Emotion dynamics concurrently and prospectively predict mood psychopathology

Abstract

Introduction: Altered emotion dynamics may represent a transdiagnostic risk factor for mood psychopathology. The present study examined whether altered emotion dynamics were associated with bipolar and depressive psychopathology concurrently and at a three-year followup. Methods: At baseline (n = 138), participants completed diagnostic interviews, questionnaires, and seven days of experience sampling assessments. Four emotion dynamics were computed for negative affect (NA) and positive affect (PA) -within-person variance (variability), mean square of successive differences and probability of acute change (instability), and autocorrelation (inertia). At the three-year follow-up, participants (n = 108) were re-assessed via interviews and questionnaires. Results: NA variability was associated with bipolar spectrum disorders at baseline and follow-up. NA instability predicted depressive symptoms and hypomanic personality at baseline, and bipolar spectrum disorders at the follow-up. NA inertia did not predict diagnoses or symptoms at either assessment. PA inertia predicted hyperthymic temperament at baseline but not follow-up. Notably, NA variability and instability predicted the development of new bipolar spectrum disorders at the follow-up. Limitations: Consistent with the recruitment strategy and young age of the participants, only 50% had developed diagnosable psychopathology by the time of the follow-up assessment. Conclusions: The present study provided a unique demonstration that altered emotion dynamics differentially predicted bipolar and depressive psychopathology concurrently and prospectively. Emotion dynamics are important to both digital phenotyping and mobile-based interventions as emotional instability offers a measurable risk factor that is identifiable prior to illness onset.

Early warning signals in psychopathology: what do they tell?

“…A second limitation is that neither the reliability of individual experience sampling items nor the reliability of autocorrelations could be verified, which highlights the need for psychometric reliability metrics suitable for experience sampling data. Finally, the present group-level design could only indirectly investigate the relation between EWS and psychopathology and, like earlier studies [ 4 , 11 , 14 , 21 , 24 , 26 , 27 , 42 , 56 ], does not allow for within-individual inferences. A more direct evaluation of the hypotheses that follow from a complex systems perspective on psychopathology requires designs where individuals who are at increased risk for psychopathology prospectively monitor their mental state for a prolonged time period (e.g., several months) during which a shift is likely to occur [ 5 ].…”

Emotion Network Analysis During COVID-19 Quarantine ‐ A Longitudinal Study

“…This network of emotions changes as a result of internal and external factors ( Frijda, 2007 ). These fluctuations better characterize emotional response than mean levels of emotions ( Kuppens et al, 2007 ; Sperry and Kwapil, 2019 ) and can be used to predict mood psychopathology ( Wichers et al, 2015 ; Sperry et al, 2020 ). In fact, emotion dynamics may be key to understand pathways to psychopathology and well-being ( Wichers et al, 2015 ).…”

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“…Emotion dynamics captured over time via digital technology have been shown to differentially predict bipolar and depressive symptoms concurrently and prospectively [ 67 ]. And, EMA data captured on smartphones has been shown to predict future mood among persons with bipolar disorder [ 68 ].…”