Netzwerkanalyse deckt Zusammenhänge zwischen Depressionen und digitalem Burnout auf

A recent study utilized network analysis to explore the intricate relationships among emotion regulation strategies, depression, and digital burnout, emphasizing the role of protective psychological factors. This study highlights the need for advanced psychological models to understand human behavior, moving beyond simple linear explanations.

Depression’s multifaceted nature makes it an ideal candidate for network analysis. Approximately 280 million people globally experience depression, making it a leading cause of disability. Recent trends show a rising incidence of depression, exacerbated by digital-age stressors, including digital burnout. Defined as emotional fatigue from excessive technology use, digital burnout affects 20-30% of adults in developed nations, with even higher rates among younger demographics. In urban China, surveys indicate that 25-35% of professionals and up to 40% of university students experience digital burnout.

This study challenges traditional biomedical perspectives, which posit that symptoms of depression arise from a common underlying cause. Instead, the network model views psychological issues as complex interrelated systems where symptoms directly influence one another. Such a framework can improve the understanding of how various factors contribute to depression.

Emotion regulation strategies are pivotal in the relationship between stress and depression. Two primary strategies are cognitive reappraisal, which involves reinterpreting emotional experiences, and emotion suppression, which inhibits emotional expression. Meta-analyses reveal cognitive reappraisal correlates negatively with depressive symptoms (r = −0.5 to −0.6), whereas emotion suppression correlates positively (r = 0.5 to 0.6).

Protective psychological factors, including resilience, self-compassion, and mindfulness, demonstrate the ability to buffer against depression. Resilience, the capacity to bounce back from adversity, shows consistent negative correlations with depression (r = −0.5 to −0.6) and positive links with self-compassion (r = 0.5 to 0.7) and mindfulness (r = 0.4 to 0.6). Self-compassion, characterized by kindness towards oneself during difficulties, correlates negatively with depression (r = −0.4 to −0.6) and positively with mindfulness (r = 0.5 to 0.7).

Mindfulness, defined as non-judgmental present-moment awareness, also shows a negative correlation with depression (r = −0.5 to −0.6) and positive relationships with adaptive emotion regulation strategies like cognitive reappraisal (r = 0.4 to 0.6). Furthermore, sleep quality plays a crucial role in the depression network, with poor sleep showing strong positive correlations with depression (r = 0.6 to 0.7) and negative associations with mindfulness and emotional well-being (r = −0.4 to −0.5).

Digital burnout complicates the relationship between emotion regulation and depression. Research indicates that excessive technology use can impair cognitive reappraisal abilities and increase reliance on emotion suppression, creating a feedback loop that elevates depression risk and diminishes protective emotional regulation strategies.

Despite existing research, gaps remain in understanding the interplay between digital burnout and depression networks. Few studies integrate modern stressors like digital burnout into comprehensive depression models. Moreover, the simultaneous interaction of protective factors within depression networks is not well studied, nor are the mechanisms through which emotion regulation strategies influence depression in the context of digital burnout.

This study addresses these gaps through a large-scale network analysis. With a sample of 9,400 participants, the research aims to: 1. Explore the network structure of depression in relation to traditional psychological factors and digital burnout. 2. Identify key nodes that could serve as intervention targets, focusing on emotion regulation and sleep quality. 3. Investigate the interactions of protective factors like resilience, self-compassion, and mindfulness within the network.

The researchers hypothesize that digital burnout will emerge as a significant node affecting both sleep quality and depression. They expect emotion regulation strategies, particularly emotion suppression, to show high centrality in the network, with sleep quality acting as a connecting variable between digital burnout and other components of the network. The study also anticipates identifying clusters of risk and protective factors, with resilience, self-compassion, and mindfulness forming a protective group negatively associated with both digital burnout and depression.

In summary, this study sheds light on the intricate dynamics of depression in the digital era, highlighting the importance of understanding both modern stressors and protective psychological factors in addressing mental health challenges.