The psychophysiological determinants of fatigue

Measuring Vocal Fatigability in Teachers: The Vocal Fatigability Scale for Teachers (VFS-T)

OBJECTIVES

This study focuses on the distinction between vocal fatigability and vocal fatigue (VF), highlighting the importance of vocal fatigability when assessing teachers' quality of life. Vocal fatigability is more critical for quality of life than performance or perceived VF because it considers the accumulation and chronicity of fatigue over isolated tasks. The objectives are to develop and validate the Vocal Fatigability Scale for Teachers (VFS-T), to assess teachers' levels of vocal fatigability, and to analyze variations based on personal and occupational traits. The VFS-T construct encompasses items related to vocal sensations and performance, their connection to accumulated demanding vocal activity, descriptions of recovery strategies, necessary rest periods, and the impact of VF on daily activities.

STUDY DESIGN

A single cross-sectional study with an ex post facto design was conducted.

METHODS

The sample consisted of 184 active teachers with various vocal conditions. They completed the VFS-T questionnaire, provided personal and occupational data, and completed the VHI-10 questionnaire. The data collected were analyzed for item refinement, internal structure, reliability, validity, diagnostic capacity, cutoff values, and group differences.

RESULTS

The item refinement and internal structure analysis revealed a scale comprising 17 items grouped into two factors. The VFS-T shows good reliability and validity, with cutoffs at <15 for no fatigability, 15-27 for moderate fatigability, and >28 for high fatigability. Most teachers fall into the moderate fatigability range, with 72% experiencing vocal fatigability.

CONCLUSIONS

The VFS-T is a reliable tool for detecting vocal fatigability in teachers. This scale focuses on the construct of vocal fatigability, which is related to chronicity of VF and limitations in daily activities in teachers.

Executive Functions Assessment Based on Wireless EEG and 3D Gait Analysis During Dual-Task: A Feasibility Study

Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.

The impact of videogames on the mood of amateur youth players during consecutive games

Introduction

Esports have experienced tremendous growth in recent years. In the scientific field, previous research has shown the determining role of psychology in competitive performance, but little is known about the factors that may be more determinant. In addition, in the amateur field, where fun and enjoyment are the most important factors, it has been observed that players can see their psychological state altered due to different factors, but it is not known if the outcome of the game (win or lose) can be influential. For this reason, the aim of the present investigation was to analyze changes in players' mood between three consecutive games as a function of the outcome of each game.

Methods

A total of 14 amateur players participated in the research, all of them with previous experience and being regular League of Legends players. The participants completed the POMS questionnaire before the start of each game and the outcome of each game was recorded at the end.

Results

The results showed that no significant pre-game differences were found in any of the games, regardless of winning or losing. Significant differences were found in the pre-game mood between the first and second game, according to the outcome of the first game, and between the second and third game, according to the outcome of the second game. Between the first and second games, there was a significant increase in depression (p = 0.038) and anger (p = 0.003) when the first game was lost; and between the second and third games, there was a decrease in tension (p = 0.003) and anger (p = 0.022) when the second game was won. In addition, it should be noted that fatigue increased significantly after each game, regardless of the outcome, and with respect to the change in mood, this was more noticeable when the first game was lost and the second was won, as significant changes were observed in tension (p = 0.028), depression (p = 0.030) and anger (p = 0.006).

Conclusion

Pre-match mood does not influence post-match performance, but mood changes do occur between successive matches depending on the outcome of the match.

How compulsive WeChat use and information overload affect social media fatigue and well-being during the COVID-19 pandemic? A stressor-strain-outcome perspective

Social media has been increasingly utilized as an effective avenue for individuals to obtain needed social support and health-related information, especially during the on-going global COVID-19 pandemic. However, surprisingly few empirical studies have concentrated on the detrimental impact of social media adoption on young adults' psychosocial well-being and mental health. Drawing upon previous stressor-strain-outcome theoretical paradigm (SSO), the present research investigates how psychosocial well-being assessments, especially compulsive WeChat use and information overload could trigger social media fatigue and, furthermore, how social media fatigue would ultimately result in emotional stress and social anxiety. This article utilized the cross-sectional design whereby statistical data were collected from 566 young people to test the conceptual research model. This research results demonstrate that perceived information overload through WeChat could significantly trigger social media fatigue among young people. Moreover, perceived information overload could indirectly predict emotional stress and social anxiety through the mediation of social media fatigue. This present work has vital theoretical and practical implications for widespread adoption of newly emerging communication technologies to enhance mental health and well-being among younger generation during recent public health crises.

