Negative Affect, but Not Inhibitory Control, Predicts Real-World Smoking: A Prospective Ecological Momentary Assessment Study
Mor Nahum, Yafit Gilboa, Netta Assa, Neta YitzhakABSTRACT
Introduction
Real-world smoking may be shaped by dynamic interactions between transient affective states and cognitive control processes. Ecological momentary assessment studies show that negative affect predicts smoking-related outcomes in daily life, however it is unclear whether momentary fluctuations in performance-based inhibitory control contribute to real-world smoking beyond affective state.
Methods
Adult light-to-moderate smokers (N = 77; ages 18–65) completed a 10-day ecological momentary assessment protocol on their smartphones, with three prompts per day. At each prompt, participants completed a Stop-Signal Task indexing inhibitory control and reported momentary affect on the Positive and Negative Affect Schedule; 45–105 minutes later they reported their actual cigarette consumption. Multilevel generalized linear mixed-effects models tested whether within-person fluctuations in inhibitory control and negative affect prospectively predicted subsequent smoking. The study and hypotheses were preregistered.
Results
Within-person increases in negative affect significantly predicted higher smoking rates at the subsequent assessment interval. Momentary inhibitory control was not significantly associated with subsequent smoking, and Bayesian model comparison favored the model without inhibitory control. Adding baseline psychological distress (Depression Anxiety Stress Scales-21) improved model fit, with higher distress predicting greater overall smoking, but it did not alter the within-person link between negative affect and smoking. Neither negative affect nor inhibitory control predicted craving.
Conclusions
In everyday life, transient negative affect is a more proximal predictor of smoking than momentary fluctuations in performance-based inhibitory control. More broadly, the findings highlight the value of intensive longitudinal designs for testing mechanistic models of smoking in naturalistic settings.