Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications.

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited. As such, the present study psychophysiologically examined monotonous driving, fatigue, and sleepiness in a group of passenger train drivers and a group of non-professional drivers. Sixty-three train drivers and thirty non-professional drivers participated in the present study, which captured 32-lead electroencephalogram (EEG) data during a monotonous driving task. Fatigue and sleepiness were self-evaluated using the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Karolinksa Sleepiness Scale, and the Checklist of Individual Strength. Unexpectedly, fatigue and sleepiness scores did not significantly differ between the groups; however, train drivers generally scored lower than non-professional drivers, which may be indicative of individual and/or industry attempts to reduce fatigue. Across both groups, fatigue and sleepiness scores were negatively correlated with theta, alpha, and beta EEG variables clustered towards the fronto-central and temporal regions. Broadly, these associations may reflect a monotony-associated blunting of neural activity that is associated with a self-reported fatigue state.

Evaluating High and Low Heart Rate Variability Response and Neurocognitive Performance in Workers: An Exploratory Study.

Heart rate variability (HRV) has the potential to be a predicting factor of cognitive performance. The present research aimed to explore the differences in neurocognitive performance of workers with high HRV and low HRV. A total of 48 white-collar workers and 53 blue-collar workers were assessed. An electrocardiogram was used to obtain HRV data, whereby a 10 min baseline and an active (neuropsychological task) recording were taken. Median splits were performed on data to obtain high- and low-HRV groups. The Cambridge Neuropsychological Test Automated Battery, specifically, the spatial working memory, attention-switching task, rapid visual processing, and spatial span were used. Higher HRV (RMSSD and HF) was linked to better neurocognitive performance measures. Interestingly, the blue- and white-collar groups exhibited different correlations and, in some cases, showed an inverse relationship with the same variables. The differences observed in the present study demonstrate the importance of assessing task-dependent HRV parameters.

Neuropsychological Performance and Cardiac Autonomic Function in Blue- and White-Collar Workers: A Psychometric and Heart Rate Variability Evaluation.

The 21st century has brought a growing and significant focus on performance and health within the workforce, with the aim of improving the health and performance of the blue- and white-collar workforce. The present research investigated heart rate variability (HRV) and psychological performance between blue and white-collar workers to determine if differences were evident. A total of 101 workers (n = 48 white-collar, n = 53 blue-collar, aged 19-61 years) underwent a three lead electrocardiogram to obtain HRV data during baseline (10 min) and active (working memory and attention) phases. The Cambridge Neuropsychological Test Automated Battery, specifically the spatial working memory, attention switching task, rapid visual processing and the spatial span, were used. Differences in neurocognitive performance measures indicated that white-collar workers were better able to detect sequences and make less errors than blue-collar workers. The heart rate variability differences showed that white-collar workers exhibit lower levels of cardiac vagal control during these neuropsychological tasks. These initial findings provide some novel insights into the relationship between occupation and psychophysiological processes and further highlight the interactions between cardiac autonomic variables and neurocognitive performance in blue and white-collar workers.

Associations between Sleep Quality and Heart Rate Variability: Implications for a Biological Model of Stress Detection Using Wearable Technology.

INTRODUCTION: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals. AIM: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. METHODS: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). RESULT: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. CONCLUSION: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.

Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks.

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

The relationship between neurocognitive performance and HRV parameters in nurses and non-healthcare participants.

Nurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non-invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non-nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini-Mental State Examination and Cognistat psychometric questionnaires. ECG-derived HRV parameters were acquired during the Trier Social Stress Test. Between-group differences were found in domain-specific cognitive performance for the similarities (p = .03), and judgment (p = .002) domains and in the following HRV parameters: SDNNbaseline, (p = .004), LFpreparation (p = .002), SDNNpreparation (p = .002), HFpreparation (p = .02), and TPpreparation (p = .003). Negative correlations were found between HF power and domain-specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain-specific cognitive performance in the non-nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition.

Anxiety Linked to COVID-19: A Systematic Review Comparing Anxiety Rates in Different Populations.

The COVID-19 pandemic has incited a rise in anxiety, with uncertainty regarding the specific impacts and risk factors across multiple populations. A qualitative systematic review was conducted to investigate the prevalence and associations of anxiety in different sample populations in relation to the COVID-19 pandemic. Four databases were utilised in the search (Medline, EMBASE, CINAHL, and PsycINFO). The review period commenced in April 2021 and was finalised on 5 July 2021. A total of 3537 studies were identified of which 87 were included in the review (sample size: 755,180). Healthcare workers had the highest prevalence of anxiety (36%), followed by university students (34.7%), the general population (34%), teachers (27.2%), parents (23.3%), pregnant women (19.5%), and police (8.79%). Risk factors such as being female, having pre-existing mental conditions, lower socioeconomic status, increased exposure to infection, and being younger all contributed to worsened anxiety. The review included studies published before July 2021; due to the ongoing nature of the COVID-19 pandemic, this may have excluded relevant papers. Restriction to only English papers and a sample size > 1000 may have also limited the range of papers included. These findings identify groups who are most vulnerable to developing anxiety in a pandemic and what specific risk factors are most common across multiple populations.