Effectiveness of Mobile Technology in Managing Fatigue: Balert App

The performance of professional tasks with a high cognitive, emotional, and even physiological demand, can cause a state of mental fatigue, which implies attentional alterations, greater errors in the tasks performed and a decrease in personal and work productivity caused by a deterioration of the cognitive control processes. The present study presents a mobile phone application named BAlert that allows monitoring and controlling the body's fatigue processes based on the scores obtained in the Stroop effect and the heart rate variability. A pilot study has been carried out with a sample of 63 adults who have used the application a total of 942 times. The results allow us to classify the subjects, by logistic regression analysis, in their fatigue levels in 74% of the occasions. These results highlight the importance of this mobile application to control work fatigue processes in different possible scenarios (military, health, sports, business, etc.).

Using Muse: Rapid Mobile Assessment of Brain Performance

The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue. Cognitive fatigue - a neural state that is associated with an increased incidence of errorful performance - is responsible for accidents on a daily basis which at times can cost human lives. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain.

Impact of Weekly Physical Activity on Stress Response: An Experimental Study

The aim of this research is focused on analyzing the alteration of the psychophysiological and cognitive response to an objective computerized stress test (Determination Test - DT-, Vienna test System^®), when the behavioral response is controlled. The sample used was sports science students (N = 22), with a mean age of 22.82 (M_age = 22.82; SD_years = 3.67; M_{PhysicalActivity hours/Week} = 7.77; SD_hours/_week = 3.32) A quasi-experimental design was used in which the response of each participant to the DT test was evaluated. The variable “number of hours of physical activity per week” and the variable “level of behavioral response to stress” were controlled. Before and after this test, the following parameters were measured: activation and central fatigue (Critical Flicker Fusion Threshold (CFF Critical flicker fusion ascending and Critical flicker fusion descending; DC potential), and perceived exertion (Central Rating of Perceived Exertion and Peripheral Rating of Perceived Exertion). Significant differences were found in all of the measures indicated. The usefulness of this protocol and the measures used to analyze the stress response capacity of the study subjects are discussed.

Early Alpha Reactivity is Associated with Long-Term Mental Fatigue Behavioral Impairments

The quantitative analysis of electroencephalogram (qEEG) is a suitable tool for mental fatigue (MF) assessment. Here, we evaluated the effects of MF on behavioral performance and alpha power spectral density (PSD) and the association between early alpha PSD reactivity and long-term behavioral MF impairments. Nineteen right-handed adults (21.21 ± 1.77 years old) had their EEG measured during five blocks of the visual oddball paradigm (~ 60 min). A paired t-test was used to compare first and last block values of cognitive performance and alpha PSD. The sample was divided into high (HAG) and low alpha group (LAG) by early alpha PSD median values. The behavioral performance of the HAG and LAG was compared across the blocks by a two-way ANOVA with repeated measures (groups and blocks). MF impairs general behavioral performance and increases alpha PSD. The HAG presents more behavioral impairment when compared to LAG across the task. Simple linear regression between early alpha PSD and behavioral performance across the task can predict 19 to 39% of variation in general behavior impairment by MF. In conclusion, MF induction impairs general behavioral and increases alpha PSD. The other finding was that higher alpha PSD reactivity is associated to higher long-term behavioral impairments of MF. This work contributes to existing knowledge of MF by providing evidence that the possibility of investigating early electrophysiological biomarkers to predict long-term MF impairments.

Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data

Driver fatigue can impair performance as much as alcohol does. It is the most important road safety concern, causing thousands of accidents and fatalities every year. Thanks to technological developments, wearable, single-channel EEG devices are now getting considerable attention as fatigue monitors, as they could help drivers to assess their own levels of fatigue and, therefore, prevent the deterioration of performance. However, the few studies that have used single-channel EEG devices to investigate the physiological effects of driver fatigue have had inconsistent results, and the question of whether we can monitor driver fatigue reliably with these EEG devices remains open. Here, we assessed the validity of a single-channel EEG device (TGAM-based chip) to monitor changes in mental state (from alertness to fatigue). Fifteen drivers performed a 2-h simulated driving task while we recorded, simultaneously, their prefrontal brain activity and saccadic velocity. We used saccadic velocity as the reference index of fatigue. We also collected subjective ratings of alertness and fatigue, as well as driving performance. We found that the power spectra of the delta EEG band showed an inverted U-shaped quadratic trend (EEG power spectra increased for the first hour and half, and decreased during the last thirty minutes), while the power spectra of the beta band linearly increased as the driving session progressed. Coherently, saccadic velocity linearly decreased and speeding time increased, suggesting a clear effect of fatigue. Subjective data corroborated these conclusions. Overall, our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task as driving.

