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


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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

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 ( 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 (


  1. 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).

  2. 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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 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).

  8. 8.

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

  9. 9.

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

  10. 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).

  11. 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).

  12. 12.

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

  13. 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. 14.

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

  15. 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).

  16. 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).

  17. 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).

  18. 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).

  19. 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).

  20. 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).

  21. 21.

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

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 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. 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).

  27. 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. 28.

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

  29. 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. (2018).

  30. 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).

  31. 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).

  32. 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).

  33. 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. (2018).

  34. 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).

  35. 35.

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

  36. 36.

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

  37. 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).

  38. 38.

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

  39. 39.

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

  40. 40.

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

  41. 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).

  42. 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).

  43. 43.

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

  44. 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).

  45. 45.

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

  46. 46.

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

  47. 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).

  48. 48.

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

  49. 49.

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

  50. 50.

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

  51. 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).

  52. 52.

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

  53. 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. 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).

  55. 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).

  56. 56.

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

  57. 57.

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

  58. 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).

  59. 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 (2018).

  60. 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. 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).

  62. 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).

  63. 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).

  64. 64.

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

  65. 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).

  66. 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).

  67. 67.

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

  68. 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).

  69. 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).

  70. 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).

  71. 71.

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

  72. 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. 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).

  74. 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).

  75. 75.

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

  76. 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. 77.

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

  78. 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).

  79. 79.

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

  80. 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).

  81. 81.

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

  82. 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).

  83. 83.

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

  84. 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).

  85. 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).

  86. 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).

  87. 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).

  88. 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).

Download references


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.

Author information

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.

Correspondence to Egon Dejonckheere or Merijn Mestdagh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–19, Supplementary Tables 1–16, Supplementary Note, and Supplementary References.

Reporting Summary

Supplementary R code

R code to reproduce analyses.

Supplementary Matlab Code

Matlab code to reproduce analyses.

Supplementary Data

Part of the data underlying the analyses in the Article.

SI Guide

Description: SI guide explaining file identity.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark