Identifying viable theoretical frameworks with essential parameters for real-time and real world alcohol craving research: a systematic review of craving models

Abstract

Background: Substance use is known to be episodic, dynamic, complex, and highly influenced by the environment, therefore a situational and momentary focus to alcohol craving research is appropriate. Current advances in mobile and wearable technology provide novel opportunities for craving research. However, the lack of consensus within craving theory impedes the identification and prioritization of parameters to be monitored. The aim of this study is to critically review current craving models in order to determine viable theoretical frameworks of alcohol craving and its essential parameters.

Methods: Eighteen models of craving were reviewed by applying a literature search with a five-step strategy that accounted for the momentary nature of craving and included a snowballing search and a key term extraction algorithm. Based on this review, multiple decision criteria were defined upon which to evaluate the models.

Results: Six models for alcohol craving were supported by sufficient empirical research to be eligible. The inferences drawn on these six models resulted in three decision criteria: the model should (1) incorporate negative affect as a predictor of relapse; (2) explain that dependent drinkers have a higher attentional bias towards alcohol cues than nondependent drinkers; (3) incorporate increased risk of relapse with heightened stress levels.

Conclusions: The affective processing model of negative reinforcement, the cognitive processing model, the incentive sensitization theory of addiction and the theory of neural opponent motivation are classified as viable theoretical frameworks, resulting in negative affect and stress as relevant parameters to include in real-time craving monitoring research.

Keywords:

• Alcohol craving
• relapse
• negative affect
• stress
• attentional bias
• alcohol cues

Acknowledgements

The authors thank Paul Mannée for constructing the key extraction algorithm in Matlab.

Disclosure statement

The authors report no conflicts of interest.

Additional information

Funding

This work was supported by the University of Twente’s Tech4people program.