The Effectiveness of an Internet Intervention Aimed at Reducing Alcohol Consumption in Adults
Results of a randomized controlled trial (Vorvida)
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Background: In 2012, approximately 3.38 million people in Germany had an alcohol-related disorder. Internet interventions can help lower alcohol consumption, albeit with mostly small effect sizes. It is still unclear whether the effectiveness of programs aimed at lowering alcohol consumption can be improved by individually adjusting program content for each participant. We studied the effectiveness of Vorvida, a new cognitive-behavioral internet intervention with individual adjustment of content.
Methods: A randomized controlled trial was conducted on 608 adults with problematic alcohol consumption. The primary outcome was self-reported alcohol consumption in the past 30 days (as determined by the Quantity-Frequency-Index, QFI) and in the past 7 days (using the Timeline Follow-Back method, TFB). The secondary outcomes were drinking behavior (binge drinking/drunkenness) and satisfaction with Vorvida. Data were collected at three time points: at baseline (t0) and three and six months later (t1, t2). Trial registration: DRKS00006104.
Results: The intention-to-treat (ITT) analysis revealed significant differences between groups at time t1 with respect to alcohol consumption (QFI: d = 0.28; TFB: d = 0.42), binge drinking (d = 0.87), and drunkenness (d = 0.39). Satisfaction with the intervention was high (27.4 [standard deviation, SD: 5.3] out of 32 points). All effects persisted, or were stronger, at time t2. Alcohol consumption, as measured by the QFI, declined over the interval from t0 to t2 in both groups: from 63.69 g/day (SD: 61.4) to 32.67 g/day (SD: 39.78) in the intervention group, and from 61.64 g/day (SD: 58.84) to 43.75 g/day (SD: 43.68) in the control group.
Conclusion: Vorvida was found to be effective in persons with risky, problematic alcohol consumption. Further studies should determine which elements of the program contribute most to effectiveness in routine clinical practice, and what long-term effects can be achieved.
Excess alcohol consumption is a global public health problem associated with severe harms (1, 2). Approximately 3.38 million Germans met the diagnostic criteria for an alcohol-related disorder (harmful use: 1.61 million; dependence: 1.77 million) in 2012 (3) and about 7.8 million showed risky alcohol consumption in 2014 (4). In 2016, a diagnosis of alcohol-related disorders (ICD-10, F10) was the second most common diagnosis in hospitals, with 74 000 related deaths per year (5, 6). Germany’s Federal Centre for Health Education (Bundeszentrale für gesundheitliche Aufklärung; BZgA) defines the limits for low-risk alcohol consumption at a pure alcohol level of 12g per day for women and 24g per day for men (7).
Internet interventions have become a promising approach to reduce treatment barriers (8). Systematic reviews and meta-analyses (9–11) reported that such interventions are generally effective in reducing alcohol consumption, albeit with mostly small effect sizes. However, few of the interventions examined in recent meta-analyses automatically custom-tailor the program content to match individual user needs or preferences (12), even though such tailoring may increase engagement and boost effectiveness (12–14). However, the advantages of custom-tailoring remain unclear, as some recent studies have failed to find consistent advantages of tailored over non-tailored interventions (15), and some have found equivalence (16).
Moreover, Internet interventions generally differ substantially in their content, depth, delivery method, safety, and effectiveness, which justifies efforts to examine specific interventions separately rather than assume equivalence among them (17).
The aim of this study was to test the effectiveness of Vorvida, a new, German Internet intervention based on cognitive behavioural therapy (CBT) methods, which automatically tailors content to match individual user characteristics. We aimed to test this intervention against a care as usual/waiting list (CAU/WL) condition because, following the logic of pragmatic randomized controlled trials (RCTs) (18), such a comparison can show how the intervention fares when used adjunctively to highly heterogeneous usual care conditions.
A parallel-group pragmatic RCT was conducted. Participants were randomized after completing the baseline questionnaire (t0) with a ratio of 1:1 to one of the two arms. Data were collected at three time points: at baseline (t0) and three and six months later (t1, t2). Immediately after randomization, the intervention group (IG) received access to Vorvida for 180 days. The control group (CG; respectively CAU/WL) received access 6 months after completing the t2 questionnaire. Vorvida targets adults with problematic alcohol consumption. It does not require human guidance or support.
