BackgroundFace-to-face brief interventions for problem drinking are effective, but they have found limited implementation in routine care and the community. Internet-based interventions could overcome this treatment gap. We investigated effectiveness and moderators of treatment outcomes in internet-based interventions for adult problem drinking (iAIs).Methods and findingsSystematic searches were performed in medical and psychological databases to 31 December 2016. A one-stage individual patient data meta-analysis (IPDMA) was conducted with a linear mixed model complete-case approach, using baseline and first follow-up data. The primary outcome measure was mean weekly alcohol consumption in standard units (SUs, 10 grams of ethanol). Secondary outcome was treatment response (TR), defined as less than 14/21 SUs for women/men weekly. Putative participant, intervention, and study moderators were included. Robustness was verified in three sensitivity analyses: a two-stage IPDMA, a one-stage IPDMA using multiple imputation, and a missing-not-at-random (MNAR) analysis. We obtained baseline data for 14,198 adult participants (19 randomised controlled trials [RCTs], mean age 40.7 [SD = 13.2], 47.6% women). Their baseline mean weekly alcohol consumption was 38.1 SUs (SD = 26.9). Most were regular problem drinkers (80.1%, SUs 44.7, SD = 26.4) and 19.9% (SUs 11.9, SD = 4.1) were binge-only drinkers. About one third were heavy drinkers, meaning that women/men consumed, respectively, more than 35/50 SUs of alcohol at baseline (34.2%, SUs 65.9, SD = 27.1). Post-intervention data were available for 8,095 participants. Compared with controls, iAI participants showed a greater mean weekly decrease at follow-up of 5.02 SUs (95% CI −7.57 to −2.48, p < 0.001) and a higher rate of TR (odds ratio [OR] 2.20, 95% CI 1.63–2.95, p < 0.001, number needed to treat [NNT] = 4.15, 95% CI 3.06–6.62). Persons above age 55 showed higher TR than their younger counterparts (OR = 1.66, 95% CI 1.21–2.27, p = 0.002). Drinking profiles were not significantly associated with treatment outcomes. Human-supported interventions were superior to fully automated ones on both outcome measures (comparative reduction: −6.78 SUs, 95% CI −12.11 to −1.45, p = 0.013; TR: OR = 2.23, 95% CI 1.22–4.08, p = 0.009). Participants treated in iAIs based on personalised normative feedback (PNF) alone were significantly less likely to sustain low-risk drinking at follow-up than those in iAIs based on integrated therapeutic principles (OR = 0.52, 95% CI 0.29–0.93, p = 0.029). The use of waitlist control in RCTs was associated with significantly better treatment outcomes than the use of other types of control (comparative reduction: −9.27 SUs, 95% CI −13.97 to −4.57, p < 0.001; TR: OR = 3.74, 95% CI 2.13–6.53, p < 0.001). The overall quality of the RCTs was high; a major limitation included high study dropout (43%). Sensitivity analyses confirmed the robustness of our primary analyses.ConclusionTo our knowledge, this is the first IPDMA on internet-based interventions that has show...

BackgroundBrief interventions via the internet have been shown to reduce university students’ alcohol intake. This study tested two smartphone applications (apps) targeting drinking choices on party occasions, with the goal of reducing problematic alcohol intake among Swedish university students.MethodsStudents were recruited via e-mails sent to student union members at two universities. Those who gave informed consent, had a smartphone, and showed risky alcohol consumption according to the Alcohol Use Disorders Identification Test (AUDIT) were randomized into three groups. Group 1 had access to the Swedish government alcohol monopoly’s app, Promillekoll, offering real-time estimated blood alcohol concentration (eBAC) calculation; Group 2 had access to a web-based app, PartyPlanner, developed by the research group, offering real-time eBAC calculation with planning and follow-up functions; and Group 3 participants were controls. Follow-up was conducted at 7 weeks.ResultsAmong 28574 students offered participation, 4823 agreed to join; 415 were excluded due to incomplete data, and 1932 fulfilled eligibility criteria for randomization. Attrition was 22.7–39.3 percent, higher among heavier drinkers and highest in Group 2. Self-reported app use was higher in Group 1 (74%) compared to Group 2 (41%). Per-protocol analyses revealed only one significant time-by-group interaction, where Group 1 participants increased the frequency of their drinking occasions compared to controls (p = 0.001). Secondary analyses by gender showed a significant difference among men in Group 1 for frequency of drinking occasions per week (p = 0.001), but not among women. Among all participants, 29 percent showed high-risk drinking, over the recommended weekly drinking levels of 9 (women) and 14 (men) standard glasses.ConclusionsSmartphone apps can make brief interventions available to large numbers of university students. The apps studied using eBAC calculation did not, however, seem to affect alcohol consumption among university students and one app may have led to a negative effect among men. Future research should: 1) explore ways to increase user retention, 2) include apps facilitating technical manipulation for evaluation of added components, 3) explore the effects of adapting app content to possible gender differences, and 4) offer additional interventions to high-risk users.Trial registrationclinicaltrials.gov: NCT01958398.

PurposeUniversity students in a study on estimated blood alcohol concentration (eBAC) feedback apps were offered participation in a second study, if reporting continued excessive consumption at 6-week follow-up. This study evaluated the effects on excessive alcohol consumption of offering access to an additional skills training app.MethodA total of 186 students with excessive alcohol consumption were randomized to an intervention group or a wait list group. Both groups completed online follow-ups regarding alcohol consumption after 6 and 12 weeks. Wait list participants were given access to the intervention at 6-week follow-up. Assessment-only controls (n = 144) with excessive alcohol consumption from the ongoing study were used for comparison.ResultsThe proportion of participants with excessive alcohol consumption declined in both intervention and wait list groups compared to controls at first (p < 0.001) and second follow-ups (p = 0.054). Secondary analyses showed reductions for the intervention group in quantity of drinking at first follow-up (−4.76, 95% CI [−6.67, −2.85], Z = −2.09, p = 0.037) and in frequency of drinking at both follow-ups (−0.83, 95% CI [−1.14, −0.52], Z = −2.04, p = 0.041; −0.89, 95% CI [−1.16, −0.62], Z = −2.12, p = 0.034). The odds ratio for not having excessive alcohol consumption among men in the intervention group compared to male controls was 2.68, 95% CI [1.37, 5.25] (Z = 2.88, p = 0.004); the figure for women was 1.71, 95% CI [1.11, 2.64] (Z = 2.41, p = 0.016).ConclusionSkills training apps have potential for reducing excessive alcohol use among university students. Future research is still needed to disentangle effects of app use from emailed feedback on excessive alcohol consumption and study participation.Trial RegistrationNCT02064998Electronic supplementary materialThe online version of this article (doi:10.1007/s12529-016-9629-9) contains supplementary material, which is available to authorized users.