Show Summary Details

Page of

PRINTED FROM the OXFORD RESEARCH ENCYCLOPEDIA, PSYCHOLOGY (psychology.oxfordre.com). (c) Oxford University Press USA, 2016. All Rights Reserved. Personal use only; commercial use is strictly prohibited. Please see applicable Privacy Policy and Legal Notice (for details see Privacy Policy and Legal Notice).

date: 25 June 2018

Internet-Based Methods in Managing Alcohol Misuse

Summary and Keywords

Alcohol-use disorders are widespread and associated with a greatly increased risk of health-related and societal harms. The majority of harms associated with consumption are experienced by those who drink above recommended guidelines, rather than those who are alcohol dependent. Brief interventions and treatments based on screening questionnaires and feedback have been developed for this group, which are effective tools for reducing consumption in primary care and in other settings. Most people who drink excessively do not receive help to reduce the risks associated with excessive consumption. Digital versions of brief and extended interventions have the potential to reach populations that might derive benefit from them. Digital interventions utilize the same principles as do traditional face-to-face versions, but they have the advantages of availability, confidentiality, flexibility, low marginal costs, and treatment integrity. The evidence for the feasibility, acceptability, costs, and effectiveness of digital interventions is encouraging, and the evidence for effectiveness is particularly strong in studies of student populations. There are, however, a number of unresolved questions. It is not clear which components of interventions are required to maximize effectiveness, whether digital versions are enhanced by the addition of personal contact from a facilitator or a health professional, or how to increase take up of the offer of a digital intervention and reduce attrition from a program. These questions are common to many online behavior-change interventions and there are opportunities for cross-disciplinary learning between psychologists, health professionals, computer scientists, and e-health researchers.

Keywords: alcohol drinking, alcohol-induced disorders, harm reduction, cognitive therapy, Internet, digital health interventions, behavior change, alcohol screening, brief intervention, treatment of alcohol-use disorders

Introduction

Excessive and harmful alcohol consumption is a significant problem worldwide (World Health Organization, 2014). There is a need for evidence-based interventions that can be deployed at scale to assist in ameliorating this situation.

Background

Definitions of Alcohol-Related Problems and Terminology in Common Use

Measurement of Alcohol Consumption

The measurement of alcohol consumption relies on self-assessment because it is rarely possible to obtain objective physiological measures, such as blood alcohol levels. Self-reports and surveys are frequently used, however, these may be subject to error through memory or response biases. Frequency and quantity measures are obtained through drinking diaries. For research purposes, the standard measure of drinking is the alcohol Timeline Follow Back (TLFB). This uses a calendar of dates and events to prompt recall to better enable retrospective estimates of daily drinking over a specified time period. The TLFB has been shown to have good psychometric characteristics with a variety of drinker groups (Sobell & Sobell, 1992).

Alcohol consumption is measured in standard drinks or standard units. The definitions of these terms vary between countries and so are converted to grams of pure ethanol to aid comparison. In the United States, a standard drink contains about 14 grams of alcohol; in the United Kingdom, a standard unit contains 8 grams; whereas in Ireland and Australia, it is 10 grams. Charts have been developed to help drinkers calculate their consumption by providing local specific information about how much alcohol is contained in a normal serving or a standard glass. For example, in the United Kingdom, a large glass of standard-strength wine contains 3 units, and a small glass contains 1.5 units. These amounts are not the same in each country and also change over time. To continue with the U.K. example, pubs will often sell 250 ml glasses of wine as standard; whereas in the past, 125ml glasses were more common.

Terminology Commonly Used to Describe Alcohol-Related Problems

Alcohol-related risk and harms refer to the direct effects of alcohol on the body; increased risk of accidents, violence, and antisocial behavior; risky behaviors, increased personal vulnerability, and negative impacts on occupation and education. Guidance on risk reduction is often produced by public bodies. The U.K. chief medical officer states, for example, that both men and women should not regularly drink more than 14 units per week. If 14 units per week are consumed, it is best to spread them evenly over three days or more, because heavy drinking sessions increase the risks of death from long-term illnesses, accidents, and injuries (Chief Medical Officer, 2015).

The World Health Organization distinguishes between hazardous drinking, a pattern of alcohol consumption that increases the risk of harmful consequences for the user or others; harmful drinking, which refers to alcohol consumption that results in harms to physical and mental health or detrimental social consequences; and alcohol dependence, which is a cluster of behavioral, cognitive, and physiological phenomena that may develop after repeated alcohol use (Fleischmann, Fuhr, Poznyak, & Rekve, 2011).

Alcohol-use disorders are medical terms describing problem drinking that include both “alcohol dependence” and “alcohol abuse.” These may be mild, moderate, or severe.

Heavy episodic drinking or binge drinking is defined as drinking at least 60 grams or more of pure alcohol on at least one occasion in the past 30 days or as a pattern of drinking that brings blood alcohol concentration (BAC) levels to 0.08 g/dL. Binge drinking is one of the most important indicators for acute consequences of alcohol use, such as accidents and injuries.

Prevalence

On average, every person in the world aged 15 years or older drinks 6.2 liters of pure alcohol per year (recorded consumption). But less than half the population (38.3%) actually drinks alcohol, so this means that those who do drink consume on average 17 liters of pure alcohol annually (World Health Organization, 2014).

Alcohol-Related Risks and Harms

Alcohol is the world’s third largest risk factor for disease; it is the leading risk factor in the Western Pacific and the Americas and the second largest in Europe. Alcohol use results in approximately 2.5 million deaths each year (Fleischmann et al., 2011).

In the United Kingdom 2.5 million people (9% of adults) drank more units on their heaviest drinking day than the weekly recommended amount (Health and Social Care Information Centre, 2016). Alcohol-related harm costs England around £21bn per year, with £3.5bn to the National Health Service (NHS), £11bn tackling alcohol-related crime, and £7.3bn from lost work days and productivity costs (Cabinet Office Strategy, 2004). There were 8,697 alcohol-related deaths in the United Kingdom in 2014.

Alcohol-Use Disorders

Global prevalence rates of alcohol-use disorders among adults were estimated by the World Health Organization to range from 0% to 16% (World Health Organization, 2014).

