A survey of the prevalence of fatigue, its precursors and individual coping mechanisms among U.S. manufacturing workers

High-speed train drivers' human error under fatigue and stress: the role of situation awareness and individual differences

Fatigue and stress are critical variables that impair railway train drivers' safety performance, and individual differences may influence these effects. This study investigates how fatigue and stress affect high-speed train drivers' human error and the role of individual differences. We hypothesised that situation awareness (SA) mediates the effects of fatigue and stress on human error, and individual differences (age and work experience) moderate these effects. We surveyed 1,391 male drivers from eight Chinese railway bureaus and used PROCESS Macro for data analysis. The results revealed that fatigue and stress increased human error, directly and indirectly through SA. Age and work experience moderated the effect of fatigue and stress on SA, respectively. Older drivers had better SA under high fatigue, while more experienced drivers had better SA under high stress. These findings can inform more tailored safety management strategies to lower human error and enhance the safety of high-speed train operations.

Physical and psychological job demands and fatigue experience among offshore workers

Offshore operations are generally challenging and hazardous, and the workers are exposed to conditions that may lead to fatigue. A cross-sectional study evaluated physical and psychological job demands and their associations with fatigue among offshore workers. The offshore workers (n = 251) completed a questionnaire including demographic/job details, Multidimensional Fatigue Inventory (MFI‒20), and Job Content Questionnaire (JCQ). Data were analysed using linear regression modelling. Results indicated that the physical (particularly performing repetitive motions and applying pressure with hands and wrists) and psychological (e.g., intense task concentration and fast working) job demands were relatively high. The total mean (SD) fatigue score (MFI‒20) was 56.3 (13.9). Individual factors (Body Mass Index ‒ BMI), physical job demands (awkward working postures, frequent moving/lifting heavy objects and doing lots of physical efforts) and psychological job demands (task interruptions by other people and doing an excessive amount of work) were the main variables associated with different dimensions of fatigue in the multivariate models. While physical fatigue was only associated with the physical job demands, both physical and psychological job demands were significantly associated with mental fatigue in the multivariate context. The findings have possible implications for job design and implementation of intervention programmes to promote health and performance of the employees.

Physiological and perceptual consequences of trust in collaborative robots: An empirical investigation of human and robot factors

Measuring trust is an important element of effective human-robot collaborations (HRCs). It has largely relied on subjective responses and thus cannot be readily used for adapting robots in shared operations, particularly in shared-space manufacturing applications. Additionally, whether trust in such HRCs differ under altered operator cognitive states or with sex remains unknown. This study examined the impacts of operator cognitive fatigue, robot reliability, and operator sex on trust symptoms in collaborative robots through both objective measures (i.e., performance, heart rate variability) and subjective measures (i.e., surveys). Male and female participants were recruited to perform a metal surface polishing task in partnership with a collaborative robot (UR10), in which they underwent reliability conditions (reliable, unreliable) and cognitive fatigue conditions (fatigued, not fatigued). As compared to the reliable conditions, unreliable robot manipulations resulted in perceived trust, an increase in both sympathetic and parasympathetic activity, and operator-induced reduction in task efficiency and accuracy but not precision. Cognitive fatigue was shown to correlate with higher fatigue scores and reduced task efficiency, more severely impacting females. The results highlight key interplays between operator states of fatigue, sex, and robot reliability on both subjective and objective responses of trust. These findings provide a strong foundation for future investigations on better understanding the relationship between human factors and trust in HRC as well as aid in developing more diagnostic and deployable measures of trust.

Automated and Continuous Fatigue Monitoring in Construction Workers Using Forearm EMG and IMU Wearable Sensors and Recurrent Neural Network

About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Therefore, it is essential to monitor fatigue to reduce its adverse effects and prevent long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This study aims to develop a system for continuous workers' fatigue monitoring by predicting the aerobic fatigue threshold (AFT) using forearm muscle activity and motion data. The proposed system consists of five modules: Data acquisition, activity recognition, oxygen uptake prediction, maximum aerobic capacity (MAC) estimation, and continuous AFT monitoring. The proposed system was evaluated on the participants performing fourteen scaffold-building activities. The results show that the AFT features have achieved a higher accuracy of 92.31% in assessing the workers' fatigue level compared to heart rate (51.28%) and percentage heart rate reserve (50.43%) features. Moreover, the overall performance of the proposed system on unseen data using average two-min AFT features was 76.74%. The study validates the feasibility of using forearm muscle activity and motion data to workers' fatigue levels continuously.

