%0 Journal Article
%T Effectiveness and treatment moderators of internet interventions for adult problem drinking: An individual patient data meta-analysis of 19 randomised controlled trials
%A Adriaan Hoogendoorn
%A Adriana Mira
%A Anders Hansen
%A Anne H. Berman
%A Brian Suffoletto
%A Brigitte Boon
%A Christopher Sundstrˋm
%A Daniela Schulz
%A David Ebert
%A Eirini Karyotaki
%A Elizabeth Murray
%A Gallus Bischof
%A Gerhard Andersson
%A Hein de Vries
%A Heleen Riper
%A Hˋvar Brendryen
%A Jeannet Kramer
%A Johannes H. Smit
%A John Cunningham
%A Kristina Sinadinovic
%A Leif Boˋ
%A Marloes Postel
%A Matthijs Blankers
%A Nicolas Bertholet
%A Nikolaos Boumparis
%A Paul Wallace
%A Pim Cuijpers
%A Reid Hester
%A Reinout W. Wiers
%A Zarnie Khadjesari
%J -
%D 2018
%R 10.1371/journal.pmed.1002714
%X Background Face-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 findings Systematic 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 ˋ7.57 to ˋ2.48, p < 0.001) and a higher rate of TR (odds ratio [OR] 2.20, 95% CI 1.63每2.95, p < 0.001, number needed to treat [NNT] = 4.15, 95% CI 3.06每6.62). Persons above age 55 showed higher TR than their younger counterparts (OR = 1.66, 95% CI 1.21每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: ˋ6.78 SUs, 95% CI ˋ12.11 to ˋ1.45, p = 0.013; TR: OR = 2.23, 95% CI 1.22每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每0.93, p = 0.029). The use of waitlist control in RCTs
%K Alcohol consumption
%K Metaanalysis
%K Internet
%K Randomized controlled trials
%K Alcoholism
%K Behavior
%K Primary care
%K Ethanol
%U https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002714