The region with the greatest number of alcohol-use disorders is Eastern Europe. Prevalence in the Russian Federation, for example, is 16.29%. Western European rates typically range between 4.5% (France and Germany) and 6.4% (United Kingdom). There is considerable variability between American countries (5%–10%). The United States prevalence is 5.5%; whereas Colombia is 10.3% (World Health Organization, 2014). However, a recent study using an updated definition of an alcohol-use disorder found the 12-month and lifetime prevalence of alcohol-use disorders in the United States to be 13.9% and 29.1%, respectively (Grant et al., 2015).

The regions with lowest prevalence of alcohol-use disorders are Southeast Asia (mostly less than 3%) and Africa (0.5%–1.5%), with Thailand (10.2%), China (7%), and South Africa (3.64%) being exceptions (World Health Organization, 2014).

Binge Drinking

In the United States, 25% of those aged 18 or older reported that they had engaged in binge drinking in the past month (Substance Abuse and Mental Health Services Administration, 2014). In Great Britain, 12.9 million drank more than 4.67 units on their heaviest drinking day, and of these, 2.5 million (9%) drank more units on their heaviest drinking day than the weekly recommended amount of 14 units (Office for National Statistics, 2016).

Changes Over Time

Alcohol consumption has gone down in the last decade, but the associated mortality and morbidity has not. For example, in England in 2013, there were 6,592 alcohol-related deaths, representing an increase of 1% from 2012 (6,495) and an increase of 10% from 2003 (5,984) (Office for National Statistics, 2016).

Different segments of the population show different trends. Data from the Office for National Statistics in the United Kingdom show that in 2016 younger people drank less than middle-aged and older people, whereas in 2005 (when the surveys began) they had drunk more. Furthermore, consumption levels among women have changed dramatically since the beginning of the 20th century. Whereas reported consumption was historically much greater for men, it is now close to parity, especially among younger cohorts, and women now appear to be drinking more and experiencing greater levels of harm and more alcohol-use disorders (Slade et al., 2016).

Summary

Harms associated with alcohol misuse place a significant burden on individuals, families, communities, and societies. Patterns of consumption are changing; but the harms associated with alcohol consumption are not necessarily related to the presence of a specific alcohol-use disorder. Most of the harms associated with alcohol use occur among people who are not dependent but drink above recommended levels (Babor & Higgins-Biddle, 2001). There is a need for interventions directed toward the treatment of individuals and reducing population risks.

Interventions for Alcohol-Use Disorders

Treatments for Alcohol-Use Disorders

Historically, treatment was reserved for “alcoholics” (dependent drinkers) and provided from intensive and expensive treatment units. However, these centers were largely abandoned after a study reported no significant differences in outcome between a group that received one counseling session and a group that had received several months of intensive treatment (Edwards et al., 1977).

Treatments for alcohol-use disorders are currently differentiated by levels of severity. Medically supervised detoxification is provided to those with a physical dependence on alcohol. Psychological or psychosocial treatments are for those with less severe problems, but these two groups often overlap.

The U.K. Model of Care for Alcohol Misusers (MoCAM) proposes a comprehensive model of integrated alcohol treatment (National Treatment Agency for Substance Misuse, 2006). MoCAM has determined that dependent drinkers can largely be managed by specialist teams in community settings; but a minority of the most heavily dependent drinkers will require inpatient assisted alcohol withdrawal and residential rehabilitation.

Hazardous and harmful drinkers require comprehensive assessment and may need some medical help, but the primary treatments are psychological therapies alongside social interventions such as support, occupational and creative activities, groups, or mentoring.

Psychological therapies are provided by trained professionals or peer support workers. The main treatments are the following:

  • Motivational interviewing or motivational-enhancement therapy. This is based on theories of cognitive dissonance and attempts to promote a favorable attitude to change and make choices that will realistically support change (Miller & Rollnick, 2002).

  • Cognitive-behavior therapy. This is a broad term that includes approaches from learning theory, social learning theory, and cognitive psychology. It includes techniques such as self-monitoring, cue exposure, relapse prevention, and community reinforcement (Marlatt & Gordon, 1985, pp. 85–101). In recent years, this general approach has been extended to include resilience training and mindfulness.

  • 12-step program facilitation. This is primarily associated with Alcoholics Anonymous and directs people toward participating in the AA community (Nowinski, Baker, & Carroll, 1992).

Evidence for Psychological Treatments for Alcohol-Use Disorders

The efficacy of psychological treatments has been evaluated in a series of randomized controlled trials. Two of these, Project MATCH in the United States and the United Kingdom Alcohol Treatment Trial (UKATT), were large scale and evaluated the relative effectiveness of the main alcohol-treatment approaches. Project MATCH compared cognitive-behavior therapy, motivational-enhancement therapy, and the 12-step program and found that all three approaches were equally effective (Project MATCH Research Group, 1998). The UKATT trial compared a new approach called social-behavior network therapy with motivational-enhancement therapy and also found equal levels of effectiveness (UKATT Research Team, 2005).

The development of psychological approaches and the understanding of treatment has not remained static. There is interest in understanding the importance of the therapist–client relationship factors in underpinning successful treatment outcomes (Cook, Heather, & McCambridge, 2015), as well as in developing innovative and eclectic approaches to treatment.

Problems With Treatment Provision

The majority of people with alcohol-use disorders are untreated. Australian data suggest that lifetime prevalence of treatment for alcohol misuse is around 27% (Chapman, Slade, Hunt, & Teesson, 2015), meaning that nearly three in four people who could benefit from treatment do not receive it. Traditional face-to-face services, when they are available, are often not well used, for a variety of reasons. Treatment is not free or covered by insurance in some jurisdictions, and this can be a significant barrier to seeking help. Clinics tend to be open during office hours, when people are at work, and they may not be located near to where someone lives, which can create transport difficulties. This is a particular problem for people who are responsible for young children, are carers, or have mobility problems. Furthermore, people who may potentially benefit from services may not know about their existence and are not aware that they may benefit from them.

