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Complex affect dynamics add limited information to the prediction of psychological well-being

Nature Human Behaviour volume 3, pages 478–491 (2019)Cite this article

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Matters Arising to this article was published on 27 April 2020

Abstract

Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being1. Because our emotional life is inherently time-dynamic2,3,4,5,6, affective scientists argue that, next to how positive or negative we feel on average, patterns of emotional change are informative for mental health7,8,9,10. This growing interest initiated a surge in new affect dynamic measures, each claiming to capture a unique dynamical aspect of our emotional life, crucial for understanding well-being. Although this accumulation suggests scientific progress, researchers have not always evaluated (a) how different affect dynamic measures empirically interrelate and (b) what their added value is in the prediction of psychological well-being. Here, we address these questions by analysing affective time series data from 15 studies (n = 1,777). We show that (a) considerable interdependencies between measures exist, suggesting that single dynamics often do not convey unique information, and (b) dynamic measures have little added value over mean levels of positive and negative affect (and variance in these affective states) when predicting individual differences in three indicators of well-being (life satisfaction, depressive symptoms and borderline symptoms). Our findings indicate that conventional emotion research is currently unable to demonstrate independent relations between affect dynamics and psychological well-being.

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Fig. 1: Simulated data for each affect dynamic measure, showing how low and high values for each measure would manifest in a person’s affective time series.
Fig. 2: Evaluating the empirical overlap between all affect dynamic measures.
Fig. 3: Evaluating the added explanatory power of all affect dynamic measures in the linear prediction of psychological well-being outcomes above and beyond mean levels of, and standard deviations in, positive and negative affect.

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Data availability

To run the code and reproduce our analyses, two datasets17,29 are provided in the Supplementary Data or are available online from the Open Science Framework (http://osf.io/zm6uw). The other datasets used in this article are available upon reasonable request from the original sources referenced in Supplementary Table 1, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available.

Code availability

All analyses reported in this paper were conducted in MATLAB (R2017a), except the visualization of the correlational network, which was performed in R (v.3.4.0). The reproducible MATLAB and R code are provided as Supplementary Matlab Code and Supplementary R Code, respectively, or are available online from the Open Science Framework (http://osf.io/zm6uw).

References

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

    PubMed  Google Scholar 

  2. Davidson, R. J., Jackson, D. C. & Kalin, N. H. Emotion, plasticity, context, and regulation: perspectives from affective neuroscience. Psychol. Bull. 126, 890–909 (2000).

    PubMed  Google Scholar 

  3. Frijda, N. H. The Laws of Emotion (Erlbaum, Hillsdale, 2007).

  4. Kuppens, P. It’s about time: a special section on affect dynamics. Emot. Rev. 7, 297–300 (2015).

    Google Scholar 

  5. Larsen, R. J. Towards a science of mood regulation. Psychol. Inq. 11, 129–141 (2000).

    Google Scholar 

  6. Scherer, K. R. The dynamic architecture of emotion: evidence for the component process model. Cogn. Emot. 23, 1307–1351 (2009).

    Google Scholar 

  7. Koval, P., Sütterlin, S. & Kuppens, P. Emotional inertia is associated with lower well-being when controlling for differences in emotional context. Front. Psychol. 6, 1–11 (2015).

    Google Scholar 

  8. Kuppens, P., Allen, N. B. & Sheeber, L. B. Emotional inertia and psychological maladjustment. Psychol. Sci. 21, 984–991 (2010).

    PubMed  PubMed Central  Google Scholar 

  9. Kuppens, P. et al. Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion 12, 283–289 (2012).

    PubMed  Google Scholar 

  10. van de Leemput, I. A. et al. Critical slowing down as early warning for the onset and termination of depression. Proc. Natl Acad. Sci. USA 111, 87–92 (2014).

    PubMed  Google Scholar 

  11. Kuppens, P., Oravecz, Z. & Tuerlinckx, F. Feelings change: accounting for individual differences in the temporal dynamics of affect. J. Pers. Soc. Psychol. 99, 1042–1060 (2010).