Assessing cardiovascular links to depression and anxiety in Australian professional drivers.

Train and truck drivers experience a myriad of unique occupational factors, which have been postulated to contribute to a high incidence of health conditions such as depression anxiety and cardiovascular disease amongst this population. The present study aimed to identify associations between heart rate variability and negative mood states such as depression and anxiety in a cohort of Australian truck and train drivers. 120 professional drivers (60 truck drivers, 60 train drivers) were recruited from the local community. Participants complete a battery of psychometric questionnaires to assess levels of negative mood states such as depression and anxiety. Participants then completed a baseline (resting) and active (driving) task while concurrent electrocardiography data was collected to obtain heart rate variability parameters. Anxiety and depression were found to be associated with increases in low frequency heart rate variability and sympathovagal balance, and a reduction in total power. The present study identified associations between negative mood states and heart rate variability parameters that are unique to this cohort.

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review.

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder.

OBJECTIVE: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress. METHODS: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers. RESULTS: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving.

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual's cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task.

Associations Between Workplace Factors and Depression and Anxiety in Australian Heavy Vehicle Truck Drivers.

INTRODUCTION: A number of health issues have been identified as prevalent within the Australian heavy vehicle driving population. Mental illnesses, such as depression and anxiety, are among those disorders that have been regularly reported, however, the contributing factors are yet to be elucidated. METHODS: This study aimed to assess the associations between workplace factors such as years of employment, social interaction and shift length, with depressive and anxious symptomology in a cohort of 60 Australian heavy vehicle drivers. RESULTS: Significant positive associations were identified between depression and alcohol use (P = 0.044), coffee consumption (P = 0.037), number of accidents during career (P = < 0.004), and number of hours driving per shift (P ≤ 0.001). Anxiety was found to be positively associated with a number of hours driving per week (P ≤ 0.001), and the number of accidents or near misses during a driving career (P = 0.039). CONCLUSION: Several workplace factors were identified as being correlated to depression or anxiety within this cohort, suggesting potential changes to rostering systems and education regarding alcohol use may benefit the mental health of this driver population.

Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables.

Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ensure an accurate measure of the effects of additive acute stress.

Electroencephalographic prediction of global and domain specific cognitive performance of clinically active Australian Nurses.

OBJECTIVE: To investigate the relationship between EEG activity and the global and domain specific cognitive performance of healthy nurses, and determine the predictive capabilities of these relationships. APPROACH: Sixty-four nurses were recruited for the present study, and data from 61 were utilised in the present analysis. Global and domain specific cognitive performance of each participant was assessed psychometrically using the Mini-mental state exam and the Cognistat, and a 32-lead monopolar EEG was recorded during a resting baseline phase and an active phase in which participants completed the Stroop test. MAIN RESULTS: Global cognitive performance was successfully predicted (81%-85% of variance) by a combination of fast wave activity variables in the alpha, beta and theta frequency bands. Interestingly, predicting domain specific performance had varying degrees of success (42%-99% of the variance predicted) and relied on combinations of both slow and fast wave activity, with delta and gamma activity predicting attention performance; delta, theta, and gamma activity predicting memory performance; and delta and beta variables predicting judgement performance. SIGNIFICANCE: Global and domain specific cognitive performance of Australian nurses may be predicted with varying degrees of success by a unique combination of EEG variables. These proposed models image transitory cognitive declines and as such may prove useful in the prediction of early cognitive impairment, and may enable better diagnosis, and management of cognitive impairment.

Impact of acute stress on cortical electrical activity and cardiac autonomic coupling.

Assessment of heart rate variability (reflective of the cardiac autonomic nervous system) has shown some predictive power for stress. Further, the predictive power of the distinct patterns of cortical brain activity and - cardiac autonomic interactions are yet to be explored in the context of acute stress, as assessed by an electrocardiogram and electroencephalogram. The present study identified distinct patterns of neural-cardiac autonomic coupling during both resting and acute stress states. In particular, during the stress task, frontal delta waves activity was positively associated with low-frequency heart rate variability and negatively associated with high-frequency heart rate variability. Low high-frequency power is associated with stress and anxiety and reduced vagal control. A positive association between resting high-frequency heart rate variability and frontocentral gamma activity was found, with a direct inverse relationship of low-frequency heart rate variability and gamma wave coupling at rest. During the stress task, low-frequency heart rate variability was positively associated with frontal delta activity. That is, the parasympathetic nervous system is reduced during a stress task, whereas frontal delta wave activity is increased. Our findings suggest an association between cardiac parasympathetic nervous system activity and frontocentral gamma and delta activity at rest and during acute stress. This suggests that parasympathetic activity is decreased during acute stress, and this is coupled with neuronal cortical prefrontal activity. The distinct patterns of neural-cardiac coupling identified in this study provide a unique insight into the dynamic associations between brain and heart function during both resting and acute stress states.

The roles of dispositional coping style and social support in helping people with respiratory disease cope with a breathlessness crisis.

AIM: To explore the role of coping moderators in self-management of breathlessness crises by people with advanced respiratory disease. DESIGN: A secondary analysis of semi-structured interview data. METHODS: Interviews with patients who had advanced respiratory disease, chronic breathlessness and at least one experience where they considered presenting to Emergency but self-managed instead (a "near miss"). Participants were recruited from New South Wales, Queensland, Victoria, South Australia or Tasmania. Eligible caregivers were those who contributed to Emergency-related decision-making. Interviews were coded inductively and then deductively against the coping moderators social support and dispositional coping style, defined by the Transactional Model of Stress and Coping. RESULTS: Interviews were conducted between October 2015 - April 2016 with 20 patients and three caregivers. Social networks offered emotional and practical support but also had potential for conflict with patients' 'hardy' coping style. Patient hardiness (characterized by a sense of 'commitment' and 'challenge') promoted a proactive approach to self-management but made some patients less willing to accept support. Information-seeking tendencies varied between patients and were sometimes shared with caregivers. An optimistic coping style appeared to be less equivocally beneficial. CONCLUSION: This study shows that social support and coping style may influence how people self-manage through their breathlessness crises and identified ways coping moderators can facilitate or hinder effective self-management. IMPACT: This study confers insights into how social-support and coping style can be supported and optimized to facilitate breathlessness self-management. Acknowledging coping moderator interactions is beneficial for developing resources and strategies that recognise patient mastery.

A systematic review of the current evidence regarding interventions for anxiety, PTSD, sleepiness and fatigue in the law enforcement workplace.

Law enforcement is inherently stressful, and police officers are particularly vulnerable to mental and physical disorders. As such, researchers are currently assessing intervention strategies that may combat or manage these psychological, physical and mental issues. To review most recent information regarding anxiety, PTSD, and sleepiness and fatigue and identify the interventions and treatments proposed to overcome work related stressors and associated mental illnesses inflicting law enforcement officers. The EMBASE, OVID MEDLINE and PsycINFO databases were canvassed for articles investigating anxiety, post-traumatic stress disorder, sleepiness, and fatigue. Initial article selections were made based on title, whilst final inclusion was informed by a full critical appraisal with respect to the primary and secondary effects. The systematic search returned 363 records, of which 183 were unique. Following screening, 43 records were included in the final review. The included literature assessed the efficacy of several interventions, and provided a number of recommendations regarding interventions, and policy. Moreover, literature indicates that police officers benefit from interventions targeting work-related stress and potential psychological disorders, if these interventions are continuous. Furthermore, larger controlled studies are required to further elucidate the benefits of psychosocial intervention in law enforcement.

Prevalence and Risk Factors of Depression, Anxiety, and Stress in a Cohort of Australian Nurses.

Nurses remain at the forefront of patient care. However, their heavy workload as a career can leave them overworked and stressed. The demanding nature of the occupation exposes nurses to a higher risk of developing negative mental states such as depression, anxiety, and stress. Hence, the current study aimed to assess the prevalence and risk factors of these mental states in a representative sample of Australian nurses. The Depression Anxiety Stress Scale was administered to 102 nurses. Information about demographic and work characteristics were obtained using lifestyle and in-house designed questionnaires. Prevalence rates of depression, anxiety, and stress were found to be 32.4%, 41.2%, and 41.2% respectively. Binominal logistic regressions for depression and stress were significant (p = 0.007, p = 0.009). Job dissatisfaction significantly predicted a higher risk of nurses developing symptoms of depression and stress respectively (p = 0.009, p = 0.011). Poor mental health among nurses may not only be detrimental to the individual but may also hinder professional performance and in turn, the quality of patient care provided. Further research in the area is required to identify support strategies and interventions that may improve the health and wellbeing of nursing professionals and hence the quality of care delivered.

Peripheral Biomarker for Vascular Disorders.

Atherosclerosis is the underlying cause of most myocardial infarction (MI) and ischaemic stroke episodes. An early sign of atherosclerosis is hypertrophy of the arterial wall. It is known that increased intima media thickness (IMT) is a non-invasive marker of arterial wall alteration, which can easily be assessed in the carotid arteries by high-resolution B-mode ultrasound. Similarly, the other key element of MI and ischaemic strokes is the N-methyl-D-aspartate (NMDA) receptor which is an ionotropic glutamate receptor that mediates the vast majority of excitatory neurotransmission in the brain. NMDA activation requires the binding of both glutamate and a coagonist like D-serine to its glycine site. A special enzyme, serine racemase (SR), is required for the conversion of L-serine into D-serine, and alterations in SR activities lead to a variety of physiological and pathological conditions ranging from synaptic plasticity to ischemia, MI, and stroke. The amount of D-serine available for the activation of glutamatergic signalling is largely determined by SR and we have developed ways to estimate its levels in human blood samples and correlate it with the IMT. This research based short communication describes our pilot study, which clearly suggests that there is a direct relationship between the SR, D-serine, and IMT. In this article, we will discuss whether the activity of SR can determine the future consequences resulting from vascular pathologies such as MI and stroke.

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers.

OBJECTIVE: In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers. APPROACH: Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20). MAIN RESULTS: Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores. SIGNIFICANCE: Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.