The association between inhibitory deficits, addictive behaviors and smoking cravings in real life

Addictive behaviors entail maladaptive patterns which can have significant negative consequences in terms of health, quality of life, and function. Cognitive models of addiction and craving behaviors suggest that the common difficulties in controlling an undesired behavior may involve a reduced capacity of inhibitory control. While the involvement of impaired inhibitory control has been documented in relation to craving and addictive behaviors in both animal and human models, it is unclear how inhibitory control difficulties trigger addictive behaviors and cravings in real-life. In this project, we aim to understand the dynamic manifestation of inhibitory control and how it unfolds ecologically in relation to affect, urges, and addictive behaviors among smokers. With this understanding, we will generate a predictive model for smoking behavior from immediately preceding inhibitory control performance and current affective state. For this, we conducted an Ecological Momentary Assessment (EMA) study using a mobile app (m-path). A sample of N=78 participants, was included in the study. Interested participants were provided information about the study and signed an informed consent form. After confirming smoking patterns suitable for the study, as well as screening for psychiatric disorders, participants completed baseline assessments of smoking behavior, smoking attitudes and mental health status, followed by a 2-week EMA protocol. The EMA schedule included 3 measurements/day of momentary affect, current smoking urges, and inhibitory control level (a short version of the Stop Signal Task, SST), followed 45-min later by questions related to their smoking behavior since the last ping. To test our working hypotheses (see below), we will apply time-lagged hierarchical models, predicting smoking behavior at time t+1 from mood and inhibitory control data at time t. The successful completion of this project will not only offer a novel mechanistic understanding of addictive behaviors in real life, but may also have clinical utility, as the data can be used to predict and prevent addictive behaviors, using novel therapeutic approaches.