    PubMed  Google Scholar 

  12. Russell, J. A. Core affect and the psychological construction of emotion. Psychol. Rev. 110, 145–172 (2003).

    PubMed  Google Scholar 

  13. Scherer, K. R. in Introduction to Social Psychology: A European Perspective 3rd edn (eds Hewstone, M. & Stroebe, W.) 151–191 (Blackwell, Oxford, 2000).

  14. Dejonckheere, E. et al. The bipolarity of affect and depressive symptoms. J. Pers. Soc. Psychol. 114, 323–341 (2018).

    PubMed  Google Scholar 

  15. Grühn, D., Lumley, M. A., Diehl, M. & Labouvie-vief, G. Time-based indicators of emotional complexity: interrelations and correlates. Emotion 13, 226–237 (2013).

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  18. Van der Gucht, K. et al. An experience sampling study examining the potential impact of a mindfulness-based intervention on emotion differentiation. Emotion 19, 123–131 (2018).

    PubMed  Google Scholar 

  19. Thompson, R. J. et al. The everyday emotional experience of adults with major depressive disorder: examining emotional instability, inertia, and reactivity. J. Abnorm. Psychol. 121, 819–829 (2012).

    PubMed  PubMed Central  Google Scholar 

  20. Mestdagh, M. et al. The relative variability index as a generic mean-corrected variability measure for bounded variables. Psychol. Methods 23, 690–707 (2019).

    Google Scholar 

  21. Brown, N. J. L. & Coyne, J. C. Emodiversity: robust predictor of outcomes or statistical artifact?. J. Exp. Psychol. Gen. 146, 1372–1377 (2017).

    PubMed  Google Scholar 

  22. Clark, L. A. & Watson, D. Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. J. Abnorm. Psychol. 100, 316–336 (1991).

    CAS  PubMed  Google Scholar 

  23. Larson, R. M., Csikszentmihalyi, M. & Graef, R. Mood variability and the psychosocial adjustment of adolescents. J. Youth Adolesc. 9, 469–490 (1980).

    CAS  PubMed  Google Scholar 

  24. von Mises, R. Mathematical Theory and Probability and Statistics (Academic Press, New York, 1964).

  25. Diener, E., Sandvik, E., & Pavot, W. in Subjective Well-being: An Interdisciplinary Perspective (eds Strack, F., Argyle, M. & Schwarz, N.) 119–139 (Pergamon, New York, 1991).

  26. Dejonckheere, E., Bastian, B., Fried, E. I., Murphy, S. & Kuppens, P. Perceiving social pressure not to feel negative predicts depressive symptoms in daily life. Depress. Anxiety 34, 836–844 (2017).

    PubMed  Google Scholar 

  27. Heininga, V. E., et al. The dynamical signature of anhedonia in major depressive disorder: positive emotion dynamics, reactivity, and recovery. BMC Psychiatry 19, 59 (2019).

  28. Houben, M. et al. Emotional switching in borderline personality disorder: a daily life study. J. Pers. Disord. 7, 50–60 (2016).

    Google Scholar 

  29. Dejonckheere, E., Kalokerinos, E. K., Bastian, B., & Kuppens, P. Poor emotion regulation ability mediates the link between depressive symptoms and affective bipolarity. Cogn. Emot. https://doi.org/10.1080/02699931.2018.1524747 (2018).

  30. Pe, M. L., Brose, A., Gotlib, I. H. & Kuppens, P. Affective updating ability and stressful events interact to prospectively predict increases in depressive symptoms over time. Emotion 16, 73–82 (2016).

    PubMed  Google Scholar 

  31. Schmiedek, F., Lövdén, M. & Lindenberger, U. Hundred days of cognitive training enhance broad cognitive abilities in adulthood: findings from the COGITO study. Front. Aging Neurosci. 2, 1–27 (2010).

    Google Scholar 

  32. Sels, L., Ceulemans, E. & Kuppens, P. Partner-expected affect: how you feel now is predicted by how your partner thought you felt before. Emotion 17, 1066–1077 (2017).

    PubMed  Google Scholar 

  33. Sels, L., Ceulemans, E., & Kuppens, P. All’s well that ends well? A test of the peak-end rule in couples’ conflict discussions. Eur. J. Soc. Psychol. https://doi.org/10.1002/ejsp.2547 (2018).

