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Keywords:

  • Alcohol consumption;
  • computer-based intervention;
  • meta-analysis;
  • systematic review

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

Aim  To determine the effects of computer-based interventions aimed at reducing alcohol consumption in adult populations.

Methods  The review was undertaken following standard Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance for systematic reviews. The literature was searched until December 2008, with no restrictions on language. Randomized trials with parallel comparator groups were identified in the form of published and unpublished data. Two authors independently screened abstracts and papers for inclusion. Data extraction and bias assessment was undertaken by one author and checked by a second author. Studies that measured total alcohol consumption and frequency of binge drinking episodes were eligible for inclusion in meta-analyses. A random-effects model was used to pool mean differences.

Results  Twenty-four studies were included in the review (19 combined in meta-analyses). The meta-analyses suggested that computer-based interventions were more effective than minimally active comparator groups (e.g. assessment-only) at reducing alcohol consumed per week in student and non-student populations. However, most studies used the mean to summarize skewed data, which could be misleading in small samples. A sensitivity analysis of those studies that used suitable measures of central tendency found that there was no difference between intervention and minimally active comparator groups in alcohol consumed per week by students. Few studies investigated non-student populations or compared interventions with active comparator groups.

Conclusion  Computer-based interventions may reduce alcohol consumption compared with assessment-only; the conclusion remains tentative because of methodological weaknesses in the studies. Future research should consider that the distribution of alcohol consumption data is likely to be skewed and that appropriate measures of central tendency are reported.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

There is strong international evidence for the use of brief interventions to reduce hazardous and harmful alcohol consumption, particularly in the primary care setting [1–3]. The World Health Organization provides a manual for their implementation in primary care and a dissemination strategy for developing countries is under way [4]. Brief interventions provide a means to fill the apparent gap between primary prevention and intensive treatment approaches [5]; however, hazardous and harmful drinking are rarely identified in family practice and so the opportunity for early identification and brief intervention is often missed [6,7]. In addition, health-care professionals report similar barriers to implementation across the world. These include lack of financial incentive, time constraints, lack of training and support [8,9] and a fear of offending patients by discussing their alcohol consumption [8].

Delivering brief interventions over the internet may address some of the barriers to implementing the conventional face-to-face approach. In 2009, 76% of adults in the United Kingdom (37.4 million people) were accessing the internet [10], with a slightly lower proportion in Europe as a whole (52%), but similar proportions in the United States (74%) and Australia (80%) [11]. The internet provides a means of combining the scalability of a public health intervention, with the capacity to deliver an individualized approach [3,12]. The internet setting allows for increased access to the intervention and flexibility of use. There are also cost advantages of internet-based interventions delivered on this scale, in that the marginal cost per additional user is low, unlike conventional face-to-face interventions [13,14]. Internet-based interventions may be integrated into health-care and other settings such as the work-place or higher education, but are also available from any location with internet access. The possibility of accessing these interventions autonomously via the internet allows for anonymity, which is a major advantage with sensitive or stigmatized behaviours such as alcohol consumption [15,16].

Internet-based interventions have demonstrated their ability to attract large numbers of people interested in reducing their drinking [17–21]. There has been a notable increase in recent years in the number of trials assessing the effectiveness of these interventions. In 2004, Copeland & Martin conducted a qualitative review of web-based interventions for substance use disorders in all adult populations, concluding that there was limited research on the efficacy of these interventions in changing substance use behaviour [12]. Four years later, Elliot et al. (2008) identified 17 trials of computer-based interventions (on- and offline) for college drinkers, finding them to be more effective than no treatment and as effective as alternative treatment approaches [22]. The first avowedly systematic review in this field, conducted by Bewick et al. (2008), concluded that there was inconsistent evidence for the use of web-based electronic screening and brief intervention for reducing alcohol intake based on five trials in all adult populations [23]. A recent meta-analysis by Carey et al. (2009) supported those findings of Elliot et al. and found computer-delivered interventions to reduce the quantity and frequency of drinking in student populations when compared with assessment-only controls, and found them as effective as other alcohol-related interventions [24]. The most recent meta-analysis (Rooke et al. 2010) pooled computer-based interventions (both stand-alone and therapeutically guided) for alcohol and tobacco use in all populations, and reported a significant reduction in substance use [25].

One limitation of the studies in this field, highlighted by the Bewick review, is the lack of appropriate statistics to account for the skewed distribution of the data [23]. The Cochrane handbook states that ‘analyses based on means are appropriate for data that are at least approximately normally distributed, and for data from very large trials’[26]. The distribution of alcohol consumption data in the population is thought to be positively skewed, where most people are abstinent or drinking relatively low levels of alcohol, while fewer people are drinking very large quantities of alcohol [27–29]. In a skewed distribution, where sample sizes are small and the data contain extreme outliers, study data should be characterized by non-parametric methods or by transformation [30]. The extent to which different measures of central tendency (e.g. mean versus median) impact upon the results are unknown, but any differences may have clinical significance and compromise the robustness of the outcomes. Furthermore, it has been highlighted that alcohol consumption is measured typically as count data, e.g. number of drinks consumed within a given time-frame [27,28], and as such the data are not continuous and a normal distribution cannot be assumed.

This review builds on those studies conducted previously in this field. It is the first to include meta-analyses of mean differences in grams of alcohol and frequency of binges, giving the findings immediate clinical relevance. It is also novel in comparing the findings of those studies that presented appropriate measures of central tendency, given the distribution of the data, with those that did not (i.e. means in the presence of skew). Of the aforementioned reviews conducted in this field, two were restricted to student samples [22,24], two included web-based interventions alone (as opposed to computer-based) [12,23] and the most recent meta-analysis included smoking and alcohol interventions as both stand-alone and therapeutically guided interventions [25]. This review adds to the field by including stand-alone computer-based interventions (available on- and offline) in all adult populations. Students represent a highly selective population who lack generalizability to the general adult population drinking at hazardous and harmful levels. Interventions that are computer-based have the potential to be made available online, with online interventions often evaluated on computers in a fixed location. Finally, it is important to gauge the effectiveness of stand-alone interventions as they carry the benefits of reach, availability, anonymity and cost savings.

METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

Search strategy

The following databases were searched from inception to December 2008 with no restrictions on language: the Cochrane Library (2008, issue 4), MEDLINE, EMBASE, CINAHL, PsychINFO, ERIC, Web of Science and International Bibliography of the Social Sciences (IBSS). Unpublished data were sought in the form of conference proceedings (Conference Proceedings Citation Index, formally ISI Proceedings) and theses (Index to Theses). Search terms were selected through discussions with an information specialist and the research team by considering the inclusion criteria, scanning the background literature and by browsing the MEDLINE Thesaurus (MeSH) (see Appendix 1 for MEDLINE search strategy). The thesaurus terms were redefined for each database. The included studies were citation-tracked through Web of Science. The reference lists of relevant reviews and included studies were hand-searched.

Selection criteria

Randomized controlled trials were eligible for inclusion. All adult populations (aged 18 years and over) with any level of alcohol consumption were included. Eligible computer-based interventions were those considered behavioural interventions, aimed at bringing about positive behaviour change, adapted for a computer-based format [31]. Inclusion was restricted to stand-alone (non-guided) computer-based interventions. Eligible studies compared computer-based interventions with either a minimally active (e.g. assessment-only, usual care, generic non-tailored information or educational materials) or an active comparator group (e.g. brief intervention). This review included studies that measured a change in alcohol consumption. A reduction in alcohol consumption was considered a positive behaviour change.

Study screening and data extraction

Study references identified by the search strategy were screened by two independent reviewers trained in systematic review methodology (Z.K. and S.H.). Full papers were ordered for all potentially relevant studies and screened in duplicate. Discrepancies were resolved by a third party (E.M.). One reviewer (Z.K.) extracted data from the included studies into pre-designed forms (Microsoft Excel), which were piloted on three studies for suitability. The data extraction was verified for accuracy by a second reviewer (E.M.). Authors were contacted for missing data.

Bias assessment

The risk of bias associated with allocation concealment was assessed in each of the included studies, as it is shown to have the greatest impact on treatment effect compared with other potential sources of bias [32–34]. Bias assessment, as advocated by the Cochrane handbook [35], considers the likelihood that a particular aspect of trial quality would have biased the findings, given the design of the trial. Studies were classified by one author (Z.K.) and checked by a second (E.M.) as having high, low or unclear risk of bias. A third party helped resolve any discrepancies (C.G.).

Data synthesis

There is no gold-standard measure of alcohol consumption, therefore two outcomes that represent different patterns of drinking were chosen for inclusion in meta-analyses. These were total alcohol consumption and number of binge drinking episodes (‘binge’ defined by the authors of the primary studies). Mean weekly alcohol intake (measured in grams) or number of binges per week, corresponding standard deviation and number of participants in the intervention and comparator groups at follow-up were entered into Review Manager software version 5. Where outcomes were not presented per week, data were adjusted to represent this time-frame [2]. Where studies did not detail the number of grams included in a standard drink, information on country-specific standard units was obtained from an established source [36]. Furthest point of follow-up was used unless a primary time-point was specified.

The distribution of alcohol consumption is often skewed [27,28]: ‘when the data are skewed we can either use a non-parametric method, or try a transformation of the raw data’ ([30], p. 199). A preliminary look at the data found the majority of studies reported the mean, while a few reported the median and transformed data. To allow for pooling of all data in meta-analyses, medians were used as the best estimate of the sample mean and an estimated standard deviation was generated from the range, using a method that makes no assumption on the distribution of the underlying data [37]. Transformed data were back-transformed.

Studies were pooled using the inverse variance method with a random effects model; all analyses were two-tailed. Studies comparing a computer-based intervention with a minimally active comparator group were pooled separately to those with an active comparator. Heterogeneity was examined through use of forest plots, χ2 test and I2 test. A subgroup analysis by population (student versus non-student) was planned a priori.

The data included in the meta-analyses were assessed for skew. The test for normality, advocated by Altman & Bland, was applied by dividing the mean by the standard deviation; where the ratio was less than two this indicated a skewed distribution [38]. A sensitivity analysis was conducted of those studies that used appropriate measures of central tendency, given the distribution of the data.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

Study description

A total of 24 studies were included in the review (see Fig. 1). The earliest study was published in 1997, with most studies published recently in 2007 and 2008. The majority were conducted in the United States (n = 18). Students were the most commonly studied population group (n = 18) [39–56], with three studies of adult problem drinkers from the general population [57–59], two of work-place employees [60,61] and one of emergency department attendees [62]. Eight studies appeared to screen for hazardous drinking, either in the form of binge drinking, total number of drinks per week, Alcohol Use Disorders Identification Test (AUDIT) cut-off score (generally reported as ≥8) or some combination of these. The other studies used either a lower cut-off score or did not restrict inclusion based on alcohol intake (see Table 1).

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Figure 1. Flow-chart of study selection

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Table 1.  Characteristics of study participants.
StudyFemale (%)Age (mean years)White (%)Screening test and cut-off score
  1. AUDIT: Alcohol Use Disorders Identification Test [74]; MAST: Michigan Alcohol Screening Test [75]; CAGE: mnemonic for cut-down, annoyed, guilty, eye-opener [76].

