Exploring the predictive value of different affect dynamics for psychological treatment outcome

Ecological momentary assessment (EMA) is increasingly used to assess affect dynamics for predicting treatment outcome. While multiple methodological approaches exist, their added predictive value remains unclear, with previous results suggesting a low predictive value beyond mean level (M), standard deviation (SD) or initial impairment (II). To this end, this study aims to evaluate the predictive value of different affect dynamic measures (ADM) for psychological treatment outcome using a naturalistic dataset. 140 white outpatients (63.6% female, 33.6% male) from the psychotherapy clinic of the Trier University reported positive affect and negative affect four times daily over two weeks. We applied continuous-time dynamic modeling (CTDM), drop- and recovery rates, time-varying change point analyses, control charts, and multilevel modeling to each patient’s time series. Interdependencies among indicators were analyzed using principal component analysis. Each ADM's performance for predicting treatment outcome beyond II was assessed via R². The model with the optimal predictor combination (using elastic net regularization) was compared in predictive performance to a model including M and SD of positive and negative affect. Significant interdependencies were found among ADMs. The predictor selection identified the cross-effect of positive affect on negative affect (from the CTDM) as the best predictor, while the model including M and SD of positive and negative affect had the greatest predictive power. In this naturalistic clinical sample, complex ADMs offered limited additional predictive value beyond M or SD for treatment outcome. Considerations and R scripts for each ADM are provided.