Experimental study on visual detection for fatigue of fixed-position staff

Fatigue can lead to decreased work performance and poorer safety and health condition. Fatigue is ubiquitous in production and in life, while the research on it is mainly concentrated in the automotive driving, aircraft piloting and other fields, and it is insufficient to study on the fatigue of fixed-position staff. This paper puts forward a non-contact visual image method, which can monitor the extent of fatigue of fixed-position staff. Fatigue threshold used in judgment is obtained by processing the recorded data of visual images of the experimental subjects when fatiguing and by analyzing eye closure time, percentage of eyelid closure (PERCLOS) value, frequency and number of blinks. The results show that there is significant difference among the four indicators before and after experiment subjects undergo fatigue. The fatigue of experimental subjects is obvious when eye closure time is 3.5 s/min, PERCLOS value 6%, and blink frequency 0.4 times/s. This provides a reference for a wider range of detection of fatigue and a method for avoiding mistakes and accidents.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

A comprehensive conceptual framework for road safety strategies

Road safety strategies (generally called Strategic Highway Safety Plans in the USA) provide essential guidance for actions to improve road safety, but often lack a conceptual framework that is comprehensive, systems theory based, and underpinned by evidence from research and practice. This paper aims to incorporate all components, policy tools by which they are changed, and the general interactions between them. A framework of nine mutually interacting components that contribute to crashes and ten generic policy tools which can be applied to reduce the outcomes of these crashes was developed and used to assess 58 road safety strategies from 22 countries across 15 years. The work identifies the policy tools that are most and least widely applied to components, highlighting the potential for improvements to any individual road safety strategy, and the potential strengths and weaknesses of road safety strategies in general. The framework also provides guidance for the development of new road safety strategies, identifying potential consequences of policy tool based measures with regard to exposure and risk, useful for both mobility and safety objectives.

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Dimensions of pure chronic fatigue: psychophysical, cognitive and biological correlates in the chronic fatigue syndrome

OBJECTIVES

To investigate associated dimensions of fatigue regarding cognitive impairment, psychomotor performances, muscular effort power and circulating cytokine levels and their relations to symptom intensity in a sample of pure chronic fatigue syndrome (CFS) patients without overlapping objective sleepiness or sleep disorders.

METHODS

16 CFS patients were compared to 14 matched controls. We assessed structured symptom-scales, polysomnography, multiple sleep latency tests, attention (Zazzo-Cancellation ZCT, digit-symbol-substitution DSST), psychomotor vigilance and speed (PVT, finger tapping test, FTT), dynamometer handgrip force (tonic and phasic trials) and circulating cytokines (IFN-γ, IL-1b, IL-6, IL-8, IL-10, TNF-α).

RESULTS

In addition to fatigue, CFS patients presented with higher affective symptom intensity and worse perceived sleep quality. Polysomnography showed more slow-wave sleep and microarousals in CFS but similar sleep time, efficiency and light-sleep durations than controls. Patients presented with impaired attention (DSST, ZCT), slower reaction times (PVT) but not with lower hit rates (FTT). Notwithstanding lower grip strength during tonic and phasic trials, CFS also presented with higher fatigability during phasic trials. Cytokine levels were increased for IL-1b, IL-8, IL-10 and TNF-α and fatigue intensity was correlated to grip strength and IL-8.

CONCLUSIONS

In contrast to sleepiness, chronic fatigue is a more complex phenomenon that cannot be reduced to one single measured dimension (i.e., sleep propensity). Showing its relations to different measurements, our study reflects this multidimensionality, in a psychosomatic disorder such as CFS. To obtain objective information, routine assessments of fatigue should rule out sleepiness, combine aspects of mental and physical fatigue and focus on fatigability.

Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts

Despite the known dangers of driver fatigue, it is a difficult construct to study empirically. Different forms of task-induced fatigue may differ in their effects on driver performance and safety. Desmond and Hancock (2001) defined active and passive fatigue states that reflect different styles of workload regulation. In 2 driving simulator studies we investigated the multidimensional subjective states and safety outcomes associated with active and passive fatigue. Wind gusts were used to induce active fatigue, and full vehicle automation to induce passive fatigue. Drive duration was independently manipulated to track the development of fatigue states over time. Participants were undergraduate students. Study 1 (N = 108) focused on subjective response and associated cognitive stress processes, while Study 2 (N = 168) tested fatigue effects on vehicle control and alertness. In both studies the 2 fatigue manipulations produced different patterns of subjective response reflecting different styles of workload regulation, appraisal, and coping. Active fatigue was associated with distress, overload, and heightened coping efforts, whereas passive fatigue corresponded to large-magnitude declines in task engagement, cognitive underload, and reduced challenge appraisal. Study 2 showed that only passive fatigue reduced alertness, operationalized as speed of braking and steering responses to an emergency event. Passive fatigue also increased crash probability, but did not affect a measure of vehicle control. Findings support theories that see fatigue as an outcome of strategies for managing workload. The distinction between active and passive fatigue is important for assessment of fatigue and for evaluating automated driving systems which may induce dangerous levels of passive fatigue.