A Survey of Human Gait-Based Artificial Intelligence Applications

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients' intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual's performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual's characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

The Silhouettes Fatigue Scale: a validity study with individuals with physical disabilities and chronic pain

PURPOSE

Fatigue is known to interfere with function in individuals with physical disabilities. In order to monitor changes in fatigue over time and evaluate the efficacy of treatments, psychometrically sound measures of fatigue are needed. The aim of this work was to evaluate the validity of the Silhouettes Fatigue Scale with English instructions (SFS-EN) in a sample of adults with physical disabilities living in the USA.

METHODS

Individuals with medical conditions associated with physical disabilities responded to an online survey that included the SFS-EN as well as another validated measure of fatigue (PROMIS short form-4a Fatigue Scale), and measures of pain intensity and pain catastrophizing.

RESULTS

523 individuals participated (mean age = 59.1 years; SD = 11.4). Most participants were Caucasian (89%), women (59%) and unemployed (71%). Results showed strong positive correlations between both measures of fatigue, supporting the convergent validity of the SFS-EN. In addition, the magnitude of this association was significantly greater than the association between the scores of the SFS-EN and the measures of pain intensity and pain catastrophizing, supporting the former's discriminant validity.

CONCLUSIONS

The findings extend previous results supporting the SFS as a brief, easy to administer and understand, and valid measure of fatigue.IMPLICATIONS FOR REHABILITATIONFatigue is common in adults with physical disabilities and chronic pain.The Silhouettes Fatigue Scale (SFS) is a new single-item measure of general fatigue.Findings show that the SFS with English instructions (SFS-EN) is an easy to understand measure.Results support the convergent and discriminant validity of the SFS-EN score in adults with physical disabilities and chronic pain.

The effect of flaxseed on physical and mental fatigue in children and adolescents with overweight/obesity: a randomised controlled trial

Anti-inflammatory agents such as long-chain n-3 fatty acids have been recognised to improve disease-related fatigue. We hypothesised that α-linolenic acid may also benefit in reduction of fatigue in low-grade inflammation such as overweight/obesity. Seventy-two healthy children and adolescents with BMI > 25 kg/m2 were randomised to flaxseed (n 38) and puffed wheat (n 34) groups. Participants consumed isoenergetic amounts of either 20 g/d flaxseed or 25 g/d puffed wheat for 4 weeks. Fatigue, mood feelings (depression, anxiety and stress), appetite, energy intake, weight, height and waist circumference were measured. Analysis was performed based on per-protocol and intention-to-treat (ITT) approaches. Consumption of flaxseed decreased mental fatigue and caused a significant between-group difference (P < 0·001). Although no significant change in physical and total score of fatigue was observed in either group, a significant between-group difference occurred due to a more remarkable change in these parameters in the flaxseed group. General fatigue, motivation, activity and mood feelings did not change significantly between groups. Flaxseed showed less benefit than puffed wheat on anthropometric measures as it caused a smaller increase in height (0·53 (sd 0·89) v. 1·09 (sd 0·87) cm, P = 0·03) and reduction in BMI (-0·25 (sd 0·63) v. -0·67 (sd 0·56) kg/m2, P = 0·01) than puffed wheat. Appetite and waist circumference decreased in both groups, but no significant difference was observed between groups. In ITT analysis, only alteration in mental fatigue was significant. In conclusion, consumption of flaxseed may improve mental fatigue in children with overweight/obesity. However, because of smaller increase in height, it is better to hinder administration of flaxseed during periods of growth.

A systematic review of the association between fatigue and cognition in chronic noncommunicable diseases

OBJECTIVES

Fatigue is one of the most common symptoms associated with chronic noncommunicable diseases, and it may also increase cognitive impairment. However, associations between fatigue and cognitive impairment in chronic illnesses remain unclear. Therefore, the purpose of this systematic review was to examine research that investigated associations between level of fatigue and cognitive status.

METHODS

PubMed/Medline, PsycINFO, CINAHL, and Cochrane Database were searched for articles published between 2012 and 2018 using search terms fatigue, cognition, and various iterations of these terms. Study quality was assessed by the Joanna Briggs Institute Critical Appraisal Checklist tool.

RESULTS

Of 1799 citations, 10 studies in samples of individuals with cancer, multiple sclerosis, neurosarcoidosis, and chronic fatigue syndrome met the inclusion criteria. Fatigue was found to be significantly correlated with cognitive impairment in one cancer-related study (r = -.480, p < .001), one multiple sclerosis study (β= -0.52, p < .0001), and two chronic fatigue syndrome studies (r = 0.397, p < .001; r = 0.388, p < .001).

DISCUSSION

There is insufficient evidence examining the relationship between fatigue and cognitive impairment in patients with chronic illnesses. As a result, more studies are needed that examine potential relationships between these two symptoms in order to develop effective treatments for individuals living with a chronic noncommunicable disease.

