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Updated: Oct 17, 2025
A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
Published on: January 5, 2018
Refining the content and design of an alcohol reduction app, Drink Less, to improve its usability and effectiveness:
Claire Garnett1, Olga Perski1, Susan Michie2
1Department of Behavioural Science and Health, University College London, London, WC1E 7HB, UK.
Abstract:
Background: Digital interventions have the potential to reduce alcohol consumption, although evidence on the effectiveness of apps is lacking. Drink Less is a popular, evidence-informed app with good usability, putting it in a strong position to be improved upon prior to conducting a confirmatory evaluation. This paper describes the process of refining Drink Less to improve its usability and likely effectiveness. Methods: The refinement consisted of three phases and involved qualitative and quantitative (mixed) methods: i) identifying changes to app content, based on findings from an initial evaluation of Drink Less, an updated review of digital alcohol interventions and a content analysis of user feedback; ii) designing new app modules with public input and a consultation with app developers and researchers; and iii) improving the app's usability through user testing. Results: As a result of the updated review of digital alcohol interventions and user feedback analysis in Phase 1, three new modules: 'Behaviour Substitution', 'Information about Antecedents' and 'Insights', were added to the app. One existing module - 'Identity Change' - was removed based on the initial evaluation of Drink Less. Phases 2 and 3 resulted in changes to existing features, such as improving the navigational structure and onboarding process, and clarifying how to edit drinks and goals. Conclusions: A mixed methods approach was used to refine the content and design of Drink Less, providing insights into how to improve its usability and likely effectiveness. Drink Less is now ready for a confirmatory evaluation.
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