Dataset Information

Emotion network density in burnout

ABSTRACT:

Background

Health care workers are often affected by burnout, resulting in reduced personal well-being and professional functioning. Although emotional exhaustion is considered a core component of burnout, little is known about the dynamics of emotions and their relation to burnout. We used network analysis to investigate the correlation between the density of a negative emotion network, a marker for emotional rigidity in person-specific networks, and burnout severity.

Methods

Using an ecological momentary assessment design, the intensity of negative emotions of forty-three health care workers and medical students was assessed five times per day (between 6 am and 8 pm) for 17 days. Burnout symptoms were assessed at the end of the study period with the Maslach Burnout Inventory. Multilevel vector autoregressive models were computed to calculate network density of subject-specific temporal networks. The one-sided correlation between network density and burnout severity was assessed. The study protocol and analytic plan were registered prior to the data collection.

Results

We found a medium-sized correlation between the negative emotion network density and burnout severity at the end of the study period r(45) = .32, 95% CI = .09–1.0, p = .014).

Conclusions

The strength of the temporal interplay of negative emotions is associated with burnout, highlighting the importance of emotions and emotional exhaustion in reaction to occupational-related distress in health care workers. Moreover, our findings align with previous investigations of emotion network density and impaired psychological functioning, demonstrating the utility of conceptualizing the dynamics of emotions as a network.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00670-y.

Background

Methods

Results

We found a medium-sized correlation between the negative emotion network density and burnout severity at the end of the study period r(45) = .32, 95% CI = .09–1.0, p = .014).

Conclusions

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00670-y.

SUBMITTER: Spiller T

PROVIDER: S-EPMC8556828 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The substantial body of research employing subjective measures indicates that burnout syndrome is associated with cognitive and emotional dysfunctions. The growing amount of neurophysiological and neuroimaging research helps in broadening existing knowledge of the neural mechanisms underlying core burnout components (emotional exhaustion and depersonalization/cynicism) that are inextricably associated with emotional processing. In the presented EEG study, a group of 93 participants (55 women; mean age = 35.8) were selected for the burnout group or the demographically matched control group on the basis of the results of the Maslach Burnout Inventory - General Survey (MBI-GS) and the Areas of Worklife Survey (AWS). Subjects then participated in an EEG experiment using two experimental procedures: a facial recognition task and viewing of passive pictures. The study focuses on analyzing event-related potentials (ERPs): N170, VPP, EPN, and LPP, as indicators of emotional information processing. Our results show that burnout subjects, as compared to the control group, demonstrate significantly weaker response to affect-evoking stimuli, indexed by a decline in VPP amplitude to emotional faces and decreased EPN amplitude in processing emotional scenes. The analysis of N170 and LPP showed no significant between-group difference. The correlation analyses revealed that VPP and EPN, which are ERP components related to emotional processing, are associated with two core burnout symptoms: emotional exhaustion and cynicism. To our knowledge, we are one of the first research groups to use ERPs to demonstrate such a relationship between neurophysiological activity and burnout syndrome in the context of emotional processing. Thus, in conclusion we emphasized that the decreased amplitude of VPP and EPN components in the burnout group may be a neurophysiological manifestation of emotional blunting and may be considered as neurophysiological markers of emotional exhaustion and cynicism. Additionally, we did not observe a decrease in LPP, which may be considered as a marker that significantly differentiates burnout from depression.

Project description:BackgroundAs a common social phenomenon, nurses' occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention.MethodsA total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks.ResultsIn the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) "I feel burned out from my work", C1 "I have become more callous toward work since I took this job", and R3 "In my opinion, I am good at my job" had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 "I feel emotionally drained from my work" and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65).ConclusionsThe network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice.

Project description:ObjectivesUsing two intensive longitudinal data sets with different timescales (90 minutes, daily), we examined emotion network density, a metric of emotional inflexibility, as a predictor of clinical-level anxiety and depression.DesignMobile-based intensive longitudinal assessments.Methods119 participants (61 anxious and depressed, 58 healthy controls) completed ecological momentary assessment (EMA) to rate a variety of negative (NE) and positive emotions (PE) 9 times per day for 8 days using a mobile phone application. 169 participants (97 anxious and depressed and 72 healthy controls) completed an online daily diary on their NE and PE for 50 days. Multilevel vector autoregressive models were run to compute NE and PE network densities in each data set.ResultsIn the EMA data set, both NE and PE network densities significantly predicted participants' diagnostic status above and beyond demographics and the mean and standard deviation of NE and PE. Greater NE network density and lower PE network density were associated with anxiety and depression diagnoses. In the daily diary data set, NE and PE network densities did not significantly predict the diagnostic status.ConclusionsGreater inflexibility of NE and lower inflexibility of PE, indexed by emotion network density, are potential clinical markers of anxiety and depressive disorders when assessed at intra-daily levels as opposed to daily levels. Considering emotion network density, as well as the mean level and variability of emotions in daily life, may contribute to diagnostic prediction of anxiety and depressive disorders.Practitioner pointsEmotion network density, or the degree to which prior emotions predict and influence current emotions, indicates an inflexible or change-resistant emotion system. Emotional inflexibility or change resistance over a few hours, but not daily, may characterize anxiety and depressive disorders. Inflexible negative emotion systems are associated with anxiety and depressive disorders, whereas inflexible positive emotion systems may indicate psychological health. Considering emotional inflexibility within days may provide additional information beyond demographics and mean level and variability of emotions in daily life for detecting anxiety and depressive disorders.

Dataset Information

Emotion network density in burnout

Background

Methods

Results

Conclusions

Supplementary Information

Background

Methods

Results

Conclusions

Supplementary Information

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