with work. METHODS: This paper provides an in-depth review of fatigue detection approaches using physiological signals to pinpoint their main achievements, identify research gaps, and recommend avenues for future research. The review results are presented under three headings, including: signal modality, experimental environments, and fatigue detection models. Fatigue detection studies are first divided based on signal modality into uni-modal and multi-modal approaches. Then, the experimental environments utilized for fatigue data collection are critically analyzed. At the end, the machine learning models used for the classification of fatigue state are reviewed. PRACTICAL APPLICATIONS: The directions for future research are provided based on critical analysis of past studies. Finally, the challenges of objective fatigue detection in the real-world scenario are discussed.

The Impact of COVID-19 on Fatigue in Multiple Sclerosis.

BACKGROUND: Although the COVID-19 quarantine required everyone to make lifestyle changes, it may have had especially profound implications for individuals who experience multiple sclerosis (MS)-related fatigue. Individuals with MS who suffer from fatigue are at risk of worsening symptoms and already predisposed to inactivity and social isolation. The objective of this study was to examine the impact of the COVID-19 national quarantine and related restrictions on mental, emotional, and physical fatigue in persons with MS in the United States. METHODS: We conducted a survey open to all adults (>18 years) with MS within the United States. The survey gathered demographic information and asked how the COVID-19 pandemic impacted their physical, mental, and emotional fatigue. RESULTS: The survey was completed by 600 individuals, 478 with relapsing MS and 122 with progressive MS. There was a significant 2-way interaction of time by fatigue type; both physical and emotional fatigue significantly increased during the pandemic (P <.01) and remained significantly higher after the pandemic than prior to the pandemic (P <.01). Mental fatigue increased significantly during the pandemic (P <.01) and although it remained higher, on average, after the pandemic, it was not significantly different from the level before the pandemic. CONCLUSIONS: Individuals with MS experienced increases in physical, mental, and emotional fatigue over the course of the COVID-19 quarantine. Even after the lifting of quarantine restrictions, these levels have not returned to baseline. To adequately address fatigue, it is critical that health care professionals inquire about all types of fatigue in persons with MS.

Bioactive Compounds in Citrus reticulata Peel Are Potential Candidates for Alleviating Physical Fatigue through a Triad Approach of Network Pharmacology, Molecular Docking, and Molecular Dynamics Modeling.

Physical fatigue (peripheral fatigue), which affects a considerable portion of the world population, is a decline in the ability of muscle fibers to contract effectively due to alterations in the regulatory processes of muscle action potentials. However, it lacks an efficacious therapeutic intervention. The present study explored bioactive compounds and the mechanism of action of Citrus reticulata peel (CR-P) in treating physical fatigue by utilizing network pharmacology (NP), molecular docking, and simulation-based molecular dynamics (MD). The bioactive ingredients of CR-P and prospective targets of CR-P and physical fatigue were obtained from various databases. A PPI network was generated by the STRING database, while the key overlapping targets were analyzed for enrichment by adopting KEGG and GO. The binding affinities of bioactive ingredients to the hub targets were determined by molecular docking. The results were further validated by MD simulation. Five bioactive compounds were screened, and 56 key overlapping targets were identified for CR-P and physical fatigue, whereas the hub targets with a greater degree in the PPI network were AKT1, TP53, STAT3, MTOR, KRAS, HRAS, JAK2, IL6, EGFR, and ESR1. The findings of the enrichment analysis indicated significant enrichment of the targets in three key signaling pathways, namely PI3K-AKT, MAPK, and JAK-STAT. The molecular docking and MD simulation results revealed that the bioactive compounds of CR-P exhibit a stronger affinity for interacting with the hub targets. The present work suggests that bioactive compounds of CR-P, specifically Hesperetin and Sitosterol, may ameliorate physical fatigue via the PI3K-AKT signaling pathway by targeting AKT1, KRAS, and MTOR proteins.

Relationship between physical fatigue and mental fatigue based on multimodal measurement under different load levels.

