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There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely.
Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer*; (3) alcohol*; and (4) E\effect*, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention.
The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66.
The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing.
The World Health Organization (WHO) has estimated that there are about 2 billion people worldwide who consume alcoholic beverages and 76.3 million with diagnosable alcohol use disorders [
The size of the community-wide challenges posed by alcohol consumption has triggered a substantial body of research into brief, low-cost interventions. These interventions have demonstrated efficacy [
In 2009 it was estimated that over a quarter of the world’s population used the Internet [
Several interactive computer-based alcohol screening and intervention programs have been developed to be delivered either through stand-alone computers [
Much of the published literature concerning online alcohol interventions has been descriptive [
Internet available AOD information and services have considerable reach and are often accessed by populations who do not necessarily access standard AOD services. For example, over half of the users of the 6-week Down Your Drink Internet intervention were women [
The Internet is a medium that is increasingly being used to deliver alcohol resources and services. In parallel with this has been burgeoning research on the Internet’s impact, with an increasing number of studies now being published in this area. It is, therefore, timely to assess the current status of the efficacy of online alcohol intervention programs to inform both the clinical application of such interventions, as well as identify directions for future research.
Relevant articles published in English from 1998 up to and including December 2009 were identified through electronic searches of Medline, PsycINFO, Web of Science and Scopus databases. The following terms were used in the search: (1) Internet, Web*; (2) online, computer*; (3) alcohol*; and (4) E\effect*, trial*, random* (where * denotes the relevant wildcard for the database). Titles and abstracts of all potentially relevant articles were independently reviewed for possible inclusion by 3 of the authors (AW, HS, DK). Articles were included if (1) the primary intervention was delivered and accessed via the Internet (including password-protected sites), (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. Unpublished dissertations were not included.
Data extraction was carried out independently by 3 authors (AW, HS, DK). The primary outcome measure employed in this review was the number of 10-gram units of alcohol; wherever possible, reported outcomes were converted into this metric. Effect sizes were estimated using the pooled baseline standard deviation [
The literature search identified 31 studies, 17 of which were of online Internet alcohol intervention programs that met inclusion criteria (see
Study identification and analysis flow diagram
Most studies that met inclusion criteria targeted university students (12/17 or 70.6%), although some recruited general company employees [
Study sample sizes ranged from 40 to 3216 (median 196) with 64.7% (11/17) of the RCTs targeting at-risk, heavy, or binge drinkers. The percentage of females ranged from 27.6% to 77.9% (mean 54.5%, median 52%), which is substantially greater than in most AOD clinics (
Of the studies that met criteria, 70.6% (12/17) evaluated the impact of brief personalized feedback, and 41.2% (7/17) examined an online multi-module information/education treatment (often incorporating personalized feedback). Control groups typically received psychoeducational resources (10/17 or 58.8%) or completed an online assessment.