Investigation of hand muscle fatigue and its influential factors in manual tasks

Muscle fatigue (MF) can lead to musculoskeletal disorders (MSDs) in the long term; however, it can be managed if the causes are well known. This study aimed to examine the grip force (GF) and grip fatigue (GFa) of employees with light, moderate and heavy manual tasks using a dynamometer and find their possible relationship with other factors. The nature of heavy manual tasks led to more experience of GFa and GF of the right hand. Moreover, the equal need for both hands in occupations with light and moderate manual tasks is the reason for more GFa in the left hand. In this primary study, the height, weight and age of subjects and their exposure to vibration had a decisive effect on GF. In order to determine the accurate effects of the aforementioned risk factors on MF, it is recommended for future studies to be performed on larger populations.

Interventions to Mitigate Fatigue Induced by Physical Work: A Systematic Review of Research Quality and Levels of Evidence for Intervention Efficacy

OBJECTIVE

We present a literature review on workplace physical fatigue interventions, focusing on evaluating the methodological quality and strength of evidence.

BACKGROUND

Physical fatigue is a recognized workplace problem, with negative effects on performance and health-related complaints. Although many studies have focused on the mechanisms and consequences of fatigue, few have considered the effectiveness of interventions to mitigate fatigue.

METHOD

A systematic review of the workplace safety literature for controlled trials of physical fatigue interventions was conducted. Data on intervention type, subject characteristics, targeted tasks and body locations, outcome measures, and study design were extracted. The methodological quality for each study was evaluated using the PEDro scale, and the level of evidence was based on quality, amount, and consistency.

RESULTS

Forty-five controlled trials were reviewed, examining 18 interventions. We categorized those interventions into individual-focused (N = 28 studies, nine interventions), workplace-focused (N = 12 studies, five interventions), and multiple interventions (N = 5 studies, four interventions). We identified moderate evidence for interventions related to assistive devices and task variation. There was moderate evidence supporting no fatigue attenuation for the garment change category of interventions. The interventions in the remaining categories had limited to minimal evidence of efficacy. The heterogeneity of the included trials precludes the determination of effect size.

CONCLUSION

This review showed a lack of high levels of evidence for the effectiveness of most physical fatigue interventions.

APPLICATION

Due to a lack of high levels of evidence for any category of reviewed physical fatigue interventions, further high-quality studies are needed to establish the efficacy of others.

A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker's condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.

Assessing Rural Health Coalitions Using the Public Health Logic Model: A Systematic Review

CONTEXT

Rural communities face unique challenges including fewer healthcare providers and restricted access to nutritious foods, likely leading to poor health outcomes. Community health coalitions are groups of local organizations partnering to address local health needs. Employing such coalitions is one strategy for implementing policy-system-environment changes for improving rural health. However, their success is variable without standardized evaluation. In this review, rural community health coalitions were retrospectively assessed using the W.K. Kellogg Foundation Logic Model. Community health coalition-reported pathways through this model were explored using market basket analysis.

EVIDENCE ACQUISITION

During Spring 2018, PubMed, Web of Science, ScienceDirect, CINAHL, and PsycINFO were searched for (coalition) AND (rural) AND (health) AND (effectiveness OR impact OR outcome OR logic model). Full-text, peer-reviewed, English articles meeting PICOS criteria (Population, rural communities; Intervention, presence of a community health coalition; Comparator, the coalition over time; Outcomes, logic model pathways) were reviewed. During Summer and Fall 2018, coalition-reported pathways were categorized according to logic model inputs and resources; internal and external activities; outputs; short-, medium-, and long-term outcomes; and impact. Market basket analysis was conducted during Winter 2018.

EVIDENCE SYNTHESIS

The 10 most frequently reported pathway items were partner diversity; organizational structures; implementing pilot studies, programs, and interventions; funding; community engagement and outreach; university partners; holding regular meetings; having working groups and subcommittees; operating under or partnering with a regional research initiative; and conducting a community health and needs assessment. Half of community health coalitions reported 4 or more of the following: funding; partner diversity; university partners; organizational structures; community engagement and outreach; and implementing pilot studies, programs, and interventions.

CONCLUSIONS

Many rural community health coalitions reported inputs and capacity building; few impacted health. Recommending common early phase logic model pathways may facilitate downstream success.

A pilot study on the effects of DHA/EPA-enriched phospholipids on aerobic and anaerobic exercises in mice

DHA/EPA-PL andl-carnitine had significant effects on aerobic exercise, while astaxanthin improved anaerobic exercise. The possible mechanism involved carbohydrate and lipid metabolism, mitochondrial respiratory chain and tricarboxylic acid cycle.

