Gender differences in cannabis use disorder symptoms: A network analysis

Kroon, Emese; Mansueto, Alessandra C.; Kuhns, Lauren; Filbey, Francesca M.; Wiers, Reínout W.; Cousijn, Janna

doi:10.1016/j.drugalcdep.2022.109733

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Such complex interactions can be estimated using network analysis, which results in networks graphing the relationships between different variables while controlling for all other variables in the model. Between-subjects networks estimated from cross-sectional data have been recently applied to addiction (Anker et al, 2017; Huth et al, 2022; Kroon et al, 2023; Rhemtulla et al, 2016). For example, an exploratory network analysis of cannabis use disorder (CUD) symptoms in women suggested that a mood disorder was associated with experiencing cannabis craving and withdrawal, which in turn was associated with experiencing other CUD symptoms such as failing to reduce or quit use (Kroon et al, 2023).…”

Section: Ema Personalized Feedback and Personalized Network In Addictionmentioning

confidence: 99%

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

Mansueto

Pan

Dessel

et al. 2023

Journal of Experimental Psychopathology

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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.

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