  34. Trull, T. J. et al. Affective instability: measuring a core feature of borderline personality disorder with ecological momentary assessment. J. Abnorm. Psychol. 117, 647–661 (2008).

    PubMed  Google Scholar 

  35. Csikszentmihalyi, M. & Larson, R. Validity and reliability of the experience-sampling method. J. Nerv. Ment. Dis. 175, 526–536 (1987).

    CAS  PubMed  Google Scholar 

  36. Bolger, N., Davis, A. & Rafaeli, E. Diary methods: capturing life as it is lived. Annu. Rev. Psychol. 54, 579–616 (2003).

    PubMed  Google Scholar 

  37. Diener, E., Suh, E. M., Lucas, R. E. & Smith, H. L. Subjective well-being: three decades of progress. Psychol. Bull. 125, 276–302 (1999).

    Google Scholar 

  38. Waterman, A. S. Two conceptions of happiness: contrasts of personal expressiveness (eudaimonia) and hedonic enjoyment. J. Pers. Soc. Psychol. 64, 678–691 (1993).

    Google Scholar 

  39. Vuillier, L. et al. Amount and diversity of digital emotional expression predicts happiness. Harvard Business School 18, 2–42 (2018).

    Google Scholar 

  40. Davis, M. C., Zautra, A. J. & Smith, B. Chronic pain, stress, and the dynamics of affective differentiation. J. Pers. 72, 1133–1159 (2004).

    PubMed  PubMed Central  Google Scholar 

  41. Santangelo, P. et al. Specificity of affective instability in patients with borderline personality disorder compared to posttraumatic stress disorder, bulimia nervosa, and healthy controls. J. Abnorm. Psychol. 123, 258–272 (2014).

    PubMed  PubMed Central  Google Scholar 

  42. Coifman, K. G., Berenson, K. R., Rafaeli, E. & Downey, G. From negative to positive and back again: polarized affective and relational experience in borderline personality disorder. J. Abnorm. Psychol. 121, 668–679 (2012).

    PubMed  Google Scholar 

  43. Demiralp, E. et al. Feeling blue or turquoise? Emotional differentiation in major depressive disorder. Psychol. Sci. 23, 1410–1416 (2012).

    PubMed  PubMed Central  Google Scholar 

  44. Wolff, S., Stiglmayr, C., Bretz, H. J., Lammers, C. H. & Auckenthaler, A. Emotion identification and tension in female patients with borderline personality disorder. Br. J. Psychol. 46, 347–360 (2007).

    Google Scholar 

  45. Pe, M. L. et al. Emotion-network density in major depressive disorder. Clin. Psychol. Sci. 3, 292–300 (2015).

    Google Scholar 

  46. Quoidbach, J. et al. Emodiversity and the emotional ecosystem. J. Exp. Psychol .Gen. 143, 2057–2066 (2014).

    PubMed  Google Scholar 

  47. Erbas, Y., Ceulemans, E., Koval, P. & Kuppens, P. The role of valence focus and appraisal overlap in emotion differentiation. Emotion 15, 373–382 (2015).

    PubMed  Google Scholar 

  48. Meinshausen, N. & Bühlmann, P. Stability selection. J. R. Stat. Soc. 72, 417–473 (2010).

    Google Scholar 

  49. Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. 58, 267–288 (1996).

    Google Scholar 

  50. Geisser, S. The predictive sample reuse method with applications. J. Am. Stat. Assoc. 70, 320–328 (1975).

    Google Scholar 

  51. Ebner-Priemer, U. W. & Sawitzki, G. Ambulatory assessment of affective instability in borderline personality disorder: the effect of the sampling frequency. Eur. J. Psychol. Assess. 23, 238–247 (2007).

    Google Scholar 

  52. Moors, A. On the causal role of appraisal in emotion. Emot. Rev. 5, 132–140 (2013).

    Google Scholar 

  53. Frijda, N. H., Kuipers, P., & ter Schure, E. Relations among emotion, appraisal, and emotional action readiness. J. Pers. Soc. Psychol. 57, 212–228 (1989).

