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Drink Less app

Development and evaluation of a theory and evidence-based smartphone application to reduce excessive alcohol consumption.

Principal investigatorsDr Claire Garnett,
Professor Jamie Brown
Co-investigators

Professor Robert West,
Professor Susan Michie,
Professor Eileen Kaner,
Professor Matthew Hickman,
Professor Marcus Munafo,
Professor Matt Field,
Dr Felix Greaves,
Matthew Walmsley,
Dr Robyn Burton

Other UCL team membersDr Olga Perski
Funding

NIHR School for Public Health Research,
Society for the Study of Addiction,
Cancer Research UK

Previous funders: UK Centre for Tobacco and Alcohol Studies

Project start and end datesJan 2018 - July 2019
Project websitehttp://www.drinklessalcohol.com 
https://apps.apple.com/gb/app/drink-less-get-help-reducing/id1020579244

Drink less app Icon
Project aims

The objectives of this research were to:

  1. Optimise the Drink Less app to improve its likely effectiveness at reducing alcohol consumption in excessive drinkers
  2. Improve the acceptability and usability of an optimised version of Drink Less
  3. Plan and submit a bid for a confirmatory trial of an optimised version of Drink Less

 

Project details

Drink Less was developed as an evidence- and theory-based alcohol-reduction app for the general population of drinkers with a robust evidence base, and rigorous development and evaluation strategy.

The initial development of the app was guided by theory and a number of sources of evidence and the app was professionally designed and built. Usability studies were conducted with potential users and changes were made to the app in light of these findings to ensure the app was easy to use and provided a good user experience.

Drink Less was developed for iOS devices and structured around goal setting – an activity to engage the users and allow experimental manipulation of other modules. There were five other modules, each focusing on a different behaviour change technique or intervention component – normative feedback; self-monitoring; action planning; cognitive bias re-training; and identity change. These five modules were evaluated in a randomised factorial screening trial as part of the Multiphase Optimisation Strategy (MOST), a sequenced, experimental approach to intervention development and evaluation. Treatment-seeking individuals who were excessive drinkers found Drink Less to be acceptable and used the app frequently. The randomised factorial trial found that the combinations of Normative Feedback and Cognitive Bias Re-training, and Self-monitoring and Action Planning produced modest improvements in alcohol-related outcomes after four weeks.

This project built directly on previous research and conducted the next step in the MOST approach - optimisation of the effectiveness and usability of the intervention.

The project involved three work packages 1) optimisation of the effectiveness of the Drink Less app; 2) user testing to improve acceptability and usability, and 3) development of a funding proposal for a confirmatory trial. The optimisation of the Drink Less app involved collating the evidence from the randomised factorial screening trial and the Cochrane systematic review of digital alcohol interventions to identify the intervention components to include or remove from the app. The user testing involved input from users of varying socio-economic status (SES) to maximise the acceptability and usability of the Drink Less app across the social spectrum. The third work package involved submitting a funding bid for a confirmatory trial of the optimised version of the Drink Less app. PHE collaborators provided insight during meetings into the app’s long-term adoption and help to support its implementation in practice.

 

Next steps
To conduct a randomised controlled trial of the Drink Less app.

 

Publications

Relevant publications are available here:
https://www.researchgate.net/project/Development-and-evaluation-of-a-smartphone-app-Drink-Less-for-reducing-excessive-alcohol-consumption

Short reports on the work packages involved in this project are available here:
https://osf.io/mc8yz/