Many people may be reluctant to seek help because they feel embarrassed or ashamed about their drinking. Individuals are often not willing to share information about their drinking with a healthcare or other professional because they are concerned that it will have detrimental consequences for them. Attending specialist services can be stigmatizing. Khadjesari, Stevenson, Godfrey, and Murray (2015) conducted interviews with participants in an online study of a digital intervention. They found that the privacy of the Internet was particularly important because it helped to reduce stigma and embarrassment. Many of the participants were also aware of the shortage of services for people with alcohol-use disorders.

Interventions for Hazardous and Harmful Drinkers

Hazardous and harmful drinkers carry the burden of most alcohol-related harm, therefore interventions directed toward this group may be able to significantly reduce the harms associated with excessive consumption. The majority of these drinkers do not seek assistance and are not offered treatment, and brief interventions have been developed for this group. These provide opportunities for such drinkers to identify and assess the extent of their drinking and to receive information on sensible drinking. Brief interventions are important because they can help reduce the aggregate level of alcohol consumed and thus lower the risk of alcohol-related harms for the entire population (National Institute for Health and Care Excellence, 2010).

Brief interventions for alcohol problems are known as either identification and brief advice (IBA) or screening and brief intervention (SBI). The elements of a brief intervention are derived from the basic principles of motivational interviewing and are summarized in the FRAMES model (Bien, Miller, & Tonigan, 1993):

  • Feedback on the risk for alcohol problems.

  • Responsibility: where the individual with alcohol misuse is responsible for change.

  • Advice: about reduction or explicit direction to change.

  • Menu: providing a variety of strategies for change.

  • Empathy, with a warm, reflective, empathic and understanding approach.

  • Self-efficacy of the misusing person in making a change.

The means by which brief interventions are delivered varies between settings. Feedback is usually provided opportunistically following screening in a routine setting, such as a doctor’s clinic or other public-service setting. This can be done face to face or by means of a self-report questionnaire. A number of questionnaires have been developed for this purpose, but the instrument used in most settings, and considered the gold standard, is the Alcohol Use Identification Test (AUDIT; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). This is a 10-item multiple-choice questionnaire that assesses risk in the domains of hazards from drinking (frequency and quantity), dependency (control over drinking, salience of drinking, and morning drinking), and harms from drinking (guilt, blackouts, alcohol-related drinking, and others’ concern about someone’s drinking). A briefer version of the AUDIT, the AUDIT C, includes only the three consumption questions from the AUDIT and has been shown to effectively identify hazardous drinkers in a number of settings (National Institute for Health and Care Excellence, 2010).

The feedback element of a brief intervention may be provided simply as information that informs the individual about their level of risk or as a more complex and personalized encounter with a trained healthcare professional or a specialized alcohol counselor. Similarly, the provision of options about strategies for change may be a printed list or a personal interview. Clearly, these are very different modalities, and the full FRAMES approach assumes that the feedback is given by someone with the appropriate personal qualities (warmth and empathy) and therapeutic training (such as in motivational interviewing).

Evidence for Brief Interventions

The evidence for the efficacy of SBI and IBA is very strong and there have been numerous studies and meta-analyses demonstrating positive outcomes for the approach. Kaner et al. (2007), for example, conducted a Cochrane review that included 24 trials in general practice and five trials in an emergency setting. Across these studies, over 7,000 participants with a mean age of 43 years were randomized to receive a brief intervention or a control intervention, including assessment only. Their conclusion was that after one year or more, those people in trials who had received a brief intervention drank less alcohol than people in the control groups (average difference, 38 grams a week; range, 23 to 54 grams; Kaner et al., 2007).

Problems With the Implementation of Brief Interventions

Pragmatic studies in routine settings have not found the same benefits as those reported in the original trials. The U.K.-based Screening and Intervention Programme for Sensible drinking (SIPS), an alcohol screening and brief intervention research program consisting of cluster randomized controlled trials in three different settings: primary care, emergency departments, and probation services. In primary care, brief interventions had no benefit over a simple information leaflet (Kaner et al., 2013). The study in emergency departments was a large, multicenter study that found that it was difficult to implement brief interventions in emergency-department settings for a variety of practical reasons. However, when these difficulties were overcome, they also found no benefits for a brief intervention over simple clinical advice or an alcohol information leaflet (Drummond et al., 2014). Similarly, structured brief advice or lifestyle counseling had no advantages over an information leaflet delivered to offenders by probation officers (Newbury-Birch et al., 2014).

Healthcare professionals may not provide brief interventions because they are reluctant to ask about drinking, for a variety of reasons (Linke, Harrison, & Wallace, 2005). These include not seeing this as part of their role or worrying that holding such information could be damaging to the welfare of a patient (e.g., if the they have to produce a report for insurance purposes). They may also be concerned that they do not know how to ask the right questions or that if they do ask they will be able to provide the patient with the service that is required.

There has been a lot of attention paid to methods of training and evaluating professional and lay practitioners in delivering brief interventions. However, in routine practice the actual delivery may vary for a range of reasons, and important elements can be omitted. This is similar to the “therapy drift” found in other behavior-change settings and can weaken their effectiveness (Waller, 2009).

Researchers and practitioners have criticized other ways in which brief interventions have been implemented in real-life settings. Practitioners have argued that when delivering a brief intervention, it is important to ensure that staff have the appropriate knowledge, skills, training, supervision, and support, along with the right personal qualities. Without these, the FRAMES approach may not be implemented fully and the interventions are less likely to be beneficial. From the researchers’ perspective, Nick Heather (2014) has argued that policymakers have been too quick to implement brief interventions before they have done the necessary foundational research in the real-life settings to maximize their effectiveness. Furthermore, implementation of brief interventions in complex multicenter trials is difficult, and the interventions themselves may not have been delivered with sufficient fidelity to the original model. Research into the challenges and opportunities for deploying brief interventions at scale is a continuing area of development. The International Network on Brief Interventions for Alcohol and Other Drugs (INEBRIA) is an international network of researchers, policymakers, practitioners, and other stakeholders interested in the potential of brief interventions. Along with outcome studies in traditional health settings, they are interested in developing combinations of delivery modes, new models and theoretical approaches, current information technologies, unique settings, and different target populations.