  54. Fried, E. I. Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Front. Psychol. 6, 1–11 (2015).

    Google Scholar 

  55. Fried, E. I. & Nesse, R. M. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 13, 1–11 (2015).

    Google Scholar 

  56. Shmueli, G. To explain or to predict?. Stat. Sci. 25, 289–310 (2010).

    Google Scholar 

  57. Linehan, M. Cognitive Behavioral Treatment of Borderline Personality Disorder (Guilford Press, New York, 1993).

  58. Ebner-Priemer, U. W. et al. Unraveling affective dysregulation in borderline personality disorder: a theoretical model and empirical evidence. J. Abnorm. Psychol. 124, 186–198 (2015).

    PubMed  Google Scholar 

  59. Bulteel, K., Mestdagh, M., Tuerlinckx, F., & Ceulemans, E. VAR(1) based models do not always outpredict AR(1) models in typical psychological applications. Psychol. Methods https://doi.org/10.1037/met0000178 (2018).

  60. Chow, P. I. et al. Using mobile sensing to test clinical models of depression, social anxiety, state affect, and social isolation among college students, J. Med. Internet Res. 19, e62 (2017).

  61. Carreiro, S. et al. Real-time mobile detection of drug use with wearable biosensors: a pilot study. J. Med. Toxicol. 11, 73–77 (2014).

    PubMed Central  Google Scholar 

  62. Trull, T. J. & Ebner-Priemer, U. W. Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: introduction to the special section. Psychol. Assess. 21, 457–462 (2014).

    Google Scholar 

  63. Erbas, Y. et al. Why I don’t always know what I’m feeling: the role of stress in within-person fluctuations in emotion differentiation. J. Pers. Soc. Psychol. 115, 179–191 (2018).

    PubMed  Google Scholar 

  64. Bringmann, L. F. et al. A network approach to psychopathology: new insights into clinical longitudinal data. PloS One 8, e60188 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Ong, A. D., Zautra, A. J. & Finan, P. H. Inter- and intra-individual variation in emotional complexity: methodological considerations and theoretical implications. Curr. Opin. Behav. Sci. 15, 22–26 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. Erbas, Y., Ceulemans, E., Pe, M. L., Koval, P. & Kuppens, P. Negative emotion differentiation: its personality and well-being correlates and a comparison of different assessment methods. Cogn. Emot. 28, 1196–1213 (2014).

    PubMed  Google Scholar 

  67. Diener, E., Emmons, R. A., Larsen, R. J. & Griffin, S. The satisfaction with life scale. J. Pers. Assess. 49, 71–75 (1985).

    CAS  PubMed  Google Scholar 

  68. Radloff, L. S. The CES-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas. 1, 384–401 (1977).

    Google Scholar 

  69. Kroenke, K., Spitzer, R. L. & Williams, J. B. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Rush, A. J. et al. An evaluation of the quick inventory of depressive symptomatology and the hamilton rating scale for depression: a sequenced treatment alternatives to relieve depression trial report. Biol. Psychiatry 59, 493–501 (2006).

    PubMed  Google Scholar 

  71. Beck, A. T., Steer, R. A. & Brown, G. K. Manual for the Beck Depression Inventory–II (Psychological Corporation, San Antonio, 1996).

  72. First, M. B, Spitzer, R. L, Gibbon, M. & Williams, J. B. W. in Structured Clinical Interview for DSM-IV-TR Axis I Disorders Research Version, Patient Edition. (BiometricsResearch: New York, 2002).

  73. Schotte, C. K. W., de Doncker, D., Vankerckhoven, C., Vertommen, H. & Cosyns, P. Self-report assessment of the DSM–IV personality disorders. Measurement of trait and distress characteristics: the ADP-IV. Psychol. Med. 28, 1179–1188 (1998).

    CAS  PubMed  Google Scholar 

  74. Distel, M. A., de Moor, H. M. & Boomsma, D. I. Dutch translation of the personality assessment inventory borderline features scale (PAI-BOR): norms, factor structure and reliability. Psychol. Health 37, 38–46 (2009).

    Google Scholar 

  75. Morey, L. C. The personality Assessment Inventory: Professional Manual (Psychological Assessment Resources, Odessa, 1991).

  76. First, M. B., Gibbon, M., Spitzer, R. L., Williams, J. B. W. & Benjamin, L. S. Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) (American Psychiatric Press, Washington, 1997).

