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Socioemotional Dynamics of Emotion Regulation and Depressive Symptoms: A Person-Specific Network Approach

Author

Listed:
  • Xiao Yang
  • Nilam Ram
  • Scott D. Gest
  • David M. Lydon-Staley
  • David E. Conroy
  • Aaron L. Pincus
  • Peter C. M. Molenaar

Abstract

Socioemotional processes engaged in daily life may afford and/or constrain individuals’ emotion regulation in ways that affect psychological health. Recent findings from experience sampling studies suggest that persistence of negative emotions (emotion inertia), the strength of relations among an individual’s negative emotions (density of the emotion network), and cycles of negative/aggressive interpersonal transactions are related to psychological health. Using multiple bursts of intensive experience sampling data obtained from 150 persons over one year, person-specific analysis, and impulse response analysis, this study quantifies the complex and interconnected socioemotional processes that surround individuals’ daily social interactions and on-going regulation of negative emotion in terms of recovery time. We also examine how this measure of regulatory inefficiency is related to interindividual differences and intraindividual change in level of depressive symptoms. Individuals with longer recovery times had higher overall level of depressive symptoms. Also, during periods where recovery time of sadness was longer than usual, individuals’ depressive symptoms were also higher than usual, particularly among individuals who experienced higher overall level of stressful life events. The findings and analysis highlight the utility of a person-specific network approach to study emotion regulation, how regulatory processes change over time, and potentially how planned changes in the configuration of individuals’ systems may contribute to psychological health.

Suggested Citation

  • Xiao Yang & Nilam Ram & Scott D. Gest & David M. Lydon-Staley & David E. Conroy & Aaron L. Pincus & Peter C. M. Molenaar, 2018. "Socioemotional Dynamics of Emotion Regulation and Depressive Symptoms: A Person-Specific Network Approach," Complexity, Hindawi, vol. 2018, pages 1-14, November.
    • Handle: RePEc:hin:complx:5094179
    DOI: 10.1155/2018/5094179
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    References listed on IDEAS

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    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Brandt, Patrick T. & Sandler, Todd, 2012. "A Bayesian Poisson Vector Autoregression Model," Political Analysis, Cambridge University Press, vol. 20(3), pages 292-315, July.
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    1. Kanas, Angelos & Molyneux, Philip & Zervopoulos, Panagiotis D., 2023. "Systemic risk and CO2 emissions in the U.S," Journal of Financial Stability, Elsevier, vol. 64(C).

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