The Case for Digital Brief Interventions

Access

Online services have the potential to reach large numbers of people who would not otherwise have access to a brief intervention. This has been facilitated by the rapid growth of Internet access. The International Telecommunication Union estimated that in 2000, 7% of the world’s population had used the Internet at least once during that year, and in 2015, this number had risen to 44% (International Telecommunication Union, 2016). In 2015, the greatest penetration was in Europe (82%), the Americas (60%), Asia (39%) and the lowest was in Africa (11%) (Telecommunication Development Bureau, 2017). We have already seen that Europe is the heaviest drinking region of the world, whereas Asia and Africa have the lowest levels of consumption. The greatest level of Internet access coincides with highest level of alcohol consumption. There are, therefore, opportunities to deploy brief interventions via the Internet in the regions with the greatest needs.

Online communication can help to resolve the barriers of limited access to treatment by providing information about treatment facilities and about alcohol consumption and the associated risks.

Availability

Unlike face-to-face interactions, digital services are available 24 hours a day and seven days a week. Many people routinely use the Internet to search for health-related information. Simple screening via online quizzes, screening tests, and advertising can raise people’s awareness that their drinking may be causing them problems. Strategically placed links on websites may direct people who are concerned about a health problem or have concerning symptoms to evidence-based digital SBIs and IBAs. Online services also have the potential to resolve the problems of the stigma of going to a clinic for help because they can be completed in privacy and anonymously.

Intervention Quality

Traditional face-to-face interventions are usually provided on a single occasion, and there is limited opportunity for follow-up. Digital interventions can be repeated and automated systems can be used to provide extra information and interactive feedback and to prompt for behavior change. There is the potential to direct users to online support forums, email groups, and appropriate social media, as well as terrestrial services, for ongoing support.

Digital interventions can be designed to ensure treatment fidelity. Materials and processes that comply with the FRAMES and other evidence-based models can be included along with evidence-based behavior-change techniques. This will prevent therapist drift and other deviations from best practice. Users are, of course, free to use other sources of information and help alongside the digital intervention, which may not adhere to the same framework.

Cost

There may be cost benefits over providing traditional SBIs and IBAs. Costs vary between countries, settings, and populations. One study in 2012 estimated the costs as between $3.41 to $243 per brief intervention, with a median cost of $48 per intervention (Bray, Zarkin, Hinde, & Mills, 2012). The initial development costs of a digital intervention may be significant; however, these are one-off, upfront investments, and once they have been built the maintenance expense may be low.

Potential for Research and Evaluation

It is also possible to use Internet technology to routinely measure user behavior by utilizing free software (such as Google Analytics) or building customized modules for research and reporting purposes. This enables automated recruitment, data collection, data analysis, and reporting. Digital brief interventions are well placed to contribute to the growing science of online trials (Murray et al., 2009).

The Development of Digital Interventions for Alcohol Harm Reduction

In the light of these potential advantages, a number of researchers have developed Internet-based interventions for alcohol-use disorders.

Feasibility and Early Findings

The first digital interventions were directed at testing the feasibility of transferring established interventions from face-to-face interactions into a digital format.

Hester and Delaney (1997) utilized a stand-alone personal computer to deliver a psychological treatment program. They developed a 10-week behavioral-control training program for a Windows-based personal computer and compared a treatment group with “waiting list” controls. They found that the treatment reduced drinking and that this was maintained at 12-week follow up.

Linke, Brown, and Wallace (2004) converted a printed treatment manual into the first fully developed Web-based system. “Down Your Drink” was a printed manual delivered weekly to subscribers via the postal service. The manual guided people through exercises to help them contemplate change—derived from the “stages of change” theory (Prochaska & DiClemente, 1994), cognitive-behavior therapy, and relapse prevention. The Web-delivered program followed the same principles, contained similar exercises, and content, and was delivered in weekly modules. The homepage (Figure 1) directed people to the Fast Alcohol Screening Test (FAST) questionnaire (Hodgson, Alwyn, John, Thom, & Smith, 2002) and from there to questions and exercises, an online drinking diary, automated emails, quizzes, an online discussion group, and a recreational area (the “Cybersaloon”). Use was made of images and many interactive features such as “mouseovers.”

Early feasibility testing showed that the intervention was widely used and that many users reduced their alcohol consumption while using the site (Linke et al., 2004); however, only 16% completed the six weekly modules. In light of this and other feedback from users, the site was redesigned, changing from a structured weekly modular approach to a “free-range” one, which allowed users to access any content, when and how they wanted (Linke, McCambridge, Khadjesari, Wallace, & Murray, 2008). Forty percent of users accessed the program outside normal office hours (Linke, Murray, Butler, & Wallace, 2007).

There were trials of a number of similar interventions at around the same time. Riper, Kramer, Smit, et al. (2008), conducted a randomized controlled trial of their Web-based behavioral program and found that intervention subjects decreased their mean weekly alcohol consumption significantly more than control subjects, with a difference of 12 standardized units. In a secondary analysis of the data, they found that their intervention was suited to a heterogeneous group of problem drinkers and could be offered as a first-step treatment in a stepped-care approach (similar to the MoCaM service model) directed at problem drinkers in the general population.

“Check Your Drinking” (CYD) was developed and evaluated by a team led by John Cunningham and made more explicit use of motivational interviewing procedures by including a normative feedback module that enabled users to compare their levels of drinking with those of groups they might consider similar to themselves. Their randomized controlled trial found that the intervention group showed a six- to seven-drink reduction in their weekly alcohol consumption (a 30% reduction in typical weekly drinking) at both the three- and six-month follow-ups, compared to a one drink per week reduction among control group respondents (Cunningham, Wild, Cordingley, van Mierlo, & Humphreys, 2009).

Kypri et al. (2009) took a different approach and made use of a single session electronic SBI in a student health center and found a reduction in hazardous drinking that lasted over 12 months.