Based on multimodal measurement methods of NASA task load index (NASA-TLX), task performance, surface electromyography (sEMG), heart rate (HR), and functional near-infrared spectroscopy (fNIRS), this study conducted experimental measurements and analyses under 16 different load levels of physical fatigue and mental fatigue combination conditions. This study observed the interaction between physical fatigue and mental fatigue at different levels, and at the subjective level, the effect of physical fatigue on mental fatigue was greater than that of mental fatigue on physical fatigue. Secondly, the results of fNIRS analysis showed that the premotor cortex is affected by physical fatigue, and the dorsolateral prefrontal cortex is affected by mental fatigue. Finally, this study constructed a fatigue classification model with an accuracy of 95.3%, which takes multimodal physiological data as input and 16 fatigue states as output. The research results will provide a basis for fatigue analysis, evaluation, and improvement in complex working situations.

Based on multimodal measurement methods of NASA-TLX, task performance, sEMG, HR, and fNIRS, this study illustrated the relationship between physical fatigue and mental fatigue, and proposed a classification method for different fatigue situations.

Gait and heart rate: do they measure trait or state physical fatigue in people with multiple sclerosis?

BACKGROUND: Trait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e., heart rate, HR, r-r interval, R-R, HR variability, HRV) in trait-PF and state-PF. METHODS: Forty-eight pwMS [42 ± 1.9 years, 65% female, EDSS 2 (IQR: 0-5.5)] completed the Timed Up and Go test (simple and with dual task, TUG-DT) and the 6-min walk test (6MWT). ANS measures were measured via a POLAR H10 strap. Gait was measured using inertial-measurement units (OPALs, APDM Inc). Trait-PF was evaluated via the Modified Fatigue Impact Scale (MFIS) motor component. State-PF was evaluated via a Visual Analog Scale (VAS) scale before and after the completion of the 6MWT. Multiple linear regression models identified trait-PF and state-PF predictors. RESULTS: Both HR and gait metrics were associated with trait-PF and state-PF. HRV at rest was associated only with state-PF. In models based on the first 3 min of the 6MWT, double support (%) and cadence explained 47% of the trait-PF variance; % change in R-R explained 43% of the state-PF variance. Models based on resting R-R and TUG-DT explained 39% of the state-PF. DISCUSSION: These findings demonstrate that specific gait measures better capture trait-PF, while ANS metrics better capture state-PF. To capture both physical fatigue aspects, the first 3 min of the 6MWT are sufficient. Alternatively, TUG-DT and ANS rest metrics can be used for state-PF prediction in pwMS when the 6MWT is not feasible.

Multidisciplinary analysis of cancer-related fatigue at the time of diagnosis: preliminary results of the BIOCARE FActory cohort.

PURPOSE: Cancer-related fatigue (CRF) is a common side effect of cancer and cancer treatment that significantly impairs the quality of life and can persist for years after treatment completion. Although fatigue is often associated with cancer treatment, it is also a result of the disease itself, even before intervention. CRF at the time of diagnosis may affect treatment timing or completion and is a consistent predictor of post-treatment fatigue at any time. The mechanisms underlying CRF are multidimensional and not well understood, particularly at the time of diagnosis. METHODS: Sixty-five breast cancer patients at the time of diagnosis were included. The participants completed self-assessment questionnaires about CRF, sleep disturbances, and emotional symptoms and wore an accelerometer to assess levels of spontaneous physical activity and sleep quality. During the experimental session, the participants underwent cognitive, neuromuscular, and exercise metabolism evaluations. RESULTS: Using augmented backward elimination regression, this study found that emotional symptoms and perceived sleep disturbances were the strongest predictors of CRF (adjusted r2 = 0.51). Neuromuscular fatigability and sleep disturbance were also associated with physical dimensions, whereas cognitive performance was associated with cognitive dimensions. CONCLUSION: At the time of diagnosis, emotional and cognitive dimensions are over-represented compared to the general population, and specific subdimensions have specific predictors that support the idea of distinct mechanisms. Evaluating CRF subdimensions and their potential mechanisms at the time of diagnosis would be particularly relevant for identifying high-risk patients and offering them appropriate interventions. TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov (NCT04391543) in May, 2020.

Different subtypes of chronic fatigue in childhood cancer survivors: A DCCSS LATER study.

