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). 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 −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. To our knowledge, this is the first IPDMA on internet-based interventions that has shown them to be effective in curbing various patterns of adult problem drinking in both community and healthcare settings. Waitlist control may be conducive to inflation of treatment outcomes.

Frequent Cannabis use has been linked to a variety of negative mental, physical, and social consequences. We assessed the effects of digital prevention and treatment interventions on Cannabis use reduction in comparison with control conditions. Systematic review with two separate meta-analyses. Thirty randomized controlled trials met the inclusion criteria for the review, and 21 were included in the meta-analyses. Primary outcome was self-reported Cannabis use at post-treatment and follow-up. Hedges's g was calculated for all comparisons with non-active control. Risk of bias was examined with the Cochrane risk-of-bias tool. The systematic review included 10 prevention interventions targeting 8138 participants (aged 12 to 20) and 20 treatment interventions targeting 5195 Cannabis users (aged 16 to 40). The meta-analyses showed significantly reduced Cannabis use at post-treatment in the prevention interventions (6 studies, N = 2564, g = 0.33; 95% CI 0.13 to 0.54, p = 0.001) and in the treatment interventions (17 comparisons, N = 3813, g = 0.12; 95% CI 0.02 to 0.22, p = 0.02) as compared with controls. The effects of prevention interventions were maintained at follow-ups of up to 12 months (5 comparisons, N = 2445, g = 0.22; 95% CI 0.12 to 0.33, p < 0.001) but were no longer statistically significant for treatment interventions. Digital prevention and treatment interventions showed small, significant reduction effects on Cannabis use in diverse target populations at post-treatment compared to controls. For prevention interventions, the post-treatment effects were maintained at follow-up up to 12 months later.

Background and Aims: Research has shown that internet interventions can be effective for dependent users of various substances. However, less is known about the effects of these interventions on users of opioids, cocaine and amphetamines than for other substances. We aimed to investigate the effectiveness of internet interventions in decreasing the usage of these types of substances. Methods: We conducted a systematic literature search in the databases of PubMed, PsycINFO, Embase and the Cochrane Library to identify randomized controlled trials examining the effectiveness of internet interventions compared with control conditions in reducing the use of opioids, cocaine and amphetamines. No setting restrictions were applied. The risk of bias of the included studies was examined according to the Cochrane Risk of Bias assessment tool. The primary outcome was substance use reduction assessed through toxicology screening, self‐report or both at post‐treatment and at the follow‐up assessment. Results: Seventeen studies with 2836 adult illicit substance users were included. The risk of bias varied across the included studies. Internet interventions decreased significantly opioid [four studies, n = 606, g = 0.36; 95% confidence interval (CI) = 0.20–0.53, P < 0.001] and any illicit substance use (nine studies, n = 1749, g = 0.35; 95% CI = 0.24–0.45, P < 0.001) at post‐treatment. Conversely, the effect of internet intervention for stimulant users was small and non‐significant (four studies, n = 481, P = 0.164). Overall, internet interventions decreased substance significantly use at post‐treatment (17 studies, n = 2836, g = 0.31; 95% CI = 0.23–0.39, P < 0.001) and at the follow‐up assessments (nine studies, n = 1906, g = 0.22; 95% CI = 0.07–0.37; P = 0.003). Conclusions: Internet interventions demonstrate small but significant effects in decreasing substance use among various target populations at post‐treatment and at the follow‐up assessment. However, given the small number of available studies for certain substances, the findings should be interpreted with caution.

Purpose: Only about 20% of people suffering from substance use disorders access available treatments due to various obstacles; digital interventions could potentially overcome some of these. Meta-analyses suggest the strongest evidence for interventions targeting alcohol use reduction, followed by cannabis and illicit substances. However, most randomized controlled trials (RCTs) used unguided standalone interventions compared to non-active controls, with limited follow-up periods and disregarded comorbidity. This review examines the literature published over the last three years (2016–2019), with a focus on recent RCTs and whether they addressed some of these gaps. Recent findings: Except for digital interventions targeting alcohol use, the number of RCTs in the last three years is limited. Although there is considerable heterogeneity between the studies, most of them applied unguided add-on interventions compared to active control groups, and a limited number investigated guided interventions. In addition, there is a need for longer follow-up periods, active rather than non-active control groups, outcome standardization, and increased focus on comorbidity. Summary: Although the number of studies using guided add-on or blended interventions compared to active controls has increased, future studies should consider our identified gaps and suggestions to further strengthen the evidence of digital interventions for reducing the use of alcohol and other substances.

Background and aims: Behavioral addictions are a public health problem that causes harm to both individuals and society. Internet-based interventions offer potential benefits over face-to-face therapy for the treatment of behavioral addictions, including their accessibility, perceived anonymity, and low costs. We systematically reviewed the characteristics and effectiveness of these interventions. Methods: A systematic literature search was conducted in: PubMed, PsycINFO, Embase, and the Cochrane Central Register of Controlled Trials. A standardized methodological quality assessment was performed on all identified studies via the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool. Results: Twenty-nine studies were assessed in this systematic review. Between them, considerable heterogeneity was noted in various study characteristics, including screening tools, inclusion criteria, and outcome measures. Attrition rates also ranged widely (9–89%), as did study quality, with three of the 29 studies rated strong, 12 moderate, and 14 weak methodologically. Twenty-two studies focused on gambling disorder, most revealing significant within-group effects for the assessed intervention on gambling-related symptoms and four of these studies identified significant between-group effects. Behavioral addictions studied in the remaining studies included gaming disorder, internet use disorder, hoarding disorder, and pornography use disorder, revealing generally-promising, albeit limited results. Conclusions: Internet-based interventions seem promising at reducing gambling problems, but too few studies have been published, to date, for conclusions to be drawn for other behavioral addictions. Internet-based interventions targeting other behavioral addictions – like gaming disorder, internet use disorder, hoarding disorder, and pornography use disorder – remain under-examined, warranting considerable additional research to assess their effectiveness.