Further Developments

In parallel to the introduction of psychological alcohol-treatment models, digital interventions also made use of the recommendations drawn from the growing science of online behavior change. These included the use of interactive features instead of simply providing flat information to be read, tailoring so that users were exposed to material that is most suitable for them, gamification and reinforcement for participation and the utilization of multiple modes of communication (email, texts, etc.), and multimedia (such as animation and videos). The online drinking diary provides a good example. Users are encouraged to submit data on their alcohol consumption, which is recorded in the system and then displayed back to them using a range of graphical or pictorial displays. Prompts to complete the diary can be delivered via email or SMS texts. The extent to which advantages like these have been fully exploited, however, has been limited, largely because funding has been provided primarily for research and there have been budgetary limitations on what innovations can be achieved (McCambridge et al., 2010). Where there have been fewer budgetary restrictions, such as sites produced by alcohol charities or private organizations, further developments have been possible, but these have not been independently evaluated.

The rapid development of the new technologies has often meant that interventions have become out of date even while they are being researched. This is both in the “look and feel” of a site, but also in the modes of delivery. Digital interventions that were originally designed for desktop computers with an Internet connection have had to be updated for mobile devices, such as laptops, tablets, and smart phones in both iPhone and android environments. There has been a proliferation of downloadable apps, many of them free, and these may not be based on the established principles of behavior change, such as those established through research in either the addiction (West & Brown, 2013) or behavior-change fields (Michie, van Stralen, & West, 2011). Recommended good practice is to develop these apps or other interventions so as to include techniques and information where there is evidence of their ability to effect change.

Deployment of Internet Interventions

The most common setting for the deployment of digital interventions for alcohol-use disorders has been student populations. This is a group that it is important to reach because they may be early in their drinking careers and are often at a life stage when consumption is heavy. Student populations maybe easy to study because researchers are often based in university settings. Other settings include the military (Pemberton et al., 2011) and the workplace (Khadjesari, Newbury-Birch et al., 2015).

Digital interventions are now available in a number of countries and in different (primarily European) languages. Though sites are based in different regions users may, of course, be based outside of these specific territories. These regions, to a large extent, coincide with the incidence of heaviest drinking worldwide. The exceptions are some parts of Eastern Europe, but there are plans to translate and culturally adapt existing sites to populations in these countries.

Acceptability of Digital Internet Interventions

In developing Internet-based interventions, researchers have paid attention to participation rates, characteristics of users, and levels of engagement with the program.

Digital interventions appear to have attracted considerable interest from people who meet the criteria for hazardous drinking or have alcohol-related problems. Wallace et al. (2011), for example, attracted more than 10,000 individuals willing to participate in an online trial of “Down Your Drink” with only minimal advertising. The mean age was 38 years, 57% of respondents were women, and 52% had been educated to at least college degree level. Similarly, in Riper, Kramer, Smit, et al.’s (2008) trial, 49% were women, the mean age was 46 years, and 70% described themselves as having academic or vocational qualifications.

The actual use of an intervention does not necessarily match up to the willingness to register with a website. Wallace et al. (2011), for example, found that those with access to the intervention visited on average only 2.3 times and downloaded an average of 63 pages. However, there was a wide range of activity, with some users making heavy use of the site and others very little. The low level of actual use is a common problem in Internet interventions and raises the question of how much exposure to or use of a site is required to constitute an adequate or effective dose.

Costs

The initial cost of development may be high; especially if there is associated research activity. However, programming costs are coming down, particularly if open-source and free app development software is used.

Maintenance costs of a digital intervention can be low so, once built, the marginal costs of each new user are negligible. However, this does depend on whether the program is fully automated and how often the site needs updating. As there are no additional costs per user in fully automated digital interventions, these are able to provide a service to large numbers of people at low costs. The greater the number of users, the lower the unit cost. A warning comes from a group of researchers, however, who found that costs escalated above the original budget estimates in their grant application (McCambridge et al., 2010). There are also costs to the end users, who usually have to own and pay for their devices and Internet connections; however, it is likely that they will use equipment they already use for other purposes, and free access to fast and reliable Wi-Fi is becoming increasingly available in many countries.

Interventions that are built for research programs are not always supported after a trial has been completed, and further investment may be required. Overall, the costs of automated digital interventions are much lower per registered user than traditional face-to-face interactions. However, this advantage is lost if actual usage levels are very low. Low costs, however, do not necessarily equate to cost-effectiveness.

Summary

The original hope that it is possible to develop and implement digital interventions has been realized. They have been deployed successfully in a range of settings and attracted very large numbers of users. These users appear to be from groups that are not normally well served by traditional face-to-face services. Actual levels of use, however, are often low, and though set-up costs may be low, there may be hidden costs that are not always fully identified.

Effectiveness

Randomized Controlled Trials

The gold standard for establishing the effectiveness of an intervention is the double-blind randomized controlled trial (RCT). The Internet lends itself well to this approach because it is easy to disseminate information and data collection can be automated. The primary outcomes in studies have usually been self-report measures of consumption with a range of secondary outcomes spanning physical and mental health, social consequences of drinking, and health economic metrics. A number of early studies demonstrated benefits for an intervention over comparators (e.g., Kypri et al., 2009; Kypri, Langley, Saunders, Cashell-Smith, & Herbison, 2008; Riper, Kramer, Smit, et al., 2008; Schinke, Schwinn, Di Noia, & Cole, 2004). Subsequent studies have continued this trend and have been conducted with a large number of different heavily drinking populations and in a range of settings.

Not all trials have shown a benefit for a digital intervention. One large study, comparing an updated version of the Down Your Drink intervention and a flat, text-based website providing simple information only, showed an overall reduction in consumption; however, no differences in consumption between the two interventions were found (Wallace et al., 2011). Wallace et al. speculated about the reasons for this. The study was a pragmatic trial conducted with people seeking treatment in the general population. This group may have naturally reduced their drinking over time (regression to the mean) or made use of other sources of help (off- or online); or the trial procedures themselves, which had required people to monitor and answer questions about their drinking, may have had an impact on drinking directly, which would have affected the intervention and control group equally. This reactivity of assessment has been considered more widely in online alcohol research and shown to be an active ingredient mediating change (McCambridge, Butor-Bhavsar, Witton, & Elbourne, 2011).

