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Daily Affective Dynamics Predict Depression Symptom Trajectories Among Adults with Major and Minor Depression

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Abstract

Affective dynamics have been increasingly recognized as important indicators of emotional health and well-being. Depression has been associated with altered affective dynamics, but little is known about how daily life affective dynamics predict depression’s naturalistic course. We investigated positive and negative affective dynamics (e.g., inertia, variability, and instability) among adults with depressive disorders (N = 60) and healthy controls (N = 38) in both cross-sectional and prospective analyses predicting weekly depression symptoms over 6 months. Relative to controls, depressed individuals showed elevated daily negative affect (NA) and NA variability along with decreased positive affect (PA). However, groups did not significantly differ on other affective dynamic indices. Based on multivariate prospective analyses of depressed individuals (follow-up N = 36), higher daily NA and lower daily PA were independently associated with higher and average weekly depressive symptom severity over the subsequent 6 months. Exploratory analyses of depression symptom trajectory shape revealed that higher NA and PA variability, NA inertia, and NA instability all predicted an initial increase and eventual return to higher depression symptom levels over the 6-month follow-up period. Daily life affective dynamics may have utility for predicting the naturalistic course of depression, which may help guide interventions targeting affective dynamics in vulnerable individuals.

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Notes

  1. One participant was excluded at follow-up after publication of ESM findings (Bylsma et al., 2011) due to no longer meeting study inclusion criteria.

  2. In testing for multicollinearity, we evaluated several indices of multicollinearity possibly being present. First correlation matrices for NA and PA identified two elevated correlations among variability and instability dynamics measures, specifically, rNA = .64 and rPA = .78. This suggested that further evaluation of multicollinearity may be warranted. Indeed, multicollinearity is suspected between PA MSSD and PA SD based on the two indices that were elevated beyond acceptable boundaries: PA MSSD VIF = 5.4 and PA MSSD variance proportion = .80; PA SD VIF = 4.8 and PA SD variance proportion = .96. Specificity analyses were therefore re-run excluding PA SD from the model and results remained unchanged.

  3. We follow recommendations by Tabachnick et al. (2007) and Shek and Ma (2011) for model testing for MLM and for growth curve nested models, respectively: model 1 (linear) - model 2 (quadratic+ linear); model 2 (quadratic+ linear) - model 3 (cubic+ quadratic+ linear).

References

  • American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed., Text Revision). Washington, DC: Author.

  • Barrett, L. F., & Barrett, D. J. (2001). An introduction to computerized experience sampling in psychology. Social Science Computer Review, 19(2), 175–185.

    Article  Google Scholar 

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B: Methodological, 57(1), 289–300.

    Google Scholar 

  • Bos, E. H., de Jonge, P., & Cox, R. F. (2019). Affective variability in depression: revisiting the inertia–instability paradox. British Journal of Psychology, 110(4), 814–827.

    Article  Google Scholar 

  • Bosley, H. G., Soyster, P. D., & Fisher, A. J. (2019). Affect dynamics as predictors of symptom severity and treatment response in mood and anxiety disorders: evidence for specificity. Journal for Person-Oriented Research, 5(2), 101–113.

    Article  Google Scholar 

  • Bylsma, L. M., Taylor-Clift, A., & Rottenberg, J. (2011). Emotional reactivity to daily events in major and minor depression. Journal of Abnormal Psychology, 120, 155–167.

    Article  Google Scholar 

  • Bylsma, L. M., Salomon, K., Taylor-Clift, A., Morris, B. H., & Rottenberg, J. (2014). RSA reactivity in current and remitted major depressive disorder. Psychosomatic medicine, 76(1), 66.

  • Dejonckheere, E.*., Mestdagh, M.*., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019). Complex affect dynamics add limited information to the prediction of psychological well-being. Nature Human Behaviour, 3, 478–491.

