PLOS ONE · 9 authors, 7 centres
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This proof-of-concept study used a mixed-effects dynamical systems model to analyze daily data on alcohol consumption and craving in individuals with alcohol use disorder. It demonstrated that the model could capture both population-level and individual-level dynamics, offering a foundation for identifying potential intervention targets.
This study developed a data-driven, mixed-effects dynamical systems model to analyze daily self-reported alcohol consumption and craving data from individuals with mild-to-moderate alcohol use disorder (AUD). The model, formulated as delayed difference equations, was calibrated using Bayesian parameter estimation after an identifiability analysis reduced the parameter set. A sensitivity analysis identified which parameters were most influential on model outputs. As a proof-of-concept, the work demonstrates a framework for mathematically describing behavior change mechanisms and is intended as a foundation for future model validation and clinical application.