(Barnett et al. 2007)5118.875.6Not used (mandated students)
(Bewick et al. 2008)6921.3Not used
(Chiauzzi et al. 2005)54Intervention: 20; control: 19.873.2Daily drinking questionnaire
Binge drinkers: ≥5♂/4♀ drinks, per drinking occasion in the past week
(Donohue et al. 2004)5620.662.6Time-line follow-back
≥1 alcoholic drink in past 30 days
(Doumas & Hannah 2008)73Range: 18–2487Binge drinking (≥5♂/4♀ drinks in row, in past 2 weeks)
All participants included but separated into low and high risk for analysis
(Doumas & Haustveit 2008)4218.154Binge drinking (≥5♂/4♀ drinks in row, in past 2 weeks)
All participants included but separated into low and high risk for analysis
(Hedman 2007)5819.593.8Binge drinking: ≥5♂/4♀ drinks in row, at least once in 2 weeks preceding survey
(Hester & Delaney 1997)4036.370MAST and AUDIT
AUDIT score ≥8; ≥120♂/70♀ drinks per month; weekly drinking with ≥6 drinks per episode; drinking at least once per week
(Hester et al. 2005)4846.1♂; 45.2♀79AUDIT
AUDIT score ≥8
(Hunt 2004)021.2Presented separately for each siteTime-line follow-back
6 drinks per week and fewer than 6 drinks per day
(Kypri et al. 2004)50Intervention: 19.9; control: 20.4AUDIT
AUDIT score ≥8; >6♂/4♀ drinks on at least one occasion in preceding 4 weeks
(Kypri & McAnally 2005)4920.275Not used
(Kypri et al. 2008)Intervention: 52; control: 52Intervention: 20.1; control: 20.1AUDIT
AUDIT score ≥8
(Lau-Barraco & Dunn 2008)572076≥2 heavy episodic drinking occasions (in past 30 days), or ≥5 weekly standard drinks (but fewer than 40)
(Lewis et al. 2007)521999.6≥1 heavy drinking episode in past month (≥5♂/4♀ drinks at one setting)
(Lewis & Neighbors 2007)552097≥1 heavy drinking episode (≥5♂/4♀ drinks at one sitting) in past month
(Matano et al. 2007)784083AUDIT and CAGE
Participants separated into low and moderate risk for analysis. High-risk participants were excluded
(Neighbors et al. 2004)5918.579.5≥1 heavy drinking episode in past month (≥5♂/4♀ drinks)
(Neighbors et al. 2006)5619.798≥1 heavy drinking episode in past month (≥5♂/4♀ drinks)
(Neumann et al. 2006)Intervention: 20; control: 22Intervention: median 30; control: median 31AUDIT
AUDIT score ≥5
(Paschall et al. 2006)5218.130.3Not used
(Riper et al. 2008)Intervention: 49; control: 49Intervention: 45.9; control: 46.2Weekly recall and quantity–frequency variability index of alcohol intake
>21♂/14♀ units per week or ≥6♂/4♀ units at least 1 day per week for past 3 months (1 unit = 10 g ethanol)
(Walters et al. 2007)4872.7Not used
Participants with at least one heavy drinking episode in past month (≥5 drinks ♂, ≥4 drinks ♀) were included in the analysis
(Weitzel et al. 2007)5519.275Drinking more than once a week

The majority of studies (n = 22) compared a computer-based intervention with a minimally active comparator group. Minimally active comparators consisted mainly of assessment with some factual information about the harms of excess alcohol consumption, or a waiting-list design. Three studies compared a computer-based intervention with an active comparator group. Active comparator groups consisted of an in-person motivational interview [39], cognitive behaviour therapy [42] and an expectancy challenge [49] (see Table 2).

Table 2.  Characteristics of included studies.
StudyRecruitmentPopulationInterventionComparatorDrinking outcomesFollow-up time-pointsFollow-up at furthest time-point %
  1. AC: active comparator group; MAC: minimally active comparator group; FRAMES: Feedback, Responsibility, Advice, Menu, Empathy and Self-efficacy [64]; MI: motivational interview [63]; BASICS: Brief Alcohol Screening and Intervention for College Students [65]; BAC: blood alcohol concentration; AUDIT: Alcohol Use Disorders Identification Test; aexcluded from meta-analyses as no measure of total alcohol consumption or binge frequency; bexcluded from meta-analyses for providing proportion of binge days and no standard deviation for total alcohol consumption; cexcluded from meta-analyses for providing frequency of heavy drinking as a categorical variable.