INTRODUCTION: The aim of the current study was to investigate whether subtypes of chronic fatigue (CF) can be identified in childhood cancer survivors (CCS), and if so, to determine the characteristics of participants with a specific subtype. METHODS: Participants were included from the nationwide DCCSS LATER cohort. The Checklist Individual Strength (CIS) was completed to assess fatigue. Participants with CF (scored ≥35 on the fatigue severity subscale and indicated to suffer from fatigue for ≥6 months) were divided into subgroups using two-step cluster analysis based on the CIS concentration, motivation, and physical activity subscales. Differences between groups on demographics, psychosocial, lifestyle, and treatment-related variables were determined using ANOVA and chi-square analyses (univariable) and multinomial regression analysis (multivariable). RESULTS: A total of 1910 participants participated in the current study (n = 450 with CF; n = 1460 without CF). Three CF subgroups were identified: Subgroup 1 (n = 133, 29% of participants) had CF with problems in physical activity; Subgroup 2 (n = 111, 25% of participants) had CF with difficulty concentrating; and Subgroup 3 (n = 206, 46% of participants) had multi-dimensional CF. Compared to Subgroup 1, Subgroup 2 more often reported sleep problems, limitations in social functioning, and less often have more than two comorbidities. Subgroup 3 more often reported depression, sleep problems, a lower self-esteem, and limitations in social functioning and a lower educational level compared to Subgroup 1. CONCLUSION: Different subgroups of CCS with CF can be identified based on fatigue dimensions physical activity, motivation and concentration. Results suggest that different intervention strategies, tailored for each subgroup, might be beneficial.

When experiencing nice interactions at work: Good sleep quality via well-being.

Based on the Work-Home Resources Model and Conservation of Resources Theory, we develop dual mechanisms by which nice interactions (patients' compliments and coworkers' informational support) predict sleep quality. Specifically, we expect these nice interactions to help individuals conserve their personal energy in the form of less cognitive depletion (a cognitive process) and diminished physical fatigue (a physical process). Further, we propose employees utilise their energy resources to experience better sleep quality. To test the proposed model, we utilised an experience-sampling method by recruiting 223 female nurses working in a regional university hospital in South Korea. Specifically, we measured nice interactions and personal resources at 3 PM on Day t and sleep quality at 5-6 AM on Day t + 1, and we administered the questionnaire for 10 consecutive days. Overall, after removing 79 invalid observations (not completing questionnaire in a timely manner), we had a final total of two-wave 1997 daily observations from 223 nurses. Receiving more compliments from patients and more information from coworkers positively affects nurses' cognitive energy (less cognitive depletion) and physical energy (less physical fatigue), which predicts better sleep quality. Finally, results supported indirect effects of these nice interactions on sleep quality via cognitive and physical processes.

SARS-CoV-2 infection is associated with physical but not mental fatigue - Findings from a longitudinal controlled population-based study.

OBJECTIVE: Fatigue has been identified as the core symptom of long-Covid, however, putative pandemic-related influences remain largely unclear. We investigated trajectories of total, physical and mental fatigue and the factors associated with it in previously infected and non-infected individuals up to one year post- infection. METHODS: We used data from a longitudinal cohort study of German adults with two samples: A representative probability sample and a sample of individuals with proven SARS-CoV-2 infection. Surveys were conducted in spring 2020(T1), autumn 2020(T2) and summer 2021(T3). Fatigue was assessed using the FAS, distinguishes between physical and mental fatigue. Depression, anxiety and stress were assessed using PHQ-4 and PSQ. RESULTS: 1990 participants [mean age 47.2 (SD = 17.0), 30.5% previously infected] were included in the survey at T1 (n = 1118 at T2, n = 692 at T3). Total and physical fatigue, but not mental fatigue were significantly higher in the previously infected compared to the non-infected sample at T2, but this group difference disappeared at T3. We identified Covid-infection as a factor associated with transient total and physical fatigue at T2. Depression, anxiety and stress at T1 were associated with total, physical and mental fatigue at both follow-ups. CONCLUSIONS: Our results highlight the importance of considering physical and mental fatigue as separate entities, while suggesting a greater relevance of the physical signs of fatigue in understanding long-Covid. The results further showed that baseline mental health symptoms were the most strongly associated with fatigue trajectories.

Mental fatigue impairs repeated sprint and jump performance in team sport athletes.

