Objectives: Binge drinking (BD) among young people has significant public health implications. Thus, there is the need to target users most at risk. We estimated the discriminative accuracy of an innovative model nested in a recently developed e-Health app (Digital-Alcohol RIsk Alertness Notifying Network for Adolescents and young adults [D-ARIANNA]) for BD in young people, examining its performance to predict short-term BD episodes.
Methods: We consecutively recruited young adults in pubs, discos, or live music events. Participants self-administered the app D-ARIANNA, which incorporates an evidence-based risk estimation model for the dependent variable BD. They were re-evaluated after 2 weeks using a single-item BD behavior as reference. We estimated D-ARIANNA discriminative ability through measures of sensitivity and specificity, and also likelihood ratios. ROC curve analyses were carried out, exploring variability of discriminative ability across subgroups.
Results: The analyses included 507 subjects, of whom 18% reported at least 1 BD episode at follow-up. The majority of these had been identified as at high/moderate or high risk (65%) at induction. Higher scores from the D-ARIANNA risk estimation model reflected an increase in the likelihood of BD. Additional risk factors such as high pocket money availability and alcohol expectancies influence the predictive ability of the model.
Conclusions: The D-ARIANNA model showed an appreciable, though modest, predictive ability for subsequent BD episodes. Post-hoc model showed slightly better predictive properties. Using up-to-date technology, D-ARIANNA appears an innovative and promising screening tool for BD among young people. Long-term impact remains to be established, and also the role of additional social and environmental factors.