(Barnett et al. 2007)Voluntary alternative to individual session with university health educator after mandated health education sessionUnited States: mandated studentsAlcohol 101 (n = 113): interactive computer-delivered intervention that features a virtual party where participants can observe the effects of gender, weight, drink type and speed of consumption on BAC. Information on alcohol refusal skills, consequences of unsafe sex, multiple choice games and stories of actual campus tragedies involving alcohol. Personalized normative feedback was providedAC: brief motivational interview (n = 112)1. No. of drinking days 2. No. of heavy drinking days 3. Average no. of drinks/drinking day 4. Average estimated BAC3, 12 monthsIntervention: 94 Control: 95
Single session ± booster session
Guiding principles not stated
Intervention on CD-ROM; location determined by researcher
(Bewick et al. 2008)Respondents of student experience surveyUnited Kingdom: university studentsPersonalized feedback (n = 234): Feedback on level of alcohol consumption and associated health risk, social norms information and generic information, such as calculating units, sensible drinking guidelines, support servicesMAC: assessment-only (n = 272)1. Units/occasion 2. Units/week12 weeksIntervention: 59 Control: 72
Single session with continued access to the website for the study duration
Based on social norms approach
Intervention online; location determined by participant
(Chiauzzi et al. 2005)Newspaper advertisements, flyers and in-person at busy campus locations and eventsUnited States: university studentsMyStudentBody.com: alcohol (n = 131) interactive website that includes: Rate Myself, based on BASICS model and consists of four sets of questions regarding (i) alcohol beliefs, (ii) life-style issues, (iii) risk-taking while drinking, and (iv) consequences resulting from drinking. Responses used to tailor feedback. The site also includes: articles, interactive tools, peer stories, ask the expert, participants and campus health newsMAC: Alcohol and You: text-based, education-only website containing articles on high-risk drinking (n = 134)1. Binge drinking days/week 2. Max. no. drinks/drinking day 3. Drinks/week 4. Drinking days/week 5. Average consumption/drinking day 6. Alcohol composite score 7. Total consumption during special occasion drinking 8. Peak consumption during special occasion drinking1, 3 monthsIntervention: 80 Control: 82
Four weekly 20-minute sessions. Each session needed to be completed before advancing to the next
Based on BASICS model
Intervention online; location determined by participant
(Donohue et al. 2004)Not statedUnited States: university studentsAlcohol 101 (n = 40): interactive computer-delivered intervention that features a virtual party where participants can observe the effects of gender, weight, drink type, and speed of consumption on BAC. There is information on alcohol refusal skills, consequences of unsafe sex, multiple choice games and stories of actual campus tragedies involving alcohol. Personalized normative feedback was providedAC: cognitive behaviour therapy (n = 39)1. No. alcoholic beverages/past month 2. No. days alcohol consumed/past month 3. No. alcoholic beverages consumed/drinking occasion in past month1 monthTotal: 92
Single session lasting approx. 45 minutes
Guiding principles not stated
Intervention on CD-ROM; location determined by researcher
(Doumas & Hannah 2008)aHuman resource departments of local companies were contacted for participationUnited States: work-place employeesCheck Your Drinking (n = 60): personalized normative feedback on drinking and associated risks. Also feedback on cost and calories associated with drinking, the rate at which the body processes alcohol, risk status for negative drinking-related consequences and problematic drinking based on AUDIT score1. MAC: control—assumed assessment-only (n = 73) 2. 3rd arm excluded: Check Your Drinking plus motivational interview1. Weekend drinking 2. Peak consumption (quantity) 3. Frequency of drinking to intoxication30 daysTotal: 63
Single session
Based on social norms approach and motivational enhancement models
Intervention online http://www.checkyourdrinking.net Location determined by researcher
(Doumas & Haustveit 2008)University athletics departmentUnited States: collegiate athletesCheck Your Drinking (n = 28): personalized normative feedback on drinking and associated risks. Also feedback on cost and calories associated with drinking, the rate at which the body processes alcohol, risk status for negative drinking-related consequences and problematic drinking based on AUDIT scoreMAC: educational website containing alcohol facts and consumption guidelines http://www.radford.edu/kcastleb/toc.html (n = 24)1. Weekly drinking quantity 2. Peak consumption (quantity) 3. Frequency of drinking to intoxication6 weeks, 3 monthsIntervention: 54 Control: 75
Single session lasting 15 minutes
Based on social norms approach and motivational enhancement models
Intervention online http://notes.camh.net/efeed.nsf/newform Location determined by researcher
(Hedman 2007)Health, sport and exercise science departmentUnited States: university studentsPersonalized feedback (n = 68): personalized feedback consisted of: peak blood alcohol level, time to alcohol oxidation, dollars spent on alcohol, caloric intake, alcohol-related risks, information on sensible drinking behaviours. Feedback was supplemented with health communication messages on risks and consequences associated with heavy alcohol consumptionMAC: alcohol facts received via e-mail twice a week for 6 weeks (n = 63)1. 30-day frequency of alcohol use (>1 drink) 2. No. of typical drinks reported at one setting in past 30 days 3. 30-day frequency of binge drinking 4. 14-day frequency of binge drinking6 weeksIntervention: 60 Control: 57
Viewed feedback via e-mail, followed by health communication messages twice a week for 6 weeks
Based on Health Belief Model, Cognitive Dissonance Theory, Elaboration Likelihood Model
Intervention via e-mail; location determined by participant
(Hester & Delaney 1997)Local health centre, other health/mental health care providers, screening program for driving while intoxicated, and through media advertisementsUnited States: adult problem drinkers in the general populationBehavioural Self-Control Program for Windows (n = 20): teaches the following skills: goal-setting, self-monitoring, rate control and drink refusal, behavioural contracting, evaluating triggers and problem solving, functional analysis of drinking, and relapse prevention. Also provided normative feedbackMAC: waiting-list control (n = 20)1. Total drinks per week 2. Estimated peak BAC per week 3. No. of drinking days per week10 weeksIntervention: 100 Control: 100
Eight weekly sessions over 10 weeks
Based on Miller & Munoz (1982) protocol for self-control training [70]
Intervention on disk; location determined by researcher (except for 2 participants who used their home PCs)
(Hester et al. 2005)Media advertisementsUnited States: adult problem drinkers in the general populationDrinker's check-up (n = 35): consisted of assessment (including decisional balance exercise), feedback, and decision-making (including Rollnick's ‘Readiness Ruler’, negotiating goals of change and developing alternatives and a change plan) modulesMAC: waiting-list control (n = 26)1. Average drinks per day 2. Drinks per drinking day 3. Average peak BAC4 weeksNot reported at 4 weeks
Approx. 90 minutes to complete. Summary of worksheets and feedback from completed assessments were printed
Based on FRAMES and MI approach
Intervention online http://www.drinkerscheckup.com Location determined by researcher
(Hunt 2004)bOnline participant pools from psychology departments across three sitesUnited States: university studentsExpectancy challenge (n = 52): video of people undergoing an alcohol/placebo expectancy–disconfirming experience followed by description of alcohol expectancy concept and effect of alcohol expectancies on behaviour. The program had audiovisual elements, including games and questions requiring interaction1. MAC: PowerPoint presentation on safe driving practices (n = 54) 2. 3rd arm excluded: non-interactive power-point presentation of expectancy challenge1. Mean drinks consumed per day 2. Quantity–frequency 3. Proportion of binge days1 monthNot reported
Approx. 20 minutes
Based on the expectancy concept [71]
Intervention computer-based; location determined by researcher
(Kypri et al. 2004)University health centreNew Zealand: university studentsPersonalized feedback (n = 51): feedback consisted of a summary of recent consumption and comparison with recommended limits, estimate of BAC for heaviest drinking session (criterion feedback), normative feedback and correction of norm misperceptions. Participants also received the leaflet provided in the control conditionMAC: participants received a paper-based leaflet on alcohol facts and effects (n = 53)1. Frequency of drinking 2. Typical occasion quantity 3. Total volume 4. Frequency of heavy episodes6 weeks, 6 monthsIntervention: 92 Control: 89
Single session 10–15 minutes
Feedback component of brief intervention and motivational interviewing [63]
Intervention online; location determined by researcher
(Kypri & McAnally 2005)aUniversity health centreNew Zealand: university studentsPersonalized feedback (n = 72): feedback consisted of health authority recommendations, social norms and self-comparison. Blood pressure and demographic details were also taken1. MAC: assessment-only (comprising of blood pressure, demographic data and assessment) (n = 74) 2. 3rd arm excluded: minimal contact (blood pressure and demographic data)1. Percentage compliance with recommendations (alcohol consumed per occasion) 2. Peak estimated BAC6 weeksIntervention: 85 Control: 88
