Objective assessment of mental stress in individuals with different levels of effort reward imbalance or overcommitment using heart rate variability: a systematic review

Cognitive and Emotional Irritation in German Veterinarians with Different Levels of Overcommitment

BACKGROUND

veterinary medicine is among the most stressful professions and is characterized by long working hours and high emotional demands. This cross-sectional study aimed to investigate the relationship between overcommitment and psychological stress (on the basis of irritation) among veterinarians in Germany, with a particular focus on age differences.

METHODS

the analysis included a sample of 995 veterinarians divided into three age groups: <35 years, 35-45 years and >45 years. Sociodemographic and job-related data as well as the overcommitment scale (OC, subscale of the Effort-Reward-Imbalance (ERI) questionnaire), and the irritation scale (IS), were both collected.

RESULTS

the results reveal that younger (vs. older) veterinarians are significantly more likely to have high overcommitment levels. This group also reported higher-than-average levels of cognitive and emotional irritation, whereas the oldest age group reported comparatively lower overcommitment levels. Age and years of work were negatively correlated with overcommitment, and overcommitment was strongly positively correlated with cognitive and emotional irritation.

CONCLUSIONS

this study highlights the need for preventative measures to reduce overcommitment and mental stress, particularly among young veterinarians. Interventions during veterinarian studies and in the workplace that promote excessive expectations and stress are crucial to ensure long-term mental health and job satisfaction among this professional group.

Individual Work Attitudes and Work Ability

Work capacity depends on many factors, including the age and health status of the employee, but also on personal characteristics and attitudes, such as reduced tolerance of unfavorable working conditions (Work Annoyance, WA), excessive commitment to work (Overcommitment, OC), passion for work (Work Engagement, WE), and social interactions (Social Capital, SC). A total of 1309 workers who underwent a medical examination at work completed questionnaires on work attitudes and assessed their work ability using the Work Ability Score (WAS). The relationship between variables expressing work attitudes and WAS was studied using hierarchical linear regression and moderation analyses. WA is associated with low WAS values; SC is a positive predictor of WAS and moderates the effect of WA on WAS. OC reduces work ability, while Vigor and Dedication, components of WE, have a strong positive effect on work ability. To improve the work ability of employees, employers and managers should improve social relations in the workplace and discourage overcommitment. A positive working environment can increase engagement and avoid triggers of work annoyance.

Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery

PURPOSE

This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.

METHODS

For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.

RESULTS

The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253).

CONCLUSIONS

The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.

Physiologic measurements of cognitive load in clinical reasoning

OBJECTIVES

Cognitive load is postulated to be a significant factor in clinical reasoning performance. Monitoring physiologic measures, such as heart rate variability (HRV) may serve as a way to monitor changes in cognitive load. The pathophysiology of why HRV has a relationship to cognitive load is unclear, but it may be related to blood pressure changes that occur in a response to mental stress.

METHODS

Fourteen residents and ten attendings from Internal Medicine wore Holter monitors and watched a video depicting a medical encounter before completing a post encounter form used to evaluate their clinical reasoning and standard psychometric measures of cognitive load. Blood pressure was obtained before and after the encounter. Correlation analysis was used to investigate the relationship between HRV, blood pressure, self-reported cognitive load measures, clinical reasoning performance scores, and experience level.

RESULTS

Strong positive correlations were found between increasing HRV and increasing mean arterial pressure (MAP) (p=0.01, Cohen's d=1.41). There was a strong positive correlation with increasing MAP and increasing cognitive load (Pearson correlation 0.763; 95 % CI [; 95 % CI [-0.364, 0.983]). Clinical reasoning performance was negatively correlated with increasing MAP (Pearson correlation -0.446; 95 % CI [-0.720, -0.052]). Subjects with increased HRV, MAP and cognitive load were more likely to be a resident (Pearson correlation -0.845; 95 % CI [-0.990, 0.147]).

CONCLUSIONS

Evaluating HRV and MAP can help us to understand cognitive load and its implications on trainee and physician clinical reasoning performance, with the intent to utilize this information to improve patient care.

Work stress and changes in heart rate variability among employees after first acute coronary syndrome: a hospital-based longitudinal cohort study

Background

Work stress is considered as a risk factor for coronary heart disease, but its link with heart rate variability (HRV) among heart attack survivors is unknown yet. The aim of this study was to investigate associations between baseline work stress and the changes of HRV over one-year after onset of acute coronary syndrome (ACS).

Methods

Hundred and twenty-two patients with regular paid work before their first ACS episode were recruited into this hospital-based longitudinal cohort study. During hospitalization (baseline), all patients underwent assessments of work stress by job strain (JS) and effort-reward imbalance (ERI) models, and were assigned into low or high groups; simultaneously, sociodemographic and clinical data, as well depression, anxiety, and job burnout, were collected. Patients were followed up 1, 6, and 12 months after discharge, with HRV measurements at baseline and each follow-up point. Generalized estimating equations were used to analyze the effects of baseline work stress on HRV over the following 1 year.

Results

After adjusting for baseline characteristics and clinical data, anxiety, depression, and burnout scores, high JS was not associated with any HRV measures during follow-up (all p > 0.10), whereas high ERI was significantly related to slower recovery of 5 frequency domain HRV measures (TP, HF, LF, VLF, and ULF) (all p < 0.001), and marginally associated with one time domain measure (SDNN) (p = 0.069). When mutually adjusting for both work stress models, results of ERI remained nearly unchanged.

Conclusion

Work stress in terms of ERI predicted lower HRV during the one-year period after ACS, especially frequency domain measures.

Heart Rate Variability during Online Video Game Playing in Habitual Gamers: Effects of Internet Addiction Scale, Ranking Score and Gaming Performance

Previous studies have demonstrated that individuals with internet gaming disorder (IGD) display abnormal autonomic activities at rest and during gameplay. Here, we examined whether and how in-game autonomic activity is modulated by human characteristics and behavioral performance of the player. We measured heart rate variability (HRV) in 42 male university student habitual gamers (HGs) when they played a round of League of Legends game online. Short-term HRV indices measured in early, middle and late phases of the game were compared between the players at high risk of developing IGD and those at low risk, as assessed by the revised Chen Internet addiction scale (CIAS-R). Multiple linear regression (MLR) was used to identify significant predictors of HRV measured over the whole gameplay period (WG), among CIAS-R, ranking score, hours of weekly playing and selected in-game performance parameters. The high-risk players showed a significantly higher low-frequency power/high-frequency power ratio (LF/HF) relative to the low-risk players, regardless of game phase. MLR analysis revealed that LF/HF measured in WG was predicted by, and only by, CIAS-R. The HRV indicators of sympathetic activity were found to be predicted only by the number of slain in WG (NSlain), and the indicators of parasympathetic activity were predicted by both CIAS-R and NSlain. Collectively, the results demonstrated that risk of developing IGD is associated with dysregulated autonomic balance during gameplay, and in-game autonomic activities are modulated by complex interactions among personal attributes and in-game behavioral performance of the player, as well as situational factors embedded in game mechanics.