Computer-Based Interventions for Problematic Alcohol Use: a Review of Systematic Reviews

Two reviews reported outcomes in standard drinking units [15, 17]; Rooke et al. 2010 reported an effect size of 0.26 representing a small effect size [15] and White et al. 2010 reported an effect size of 0.42 representing a medium effect size [17]. Two reviews reported outcomes in grammes of ethanol per week [18, 21]; Khadjesari et al. 2010 found a statistically significant reduction in the intervention group of 26 g per week (95 % CI −41 to −11), an amount reflecting about three standard drinking units (8 g of ethanol) in the UK or 2.5 glasses in European standard drinking units (10 g of ethanol) [18]. Donoghue et al. 2014, the only review to compare different follow up times in relation to outcomes, concluded that effects on alcohol consumption were sustained at 3 months, with a mean difference of −32.74 g of ethanol, (95 % CI −56.80 to −8.68), slightly smaller effects at 3 months to less than 6 months (mean difference –17.33 g, 95 % CI –31.82 to −2.84) and from 6 months to less than 12 months follow-up (mean difference –14.91 g, 95 % CI −25.56 to −4.26). No significant effects were maintained after 12 months (mean difference –7.46 g, 95 % CI −25.34 to 10.43) [21]. One review without meta-analyses, Balhara et al. 2014 [22], considered evidence for the effectiveness of computer-based alcohol interventions inconclusive pointing to negative findings in some RCTs.

Three reviews reported outcomes in standard drinking units [14, 16, 26]. In Carey et al. 2009’s review, computer-based interventions were found to show small effects on alcohol use, ranging from 0.09 to 0.28 for all follow-up time-points [14], while Tait et al. 2010 found a minimal effect size in alcohol consumption reduction (d = 0.12, 95 % CI 0.10 to 0.34) [16]. Dotson et al. 2015 found a small effect size on consumption (d = 0.29, 95 % CI 0.16 to 0.42) and also reported a reduction of about three drinks [26]. Dedert et al. 2015, the only review reporting outcomes in grammes of ethanol per week among students, found a mean difference of −11.7 g of ethanol (95 % CI −19.3 to −4.1) per week at 6 months between computer-based interventions and control groups reflecting a reduction of about 1.5 UK standard units or one European standard unit. At 12 months, no significant difference between groups was found [24]. Three reviews presented effect sizes on binge drinking [14, 16, 24]. Carey et al. 2009 reported a non-significant reduction (at >5 weeks) with a minimal effect size (d = 0.10, 95 % CI 0.00 to 0.20, [14], Tait et al. 2010 reported a significant reduction with a small to medium effect size (d = −0.35, 95 % CI − 0.64 to −0.06) [16], and Dedert et al. 2015 found no effect [24]. Three reviews presented effect sizes on alcohol related problems [14, 16, 26]. Carey et al. 2009 reported a minimal effect size at <5 weeks (d = 0.16, 95 % CI 0.03 to 0.29) [14] in line with the findings of Dotson et al. 2015 (d = 0.157, 95 % CI 0.037 to 0.278) [26], while Tait et al. 2010 found a moderate effect size (d = −0.57, 95 % CI− 0.98 to −0.15 [16]. Narratively, Bhochhibhoya et al. 2015 supported these findings [23], whereas Bewick et al. 2008 [8] and Leeman et al. 2015 [25] highlighted mixed findings.

Riper et al. conducted two reviews focusing on adult problem drinkers [19, 20]; the first of the two found a medium effect size on alcohol consumption (Hedges g = 0.44, 95 % CI 0.17 to 0.71) [19] whereas the more recently published of the two found a small significant effect size (Hedges g = 0.20, 95 % CI 0.13 to 0.27). In this later review, Riper et al. also reported a significant reduction of 2.2 “alcohol consumptions” (22 g of ethanol) per week (95 % CI 0.87 to 3.46) [20]. In the review by Dedert et al. 2015, a significant reduction of 16.7 g of ethanol was found at 6 months (95 % CI −27.6 to −5.8) with no significant difference found at 12 months [24].

Khadjesari et al. 2010 conducted a sub-group analysis of studies with student populations and found the effectiveness of computer-based interventions to be less pronounced than in the wider meta-analysis of mixed student and non-student adult populations [18].