Single session
Feedback component of brief intervention and motivational interviewing [63]
Intervention online; location determined by researcher
(Kypri et al. 2008)University health centreNew Zealand: university studentsPersonalized feedback plus information pamphlet on health effects of alcohol consumption (single and multi-dose groups combined) (n = 283): feedback consisted of: risk status, summary of recent consumption, comparison of consumption with recommended limits, estimate of blood alcohol concentration for heaviest drinking occasion in past 4 weeks, comparison of consumption with national and university norms and correction of misperceptions of normsMAC: screening and information pamphlet on health effects of alcohol consumption (n = 146)1. Frequency of drinking 2. Typical occasion quantity 3. Total volume 4. Frequency of heavy episodes6, 12 monthsIntervention: 83 Control: 86
Single dose: single session of assessment and feedback at baseline. Multi-dose: assessment and feedback at baseline, 1 and 6 months
Feedback component of brief intervention and motivational interviewing [63]
Intervention online; location determined by researcher
(Lau-Barraco & Dunn 2008)Psychology classesUnited States: university studentsAlcohol 101 (n = 39): information on the effects of alcohol misuse and drinking behaviour among peers (also see Barnett et al. 2007 and Donohue et al. 2004)1. AC: expectancy challenge (n = 114) 2. MAC: assessment-only (n = 64)1. Average drinks/week 2. Heavy episodic drinking frequencyPost-test, 1 monthIntervention: 89 Control (MAC): 93 Control (AC): 91
Single session lasting 90–120 minutes
Guiding principles not stated
Intervention on CD-ROM; location determined by researcher
(Lewis et al. 2007)Orientation courseUnited States: university studentsNormative feedback (gender-specific and gender-neutral groups combined) (n = 157): feedback on personal drinking behaviour, personal perceptions of typical student drinking behaviour, information on actual norms for typical student drinking behaviourMAC: assessment-only (n = 88)1. Drinks/week 2. Drinking frequency5 monthsIntervention (combined): 83 Control: 89
Feedback viewed on screen then provided as print-out
Based on social norms approach
Intervention online; location determined by researcher
(Lewis & Neighbors 2007)Psychology classesUnited States: university studentsNormative feedback (gender-specific and gender neutral groups combined) (n = 125): feedback on personal drinking behaviour, perceptions of typical student drinking behaviour, information on actual norms for typical student drinking behaviourMAC: assessment-only (n = 57)1. Overall consumption (Alcohol Consumption Inventory) 2. Typical weekly drinking 3. Typical no. drinks consumed/drinking occasion1 monthTotal: 89
Feedback viewed on screen for 1–2 minutes then provided as print-out
Based on social norms approach
Intervention computer-based; location determined by researcher
(Matano et al. 2007)aMailed recruitment flyerUnited States: work-place employeesCoping matters (n = not reported, total sample = 145): provided individualized feedback on risk of alcohol-related problems, recommendations, mini-workshops, drinking journal and links to online resources. Feedback was also given on stress level and use of coping strategiesMAC: computer-based individualized feedback on stress level and coping strategies but not alcohol consumption (n = not reported, total sample = 145) 1. Frequency of drinking  2. Usual no. of beers consumed when drinking  3. Usual no. of glasses of wine consumed when drinking  4. Usual no. of shots of hard liquor when drinking  5. Most no. of beers consumed when drinking  6. Most no. of glasses of wine consumed when drinking  7. Most no. of shots of hard liquor when drinking  8. Frequency of beer binges  9. Frequency of wine binges 10. Frequency of hard liquor binges3 monthsTotal: 84
Participants had access to the website for 90 days
Based on concepts derived from social learning perspective
Intervention online; location determined by participant
(Neighbors et al. 2004)Psychology classesUnited States: university studentsNormative feedback (n = 126): consisted of perceived drinking norms compared with actual drinking norms, and summary of reported consumption compared with average college drinking behaviour. Also feedback on percentile ranking compared with other college student drinkingMAC: assessment-only (n = 126) 1. Overall consumption (Alcohol Consumption Index)  2. Typical weekly drinking  3. Peak quantity3, 6 monthsTotal: 82
Viewed feedback on screen for approx. 1 minute while printing
Based on social norms approach
Intervention computer-based; location determined by researcher
(Neighbors et al. 2006)Psychology classesUnited States: university studentsNormative feedback (n = 108): consisted of perceived drinking norms for quantity and frequency of alcohol intake compared with actual quantity and frequency norms, and summary of reported consumption compared with actual norms. Also feedback on percentile ranking compared with other college student drinkingMAC: assessment-only (n = 106) 1. No. of drinks/week2 monthsIntervention: 91 Control: 82
Feedback viewed on screen for 1–2 minutes then provided as print-out
Based on social norms approach.
Intervention computer-based; location determined by researcher
(Neumann et al. 2006)Emergency department after initial careGermany: emergency department attendeesBrief intervention (n = 561): feedback on current drinking status based on AUDIT and Readiness to Change responses. The intervention contained feedback on: comparison of consumption with safe drinking levels, personal responsibility for change, advice on need to change drinking and on developing goals for change. Alternative strategies for changing consumption were provided (treatment-assisted or self-change). Alcohol-related feedback was imbedded with information about other lifestyle risks. Participants also had access to usual careMAC: usual care (n = 575) 1. Proportion of at-risk drinking  2. Alcohol intake (g/day)6, 12 monthsIntervention: 55 Control: 61
Results were presented on screen, printed and provided to participant
Based on FRAMES model
Intervention computer-based; location determined by researcher
(Paschall et al. 2006)cCampus orientation sessions and by letter and e-mailUnited States: university studentsCollege Alc (n = 310): alcohol misuse and harm prevention course consisting of 5 units: college alcohol use, harm prevention, how it works, risky business and practical solutions. Encourages development of a harm prevention plan. The program includes interactive animation and assignments, challenges normative misconceptions and alcohol expectanciesMAC: assessment-only (n = 312)1. Frequency of alcohol use in past month 2. Frequency of heavy drinking in past month 3. Frequency of feeling drunk in past month30 daysIntervention: 56 Control: 63
Approx. 3 hours (participants given 6 weeks for completion)
Theories of problem and health-related behaviour [72,73]
Intervention online; location determined by participant
(Riper et al. 2008)Newspaper advertisements and via health-related websitesNetherlands: adult problem drinkers in the general populationDrinking less (n = 130): consists of four stages: (i) preparing for action, (ii) goal setting, (iii) behavioural change and (iv) maintenance of gains and relapse prevention. Access to peer-to-peer discussion forumMAC: web-based psychoeducational brochure describing impact of alcohol use on physical and social functioning (n = 131)1. No. of problem drinkers 2. Mean weekly alcohol consumption6 monthsIntervention: 54 Control: 62
Recommended treatment period of 6 weeks. Measured actual use of the intervention
Based on cognitive behavioural and self-control principles
Intervention online http://www.minderdrinken.nl Location determined by participant
(Walters et al. 2007)Not statedUnited States: university studentse-CHUG (n = 50): personalized feedback consisted of: (i) quantity/frequency drinking summary (including caloric ‘cheeseburger’ equivalent); (ii) comparison to US adult and college norms; (iii) estimated level of risk; (iv) money spent on alcohol per year; (v) no. cigarettes smoked per month; and (vi) advice and local services. Feedback was derived from responses to AUDIT, questions on genetic risk of alcoholism, weight and expenditure on alcoholMAC: assessment-only (n = 56)1. Typical drinks/week 2. Peak BAC8, 16 weeksTotal: 77
Single session where feedback was viewed on screen
Feedback based on motivational interviewing and social psychology approaches. Followed FRAMES model
Intervention online http://www.e-chug.com Location determined by participant
(Weitzel et al. 2007)Flyers, e-mails and advertisementsUnited States: university studentsHand-held computer with messaging (n = 20): tailored text messages sent to hand-held computer daily on avoiding alcohol-related consequences. Messages addressed three situations: (i) drinking with negative consequence, (ii) drinking without consequence and (iii) not drinking. Messages were tailored to behaviour, self-efficacy and outcome expectancies regarding alcohol-related consequences1. MAC: hand-held computer without messaging (n = 20) 2. 3rd arm excluded from publication1. Total drinks consumed in study period 2. Drinking days 3. Drinks/drinking day2 weeksIntervention: 100 Control: 100
Messages were sent daily to those participants providing consumption data. Number of messages sent to and read by participants was recorded
Guiding principles not stated
Intervention computer-based; location determined by participant
Intervention—delivery mode

Most studies delivered the intervention via the internet (n = 14). One study sent tailored text-messages to hand-held computers [56], while the others were available from a computer in a fixed location. Most interventions were accessed from computers at a location determined by the researchers (n = 16); the remainder were able to access the intervention online at a location and time convenient to them [40,41,44,54–56,59,61].

Intervention—content

Fifteen studies consisted of personalized feedback on current levels of drinking and comparison with safe drinking limits. This was often accompanied with normative feedback, associated health risk, information on calculating units and support services. Five studies investigated interventions designed to resemble the campus setting. These included a variety of interactive games and assignments, motivational feedback and information on risk taking and refusal skills [39,41,42,49,54]. One study presented a video of people undergoing an alcohol/placebo expectancy–disconfirming experience. This aimed to increase awareness of how participants expected alcohol to affect them, and how these expectancies can lead to detrimental effects. It was followed by a description of the alcohol expectancy concept and the effect of alcohol expectancies on behaviour. The intervention also included games and questions requiring interaction [45]. Three studies based on adult problem drinkers from the general population provided a more extensive intervention, featuring common elements from behaviour change interventions. They included components such as readiness to change, decisional-balance, goal-setting, self-monitoring, strategies for behaviour change, behavioural contracting with rewards and penalties, maintenance of change and relapse prevention [57–59]. One of these studies also provided access to a peer-to-peer discussion forum [59] (see Table 2 for more information).

Intervention—theoretical basis

The studies cite different theoretical foundations of their interventions. The authors of the primary studies provided different justifications for using personalized feedback, reporting it to have originated from: Motivational Interviewing [63], FRAMES (Feedback, Responsibility, Advice, Menu, Empathy and Self-efficacy—illustrates effective components from brief intervention) [64], BASICS (Brief Alcohol Screening and Intervention for College Students) [65] and the social norms approach [66–68]. In those studies that used a more extensive range of behaviour change techniques, self-control training and cognitive behaviour therapy were referenced [69,70]. Three studies did not state any guiding principles, possibly because the computer-based intervention was used in a comparator arm [39,42,49].

Intervention—intensity of intervention

In many studies personalized feedback was made available on screen for a few minutes, and in some cases it was possible to print and take away. The campus-based interventions comprised longer sessions of up to 3 hours. Some studies allowed participants access to the intervention over a period of time [54,61], while others recommended revisiting the website to complete different sessions [41,59]. Two studies investigated multiple exposures to the intervention as part of their study design [39,48].

Bias assessment

Three studies made explicit reference to randomization sequence generation and the procedure for allocating participants to groups. These studies were classified as having low risk of bias associated with allocation concealment [46–48]. The remainder of studies were assessed as having unclear risk of bias, meaning that there was insufficient information in the publication to judge this aspect of trial quality.

Study outcomes

A variety of different self-reported outcomes were used to measure alcohol consumption. Most of the studies reported between one and four different drinking outcomes, while one study reported eight [41] and another reported 10 [61] (see Table 2). Twelve studies measured short-term outcomes (less than 3 months), nine measured medium-term outcomes (3–6 months) and three measured long-term outcomes (longer than 6 months). The shortest length of follow-up was 2 weeks [56] and the longest was 12 months [39,48,62]. Twenty studies reported a sample size of fewer than 300 participants, six of which had fewer than 100 participants. The smallest sample size was 40, reported in two studies [56,57], while the largest comprised more than 1000 [62].