Posttreatment assessments were conducted anywhere from 1 week to 12 months posttreatment, with several studies conducting assessments at multiple time points. Across the 17 studies, 7 (47.1%) had a maximum follow up period of a month, 4 (23.5%) had a maximum 3-month follow up, and 3 (17.6%) followed participants to 6-months post intervention. Only Kypri et al [
Reported retention rates in the intervention groups ranged from 38.9% to 100%, and in controls, from 33.4% to 100%. Median reported retention in the treatment condition was 83.4% at 1 month, 74.5% 3 months, and 74.5% at 6 months. In control groups, the median retention rates at the same time points were 80%, 70.4%, and 74.9%. The Kypri et al study [
Several studies reported only combined retention data. The studies by Doumas and Hannah [
A wide variety of outcome assessments were employed across the studies with all studies including some measure of alcohol consumption (eg, unit grams of alcohol, number of standard drinks, or blood alcohol concentrations) in relation to either a typical drinking occasion or when the greatest amount was consumed on a single occasion. In many cases, the measure of frequency of alcohol consumption used was either 4 or more or 6 or more drinks per occasion or drinking to intoxication. Quantity and frequency measures related to a designated assessment period (a typical week, the previous week, 2 or 6 weeks, or up to the last 12 months). Several studies assessed alcohol use in relation to specific events (eg, 21st birthdays [
Characteristics of online alcohol-related randomized controlled trials
Author | Recruitment Pool | Description and Size of Intervention Group | Description and Size of Control Group | Age Reported Mean (SD) and/or Range (Years) | Percent Female Gender |
Bewick et al [ |
University students recruited through a student experience survey | Personalized normative feedback |
Assessment only |
Mean 21.3 (SD 3.7) | 69 |
Chiauzzi et al [ |
2nd and 4th year university students from 5 colleges who responded to local advertisement and subsequently screened as binge drinkers | MyStudentBody, a website that provides motivational feedback and alcohol-related resources |
Alcohol and You, a website that provides educational material only |
Mean 19.9 (SD 1.6) | 54 |
Croom et al [ |
All incoming 1st year university students | Participant survey, knowledge test, and online course |
Survey and knowledge test |
18 to 24 | 49.1 |
Cunningham et al [ |
Problem drinkers identified through a general population telephone survey | Web-based personalized feedback (approximately 10 minutes) |
List of alcohol education resources |
Mean 40.1 (SD 13.4) | 47 |
Doumas and Hannah [ |
Workplace employees of 5 local companies | (1) Web-based feedback (approximately 15 minutes) |
Assessment only |
18 to 24 | 73 |
Doumas et al [ |
University students mandated for alcohol counselling | Web-based personalized normative feedback (15 minutes) |
Web-based education (approximately 45 minutes) |
Mean 19.2 (SD 1.33) 18 to 24 | 27.6 |
Hester et al [ |
Newspaper advertisement recruiting heavy drinkers |
Online alcohol education resource and Web-based alcohol moderation program |
Access to online alcohol education resources |
Intervention group mean 48.7; control group mean 52.1 | 56 |
Hustad et al [ |
1st year university students | (1) AlcoholEdu, 3-hour modularized program |
Assessment only |
Mean 18.1 (SD 0.3) | 51 |
Kypri et al [ |
Heavy drinking university students majoring in psychology and attending university health care | Web-based motivational assessment and personalized feedback (10 to 15 minutes) |
Screening only |
Mean 19.7 (SD 1.8), |
45.3 |
Kypri et al [ |
Undergraduate university students, who scored ≥ 8 on Alcohol Use Disorders Identification Test (AUDIT) | (1) Multidose motivational intervention |
Information pamphlet |
Mean 20.1 (SD 2.2), |
52 |
Matano et al [ |
Workplace employee website | Full individualized feedback regarding alcohol risk, information regarding alcohol use, and feedback regarding stress and coping |
General information regarding alcohol and limited individualized feedback regarding stress and coping |
Mean 39.9 (SD 11.3) | 77.9 |
Moore et al [ |
Convenience sample of 1st year university students enrolled in 3 college courses | Web-based binge-drinking intervention |
Correspondence-based binge-drinking intervention |
Mean 21.7 (SD 0.2), 18 to 25 | 57.8 |
Neighbors et al [ |
University students turning 21 during 2 academic quarters who intended drinking 2 or more drinks on their birthday | Web-based personalized feedback |
Assessment only |
20 year olds | 51.1 |
Riper et al [ |
Advertisements in national newspapers and health-related websites recruiting adult problem drinkers | Web-based multi-component Cognitive Behaviour Therapy self-help intervention |
Online psycho-educational alcohol use brochure |
18 to 65, intervention group mean 45.9 (SD 8.9), control group mean 46.2 (SD 9.2) | 49 |
Saitz et al [ |
1st year university students identified as engaging in hazardous alcohol use ( ≥ 8 on AUDIT) | Extensive individualized brief feedback intervention |
Individualised minimal brief intervention |
18 and over | 63.7 |
Walters et al [ |
1st year university students assessed within the study as “at risk” drinkers | eCHUG, personalized normative feedback program (20 minutes) |
Assessment only |
Not specified | 48.1 |
Weitzel et al [ |
University students who self-identified as drinking more than 1 once of alcohol per week recruited through emails and on-campus advertising | Online daily diary and individualized tailored messages |
Online daily survey |
Mean 19.2, 18 and over | 55 |
a Shown are baseline sample size and data. Data shown for this study in
b Intention-to-treat analysis was conducted on some or all measures.