We recruited online and offline. Inclusion criteria:
- Age ≥ 18 years
- An average consumption of >12/24g (women/men) of pure alcohol per day) and/or an AUDIT-C score ≥ 3 (indicating unhealthy alcohol use) (20),
- Informed consent.
The Quantity-Frequency-Index (QFI) and Timeline-Follow-Back (TFB) (21–23) were used to determine average daily consumption of grams of pure alcohol. Both are self-report measures that estimate alcohol consumption based on recalling which beverages were consumed during the past 30 days (QFI) and the past 7 days (TFB), respectively.
Drinking behavior was assessed with two items:
- Binge drinking (On how many days did you drink five or more drinks on one occasion, regardless of whether this was beer, wine/sparkling wine, spirits, or mixed drinks/cocktails containing alcohol?)
- Drunkenness (On how many days within the past 30 days did you feel drunk (e.g. unsteady on the feet, blurred vision, unclear speech?) (24).
Satisfaction with Vorvida was assessed for the IG with the Patient satisfaction questionnaire (ZUF-8) (25).
These included, among others:
- Job status
- Information about the use of other treatments
- Timepoint (age) when at least one glass of alcohol was first consumed
- Start of regular alcohol consumption (22).
An intention-to-treat (ITT) analysis of primary data was conducted on all available data from all randomized participants. Multiple imputation was used to replace missing values. ANCOVAs (analyses of covariance) were conducted at t1 and t2 for primary outcomes (QFI, TFB) and secondary outcomes (binge drinking, drunkenness), controlling for the corresponding outcome at baseline (t0). Sensitivity analyses with complete cases were conducted. The mean alcohol consumption without controlling for baseline consumption as well as the number of participants who showed low-risk drinking behavior were calculated for both groups at all time points.
For satisfaction with the intervention (ZUF-8), means and standard deviations were calculated. Descriptive statistics on intervention usage time are reported.
The study was conducted in compliance with the Declaration of Helsinki (26). Approval was obtained from the Ethics Committee of the Hamburg State Chamber of Physicians (reference number: PV4802).
The trial flow of this study is shown in the Figure. The results are reported in accordance with the CONSORT-EHEALTH statement (CONSORT, Consolidated Standards of Reporting Trials). 1034 individuals were screened for inclusion and exclusion criteria. Of these, 290 were excluded for reasons shown in the Figure, and 136 persons did not reply to the invitation to complete t0. N = 608 participants were thus included and randomized to the IG (n = 306) or the CG (n = 302) after t0. The drop-out rate (non-completion of questionnaires) between randomization (t0) and t1 was 25% (t1: N = 458) and 7% from t1 to t2 (t2: N = 425), resulting in a drop-out rate of 30% from t0 to t2. Dropout was higher in IG (37% at t2) than in CG (23% at t2). Seven participants contacted us between t0 and t1 because they wanted to withdraw from the study. Other reasons for drop-out are unknown. No significant differences were observed between IG and CG in sociodemographic data or alcohol consumption at t0 (eTable 1).
ITT analyses for primary and secondary outcomes
For primary outcomes, significant differences in alcohol consumption were observed at t1 with small to medium effects of d = 0.278 for QFI (controlling for baseline consumption: average alcohol consumption was 40.8 g/day for IG; 56.8 g/day for CG) and d = 0.419 for TFB (34.3 g/day for IG; 43.7 g/day for CG). These effects were slightly larger at t2 (Table 1).
When not controlling for baseline levels, daily average alcohol consumption in grams decreased in the IG from a mean (M) = 63.69 (SD = 61.84) at baseline to M = 32.67 (SD = 39.78) at t2 (assessed with QFI). The TFB estimate of daily alcohol consumption showed a similar decrease from M = 52.91 (SD = 56.68) at baseline to M = 26.53 (SD = 24.09) at t2 (Table 2). This reduction in drinking was also reflected in an increasing proportion of low risk drinking behavior in the IG over time. According to the QFI, for example, 7.5% of participants in the IG showed low risk drinking behavior at baseline, compared to 20.9% at t1 and to 38.9% at t2. Similarly, on the TFB, low risk drinking among IG participants increased from 12.4% at baseline to 24.8% at t1 and 41.8% at t2. Among CG participants, by contrast, low risk drinking estimates remained relatively stable over time (Table 3).
