Ecological Momentary Assessment and Personalized Networks in Cognitive Bias Modification Studies on Addiction: Advances and Challenges

Abstract

Adding cognitive bias modification (CBM) to treatment as usual for alcohol use disorders has been found to reduce relapse rates. However, CBM has not yielded effects as a stand-alone intervention. One possible reason may be that this is due to CBM effects being underpinned by inferential rather than associative mental mechanisms. This change in perspective has led to a proposed improved version of CBM: Inference-based ABC training. In ABC training, participants learn to relate the antecedents (A) of their addiction behavior to alternative behaviors (B) and to their expected consequences (C) in relation to their long-term goals. Mechanisms triggering and maintaining addiction, such as those targeted during ABC training, can differ between people. Ecological Momentary Assessment (EMA) and derived personalized statistics, including models depicting relationships between variables (i.e., personalized networks), are therefore promising tools to help to optimally personalize this training. In this paper, we (1) explain the theoretical background and first implementations of ABC training; (2) present novel approaches to personalize treatment based on EMA; (3) propose ways forward to integrate improved CBM approaches and EMA to potentially advance addiction treatment; and (4) discuss promises and challenges of these proposed new approaches.