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Description: Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being. Because our emotional life is inherently time dynamic, affective scientists argue that, next to how positive or negative we feel on average, patterns of emotional change too, are informative for mental health. 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 (a) that considerable interdependencies between measures exist, suggesting that single dynamics often do not convey unique information, and (b) that 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 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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