The pragmatic “real-world” nature of the Wallace et al. study is an important consideration. In practice digital interventions may not be offered as stand-alone services but as part of routine screening, brief intervention, and referral- to-treatment services. There is a strong evidence base for the effectiveness of this in a range of traditional face-to-face settings (see Babor et al., 2017, for a review). Haskins et al. (2017) found that although a computerized version of a screening, brief intervention, and referral to treatment services approach increased referrals in an emergency department setting, it did not lead to a reduction in risky alcohol use. Further studies of digital brief interventions in routine settings are required.

Systematic Reviews

As with standard face to face brief interventions systematic reviews of outcome studies of digital interventions tend to report small, but statistically significant, benefits. This has been shown in mixed adult populations and in students (Bewick et al., 2008; Khadjesari, Murray, Hewitt, Hartley, & Godfrey, 2011; Riper et al., 2011; White et al., 2010).

Evidence from Other Studies

Not all digital alcohol interventions are the same, and they vary in the extent to which they follow the FRAMES or other evidence-based approaches and methods of deployment. This heterogeneity may render standard meta-analysis inappropriate. By examining the differences between interventions it may be possible to learn something about the active components of the interventions; but only if it is possible to adequately describe the interventions themselves.

Behavior-Change Techniques

Garnett et al. (2016) reviewed 40 trials and coded them according to the extent to which they reported having derived the intervention from a known behavior-change theory or method. They concluded that many digital interventions are inadequately described and that this needs to be addressed before it is possible to infer whether particular behavior-change techniques have an influence on outcomes. This heterogeneity was also examined in a review of 14 previous systematic reviews of outcomes of digital interventions by Sundstrom, Blankers, and Khadjesari (2016). The authors concluded that longer, multisession interventions are likely to be more effective that shorter ones. However, Cunningham et al. (2017) reported a different outcome because they found no added benefit of an extended Internet intervention over a brief one. Further research will be needed before any clear conclusions can be drawn. As with Garnett et al.’s review, Sundstrom’s meta-analysis concluded that there was insufficient evidence to state whether any specific theoretical modalities were associated with greater effectiveness.

Modes of Delivery

There have been attempts to maximize benefits of brief interventions by integrating digital technologies with other modes of delivering screening and brief interventions and identification and brief advice interventions. The SIPS trial, for example, used a hand-held device to screen potential users but did not provide a digital intervention. A novel approach has been to integrate a digital intervention within a primary care medical setting. This was informed by the recognition of the potential impact of the working alliance with a practitioner on mediating the effectiveness of a face-to-face intervention in alcohol treatment (e.g., Cook, Heather, & McCambridge, 2015; Prince et al., 2016). This is missing in digital versions and is being addressed by a group of researchers and General Practitioners in Italy. They have developed a facilitated version of a digital intervention, called “Download Your Doctor,” that includes a discussion with the doctor as part of the protocol, wherein the physician gives the patient information about the site and a log-in code. Doctors can customize the messages and visual elements presented to their patients and also upload a photograph of themselves and their signature for integration with their messages on the website. The appearance of each message can thus be customized by inserting links and images shown in a “speech bubble” next to text and alongside a photo of the doctor (Lygidakis et al., 2016). In the study nearly three quarters of the physicians chose to customize the contents of the interactions with their patients, and many of the patients (70%) recalled having noticed the personalized elements when they used the site.

Summary

There is evidence that in well-conducted RCTs, those receiving digital interventions reduce their alcohol consumption more than those in the comparator arms of a trial. The effect sizes are small, and there is no satisfactory evidence of similar findings in messier real-world situations. There is evidence of efficacy but not yet evidence of effectiveness. On the basis of the existing research, it is generally assumed by public bodies that when digital interventions are delivered at scale, there will be net benefits for the populations they are offered to. Furthermore, even if the outcomes do not show great reduction in consumption or alcohol-related harms, neither have any adverse effects have been associated with the interventions. For these reasons Internet-based interventions are considered safe to make widely available as some individuals may derive benefit from an intervention which they would not otherwise have received.

What Is Not Known About Digital Interventions for Alcohol-Related Problems?

Comparative Effectiveness

Most trials have compared digital interventions with comparators that were considered unlikely to have a direct impact on consumption or alcohol-related harms. In the real world of alcohol treatment, however, it is important to understand the comparative effectiveness of digital interventions and face-to-face versions. There are ongoing studies designed to address this. Struzzo et al. (2013) are comparing Download your Doctor with the standard brief interventions provided by general practitioners in a group of practices in Italy, and Lopez-Pelayo et al. (2014), who are conducting a similar study in northern Spain, have extended it to include nurses and practice staff, as well as doctors). These are both randomized controlled noninferiority trials comparing facilitated access to a dedicated website with standard face-to-face brief interventions. Patients will be given a leaflet inviting them to log on to a website to complete an alcohol-screening questionnaire, and follow-up will be at 1 month, 3 months and 1 year. Another study, the Digital Alcohol Management on Demand (DIAMOND) trial, which in 2017 was at the stage of feasibility testing, is based in community alcohol services in three north London boroughs (Hamilton et al., 2015). It will compare the acceptability, effectiveness, and cost-effectiveness of a psychologically informed, Web-based alcohol-treatment program, called Healthy Living for People who use Alcohol (HeLP-Alcohol), with usual (face-to-face) treatment delivered by specialist alcohol counselors.

The results of these studies are not yet available; however, some interesting observations have emerged about these approaches. Murray et al. (2012) had previously demonstrated that it was practical and acceptable to provide a facilitated Web-based service in a primary care setting in a south London borough. They also found that of the 31 patients who were referred within the 12-month study period, 10 could not be contacted or did not respond to the offer to make an appointment, and 2 declined the offer of treatment. Only 6 of the 19 patients who agreed to take part logged on at least once after the initial appointment. However, those who did log on appeared to use the intervention as intended, judging by their regular use of the website. The mean number of log-ins was 8, and the mean number of pages visited per session was 11.