Work Fatigue in a Non-Deployed Military Setting: Assessment, Prevalence, Predictors, and Outcomes

Although work fatigue represents an important issue among military personnel in combat settings, little attention has been paid to work fatigue in the non-deployed setting. This issue was addressed by (a) validating the Three-Dimensional Work Fatigue Inventory (3D-WFI) among non-deployed military personnel, (b) assessing the prevalence of work fatigue in a non-deployed setting, and (c) exploring several potential predictors and outcomes of work fatigue in this setting. Data came from a large national probability sample (N = 1375) of non-deployed Royal Canadian Air Force military personnel. Results demonstrated that the 3D-WFI provided a psychometrically sound assessment of physical, mental, and emotional work fatigue among military personnel, which was invariant across sex, age, military component, and military role. All three types of work fatigue were highly prevalent among military personnel in a non-deployed setting. In terms of predictors, job demands were positively associated, and distributive justice, perceived organizational support, physical activity and sleep quality were negatively associated with each type of work fatigue, whereas role ambiguity was positively associated with mental and emotional work fatigue, and interpersonal justice was negatively associated with physical and emotional fatigue. Abusive supervision and sleep quantity were unrelated to work fatigue. In terms of outcomes, the three types of fatigue were positively associated with workplace cognitive failures and work-to-family conflict. In contrast, mental and emotional work fatigue were negatively related to military morale and positively associated with turnover intentions. This study demonstrates that work fatigue is a critical issue among military personnel in non-deployed settings, and an essential issue for military policy development.

The rapid effects of eicosapentaenoic acid (EPA) enriched phospholipids on alleviating exercise fatigue in mice

It has been reported that docosahexaenoic acid/eicosapentaenoic acid (DHA/EPA) and phospholipids (PLs) play an important role in alleviating exercise fatigue. However, the difference of DHA and EPA in ameliorating exercise fatigue is still unclear. Furthermore, the comparative study about DHA/EPA-PLs and nonpolar DHA/EPA on exercise fatigue has not been reported. In the present study, the effects of DHA and EPA on exercise fatigue was firstly compared by conducting an exhaustion test, and the results showed that triglyceride (TG) with high ratio of EPA had a more significant effect on alleviating exercise fatigue than TG with a low ratio of EPA in mice. Therefore, eicosapentaenoic acid–ethyl ester (EPA–EE) and EPA–PL were then selected to compare the rapid effects of polar and nonpolar DHA/EPA on exercise fatigue in mice by a weight-loaded swimming exhaustion test. A single intake of EPA–PL but not EPA–EE significantly alleviated exercise fatigue in mice by increasing the lactic acid recycling rate as well as inhibiting glycogen consumption and muscle injury, suggesting that EPA–PL exhibited a rapid effect on alleviating exercise fatigue. The study might represent a potential novel candidate or targeted dietary pattern for alleviating exercise fatigue.

EPA-PL has rapid effects on alleviating exercise fatigue by inhibiting lactic acid accumulation, glycogen consumption and muscle injury.

A machine learning approach to detect changes in gait parameters following a fatiguing occupational task

The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities. Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.

Exosomes and Metabolic Function in Mice Exposed to Alternating Dark-Light Cycles Mimicking Night Shift Work Schedules

Sleep is an important modulator of metabolic function. Disruptions of sleep in circadian rhythm are common in modern societies and are associated with increased risk of developing cardiometabolic disorders. Exosomes are ubiquitous extracellular vesicles that may play a mechanistic role in metabolic derangements. We hypothesized that alternating dark-light cycles mimicking shift work in mice would alter fecal microbiota and colonic epithelium permeability and alter plasma exosome cargo and metabolic function. C57BL/6 mice were randomly assigned to (i) control day light (CL), or (ii) inverted dark-light every 2 weeks for 8 weeks (IN). Body weight, fat mass and HOMA-IR were measured, along with Tregs, metabolic, and resident macrophages in visceral white adipose tissue (vWAT). Fecal water samples were incubated with confluent colonic epithelium cell cultures in electric cell-substrate impedance sensing (ECIS) arrays, and plasma exosomes were added to differentiated adipocytes and insulin-induced pAKT/AKT expression changes were assessed by western blots. Mice exposed to IN showed elevated HOMA-IR, and their fecal samples showed altered microbiota which promote increased permeability of the colonic epithelial cell barrier. Plasma exosomes decreased pAKT/AKT responses to exogenous insulin compared to CL, and altered expression of circadian clock genes. Inflammatory macrophages (Ly-6c^high) were increased in IN-exposed vWAT, while Tregs were decreased. Thus, gut microbiota and the cargo of plasma exosomes are altered by periodic shifts in environmental lighting, and effectively alter metabolic function, possibly via induction of systemic inflammation and altered clock expression in target tissues. Further exploration of exosomal miRNA signatures in shift workers and their putative metabolic organ cell targets appears warranted.