For the secondary outcome, drinking behavior, we found a large effect at t1 for binge drinking, d = 0.873 (8.1 days within the last 30 days for IG and 14.6 days for CG, when controlling for baseline) and a small to medium effect for drunkenness, d = 0.392 (2.9 days within the past 30 days for IG and 4.6 days for CG). These effects were even larger at t2 (Table 1). Complete case analyses showed similar results with slightly larger effect sizes (eTable 2).
Descriptive statistics on secondary outcomes
Results of the ZUF-8 analyses showed a high level of satisfaction with Vorvida (M = 27.4; SD = 5.3) at t1 and t2 (M = 28.2; SD = 5.4). Both means were close to the possible maximum of 32 points (Table 4). At t2, about 94% of the participants reported they would recommend the program to a friend, 90% agreed that this had been the type of treatment they had wanted, 92% reported they would use Vorvida again (eTable 3). The mean total usage time of Vorvida was almost four hours at t1, and slightly above four hours at t2 (eTable 4).
This RCT showed statistically significant effects on the primary and secondary outcomes in favor of the IG. These effects were in the small to medium range at t1 and medium to large range at t2 (across primary and secondary outcomes, average d = 0.49 at t1 and d = 0.75 at t2). Participants in the IG decreased their daily alcohol consumption from t0 to t2 by about 31.02 g, compared to a reduction of only 17.89 g for CG participants (assessed with QFI, Table 2). IG participants reported drinking an average of 29.6 g of alcohol per day at t2, compared to 40.7 g per day among CG participants (average QFI and TFB, Table 2). Furthermore, IG participants reported an average of 10 fewer binge drinking days per month (IG: 4.6 days), compared to CG participants (CG: 14.5 days) at t2. Thus, whereas CG participants continued to binge drink about every other day at t2, this had reduced to less than two days per week, on average, among those who had used the intervention. IG participants’ satisfaction with the intervention was also very high. With 25% attrition between t0 and t1 and 7% from t1 to t2, dropout rates were less than ideal in this study, and reasons for drop-out were unfortunately not assessed in this study. However, these drop-out rates are comparable to similar trials (26).
Our results are in line with similar RCTs, given that meta-analyses have generally shown small but significant average effect sizes of Internet interventions on alcohol consumption reduction (9, 27). A recent review of several systematic reviews noted that, on average, computer-based alcohol interventions achieved reductions in weekly alcohol consumption of up to 2.5 standard European units (9). In this trial, IG participants reported drinking about one unit less per day at t2, compared to CG participants. Thus, they reported drinking about 7 fewer units per week, on average, which exceeds the average Internet intervention effect of 2–3 units less per week reported in recent reviews (9). However, the robustness of this finding requires replication, and it must be kept in mind that despite this substantial reduction, daily self-reported consumption still remained slightly above recommended limits (28). Thus, IG participants appeared to move, on average, from a pattern of clearly harmful drinking to one of risky drinking. Further research will be required to clarify the mechanisms explaining why or how the observed effects unfold.
Firstly, no face-to-face diagnostic interviews were administered, and an online self-report screening questionnaire served as the only basis for inclusion or exclusion. Face-to-face contacts or guidance in the form of regular feedback, explanations, motivation, and reminders (27, 29, 30) may boost the effects achieved by Internet interventions, but such contacts make it difficult to disentangle whether observed effects can be attributed to the automated intervention or human support.
A second limitation concerns the use of self-reported outcome measures. However, studies suggest that self-report assessments in alcohol research are reliable and valid, particularly the quantity–frequency measures we used (31).
A third limitation concerns the representativeness of our sample and, therefore, the generalizability of our findings. That is, participants in this trial were relatively highly educated and most were working. Therefore, these results may be generalizable primarily to at-risk adult drinkers who can function reasonably well on the job, who have access to the Internet, and who are sufficiently motivated to engage with an Internet intervention. It would be fair to conclude, then, that this subgroup of at-risk drinkers would be the appropriate target audience for Vorvida, not necessarily the entire population of at-risk drinkers.