c This study included a second intervention condition which consisted of Web-based feedback as well as motivational interviewing (MI). However, the motivational interviewing component was delivered face-to-face rather than via the Internet and, therefore, the effect size data from the second intervention condition is not included in calculations of mean effect sizes.
d Completion of AlcoholEdu program was a university-wide administrative requirement.
Based on 5 RCTs [
Employing Cohen’s effect size evaluation benchmarks [
Only 1 RCT allowed extraction of a follow-up effect size on alcohol units. The examination by Cunningham and colleagues [
Pre-post data on peak blood alcohol concentrations (BAC) were available from 2 RCTs [
Differential effect sizes were extracted from 5 RCTs [
Randomized controlled trials of online alcohol interventions
Study | Treatment Group |
Control Group | |||||||||
Correction for Alcohol Unitsa | n | Mean (SD) Pre |
Mean (SD) Post | Mean at Follow Up | n | Mean (SD) Pre |
Mean (SD) Post | Mean at Follow Up | Pre-Post Effect Size ( |
Pre-Follow Up Effect Size ( |
|
Bewick et al [ |
0.80 | 138 | b | 9.62 (10.86) | 179 | b | 11.88 (14.9) | 0.02b | |||
Riper et al [ |
1.00 | 130 | 43.7 (21.0) | 28.7d | 131 | 43.5 (22.3) | 40.6d | 0.56 | |||
Doumas et al [ |
1.40 | 46 | 11.42 (9.2) | 6.8 (5.43) | 31 | 9.86 (7.42) | 8.1 (8.27) | 0.33 | |||
Hustad et al [ |
1.40 | 26 | 8.9 (11.62) | 11.0 (15.54) | 24 | 9.28 (12.4) | 18.14 (17.25) | 0.56 | |||
Hustad et al [ |
1.40 | 30 | 12.4 (14.29) | 10.4 (11.09) | 24 | 9.28 (12.4) | 18.1 (17.25) | 0.81 | |||
Cunningham et al [ |
1.36 | 92 | 18.9 (14.82) | 14.96 (12.38) | 15.1 (12.1) | 93 | 16.18 (13.7) | 15.5 (14.0) | 15.64 (14.0) | 0.23 | 0.23 |
Cunningham et al [ |
1.36 | 35 | 30.6 (17.14) | 20.54 (15.23) | 21.76 (16.2) | 37 | 25.98 (16.3) | 25.02 (16.73) | 24.34 (17.0) | 0.54 | 0.43 |
a The table displays means in 10-gram alcohol units. Calculations use stated drink sizes where available. Where a paper referred only to numbers of drinks, these were adjusted using national “standard drink” sizes [
b Baseline data presented by Bewick et al [
c Means were calculated using identified comparisons.
d Post SDs were not reported.
e Units of alcohol reported are per month.