Evaluating the Effectiveness of Using Electroencephalogram Power Indices to Measure Visual Fatigue

Electroencephalography (EEG) is widely used in cognitive and behavioral research. This study evaluates the effectiveness of using the EEG power index to measure visual fatigue. Three common visual fatigue measures, critical-flicker fusion (CFF), near-point accommodation (NPA), and subjective eye-fatigue rating, were used for comparison. The study participants were 20 men with a mean age of 20.4 yr. ( SD = 1.5). The experimental task was a car-racing video game. Results indicated that the EEG power indices were valid as a visual fatigue measure and the sensitivity of the objective measures (CFF and EEG power index) was higher than the subjective measure. The EEG β and EEG α were effective for measuring visual fatigue in short- and long-duration tasks, respectively. EEG β/α were the most effective power indexes for the visual fatigue measure.

Fatigue and tiredness in people with spinal cord injury

OBJECTIVE

Fatigue is a common symptom in people with neurological injury such as spinal cord injury (SCI), though its nature and occurrence in people with SCI are not well understood. The objective of this research was to investigate fatigue and its relationship to factors such as mood states and self efficacy in adult people with SCI compared to able-bodied controls.

METHODS

Participants included 41 adults with SCI living in the community and 41 able-bodied controls matched for age and sex ratios and education. All participants first completed a comprehensive psychological assessment and were then asked to take part in a 2-3 hour session composed of a regimen of cognitive tasks that required constant concentration and attention. Participants were assessed after completing this task.

RESULTS

The SCI group was found to have significantly elevated levels of fatigue, as well as elevated depressive mood, anxiety and poor self-efficacy. The SCI group was also found to suffer excessive levels of tiredness as a consequence of the 2-3 hour task. Factors such as depressive mood and poor self-efficacy were shown to increase the risk of excessive tiredness.

CONCLUSION

People with a neurological injury such as SCI have a high risk of having fatigue and are susceptible to experiencing excessive tiredness when performing extended tasks, and the presence of elevated depressive mood or poor expectations towards self management will increase this susceptibility. Implications for managing fatigue and improving social access in SCI populations are discussed.

The Relationship Between Spectral Changes in Heart Rate Variability and Fatigue

Fatigue is a prevalent problem in the workplace and a common symptom of many diseases. However, its relationship with the autonomic nervous system, specifically with sympathetic arousal, needs clarification. The objective of this study was to determine the association between fatigue and heart rate variability (HRV). HRV is regarded as an indicator of the autonomic regulation activity of heart rate, specifically sympathetic and parasympathetic activity. Spectral changes in low-frequency (LF; 0.04–0.15 Hz) and high-frequency (HF; 0.15–0.4 Hz) components of HRV have been reported to be associated with distressing conditions such as hemorrhagic shock, acute myocardial infarction, elevated anxiety, and depressed mood. While HRV changes have been found in persons with chronic fatigue syndrome, its association with fatigue in healthy individuals still needs clarification. HRV was assessed in a total of 50 participants who were asked to perform a task until becoming fatigued. Low-frequency HRV activity increased, while indices of parasympathetic modulation such as RMSSD and pNN50 remained stable as participants experienced fatigue, suggesting that fatigue in healthy individuals may be associated with increased sympathetic arousal. In addition, employing multiple regression analyses, we could positively associate the change in LF/HF HRV ratio from baseline to fatigue with factors such as emotional stability, warmth and tension and negatively associate it with social boldness and self-reported levels of vigor.

Functional changes in cerebral and paraspinal muscle physiology of healthy women during exposure to whole-body vibration

The objective of this study was to investigate the effects of whole-body vibration on multiple tissues simultaneously in fourteen healthy women. On three separate days, participants were exposed to frequencies, 3, 4.5, or 6 Hz (at 0.9 g(r.m.s) acceleration in vertical direction) per day on a simulator for 16 min. While sitting 'with' and 'without' backrest support, participants also performed handgrip contractions for 1 min. Cerebral and lumbar muscle oxygenation and blood volume responses were measured using near-infrared spectroscopy. Cardiorespiratory responses were collected using a metabolic cart. In general, cerebral and cardiorespiratory responses increased with vibration compared to without vibration, whereas in the lumbar region oxygenation and blood volume responses decreased. Greatest cerebral responses were observed at 6 Hz (P<0.05). When compared to exposure to vibration without performing work, significant decrease in lumbar responses was observed during handgrip contractions in both conditions of sitting 'with' and 'without' a backrest (P<0.05). Such decreases in the lumbar responses suggest postural load due to prolonged sitting combined with physical activity during vibration, might reduce vascular supply to the paraspinal muscles. This study reiterates the importance of understanding the physiological basis for various health disorders in women due to exposure to whole-body vibration.