OBJECTIVES: We tested whether mental fatigue (MF), induced by a cognitively-demanding task, would impair repeated sprint ability (RSA) and repeated jump ability (RJA) performance, and whether physical fatigue and MF would impair psychomotor vigilance. DESIGN: Randomized within-participant design. METHODS: After establishing baseline peak countermovement jump (CMJ), 18 male participants performed 12 maximal 20-m (10-m linear + 10-m directional) repeated sprints (RSA random test) followed by 12 maximal repeated CMJs (RJA test) subsequent to 30-min Stroop task (MF) or a documentary (Control). Peak and mean running time and height, percent decrement score (Sdec), blood lactate, heart rate and RPE were measured for CMJ, RSA, and RJA tests. MF (M-VAS) and psychomotor vigilance [psychomotor vigilance test (PVT)] were measured at baseline, after each condition, and after the RSA/RJA tests. RESULTS: Compared to Control, the Stroop task elevated MF (p = .001), RPE ratings (all p < .031), and mean and Sdec performance in directional (but not linear) RSA (all p < .032) and RJA tests (all p < .034). PVT score worsened after Stroop task (p = .011) but not Control, declined after RSA/RJA tests in both conditions (all p < .023) and was lower in the MF condition (p = .029). No condition differences were noted for peak (CMJ, RSA and RJA tests) performance, blood lactate, and heart rate. CONCLUSIONS: MF impairs directional RSA, and RJA performance. This impairment was linked with increased RPE and without physiological changes. The progressive impairment in PVT score suggests a cumulatively negative effect of mental and physical fatigue on psychomotor vigilance.

Validating the Fatigue Scale for Motor and Cognitive Function (FSMC) in chronic stroke.

BACKGROUND: Post-stroke fatigue can manifest as both physical and mental fatigue. The Fatigue Scale for Motor and Cognitive Functions (FSMC) evaluates fatigue on the motor and cognitive domains separately, however, the psychometric properties of this measure in stroke have not been reported. OBJECTIVE: To determine the internal consistency, test-retest reliability, and concurrent validity of the FSMC in chronic stroke. METHODS: Thirty-four participants with chronic stroke (55.26±12.27 years of age; 59.53±89.21 months post-stroke) completed the FSMC on two separate visits. Internal consistency and reliability of the FSMC were examined using Cronbach's alpha and two-way mixed effects intraclass correlation coefficients (ICC), respectively. Correlation between the FSMC and the Fatigue Severity Scale and Visual Analog Scale-Fatigue was used to assess concurrent validity. RESULTS: Internal consistency was excellent (Cronbach's alpha > 0.9) and reliability was moderate to good (ICC = 0.72-0.81) for all FSMC scores. The FSMC demonstrated moderate to good concurrent validity with the Fatigue Severity Scale (ρ= 0.66-0.72) but only fair concurrent validity with the Visual Analog Scale-Fatigue (ρ= 0.37-0.44). CONCLUSION: The FSMC is a valid and reliable measure of post-stroke fatigue and may be a useful tool to examine physical fatigue and cognitive fatigue in chronic stroke.

Acute Effects of Fatigue on Cardiac Autonomic Nervous Activity.

The onset of fatigue disrupts the functioning of the autonomic nervous system (ANS), potentially elevating the risk of life-threatening incidents and impairing daily performance. Previous studies mainly focused on physical fatigue (PF) and mental fatigue (MF) effects on the ANS, with limited knowledge concerning the influence of physical-mental fatigue (PMF) on ANS functionality. This study aimed to assess the immediate impact of PMF on ANS function and to compare its effects with those of PF and MF on ANS function. Thirty-six physically active college students (17 females) without burnout performed 60-min cycling exercises, AX-Continuous Performance Task (AX-CPT), and cycling combined with AX-CPT to induce PF, MF, and PMF respectively. Subjective fatigue levels were measured using the Rating of Perceived Exertion scale and the Visual Analog Scale-Fatigue. Heart rate variability was measured before and after each protocol to assess cardiac autonomic function. The proposed tasks successfully induced PF, MF, and PMF, demonstrated by significant changes in subjective fatigue levels. Compared with baseline, PMF decreased the root mean square of successive differences (RMSSD) between normal heartbeats (P < 0.001, d = 0.50), the standard deviation of normal-to-normal RR intervals (SDNN) (P < 0.01, d = 0.33), and the normalized high-frequency (nHF) power (P < 0.001, d = 0.32) while increased the normalized low-frequency (nLF) power (P < 0.001, d = 0.35) and the nLF/nHF ratio (P < 0.001, d = 0.40). Compared with MF, PMF significantly decreased RMSSD (P < 0.001, η2 = 0.431), SDNN (P < 0.001, η2 = 0.327), nLF (P < 0.01, η2 = 0.201), and nHF (P < 0.001, η2 = 0.377) but not the nLF/nHF ratio. There were no significant differences in ΔHRV (i.e., ΔRMSSD, ΔSDNN, ΔnLF/nHF, ΔnLF, and ΔnHF), heart rate, and training impulse between PF- and PMF-inducing protocols. Cognitive performance (i.e., accuracy) in AX-CPT during the PMF-inducing protocol was significantly lower than that during the MF-inducing protocol (P < 0.001, η2 = 0.101). PF and PMF increased sympathetic activity and decreased parasympathetic activity, while MF enhanced parasympathetic activity.