How to Engage Those Who Might Most Benefit

Little information is available on the best way to recruit the people who are in most need of an intervention. One possible method is to ask staff at a health service to screen people and make referrals. In a large multicenter study in five different and diverse European countries, practitioners were offered the opportunity to refer patients to an online brief alcohol intervention as an alternative to a face-to-face brief intervention (Bendtsen et al., 2016). The results indicated a low level of engagement with this new technology. Staff referred a larger proportion of patients for face-to-face advice than to Web-based treatment, and low levels of engagement were seen among the patients who were referred to the website. Overall, only 18% of those referred logged on to the website, with a mean log-on rate across the different countries of between 0.58% and 36.95% (Bendtsen et al., 2016).

Another route to the target population could be to make a brief intervention available as part of a wider health check that takes place outside a healthcare environment. Khadjesari, Freemantle, Linke, Hunter, and Murray (2014) recruited 3,375 employees of a large U.K.-based private-sector organization to an online health check that was promoted as part of a general health campaign organized and supported by the company’s senior managers. The health check included an alcohol-screening tool, along with other questions and quizzes about health and well-being. Individuals who took the check and then screened positive for hazardous drinking were offered the opportunity to make use of a digital intervention. Only 3% did so. In discussing these results, the authors suggested that the volunteers who made use of the online health check were already a health-conscious group who may not have been experiencing any negative consequences from their drinking and so were not motivated to engage with the online alcohol treatment.

Cost-Effectiveness

Without evidence of real-world effectiveness or benefits, it is not possible to make any confident statements about cost-effectiveness. Similarly, a good understanding of all the costs is required, including the iterative costs associated with development, and the benefits, which include an understanding of the degree of success in reaching the people the intervention was intended for that was achieved. McNamee et al. (2016) discuss this in some detail and point out that digital interventions are invariably complex interventions operating in complex systems and require, therefore, the development of complex models to evaluate cost-effectiveness.

Understanding How to Optimize Digital Behavior Change Interventions

There have been no systematic investigations directed at determining the active components of Internet-based interventions for alcohol problems. We have already seen that there is a considerable range in the content, length, and functionality of the interventions and that they are generally poorly described in the research literature (Garnett et al., 2016). The implicit assumption has been that the models developed for face-to-face brief interventions and those that underpin psychological treatments will successfully transfer to an online environment. However, this has not been explicitly tested and, as with face-to-face brief interventions, the fidelity of the online versions cannot be assumed. The traditional RCT may not be the best tool for this kind of research. Taking apart the “black box” may be more suited to a factorial experimental design, where different elements can be tested against each other without sacrificing statistical power (Collins, Murphy, & Strecher, 2007).

The hybrid approach to intervention development will also require a range of approaches to evaluation. The RCT model that is tried and tested in a medical research setting may not always be suited to answering the questions posed by interventions in an online environment. Psychologists and other behavioral scientists are familiar with the single case studies and research designs (Kazdin, 2011). Murray et al. (2016) point out that engineering and computer scientists typically employ multiple cycles of development and would not attempt to formally evaluate a product with an expectation of reasonable benefits for the target group until it was relatively stable. Bringing these approaches together requires a comprehensive interdisciplinary approach and will help ensure that expensive RCTs are nor undertaken too early in the development and evaluation of a digital intervention.

Future Directions

Knowledge Transfer from Related Areas

A transfer of learning from parallel fields, such as smoking cessation, may be helpful and accelerate the development process. For example, it has become relatively easy and inexpensive to develop behavior-change apps for smart phones, and these are regularly used as part of health-behavior-change programs, such as those promoting exercise and smoking cessation. Research into how to make these most effective is underway, and a recent review of smoking-cessation apps has shown that, unlike with alcohol interventions, specific behavior-change techniques and effective ease-of-use and engagement factors can be readily identified (Ubhi et al., 2016). Many of the challenges of poor engagement and participation in a program (“stickiness”) have been encountered before, but there is no evidence, as yet, to inform developers of the theoretical or empirical basis for such a transfer. And while there are similarities, there are also differences between the fields. For example, the smoking-cessation message to stop smoking is qualitatively different and may be easier to convey than the complex message in alcohol interventions that it is possible to drink safely within recommended limits. Similarly, there have been curbs on the tobacco advertising but less so with alcohol advertising, and these messages may have an important influence on the context in which Internet interventions for alcohol harm reduction are presented to potential users.

Emerging Technologies

Potential users of Internet interventions will also be likely to be engaged in multiple other online activities in an increasingly complex virtual space of apps, digital information, wearable devices, and social media. A topic for future investigation is how to best integrate these interventions into people’s daily lives so that they are utilized and effective. Similarly, behavior-change applications are also part of public-health strategy, which increasingly promotes digital technologies and developments in healthcare that will enable people to have access to their personal health information via the Internet and to monitor their health status using smartphone applications and connected wearable devices. These future developments will likely benefit from collaboration between behavior scientists, topic experts, and app developers.

Engagement

A key focus for the future development of digital interventions will be the topic of maximizing meaningful engagement with the program as provided by the developers. Research thus far has mainly focused on improving participation in a research trial instead of with the intervention itself. Yardley et al. (2016) emphasize that simply increasing levels of use is not the issue. More important is to develop means of assuring that users engage with a program in such a way so as to facilitate health-behavior change, and they use the phrase “effective engagement” to convey this. They recommend that different approaches are used to investigate—ranging from self-report to the use of technology to observe and record users’ actual behaviors when they are using an intervention. Yardley et al. make suggestions about different aspects of intervention design that might support effective engagement and also pay attention to the role of human facilitation and support alongside the digital intervention. This is similar to the facilitation described by Struzzo et al. (2013) and also the recent introduction of blended interventions in the psychological treatment of anxiety disorders, in which a trained therapist incorporates digital technology into treatment.

Conclusions

Digital interventions for alcohol-use disorders are available and well used. However, they do not suit everyone, and additional work is required to understand how to best meet the needs of the populations that might benefit from such interventions and to identify the settings in which they may be most effective and achieve the greatest reach. Future research should focus on identifying the active ingredients of interventions and methods of deployment to maximize their impact. This may include integrating them into existing care pathways and services.