Total alcohol consumption (quantity measure)

Nineteen studies measured the quantity of alcohol as actual or average drinks/units consumed within a given time-frame. One study was excluded from the meta-analyses as it did not provide standard deviations [45]. Fifteen studies appeared to have skewed data. Five of the 15 studies presented appropriate measures of central tendency given the skewed distribution of the data: two provided transformed data [55,58] and three reported medians [46,48,62]. Hence, a total of 18 studies (10 of which were unadjusted for skewed data) were included in the meta-analyses for this outcome (analyses 1 and 2).

Analysis 1: computer-based intervention versus minimally active comparator—g/week

The primary meta-analysis compared computer-based interventions with a minimally active comparator. It included 16 trials (nine unadjusted for skewed data) with a total of 3118 participants. Participants receiving the computer-based intervention reduced the amount of alcohol consumed per week significantly more than those receiving the minimally active comparator (mean difference = −25.9 g per week; 95% confidence interval (CI): −41 to −11). The mean difference was equal to 3.24 UK units of alcohol (1 UK unit = 8 g ethanol). However, there was substantial heterogeneity between the findings of the trials, with an I2 value of 62%. This suggests that although participants in most studies appeared to benefit from the computer-based intervention, the estimated benefit varied substantially between the trials.

Analysis 1.1: subgroup analysis: students versus non-students—g/week.  This heterogeneity was explored in a subgroup analysis by population. The studies were separated into two groups: students and non-students (three studies in adult problem drinkers from the general population and one in emergency department attendees). The two groups were found to differ significantly from each other (P < 0.001), suggesting a more pronounced effect in the non-student adult population (see Fig. 2). The heterogeneity was reduced substantially within the student subgroup (I2 = 28% for students, I2 = 77% for non-students).

image

Figure 2. Forest plot of subgroup analysis by population—computer-based interventions versus minimally active comparator groups (g/week)

Download figure to PowerPoint

Analysis 1.2: sensitivity analysis (within analysis 1.1): studies presenting appropriate measures of central tendency given the distribution of the data—g/week.  This analysis included two studies presenting medians [46,48], one study that presented back-transformed data [55] and two studies that reported no evidence of skew [51,52]. These five studies in student populations (994 participants) found no significant difference between computer-based interventions and minimally active comparator groups in alcohol consumed per week. This analysis was not possible in the non-student adult population due to the small number of studies.

Analysis 2: computer-based intervention versus active comparator—g/week

Three studies (two unadjusted for skewed data), including 457 student participants, compared a computer-based intervention with an active comparator [39,42,49]. There was no significant difference between participants receiving a computer-based intervention and an active comparator group in alcohol consumed per week. There was no heterogeneity observed between the findings of the trials (I2 = 0%). However, the analysis was heavily weighted by one particular study [39].

Binge drinking (frequency measure)

Eight studies measured frequency of heavy/binge drinking days or episodes within a given time-frame. Two studies were excluded from analyses for reporting the proportion of binge days [45] and frequency of heavy drinking as a categorical variable [54]. All the studies reporting this outcome demonstrated a skewed distribution at furthest point of follow-up. Two studies accounted for this by presenting medians [46,48].

Analysis 3: computer-based intervention versus minimally active comparator—binge frequency/week

This analysis included five trials (three unadjusted for skewed data) with a total of 848 student participants [41,44,46,48,49]. Participants receiving a computer-based intervention appeared to reduce their frequency of binge drinking compared with those receiving a minimally active comparator (mean difference = −0.23 days per week; 95% CI: −0.47, 0.00; P = 0.05). There was no heterogeneity observed between the findings of the trials (I2 = 0%).

Analysis 4: computer-based intervention versus active comparator—binge frequency/week

Only two studies made this comparison [39,49], and so the findings were not pooled in a meta-analysis. Both studies reported no significant difference in binge frequency between the intervention and an active comparator group.

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

The data identified by this review suggest that computer-based interventions were more effective than minimally active comparator groups at reducing alcohol consumed per week (in both student and non-student adult populations) and binge frequency (in student populations). A small number of studies found no difference between alcohol consumed per week in those receiving the intervention or an active comparator. However, most studies reported skewed data, which was summarized using the mean. A sensitivity analysis of those studies that presented suitable measures of central tendency for the distribution of the data found that in student populations there was no difference between intervention and minimally active comparator groups in alcohol consumed per week. These findings should therefore be interpreted with caution.

Notwithstanding the limitations of the data in the current review, a mean difference of 26 g of alcohol per week was found between computer-based interventions and minimally active comparator groups. This difference is of similar magnitude to that reported in a Cochrane review of hazardous and harmful drinkers in primary care, where participants receiving conventional face-to-face brief interventions reduced their alcohol intake significantly more than those receiving a control (difference of 38 g per week) [2]. The effectiveness of computer-based interventions in student populations was less pronounced than in non-student populations and diluted the overall reduction in alcohol consumption (see Fig. 2). These differences in the size of effect between the population groups may be due to baseline risk, where non-student drinkers were consuming greater amounts of alcohol than students and therefore had greater capacity for reducing their intake. The theoretical basis of the intervention may also have influenced differences in effect, where non-student populations received more extensive brief interventions. Such factors, along with impact of length of follow-up, could have been considered in further analyses. However, this was not deemed appropriate given the limitations with the data.

The initial finding that computer-based interventions are effective in student populations supports findings from previous research [22,24,25]. However, this review has highlighted that most studies use an inappropriate measure of central tendency (i.e. mean) given the skewed distribution of the data and the small sample sizes. A problem then lies in constructing meta-analyses of mean differences. In order not to exclude studies that used appropriate measures of central tendency (three studies that reported medians [46,48,62]), we used the median to estimate the mean and the range to generate an estimated standard deviation [37]. Estimating the sample mean in this way may have introduced errors; however, an estimation of a correct statistic was considered preferable to the exclusion of these studies from meta-analyses. At present, there is no consensus on how best to pool different measures of central tendency in meta-analyses. An ideal analysis of the current data on computer-based interventions for reducing alcohol intake would include the individual patient data from all eligible studies. This would allow the pooling of rate ratios from negative binomial models, as advocated when using count data [27].