A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform.

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

Utilizing a quality of life tool to examine the presence of fatigue in subjects with diabetes mellitus.

Introduction: The prevalence of fatigue in patients with diabetes mellitus (DM) can be as high as 50 %. Physical, mental, and psychosocial components of fatigue negatively impact quality of life (QOL), morbidity and mortality. Several tools have been developed to address fatigue, but none specifically for measuring fatigue in DM. The aim of this study was to assess the impact of diabetes and neuropathy on fatigue using the Norfolk QOL-Fatigue (QOL-F) survey. Methods: 605 adult participants from [Anonymous] were recruited (400 subjects with type 1 or type 2 DM and 205 subjects without diabetes (controls)). All subjects completed the Norfolk QOL-F. Demographics, weight, BMI, and duration of diabetes were obtained. The Norfolk QOL-F, a 35-item validated questionnaire, assesses five domains: subjective fatigue, physical and cognitive fatigue, reduced activities, impaired activities of daily living, and depression. Results: Subjects with DM reported significantly higher fatigue total scores (52.63vs33.89, p < 0.0001) and in all five domains when compared to controls. Patients with DM with neuropathy were significantly more fatigued than those without (59.72vs27.83, p < 0.0001). Fatigue scores in patients with DM without neuropathy were similar to controls (27.83vs33.89, p = NS). In multivariate analysis, age, gender, and presence of neuropathy significantly impacted fatigue scores. Conclusions: The Norfolk QOL-F questionnaire can potentially identify the impact of chronic diseases such as diabetes on fatigue. Assessing the different components of fatigue is important for clinicians in improving disease management and outcomes. Further investigations are needed to confirm these observations in specific cohorts with other comorbidities.

Upper and Lower Limbs Acute Fatigue Did Not Mitigate Male Trained Air Force Soldiers' Marksmanship.

In this study, we aimed to investigate the impact of acute fatigue on pistol shooting performance among Air Force marksmen. We compared the accuracy, precision, speed-accuracy trade-off, shooting cycle time, and hits on a silhouette target among 12 Brazilian Air Force servicemen (M age = 21.5, SD - 1.6 years) under both fatigue and non-fatigue conditions in a crossover design. In the fatigued condition, the participants performed a fatigue protocol composed of side runs, vertical jumps, push-ups, running, and burpees exercises before shooting. Participants performed the countermovement jump and the plyometric push-ups tests on a contact mat before and immediately after the fatigue protocol to compare the heights achieved pre- and post-fatigue. Paired t-tests showed a significant performance reduction of 34.36% and 40.02% for the countermovement jump and plyometric push-ups, respectively, indicating that participants were fatigued in their lower and upper limbs. In the non-fatigued condition, no exercise was performed before shooting. Results indicated no significant differences between conditions on shooting precision (p = .125; ES: .54), speed-accuracy trade-off (p = .261; ES = .33), hits within the silhouette (p = .167; ES = .41), or shooting cycle times (p = .868; ES = .05); but accuracy was greater (p = .025; ES: .54) when fatigued. We concluded that overall shooting performance was not impaired by physical fatigue, and shooting accuracy appeared to be improved. Perhaps physical fatigue was not enough to impair shooting accuracy in this young adult group, as accuracy decline is expected instead when shooters are in an exhausted state. Further research is needed to confirm these findings and test this presumption.

