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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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.
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).
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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.
The authors declare no competing interests.
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Supplementary Figures 1–19, Supplementary Tables 1–16, Supplementary Note, and Supplementary References.
R code to reproduce analyses.
Matlab code to reproduce analyses.
Part of the data underlying the analyses in the Article.
Description: SI guide explaining file identity.