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.

Background: Heart rate variability (HRV) is a non-invasive measure of the function of the autonomic nervous system, and its dynamic nature may provide a means through which stroke and its associated complications may be predicted, monitored, and managed. Objective: The objective of this review is to identify and provide a critique on the most recent uses of HRV in stroke diagnosis/management and highlight areas that warrant further research. Methods: The MEDLINE, CINAHL, and OVID MEDLINE databases were canvassed using a systematic search strategy, for articles investigating the use of HRV in stroke diagnosis and management. Initial paper selections were based on title alone, and final paper inclusion was informed by a full-text critical appraisal. Results: The systematic search returned 98 records, of which 51 were unique. Following screening, 22 records were included in the final systematic review. The included papers provided some information regarding predicting incident stroke, which largely seems to be best predicted by time- and frequency-domain HRV parameters. Furthermore, post-stroke complications and functionality are similarly predicted by time- and frequency-domain parameters, as well as non-linear parameters in some instances. Conclusions: Current research provides good evidence that HRV parameters may have utility as a biomarker for stroke and for post-stroke complications and/or functionality. Future research would benefit from the integration of non-linear, and novel parameters, the hybridisation of HRV parameters, and the expansion of the utilisation of predictive regression and hazard modelling.

Background: Diabetes mellitus (DM) is a global metabolic epidemic associated with numerous adverse complications. Invasive finger prick tests or invasive monitors are currently the most common means of monitoring and controlling blood glucose levels (BGLs). Heart rate variability (HRV) is a noninvasive measure of the autonomic nervous system, and its dynamic physiological nature may provide an alternative means of blood glucose monitoring. However, the relationship between BGL and HRV parameters remains relatively unknown. Materials and Methods: Thirty-two participants with diabetes (39.97 ± 17.21 years of age) and 31 without diabetes (27.87 ± 10.55 years of age) participated in the current study. Fasting preceded a 10-min three-lead electrocardiogram (ECG), which was followed by a finger prick blood glucose assessment. Following this, a regular meal was consumed, and 30 min after ingestion, a second postprandial 10-min ECG was obtained, and blood glucose assessment was conducted. Results: Low-frequency (LF) power, high-frequency (HF) power, and total power (TP) of HRV were negatively associated with BGL in participants with DM. Additionally, the ratio of LF to HF was positively correlated with BGL. Duration of DM was also associated with multiple HRV parameters, with negative associations to both LF and HF parameters as well as TP. Conclusions: This study demonstrates links between specific HRV variables and BGL. In the future the dynamic nature of HRV could provide a unique and real-time method for monitoring BGL, for continuous noninvasive prediction and/or management of DM.

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.

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%.

Research utilizing event‐related potential (ERP) methods is generally biased with regard to sample representativeness. Among the myriad of factors that contribute to sample bias are researchers' assumptions about the extent to which racial differences in hair texture, volume, and style impact electrode placement, and subsequently, study eligibility. The current study examines these impacts using data collected from n = 213 individuals ages 17–19 years, and offers guidance on collection of ERP data across the full spectrum of hair types. Individual differences were quantified for hair texture using a visual scale, and for hair volume by measuring the amount of gel used in cap preparation. Electroencephalography data quality was assessed with multiple metrics at the preprocessing, post‐processing, and variable generation stages. Results indicate that hair volume is associated with small, but systematic differences in signal quality and signal amplitude. Such differences are highly problematic as they could be misattributed to cognitive differences among groups. However, inclusion of gel volume as a covariate to account for individual differences in hair volume significantly reduced, and in most cases eliminated, group differences. We discuss strategies for overcoming real and perceived technical barriers for researchers seeking to achieve greater inclusivity and representativeness in ERP research.