    Article  Google Scholar 

  • Demiralp, E., Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Barrett, L. F., & Jonides, J. (2012). Feeling blue or turquoise? Emotional differentiation in major depressive disorder. Psychological Science, 23, 1410–1416.

    Article  Google Scholar 

  • Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American psychologist, 55(1), 34.

  • Frijda, N. H. (2007). The laws of emotion Mahwah. NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Ghio, L., Gotelli, S., Marcenaro, M., Amore, M., & Natta, W. (2014). Duration of untreated illness and outcomes in unipolar depression: a systematic review and meta-analysis. Journal of Affective Disorders, 152–154, 45–51.

    Article  Google Scholar 

  • Heininga, V. E., Dejonckheere, E., Houben, M., Obbels, J., Sienaert, P., Leroy, B., van Roy, J., & Kuppens, P. (2019). The dynamical signature of anhedonia in major depressive disorder: positive emotion dynamics, reactivity, and recovery. BMC Psychiatry, 19(1), 59.

    Article  Google Scholar 

  • Hollenstein, T. (2015). This time, it’s real: affective flexibility, time scales, feedback loops, and the regulation of emotion. Emotion Review, 7(4), 308–315.

    Article  Google Scholar 

  • Hollenstein, T., Lichtwarck-Aschoff, A., & Potworowski, G. (2013). A model of socioemotional flexibility at three time scales. Emotion Review, 5(4), 397–405.

    Article  Google Scholar 

  • Houben, M., & Kuppens, P. (2020). Emotion dynamics and the association with depressive features and borderline personality disorder traits: unique, specific and prospective relationships. Clinical Psychological Science, 8, 226–239.

    Article  Google Scholar 

  • Houben, M., Van den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: a meta-analysis. Psychological Bulletin, 141, 901–930.

    Article  Google Scholar 

  • Husen, K., Rafaeli, E., Rubel, J. A., Bar-Kalifa, E., & Lutz, W. (2016). Daily affect dynamics predict early response in CBT: feasibility and predictive validity of EMA for outpatient psychotherapy. Journal of Affective Disorders, 206, 305–314.

    Article  Google Scholar 

  • IBM Corp. (2018). IBM SPSS statistics for windows, version 26.0. Armonk, NY: IBM Corp.

  • Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of affective instability in ecological momentary assessment: indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13(4), 354–375.

    Article  Google Scholar 

  • Kahneman, D., Diener, E., & Schwarz, N. (Eds.). (1999). Well-being: Foundations of hedonic psychology. Russell Sage Foundation.

  • Kashdan, T. B., & Rottenberg, J. (2010). Psychological flexibility as a fundamental aspect of health. Clinical Psychology Review, 30(7), 865–878.

    Article  Google Scholar 

  • Keller, M. B., Lavori, P. W., Friedman, B., Nielsen, E., Endicott, J., McDonald-Scott, P., & Andreasen, N. C. (1987). The Longitudinal Interval Follow-up Evaluation: a comprehensive method for assessing outcome in prospective longitudinal studies. Archives of General Psychiatry, 44(6), 540–548.

    Article  Google Scholar 

  • Koval, P., Kuppens, P., Allen, N. B., & Sheeber, L. B. (2012). Getting stuck in depression: the roles of rumination and emotional inertia. Cognition & Emotion, 26, 1412–1427.

    Article  Google Scholar 

  • Koval, P., Pe, M. L., Meers, K., & Kuppens, P. (2013). Affect dynamics in relation to depressive symptoms: variable, unstable or inert? Emotion, 13, 1132–1141.

    Article  Google Scholar 

  • Koval, P., Brose, A., Pe, M. L., Houben, M., Erbas, Y., Champagne, D., & Kuppens, P. (2015). Emotional inertia and external events: the roles of exposure, reactivity, and recovery. Emotion, 15(5), 625–636.

    Article  Google Scholar 

  • Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21(7), 984–991.

    Article  Google Scholar 

  • Kuppens, P., Sheeber, L. B., Yap, M. B. H., Whittle, S., Simmons, J. G., & Allen, N. B. (2012). Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion, 12, 283–289.

