Quantifying heterogeneity in mood-alcohol relationships with idiographic causal models.

Abstract

Ecological momentary assessment (EMA) studies have provided conflicting evidence for the mood regulation tenet that people drink in response to positive and negative moods. The current study examined mood-to-alcohol relationships idiographically to quantify the prevalence and intensity of relationships between positive and negative moods and drinking across individuals. We used two EMA samples: 96 heavy drinking college students (sample 1) and 19 young adults completing an ecological momentary intervention (EMI) for drinking to cope (sample 2). Mood and alcohol use were measured multiple times per day for 4-6 weeks. Mood-alcohol relationships were examined using three different analytic approaches: standard multilevel modeling, group causal modeling, and idiographic causal modeling. Both multilevel modeling and group causal modeling showed that participants in both samples drank in response to positive moods only. However, idiographic causal analyses revealed that only 63% and 21% of subjects (in samples 1 and 2, respectively) drank following any positive mood. Many subjects (24% and 58%) did not drink in response to either positive or negative mood in their daily lives, and very few (5% and 16%) drank in response to negative moods throughout the EMA protocol, despite sample 2 being selected specifically because they endorse drinking to cope with negative mood. Traditional group-level analyses and corresponding population-wide theories assume relative homogeneity within populations in mood-alcohol relationships, but this nomothetic approach failed to characterize accurately the relationship between mood and alcohol use in approximately half of the subjects in two samples that were demographically and clinically homogeneous. Given inconsistent findings in the mood-alcohol relationships to date, we conclude that idiographic causal analyses can provide a foundation for more accurate theories of mood and alcohol use. In addition, idiographic causal models may also help improve psychosocial treatments through direct use in clinical settings.