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

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. Access through your institution Buy

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CRITICAL BEFORE USING EARLY WARNING SIGNALS IN PSYCHOPATHOLOGY Article 10 October 2024 POSITIVE AFFECT AND HEART RATE VARIABILITY: A DYNAMIC ANALYSIS Article Open access 25 March 2024 THE

EFFECT OF ATTENTIONAL BIAS MODIFICATION ON POSITIVE AFFECT DYNAMICS Article Open access 09 October 2024 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

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investigations of daily life: illustrations and recommendations with emodiversity. _J. Gerontol. B_ 15, 75–86 (2017). Google Scholar  Download references 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. AUTHOR INFORMATION Author notes * These authors

contributed equally: Egon Dejonckheere, Merijn Mestdagh. AUTHORS AND AFFILIATIONS * KU Leuven—Faculty of Psychology and Educational Sciences, Leuven, Belgium Egon Dejonckheere, Merijn

Mestdagh, Marlies Houben, Isa Rutten, Laura Sels, Peter Kuppens & Francis Tuerlinckx Authors * Egon Dejonckheere View author publications You can also search for this author inPubMed 

Google Scholar * Merijn Mestdagh View author publications You can also search for this author inPubMed Google Scholar * Marlies Houben View author publications You can also search for this

author inPubMed Google Scholar * Isa Rutten View author publications You can also search for this author inPubMed Google Scholar * Laura Sels View author publications You can also search for

this author inPubMed Google Scholar * Peter Kuppens View author publications You can also search for this author inPubMed Google Scholar * Francis Tuerlinckx View author publications You

can also search for this author inPubMed Google Scholar 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. CORRESPONDING AUTHORS Correspondence to Egon Dejonckheere or Merijn Mestdagh. ETHICS DECLARATIONS

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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 CITE THIS ARTICLE 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 Download citation * Received: 28 June 2018 * Accepted: 06 February 2019 * Published: 15 April 2019 * Issue Date: May 2019 * DOI:

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