    Article  Google Scholar 

  • Lewinsohn, P. M., Joiner Jr., T. E., & Rohde, P. (2001). Evaluation of cognitive diathesis-stress models in predicting major depressive disorder in adolescents. Journal of Abnormal Psychology, 110(2), 203–215.

    Article  Google Scholar 

  • Moos, R. H., & Cronkite, R. C. (1999). Symptom-based predictors of a 10-year chronic course of treated depression. The Journal of Nervous and Mental Disease, 187(6), 360–368.

    Article  Google Scholar 

  • Morris, B. H., Bylsma, L. M., & Rottenberg, J. (2009). Does emotion predict the course of major depressive disorder? A review of prospective studies. British Journal of Clinical Psychology, 48, 255–273.

    Article  Google Scholar 

  • Myin-Germeys, I., Peeters, F. P. M. L., Havermans, R., Nicolson, N. A., DeVries, M. W., Delespaul, P. A. E. G., & Van Os, J. (2003). Emotional reactivity to daily life stress in psychosis and affective disorder: an experience sampling study. Acta Psychiatrica Scandinavica, 107(2), 124–131.

    Article  Google Scholar 

  • Myin-Germeys, I., Krabbendam, L., Delespaul, P. A., & van Os, J. (2004). Sex differences in emotional reactivity to daily life stress in psychosis. Journal of Clinical Psychiatry, 65, 805–809. https://doi.org/10.4088/JCP.v65n0611.

    Article  PubMed  Google Scholar 

  • Neumann, A., van Lier, P. A., Frijns, T., Meeus, W., & Koot, H. M. (2011). Emotional dynamics in the development of early adolescent psychopathology: a one-year longitudinal study. Journal of Abnormal Child Psychology, 39(5), 657–669.

    Article  Google Scholar 

  • Nezlek, J. B. (2001). Multilevel random coefficient analyses of event-and interval-contingent data in social and personality psychology research. Personality and Social Psychology Bulletin, 27(7), 771–785.

    Article  Google Scholar 

  • Panaite, V., Hindash, A. C., Bylsma, L. M., Small, B. J., Salomon, K., & Rottenberg, J. (2016). Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory. International Journal of Psychophysiology, 99, 108–113.

    Article  Google Scholar 

  • Peeters, F., Berkhof, J., Delespaul, P., Rottenberg, J., & Nicolson, N. A. (2006). Diurnal mood variation in major depressive disorder. Emotion, 6, 383–391.

    Article  Google Scholar 

  • Salomon, K., Bylsma, L. M., White, K. E., Panaite, V., & Rottenberg, J. (2013). Is blunted cardiovascular reactivity in depression mood-state dependent? A comparison of major depressive disorder remitted depression and healthy controls. International Journal of Psychophysiology, 90(1), 50–57.

  • Shankman, S. A., Katz, A. C., DeLizza, A. A., Sarapas, C., Gorka, S. M., & Campbell, M. L. (2014). The different facets of anhedonia and their associations with different psychopathologies. In Anhedonia: a comprehensive handbook (Vol. I, pp. 3–22). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Shek, D. T., & Ma, C. (2011). Longitudinal data analyses using linear mixed models in SPSS: concepts, procedures and illustrations. The Scientific World Journal, 11, 42–76.

    Article  Google Scholar 

  • Suls, J., Green, P., & Hillis, S. (1998). Emotional reactivity to everyday problems, affective inertia, and neuroticism. Personality and Social Psychology Bulletin, 24, 127–136.

    Article  Google Scholar 

  • Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston: Pearson.

    Google Scholar 

  • Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2012). The everyday emotional experience of adults with major depressive disorder: examining emotional instability, inertia, and reactivity. Journal of Abnormal Psychology, online, 1–11.

  • van Roekel, E., Bennik, E. C., Bastiaansen, J. A., Verhagen, M., Ormel, J., Engels, R. C., & Oldehinkel, A. J. (2016). Depressive symptoms and the experience of pleasure in daily life: an exploration of associations in early and late adolescence. Journal of Abnormal Child Psychology, 44(5), 999–1009.

    Article  Google Scholar 

  • Warshaw, M. G., Dyck, I., Allsworth, J., Stout, R. L., & Keller, M. B. (2001). Maintaining reliability in a long-term psychiatric study: an ongoing inter-rater reliability monitoring program using the Longitudinal Interval Follow-up Evaluation. Journal of Psychiatric Research, 35(5), 297–305.

    Article  Google Scholar 

  • Wichers, M., Myin-Germeys, I., Jacobs, N., Peeters, F., Kenis, G., Derom, C., et al. (2007a). Genetic risk of depression and stress-induced negative affect in daily life. The British Journal of Psychiatry, 191(3), 218–223.

    Article  Google Scholar 

  • Wichers, M. C., Myin-Germeys, I., Jacobs, N., Peeters, F., Kenis, G., Derom, C., et al. (2007b). Evidence that moment-to-moment variation in positive emotions buffer genetic risk for depression: a momentary assessment twin study. Acta Psychiatrica Scandinavica, 115(6), 451–457.

    Article  Google Scholar 

  • Wichers, M., Peeters, F., Geschwind, N., Jacobs, N., Simons, C. J. P., Derom, C., Thiery, E., Delespaul, P. H., & van Os, J. (2010). Unveiling patterns of affective responses in daily life may improve outcome prediction in depression: a momentary assessment study. Journal of Affective Disorders, 124(1), 191–195.

    Article  Google Scholar 

  • World Health Organization. (2017). Depression and other common mental disorders: Global health estimates: https://www.who.int/mental_health/management/depression/prevalence_global_health_estimates/en/

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Authors and Affiliations

  1. Research and Development Service, James A. Haley Veterans’ Hospital, Tampa, FL, USA

    Vanessa Panaite

  2. Department of Psychology, University of South Florida, Tampa, FL, USA

    Vanessa Panaite & Jonathan Rottenberg

  3. Department of Psychiatry, University of Pittsburgh, 3811 O’Hara St., Pittsburgh, 15213, PA, United States

    Lauren M. Bylsma

  4. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA

    Lauren M. Bylsma

Authors
  1. Vanessa Panaite
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  2. Jonathan Rottenberg
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  3. Lauren M. Bylsma
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Correspondence to Lauren M. Bylsma.

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Acknowledgments

We thank Marlies Houben and Merijn Mestdagh for providing the code used to produce the simulated affective dynamic data used in Fig. 1.

Authors’ Contributions

LMB and JR contributed to the study design; LMB collected the data; LMB performed preliminary data analyses and contributed to the literature review; VP and LMB contributed to the concept of this secondary data analysis; VP performed the current data analyses and drafted the manuscript. JR and LMB provided substantive edits and feedback throughout the development of this manuscript.

Funding

LMB received support from American Psychological Association of Graduate Students (APAGS) Nancy B. Forest & L. Michael Honaker Master’s Scholarship for Research in psychology to support data collection and preliminary analyses of this research. LMB also was supported by MH104325 and MH118218 during the preparation of this manuscript.

Data Availability

The data used to prepare this article can be found at https://osf.io/cepr4/.

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All data were collected in accordance with the rules and regulations of the University of South Florida Internal Review Board.

Informed Consent

Completed by all participants prior to enrollment.

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The contents of this publication do not represent the views of the Department of Veterans Affairs or the US Government.

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Handling Editor: David Almeida

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Panaite, V., Rottenberg, J. & Bylsma, L.M. Daily Affective Dynamics Predict Depression Symptom Trajectories Among Adults with Major and Minor Depression. Affec Sci 1, 186–198 (2020). https://doi.org/10.1007/s42761-020-00014-w

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