Eleven of the 14 reviews (79 %) addressed the association between therapeutic orientation and outcome. Four of these addressed this theme quantitatively [14, 15, 20, 25]. Carey et al. 2009 found that computer-based interventions were more successful at reducing heavy drinking frequency at long-term (≥6 weeks) when they did not provide feedback on alcohol-related problems (β = −0.63, p < 0.01) [14]. Rooke et al. 2010 compared interventions using normative feedback and relapse prevention with those that did not. No significant association was reported (normative feedback Q_between(1) = 0.06, p = 0.80; relapse prevention Q_between(1) = 0.01, p = 0.98) [15]. Riper et al. 2014 [20] performed subgroup analyses focussing on the relation between therapeutic orientation and outcome. Of the 16 studies included in the review, seven applied a single-focus therapeutic strategy, mostly personalized normative feedback, while the other nine studies used combined treatment approaches based on motivational interviewing, personalized normative feedback, cognitive-behavioural therapy and/or behavioural self-control and change principles. No significant association was found. Leeman et al. 2015 compared studies of personalized normative feedback interventions with studies of multi-component interventions and did not find any differences in effect sizes [25]. Other reviews addressed this issue narratively. The review by Bewick et al. 2008 suggested research on which elements of personalized feedback are related to outcome and whether those elements are different for low and high-risk drinkers [8]. Bhochhibhoya et al. 2015 found that in many studies, a theory-driven approach to interventions development was lacking, and of the included studies in this review, only 3 out of 14 utilized a theory-based intervention [23].

Eight reviews (57 %) addressed the association between length of intervention and outcome. Three of these addressed this theme quantitatively [15, 19, 20]. Rooke et al. 2010 found a non-significant association between number of sessions and treatment effect (r = 0.17, p = 0.34) [15]. Riper et al. 2011 [19] found a significant difference (p = 0.04) between single session interventions (g = 0.27, 95 % CI 0.11 to 0.43) and more extended Internet-based self-help interventions (g = 0.61, 95 % CI 0.33 to 0.90) in sub-group analyses. However, in a more recent review by Riper et al. 2014 [20], no significant associations were found in a meta-regression between number of sessions and effect size (b = −0.0001, 95 % CI −0.004 to 0.003). White et al. 2010 noted that the pre­post differential effect size for brief personalized feedback programs (d = 0.39) was somewhat smaller than the effect size for the multisession modularized programs (d = 0.56) [17]. Some reviews addressed the theme narratively; Dedert et al. 2015 stated that variability in treatment intensity was insufficient to formally test its association with outcomes [24], while Bhochhibhoya et al. 2015 concluded that more prolonged, multi-session interventions seem to be more effective than one-time interventions [23].

The association between guidance and outcome was addressed in five reviews (36 %). In three of these, some form of quantitative analysis on this theme was performed [14, 15, 20]. Carey et al. 2009 found that computer-based interventions were more successful in reducing alcohol-related problems at short-term (≤5 weeks) when including human interaction vs. using the computer alone (β = −0.53, p = 0.02) [14]. Rooke et al. 2010 compared interventions with minimal therapist contact (n = 32), moderate therapist contact (n = 8) and major therapist contact (n = 2). No significant association was reported, Q_between(2) = 3.29, p = 0.19 [15]. Riper et al. 2014 did not find an association between therapist guidance and outcome: guided (g = 0.23) and unguided (g = 0.20), p = 0.73 [20]. As suggested by both Dedert et al. 2015 [24] and Riper et al. 2014 the variability in amount of guidance, and the lack of published studies on guided interventions, may not yet be sufficient for a sound evaluation of its effect on alcohol-related outcomes.

Three reviews mentioned the possible association between trial engagement (or its reverse, drop-out) and outcome (21 %) with one of them presenting a quantitative analysis on this theme. Leeman et al. 2015 did not find differences in effect sizes between studies with retention rates at follow-up of more than 70 % versus less than 70 % [25]. White et al. 2010 reported retention in the intervention groups of the included trials ranging from 38.9 to 100 %, with a median of 74.5 % at 6 months; retention in control groups were quite similar with a range of 33.4 to 100 % and a median of 74.9 % at 6 months [17]. Dotson et al. 2015 reported drop-out rates in the included studies, ranging from 4.2 to 21 % [26]. This review in addition contended that researchers should not only measure drop-out rates but also find a way to measure whether participants actually pay attention to the intervention content as there are indications that some participants may engage in other activities while completing Internet interventions [27] in [26]].

None of the reviews explicitly report on the association between therapeutic orientation, length of intervention and guidance and trial engagement.