Randomized controlled trials of online alcohol interventions: Effect sizes (d) obtained across blood alcohol concentrations and other alcohol-related measures
Study and Outcome Measure |
Treatment Group |
Control Group |
|
||||
n | Mean (SD) Pre | Mean (SD) Post | n | Mean (SD) Pre | Mean (SD) Post |
Pre-Post |
|
Hustad et al [ |
26 | 0.08 |
0.08 (0.09) | 24 | 0.07 |
0.15 (0.15) | 0.88 |
Hustad et al [ |
30 | 0.08 |
0.08 (0.08) | 24 | 0.07 (0.08) | 0.15 (0.15) | 0.87 |
Neighbors et al [ |
150 | 0.11 (0.10) | 0.10 (0.11) | 145 | 0.12 |
0.13 (0.13) | 0.22 |
Neighbors et al [ |
150 | 7.23 (5.29) | 6.4 (6.13) | 145 | 7.14 (5.12) | 7.00 (5.57) | 0.13 |
Bewick et al [ |
138 | a | 6.77 (4.54) | 179 | a | 7.84 (5.78) | 0.23a |
Doumas and Hannah [ |
60 | 1.44(2.06) | 0.85 (1.63) | 73 | 1.19 |
1.02 (1.88) | 0.22 |
Doumas and Hannah [ |
60 | 5.12 (5.36) | 3.55 (3.91) | 73 | 4.15 (4.80) | 3.98 (4.70) | 0.28 |
Doumas et al [ |
46 | 8.77 (4.53) | 6.95 (3.92) | 31 | 6.21 (2.77) | 5.88 (3.07) | 0.38 |
Doumas et al [ |
46 | 0.84 |
0.68 (0.47) | 31 | 0.79 (0.41) | 0.71 (0.46) | 0.21 |
Moore et al [ |
53 | 4.74 (5.82) | 3.68 (4.95) | 47 | 5.38 (5.83) | 5.02 (4.94) | 0.12 |
Moore et al [ |
53 | 2.49 (2.55) | 2.53 (2.33) | 47 | 3.15 (2.6) | 2.51 (2.33) | -0.26 |
a Baseline data presented by Bewick et al [
b This study’s control group differed primarily in mode of delivery (via postal services) rather than in key content.
Internet interventions offer an alternative, accessible treatment option for people with alcohol-related problems. Their effectiveness, however, has not been systematically evaluated. To date, there have been a limited number of published RCTs of online alcohol interventions. The majority have been conducted with university or student populations and have employed a range of incentives and inducements to achieve an acceptable participation and retention rate. These groups tend to be young (early 20s) with a predominance of females. Given the high rates of binge drinking in this age group [
The brevity of intervention descriptions in the published papers, variable intervention uptake and completion rates, and the heterogeneity of outcome measures and follow-up periods across studies impede the ability to generalize about the efficacy and utility of Internet-based interventions for alcohol use. Overall, online alcohol interventions (whether only involving brief personalized feedback or comprising multiple modules) appear to bring about small but meaningful differential reductions in 10-gram alcohol units consumed, blood alcohol concentrations, and a range of other alcohol-related measures. In particular, they appear more efficacious than assessment alone or general education about alcohol.
Of studies published to date, 3 stand out. Hustad and colleagues [
Moore and colleagues [
The trial of Riper and colleagues [
The use of online interventions for the treatment of alcohol-related problems requires more extensive research to establish the clinical appropriateness and usability of online health technologies [
A significant challenge for this field is that advances in equipment, connectivity, and software capabilities are occurring much more rapidly than the evidence base can be fully established. In this context, recommendations for practice must necessarily rely to some extent on analogies from evidence that has been obtained on similar interventions using older forms of delivery. However, transfer of interventions to new modes of delivery run the risk of losing the key effective ingredients. It remains important that researchers respond rapidly to new technological advances, adapting treatments and routinely conducting trials to ensure that effects on alcohol use are retained.
As with all remotely delivered interventions, engagement of participants remains an issue. Internet-based interventions are likely to have greater reach if they are interfaced with targeted marketing campaigns or are embedded in routine primary care. Further research on the most effective marketing and widespread dissemination of these interventions is required.
While the current research evidence is fragmented and requires greater methodological rigor, it suggests that problematic or at-risk users may benefit from online alcohol interventions and that they may be a useful preventative and first step for groups such as women or young people who may not otherwise access more traditional AOD health services. Our confidence in these interventions is boosted by decades of research on bibliotherapies [
We would like to thank the Australian Commonwealth Department of Health and Ageing for providing the funding to conduct this study. The authors also wish to acknowledge the contributions of Rachael Moss, Therese McNally, Dominique Lynch, Suzi George, and Greg Picker.
The writers have been involved in the development of online or telephone-based interventions for alcohol use but derive no commercial benefit from them.
alcohol and other drug
Alcohol Use Disorders Identification Test
blood alcohol concentrations
motivational interviewing
randomized controlled trials
World Health Organization