Updating the evidence on the effectiveness of the alcohol reduction app, Drink Less: using Bayes factors to analyse trial datasets supplemented with extended recruitment

Abstract

Background : A factorial experiment evaluating the Drink Less app found no clear evidence for main effects of enhanced versus minimal versions of five components but some evidence for an interaction effect. Bayes factors (BFs) showed the data to be insensitive. This study examined the use of BFs to update the evidence with further recruitment. Methods : A between-subject factorial experiment evaluated the main and two-way interaction effects of enhanced versus minimal version of five components of Drink Less. Participants were excessive drinkers, aged 18+, and living in the UK. After the required sample size was reached (n=672), additional data were collected for five months. Outcome measures were change in past week alcohol consumption and Alcohol Use Disorders Identification Test (AUDIT) score at one-month follow-up, amongst responders only. BFs (with a half-normal distribution) were calculated for those for which we had outcome data (BF<0.33 indicate evidence for null hypothesis; 0.33