Emotion network density in burnout

Network analyses of ecological momentary emotion and avoidance assessments before and after cognitive behavioral therapy for anxiety disorders

Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.

Evaluation of symptom network density as a predictor of treatment outcome of inpatient psychotherapy

OBJECTIVE

The network approach implies that the persistence of a mental disorder is rooted in a dense causal interconnection of symptoms. This study attempts to replicate and generalize previous findings in support of the assumption that higher density predicts poorer outcomes. The study examines the predictive value of network density at admission for recovery after inpatient treatment.

METHOD

N = 1375 adult patients with various forms of mental illness were classified as recovered (28%) versus not recovered (72%) after inpatient treatment. Recovery was defined as clinically significant improvement in impairment from admission to discharge. Networks of transdiagnostic symptoms at the time of admission were estimated. Network density, measured by global strength d, was compared between the recovered and not recovered groups using a permutation test.

RESULTS

Global strength at the time of admission tended to be higher in the No-Recovery group (d = 10.83) than the Recovery group (d = 7.53) but the association was not significant (p = .12). Similar results were found after controlling for group size and symptom severity.

CONCLUSION

The predictive value of network density for treatment outcomes remains unclear. There might be structural differences between the groups that the current measure of network density does not adequately represent.

Using Ecological Momentary Assessments to Study How Daily Fluctuations in Psychological States Impact Stress, Well-Being, and Health

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.