A fourth limitation concerns the fact that no active control group was included. However, pragmatic RCTs aim to examine intervention effects in routine real-world settings, and including an active control condition would have complicated interpretability because such an intervention would, by definition, not be available in routine settings, and it might have exerted effects on drinking reduction (or increases in drinking) that could have introduced additional biases (e.g., placebo or nocebo effects). Comparing a psychological treatment with a “psychological placebo” also introduces conceptual problems because, unlike medication, psychological interventions actively use procedures such as providing a credible rationale and creating positive expectancies (32). Thus, it is inherently difficult to construct a “placebo psychological intervention” that, on the one hand, is perceived as credible and likely to produce clinical improvements but, on the other hand, does not contain any “active treatment ingredients”. Nevertheless, future replications in which Vorvida is compared to other active treatments could clarify how this program fares in the comparison to other interventions.
The fifth limitation to be named is the fact that the dropout rates were different: in the IG, more drop-outs (37.3%) were observed than in the CG (22.8%). However, other online-trials have also reported higher drop-out rates in the IGs (33, 34). The reasons for these differing drop-out rates remain unclear, and we can only speculate that the higher dropout rates might be related to the burden of using an Internet intervention or to the fact that IG participants had already received the purported benefit of the study (the intervention) by the time of post-assessment. If they no longer anticipated further benefits, motivation to complete online questionnaires might have waned, whereas receiving the anticipated benefit may have functioned as an incentive for continued participation among CG participants. Further research is needed, however, to understand the reasons for discontinuation among IG and CG participants.
A final limitation concerns the fact that no long-term follow-up data were collected; therefore, the stability of intervention effects beyond six months remains unclear.
The results of this study show that Vorvida was effective in terms of its capacity to facilitate reduction of alcohol consumption among adult problem drinkers. Further research is required to replicate these findings with objective measures of alcohol consumption and more active control groups; to examine mediators, moderators, and long-term intervention effects in routine care settings; and to examine whether certain program elements are more effective than others. Reasonable next steps might include the implementation of Vorvida in inpatient and outpatient care settings and wider dissemination among at-risk groups and in the general population.
In summary, our findings suggest that Vorvida is a scalable and effective intervention that could augment existing treatment options as well as help reduce the treatment gap for alcohol-related disorders.
This project was funded by the Federal Ministry of Education and Research (funding number: 01KQ1002B).
We would like to thank Janine Topp and Anastasia Itzotova for their help as student assistants in the project.
Conflict of interest statement
Dr Meyer is employed as research director at GAIA AG, the company that developed, owns, and operates the Internet intervention Vorvida, which was investigated in this trial.
The remaining authors declare that no conflict of interest exists.
The authors are happy to share the data from this study with other researchers.
Manuscript received on 5 March 2018, revised version accepted on 18 December 2018.
Dr. phil. Dipl.-Psych. Jördis M. Zill
Institut und Poliklinik für Medizinische Psychologie
Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg
For eReferences please refer to:
GAIA AG, Hamburg, and Department of Psychology, City, University of London: Björn Meyer, Ph. D.