This review investigated the effectiveness of computer-based interventions with two specific measures of alcohol consumption: total consumption and binge frequency. It is possible that the selection of another consumption measure may have resulted in different findings. The strength of this approach is that it provides a meaningful interpretation of the pooled data, i.e. grams of alcohol consumed per week and frequency of binge drinking episodes per week. It also acknowledges that different measures of alcohol consumption reflect different patterns of drinking.

This review considered allocation concealment as a potential source of bias. Only three studies were assessed as having low risk of bias [46–48], while the other studies provided insufficient information to pass judgement. It is likely that many studies assessed as unclear were poorly reported rather than poorly designed; for example, those conducted over the internet in their entirety would consequently have concealed allocation to randomized group. Other potential sources of bias in trial design include: inadequate sequence generation and blinding, incomplete outcome data and selective reporting. These features of valid trial design are most applicable to conventional drug trials and problems occur when applying them to trials of computer-based behavioural interventions, particularly those conducted online. In an online trial it is likely that sequence generation and allocation concealment will have been performed by a computer in a fully automated process. Blinding of participants and study personnel is not truly possible with behavioural interventions where some participants receive access to an intervention and others do not. Also, blinding of outcome assessors may not be relevant in an online trial where participants complete follow-up questionnaires from a remote location over the internet. Future trial designs and publications would benefit from explicit reference to these factors, and further attempts to identify other sources of bias unique to online trials and computer-based interventions, such as re-registration.

At present, it is not possible to interpret the evidence with any degree of certainty. It is vital that future research in this area considers that alcohol consumption data are likely to be skewed, and that appropriate measures of central tendency are reported in trial publications. The current literature is also limited by small sample sizes, short-term follow-up, insufficient information to judge potential sources of bias, few studies in non-student adult populations and few comparisons with active comparator groups. However, the volume of research is encouraging and the potential benefits of computer-based interventions for reducing alcohol consumption should continue to drive interest in this area.

Acknowledgements

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

With special thanks to Angela Young for help in designing the search strategy and searching the databases, and Giancarlo Manzi and Simon Thompson for involvement in protocol development. We are also grateful to the reviewers for their helpful suggestions on improving the paper.

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  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix
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Appendix

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Declarations of interest
  8. Acknowledgements
  9. References
  10. Appendix

APPENDIX 1

MEDLINE search strategy
Search strategy used for MEDLINE database
Computer-related terms:
#33 ((personal adj digital adj assistant) or pda) in ti,ab,kw 3325
#32 (surf* near4 internet*) in ti,ab,kw 60
#31 (surf* near4 web*) in ti,ab,kw 92
#30 (virtual adj reality) in ti,ab,kw 2096
#29 (consumer adj health adj informatic*) in ti,ab,kw 49
#28 ((e adj health) or e-health or (electronic adj health)) in ti,ab,kw 1463
#27 (interactive near ((health adj communicat*) or televis* or video* or technolog* or multimedia)) in ti,ab,kw 1420
#26 ((bulletin adj board*) or bulletinboard* or messageboard* or (message adj board*)) in ti,ab,kw 280
#25 (blog* or web-log* or weblog*) in ti,ab,kw 149
#24 ((chat adj room*) or chatroom*) in ti,ab,kw 144
#23 (online or on-line) in ti,ab,kw 27137
#22 ((internet adj based) or internet-based) in ti,ab,kw 1690
#21 ((web adj based) or web-based) in ti,ab,kw 5154
#20 ((world adj wide adj web) or (world-wide-web) or www or (world-wide adj web) or (worldwide adj web) or website*) in ti,ab,kw 6587
#19 ((electronic adj mail) or e-mail* or email*) in ti,ab,kw 3683
#18 ((mobile or cellular or cell) adj (phone* or telephone*)) in ti,ab,kw 1860
#17 ((CD adj ROM) or cd-rom or cdrom or (compact adj dis*)) in ti,ab,kw 1238
#16 (decision adj (tree* or aid*)) in ti,ab,kw 2693
#15 (internet or (local adj area adj network*)) in ti,ab,kw 15034
#14 (computer* or microcomputer* or laptop) in ti,ab,kw 175387
#13 explode ‘Software-’ / all SUBHEADINGS in MIME,MJME,PT 66293
#12 explode ‘Computer-Graphics’ / all SUBHEADINGS in MIME,MJME,PT 11752
#11 explode ‘Public-Health-Informatics’ / all SUBHEADINGS in MIME,MJME,PT 679
#10 explode ‘Computer-Assisted-Instruction’ / all SUBHEADINGS in MIME,MJME,PT 6187
#9 explode ‘Audiovisual-Aids’ / all SUBHEADINGS in MIME,MJME,PT 61028
#8 explode ‘Decision-Support-Techniques’ / WITHOUT SUBHEADINGS in MIME,MJME,PT 51993
#7 explode ‘Medical-Informatics’ / all SUBHEADINGS in MIME,MJME,PT 147451
#6 explode ‘Computer-Systems’ / all SUBHEADINGS in MIME,MJME,PT 103304
Alcohol-related terms:
#5 (alcohol* near (abuse or related disorder* or drink* or excessive or consum* or intake or reduction or misuse* or dependen*)) in ti,ab,kw 57068
#4 ((heavy or hazardous or harmful or excessive or problem or binge or controlled) adj drink*) in ti,ab,kw 6605
#3 explode ‘Alcoholic-Beverages’ / all SUBHEADINGS in MIME,MJME,PT 9255
#2 explode ‘Alcohol-Drinking’ / all SUBHEADINGS in MIME,MJME,PT 35790
#1 explode ‘Alcohol-Related-Disorders’ / all SUBHEADINGS in MIME,MJME,PT 80278