Emotion Dynamics as Predictors of Drinking Behavior: Idiographic and Pooled Approaches

Resumen

The specific factors driving alcohol-related behavior and cognition likely vary from person to person. Many theories suggest emotions are pertinent to alcohol use. Emotions and how they change over time may provide an opportunity for more precise prediction of alcohol consumption. The present study applied statistical classification methods to idiographic time series data of emotions and emotion dynamics in order to identify person-specific and between-subjects predictors of future drinking-relevant behavior, affect, and cognition (N = 33). Participants were sent eight mobile phone surveys per day for 15 days. Each survey assessed the number of drinks consumed since the previous survey, as well as emotions, alcohol craving, and the desire to drink. Each participant’s EMA data were prepared for analysis separately. To estimate emotion dynamics, we utilized the Generalized Local Linear Approximation. The data collected from each individual were split into training and testing sets for out-of-sample, person-specific validation. Elastic net regularization was used to select a subset of emotion and emotion dynamic variables to be used in models that predicted either alcohol consumption, craving, or wanting to drink roughly two hours in the future. To compare predictive performance, we tested both person-specific and between-subject prediction models. Averaging across participants, out-of-sample predictions of future drinking using idiographic models were 69% accurate. For craving, the mean out-of-sample R² value was .13. For wanting to drink, the mean out-of-sample R² value was .16. Idiographic prediction models exceeded nomothetic models in prediction accuracy. Using person-specific emotion and emotion dynamics can help predict future drinking behaviors.

Áreas y palabras clave

Cómo citar (APA 7)

Ashlock, L., Soyster, P., & Fisher, A. J. (2021). Emotion Dynamics as Predictors of Drinking Behavior: Idiographic and Pooled Approaches. https://doi.org/10.31234/osf.io/dt7js