|1.||World Health Organization: Global status on alcohol and health 2018. Geneva: World Health Organization 2018. http://apps.who.int/iris/bitstream/handle/10665/274603/9789241565639-eng.pdf (last accessed on 28 September 2018).|
|2.||Deutsche Hauptstelle für Suchtfragen (DHS) e. V.: DHS Jahrbuch Sucht 2018. Lengerich: Pabst Science Publishers 2018.|
|3.||Pabst A, Kraus L, Matos EGD, Piontek D: Substanzkonsum und substanzbezogene Störungen in Deutschland im Jahr 2012. Sucht 2013; 59: 321–31 CrossRef|
|4.||de Matos EG, Atzendorf J, Kraus L, Piontek D: Substanzkonsum in der Allgemeinbevölkerung in Deutschland. Sucht 2016; 62: 271–8 CrossRef|
|5.||John U, Hanke M: Alcohol-attributable mortality in a high per capita consumption country – Germany. Alcohol Alcohol 2002; 37: 581–5 CrossRef|
|6.||Statistisches Bundesamt (Destatis): Diagnosedaten der Patienten und Patientinnen in Krankenhäusern (einschl. Sterbe- und Stundenfälle) 2016. Fachserie 12 Reihe 621. Wiesbaden: Statistisches Bundesamt (Destatis) 2017.|
|7.||Bundeszentrale für gesundheitliche Aufklärung: Alkohol? Kenn dein Limit. www.kenn-dein-limit.de/alkohol/riskanter-konsum/ (last accessed on 27 September 2018).|
|8.||Rooke S, Thorsteinsson E, Karpin A, Copeland J, Allsop D: Computer-delivered interventions for alcohol and tobacco use: a meta-analysis. Addiction 2010; 105: 1381–90 CrossRef MEDLINE|
|9.||Sundstrom C, Blankers M, Khadjesari Z: Computer-based interventions for problematic alcohol use: a review of systematic reviews. |
Int J Behav Med 2017; 24: 646–58 CrossRef MEDLINE PubMed Central
|10.||Dedert EA, McDuffie JR, Stein R, et al.: Electronic interventions for alcohol misuse and alcohol use disorders: a systematic review. Ann Intern Med 2015; 163: 205–14 CrossRef MEDLINE PubMed Central|
|11.||Kaner EF, Beyer FR, Garnett C, et al.: Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst Rev 2017 CrossRef MEDLINE|
|12.||Krebs P, Prochaska JO, Rossi JS: A meta-analysis of computer-tailored interventions for health behavior change. Prev Med 2010; 51: 214–21 CrossRef MEDLINE PubMed Central|
|13.||Lustria MLA, Noar SM, Cortese J, Van Stee SK, Glueckauf RL, Lee J: A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun 2013; 18: 1039–69 CrossRef MEDLINE|
|14.||Noar SM, Benac CN, Harris MS: Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol Bull 2007; 133: 673 CrossRef MEDLINE|
|15.||Johansson R, Sjöberg E, Sjögren M, et al.: Tailored vs. standardized internet-based cognitive behavior therapy for depression and comorbid symptoms: a randomized controlled trial. PloS one 2012; 7: e36905 CrossRef MEDLINE PubMed Central|
|16.||Berger T, Boettcher J, Caspar F: Internet-based guided self-help for several anxiety disorders: a randomized controlled trial comparing a tailored with a standardized disorder-specific approach. Psychotherapy 2014; 51: 207 CrossRef MEDLINE|
|17.||Berger T: Internetbasierte Interventionen bei psychischen Störungen: Hogrefe Verlag; 2015.|
|18.||Treweek S, Zwarenstein M: Making trials matter: pragmatic and explanatory trials and the problem of applicability. Trials 2009; 10: 37 CrossRef MEDLINE PubMed Central|
|19.||Zill JM, Meyer B, Topp J, Daubmann A, Härter M, Dirmaier J: Vorvida: study protocol of a randomized controlled trial testing the effectiveness of internet-based self-help program for the reduction of alcohol consumption for adults. BMC Psychiatry 2016;16: 19 CrossRef MEDLINE PubMed Central|
|20.||Berner MM, Kriston L, Bentele M, Härter M: The alcohol use disorders identification test for detecting at-risk drinking:a systematic review and meta-analysis. J Stud Alcohol Drugs 2007; 68: 461–73 CrossRef|
|21.||Bloomfield K, Hope A, Kraus L: Alcohol survey measures for Europe: a literature review. Drugs Educ Prev Polic 2013; 20: 348–60 CrossRef|
|22.||Kraus L, Piontek D, Pabst A, Gomes de Matos E: Studiendesign und Methodik des Epidemiologischen Suchtsurveys 2012. Sucht 2013; 59: 309–20 CrossRef|
