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BackgroundAlcohol consumption in the student population continues to be cause for concern. Building on the established evidence base for traditional brief interventions, interventions using the Internet as a mode of delivery are being developed. Published evidence of replication of initial findings and ongoing development and modification of Web-based personalized feedback interventions for student alcohol use is relatively rare. The current paper reports on the replication of the initial Unitcheck feasibility trial. ObjectiveTo evaluate the effectiveness of Unitcheck, a Web-based intervention that provides instant personalized feedback on alcohol consumption. It was hypothesized that use of Unitcheck would be associated with a reduction in alcohol consumption. MethodsA randomized control trial with two arms (control=assessment only; intervention=fully automated personalized feedback delivered using a Web-based intervention). The intervention was available week 1 through to week 15. Students at a UK university who were completing a university-wide annual student union electronic survey were invited to participate in the current study. Participants (n=1618) were stratified by sex, age group, year of study, self-reported alcohol consumption, then randomly assigned to one of the two arms, and invited to participate in the current trial. Participants were not blind to allocation. In total, n=1478 (n=723 intervention, n=755 control) participants accepted the invitation. Of these, 70% were female, the age ranged from 17-50 years old, and 88% were white/white British. Data were collected electronically via two websites: one for each treatment arm. Participants completed assessments at weeks 1, 16, and 34. Assessment included CAGE, a 7-day retrospective drinking diary, and drinks consumed per drinking occasion. ResultsThe regression model predicted a monitoring effect, with participants who completed assessments reducing alcohol consumption over the final week. Further reductions were predicted for those allocated to receive the intervention, and additional reductions were predicted as the number of visits to the intervention website increased. ConclusionsUnitcheck can reduce the amount of alcohol consumed, and the reduction can be sustained in the medium term (ie, 19 weeks after intervention was withdrawn). The findings suggest self-monitoring is an active ingredient to Web-based personalized feedback.

Aims: The aim of the study was to investigate non-persistence with treatment for neovascular age-related macular degeneration (NvAMD) before day 720 (24 months) after initiation, explore associations with baseline characteristics and variation between sites. Methods: Anonymised demographic and clinical data were extracted from electronic medical records at treating National Health Service (NHS) Trusts for NvAMD eyes starting intra-vitreal therapy from 2017 to 2018. Time to non-persistence with treatment, defined as no recorded attendance for either monitoring or treatment for a period ≥6 months, was visualised with a Kaplan-Meier survival plot. Associations with treatment non-persistence were investigated using a Cox proportional hazards model. Results: Analysis included 7,970 eyes of 7,112 patients treated at 13 NHS trusts. Censoring deaths and those eyes in which treatment was stopped permanently, the Kaplan-Meier analyses demonstrated survival figures of 77.7% for persistence with treatment to day 360 and 71.8% to day 720. Hazard ratios for non-persistence with treatment were reduced at 10 sites, relative to the reference, with first-treated eye status and with baseline acuity worse than or equal to LogMAR 1.0. Hazard ratios increased with younger age, in the presence of other ocular co-morbidities and with baseline acuity better than or equal to LogMAR 0.5. After an episode of non-persistence, visual acuity decreased by at least 0.1 and 0.3 LogMAR in 39% and 18% of eyes, respectively. Conclusions: Non-persistence with treatment was common, especially in the first year of treatment, and was often associated with a decrease in visual acuity. Treatment site, baseline visual acuity, and age were the strongest predictors of treatment non-persistence before day 720. Understanding and addressing reasons for non-persistence are important to ensure that effective but expensive treatments are used cost-effectively and to maintain acuity. Variation in non-persistence between sites, even after adjustment for other variables, suggests that local factors in treatment provision may be particularly important. [ABSTRACT FROM AUTHOR]