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors.

Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers' inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers' inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers' physiological features and attention.

Effect of Globin Digests on Physical Fatigue in Mice.

Globin digest (GD) inhibits dietary hypertriglyceridemia; however, its effects on physical fatigue remain unknown. Therefore, this study aimed to investigate the potential anti-fatigue effects of GD. Repeated administration of GD and valine (Val)-Val-tyrosine (Tyr)-proline (Pro), a component of GD, for five days prevented the forced walking-induced decrease in locomotion. Furthermore, GD treatment reversed the forced walking-induced increase in blood lactate levels in mice and increased phosphorylated AMP-activated protein kinase (p-AMPK) in the soleus muscle, suggesting that the anti-fatigue effect of GD involves AMPK activation in the soleus muscle through reduced blood lactate.

High-Performance Work Systems and Coping Strategies in Regulating Burnout and Safety Workarounds in the Healthcare Sector.

The healthcare sector is essential for any country because it indirectly affects its economy. The productivity of land will increase if there is a healthy workforce, and it will enhance its economy, which will, in return, lead to the human welfare of the country. The present quantitative study has investigated the relationship between high-performance work systems (HPWS) on safety workarounds through the role of burnout as mediation, and explored coping strategies as a moderator between burnout and safety workarounds. These constructs play a vital role in efficiently managing different organizational activities to generate better productivity and employee performance, and educate employees about rules that can be used and adopted to ensure a healthy work-life. The data were collected from 550 nurses through a questionnaire in the healthcare sector of Lahore, Punjab (Pakistan). AMOS and SPSS were used to test the direct relationships between the constructs, and analyze the moderation of coping strategies and the mediation effect of burnout. The results have demonstrated the strong moderated mediation of coping strategies and burnout between existing HPWS and safety workarounds. The study of coping strategies would help managers and employees handle job stress and alleviate burnout in the healthcare sector through safety workarounds to increase effectiveness and efficiency.

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

BACKGROUND: Although some research has been done in the Mexican manufacturing industry regarding mental workload, none has explored its association with physical fatigue, body weight gain, and human error simultaneously. OBJECTIVE: This research examines the association between mental workload and physical fatigue, body weight gain, and human error in employees from the Mexican manufacturing systems through a mediation analysis approach. METHODS: A survey named Mental Workload Questionnaire was developed by merging the NASA-TLX with a questionnaire containing the mental workload variables mentioned above. The Mental Workload Questionnaire was applied to 167 participants in 63 manufacturing companies. In addition, the mental workload was used as an independent variable, while physical fatigue and body weight gain were mediator variables, and human error was a dependent variable. Six hypotheses were used to measure the relationships among variables and tested using the ordinary least squares regression algorithm. RESULTS: Findings indicated that mental workload significantly correlates with physical fatigue and human error. Also, the mental workload had a significant total association with human error. The highest direct association with body weight gain was provided by physical fatigue, and body weight gain had an insignificant direct association with human error. Finally, all indirect associations were insignificant. CONCLUSION: Mental workload directly affects human error, which physical fatigue does not; however, it does affect body weight gain. Managers should reduce their employees' mental workload and physical fatigue to avoid further problems associated with their health.

Correlates of depressive mood among graduate-level allied health students: An exploratory study examining trait energy and fatigue.

Objective: The objective of this study was to identify factors associated with the occurrence and severity of depressive mood states among graduate-level allied health students. Participants: Students (N = 77) completed this study. Methods: Participants completed a series of self-reported surveys measuring moods, lifestyle behaviors, trait mental and physical energy and fatigue, and objective assessments of Trail-Making Test Part-B, and muscle oxygen consumption. Multiple backwards linear regression models were fitted to identify factors associated with depressive mood states. Results: When accounting for all subjects, increased severity of depressive mood states was associated with worse sleep quality (SQ), increased sitting time (ST), and trait physical fatigue (TPF). When examining subjects reporting depressive mood states, increased severity of depressive mood states was associated with worse SQ, increased ST, decreased mental workload on non-school days, and trait physical energy (TPE). Conclusion: Adjustments in lifestyle factors such as sleep, mental workload, and ST, may ameliorate depressive mood states.