|23.||Sobell LC, Sobell MB: Timeline Follow-Back: a technique for assessing self-reported alcohol consumption. In: Litten RZ, Allen JP (eds.): Measuring alcohol consumption: psychosocial and biochemical methods. Humana Press 1992.|
|24.||Sierosławski J, Foster J, Moskalewicz J: Survey of European drinking surveys. Alcohol survey experiences of 22 European countries. Drugs Educ Prev Polic 2013; 20: 383–98 CrossRef|
|25.||Schmidt J, Lamprecht F, Wittmann W: Satisfaction with inpatient management. Development of a questionnaire and initial validity studies. Psychother Psychosom Med Psychol 1989; 39: 248–55 MEDLINE|
|26.||Postel MG, de Haan HA, ter Huurne ED, van der Palen J, Becker ES, de Jong CA: Attrition in web-based treatment for problem drinkers. J Med Internet Res 2011; 13: e117 CrossRef MEDLINE PubMed Central|
|27.||Riper H, Blankers M, Hadiwijaya H, et al.: Effectiveness of guided and unguided low-intensity internet interventions for adult alcohol misuse: a meta-analysis. PLoS One 2014; 9 CrossRef|
|28.||Furtwængler NA, de Visser RO: Lack of international consensus in low-risk drinking guidelines. Drug Alcohol Rev 2013; 32: 11–8 CrossRef MEDLINE|
|29.||Lin J, Paganini S, Sander L, et al.: An internet-based intervention for chronic pain: a three-arm randomized controlled study of the effectiveness of guided and unguided acceptance and commitment therapy. Dtsch Arztebl Int 2017; 114: 681 VOLLTEXT|
|30.||Sundström C, Gajecki M, Johansson M, et al.: Guided and unguided internet-based treatment for problematic alcohol use–a randomized controlled pilot trial. PloS One 2016; 11: e0157817 CrossRef MEDLINE PubMed Central|
|31.||McKenna H, Treanor C, O’Reilly D, Donnelly M: Evaluation of the psychometric properties of self-reported measures of alcohol consumption: a COSMIN systematic review. Subst Abuse Treat Prev Policy 2018; 13: 6 CrossRef MEDLINE|
|32.||Kirsch I, Wampold B, Kelley JM: Controlling for the placebo effect in psychotherapy: noble quest or tilting at windmills? Psychol Conscious 2016; 3: 121.|
|33.||Nahm ES, Bausell B, Resnick B, et al.: Online research in older adults: lessons learned from conducting an online randomized controlled trial. Appl Nurs Res 2011; 24: 269–75 CrossRef MEDLINE PubMed Central|
|34.||Nahm ES, Resnick B, Brown C, et al.: The effects of an online theory-based bone health program for older adults. J Appl Gerontol 2017; 36: 1117–44. CrossRef MEDLINE PubMed Central|
|e1.||SurveyMonkey Europe UC: SurveyMonkey. https://de.surveymonkey.com/ (last accessed on 27 September 2018).|
|e2.||Berger T, Urech A, Krieger T, et al.: Effects of a transdiagnostic unguided internet intervention (‘velibra’) for anxiety disorders in primary care: results of a randomized controlled trial. Psychol Med 2016: 1–14.|
|e3.||Twomey C, O‘Reilly G, Meyer B: Effectiveness of an individually-tailored computerised CBT programme (Deprexis) for depression: a meta-analysis. Psychiatry Res 2017; 256: 371–7 CrossRef MEDLINE|
|e4.||Dilling H, Mombour W, Schmidt MH, World Health Organization: Internationale Klassifikation psychischer Störungen: ICD-10, Kapitel V (F), klinisch-diagnostische Leitlinien. 1991.|
|e5.||Osman A, Bagge CL, Gutierrez PM, Konick LC, Kopper BA, Barrios FX: The Suicidal Behaviors Questionnaire-Revised (SBQ-R): validation with clinical and nonclinical samples. Assessment 2001; 8: 443–54 CrossRef MEDLINE|
|e6.||Faul F, Erdfelder E, Lang AG, Buchner A: G* Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007; 39: 175–91 CrossRef|
|e7.||Cuijpers P, Donker T, Johansson R, Mohr DC, van Straten A, Andersson G: Self-guided psychological treatment for depressive symptoms: a meta-analysis. PloS One 2011; 6: e21274 CrossRef MEDLINE PubMed Central|
|e8.||Cohen J: Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Erlbaum 1988.|
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