  77. Kutner, M., Nachtsheim, C., Neter, J. & Li, W. Applied Linear Statistical Models 5th edn (McGraw-Hill, New York, 2004).

  78. Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R² from generalized linear mixed‐effects models. Methods Ecol. Evol. 4, 133–142 (2013).

    Google Scholar 

  79. Johnson, P. C. D. Extension of Nakagawa & Schielzeth’s R²GLMM to random slopes models. Methods Ecol. Evol. 5, 944–946 (2013).

    Google Scholar 

  80. Watson, D., Clark, L. A. & Tellegen, A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Pers. Soc. Psychol. 54, 1063–1070 (1988).

    CAS  PubMed  Google Scholar 

  81. Eid, M. & Diener, E. Intraindividual variability in affect: reliability, validity, and personality correlates. J. Pers. Soc. Psychol. 76, 662–676 (1999).

    Google Scholar 

  82. Baird, B. M., Le, K. & Lucas, R. E. On the nature of intraindividual personality variability: reliability, validity, and associations with well-being. J. Pers. Soc. Psychol. 90, 512–527 (2006).

    PubMed  Google Scholar 

  83. Kalmijn, W. & Veenhoven, R. Measuring inequality of happiness in nations: in search for proper statistics. J. Happiness Stud. 6, 357–396 (2005).

    Google Scholar 

  84. Cowdry, R. W., Gardner, D. L., O’Leary, K. M., Leibenluft, E. & Rubinow, D. R. Mood variability: a study of four groups. Am. J. Psychiatry 148, 1505–1511 (1991).

    CAS  PubMed  Google Scholar 

  85. Barrett, L. F., Gross, J., Christensen, T. C. & Benvenuto, M. Knowing what you’re feeling and knowing what to do about it: mapping the relation between emotion differentiation and emotion regulation. Cogn. Emot. 15, 713–724 (2001).

    Google Scholar 

  86. Kashdan, T. B., Barrett, L. F. & McKnight, P. E. Unpacking emotion differentiation: transforming unpleasant experience by perceiving distinctions in negativity. Curr. Dir. Psychol. Sci. 24, 10–16 (2015).

    Google Scholar 

  87. Feldman, L. A. Valence focus and arousal focus: individual differences in the structure of affective experience. J. Pers. Soc. Psychol. 69, 153–166 (1995).

    Google Scholar 

  88. Benson, L., Ram, N., Almeida, D., Zautra, A. & Ong, A. D. Fusing biodiversity metrics into investigations of daily life: illustrations and recommendations with emodiversity. J. Gerontol. B 15, 75–86 (2017).

    Google Scholar 

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Acknowledgements

This research was supported by the Research Fund of KU Leuven (grant nos. GOA/15/003 and OT/11/031). M.M. and M.H. are supported by the Fund of Scientific Research Flanders. We sincerely thank the following researchers that provided data for this project: A. Brose, B. Bastian, I. Gotlib, J. Jonides, E. Kalokerinos, P. Koval, U. Lindenberger, M. Lövdén, M. Pe, F. Schmiedek, R. Thompson, T. Trull and K. Van der Gucht. This article uses data from the COGITO study, supported by a grant from the Innovation Fund of the President of the Max Planck Society to U. Lindenberger. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government, department EWI. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. These authors contributed equally: Egon Dejonckheere, Merijn Mestdagh.

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  1. KU Leuven—Faculty of Psychology and Educational Sciences, Leuven, Belgium

    Egon Dejonckheere, Merijn Mestdagh, Marlies Houben, Isa Rutten, Laura Sels, Peter Kuppens & Francis Tuerlinckx

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Contributions

M.M. performed the analyses and E.D. drafted the manuscript. Both authors conceptualized the study project and interpreted the results under P.K. and F.T.’s supervision. I.R. independently re-analysed parts of the data with different statistical software to achieve converging results. M.H. and L.S. critically revised the manuscript. All authors approved the final version of the article.

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Correspondence to Egon Dejonckheere or Merijn Mestdagh.

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Dejonckheere, E., Mestdagh, M., Houben, M. et al. Complex affect dynamics add limited information to the prediction of psychological well-being. Nat Hum Behav 3, 478–491 (2019). https://doi.org/10.1038/s41562-019-0555-0

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