References

Babor, T. F., Del Boca, F., & Bray, J. W. (2017). Screening, brief intervention and referral to treatment: Implications of SAMHSA’s SBIRT Initiative for Substance Abuse Policy and Practice. Addiction, 112(Suppl. 2), 110–117.Find this resource:

Babor, T. F., & Higgins-Biddle, J. C. (2001). Brief intervention for hazardous and harmful drinking: A manual for use in primary care. World Health Organization. Retrieved from http://whqlibdoc.who.int/hq/2001/WHO_MSD_MSB_01.6b.pdf.Find this resource:

Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). AUDIT: The Alcohol Use Disorders Identification Test guidelines for use in primary care.Find this resource:

Bendtsen, P., Mussener, U., Karlsson, N., Lopez-Pelayo, H., Palacio-Vieira, J., Colom, J., . . . Anderson, P. (2016). Implementing referral to an electronic alcohol brief advice website in primary healthcare: Results from the ODHIN implementation trial. BMJ Open, 6(6), e010271.Find this resource:

Bewick, B. M., Trusler, K., Barkham, M., Hill, A. J., Cahill, J., & Mulhern, B. (2008). The effectiveness of Web-based interventions designed to decrease alcohol consumption: A systematic review. Preventive Medicine, 47, 17–26.Find this resource:

Bien, T. H., Miller, W. R., & Tonigan, J. S. (1993). Brief interventions for alcohol problems: A review. Addiction, 88, 315–335.Find this resource:

Bray, J. W., Zarkin, G. A., Hinde, J. M., & Mills, M. J. (2012). Costs of alcohol screening and brief intervention in medical settings: A review of the literature. Journal of Studies on Alcohol and Drugs, 73, 911–919.Find this resource:

Cabinet Office Strategy. (2004). Alcohol harm reduction strategy for England. Strategy Unit, Admiralty Arch, The Mall, London SW1A 2WH.Find this resource:

Chapman, C., Slade, T., Hunt, C., & Teesson, M. (2015). Delay to first treatment contact for alcohol use disorder. Drug and Alcohol Dependence, 147, 116–121.Find this resource:

Chief Medical Officer. (2015). UK chief medical officers’ alcohol guidelines review: Summary of the proposed new guidelines. London: Department of Health.Find this resource:

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medecine, 32(Suppl. 5), S112–S118.Find this resource:

Cook, S., Heather, N., & McCambridge, J. (2015). The role of the working alliance in treatment for alcohol problems. Psychology of Addictive Behaviors, 29, 371–381.Find this resource:

Cunningham, J. A., Shorter, G. W., Murphy, M., Kushnir, V., Rehm, J., & Hendershot, C. S. (2017). Randomized controlled trial of a brief versus extended Internet intervention for problem drinkers. International Journal of Behavioral Medicine, 24(5), 760–767.Find this resource:

Cunningham, J. A., Wild, T. C., Cordingley, J., van Mierlo, T., & Humphreys, K. (2009). A randomized controlled trial of an Internet-based intervention for alcohol abusers. Addiction, 104, 2023–2032.Find this resource:

National Treatment Agency for Substance Misuse. (2006). Models of care for alcohol misusers (MoCAM). London: Department of Health.Find this resource:

Drummond, C., Deluca, P., Coulton, S., Bland, M., Cassidy, P., Crawford, M., . . . Kaner, E. (2014). The effectiveness of alcohol screening and brief intervention in emergency departments: A multicentre pragmatic cluster randomized controlled trial. PLoS One, 9(6), e99463.Find this resource:

Edwards, G., Orford, J., Egert, S., Guthrie, S., Hawker, A., Hensman, C., et al. (1977). Alcoholism: A controlled trial of treatment and advice. Journal of Studies on Alcohol, 38, 1004–1031.Find this resource:

Fleischmann, A., Fuhr, D., Poznyak, V., & Rekve, D. (2011). Global Status Report on Alcohol and Health. Geneva, Switzerland: World Health Organization.Find this resource:

Garnett, C., Brown, J., Crane, D., Kaner, E., Beyer, F., Muirhead, C., . . . Michie, S. (2016). Theory content of digital interventions for reducing alcohol consumption: A systematic review. Paper presented at the 2nd Behavior Change Conference: Digital Health and Wellbeing, London.Find this resource:

Grant, B. F., Goldstein, R. B., Saha, T. D., Chou, S. P., Jung, J., Zhang, H., . . . Hasin, D. S. (2015). Epidemiology of DSM-5 alcohol use disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry, 72, 757–766.Find this resource:

Hamilton, F. L., Hornby, J., Sheringham, J., Kerry, S., Linke, S., Solmi, F., . . . Murray, E. (2015). DIgital Alcohol Management ON Demand (DIAMOND) feasibility randomised controlled trial of a Web-based intervention to reduce alcohol consumption in people with hazardous and harmful use versus a face-to-face intervention: Protocol. Pilot and Feasibility Studies, 1(1), 1.Find this resource:

Haskins, B.L, Davis-Martin, R., Abar, B., Baumann, B. M., Harralson, T., & Boudreaux, E. D. (2017). Health evaluation and referral assistant: A randomized controlled trial of a Web-based screening, brief intervention, and referral to treatment system to reduce alcohol use among emergency department patients. Journal of Medical Internet Research, 19(5), e119.Find this resource:

Health and Social Care Information Centre. (2016). Statistics on Alcohol.

Heather, N. (2014). The efficacy-effectiveness distinction in trials of alcohol brief intervention. Addiction Science and Clinical Practice, 9, 13.Find this resource:

Hester, R. K., & Delaney, H. D. (1997). Behavioral self-control program for Windows: Results of a controlled clinical trial. Journal of Consulting and Clinical Psychology, 65, 686–693.Find this resource:

Hodgson, R., Alwyn, T., John, B., Thom, B., & Smith, A. (2002). The FAST Alcohol Screening Test. Alcohol and Alcoholism, 37, 61–66.Find this resource:

International Telecommunication Union. (2016). ICT Facts and Figures: The World in 2015.

Kaner, E., Beyer, F., Dickinson, H. O., Pienaar, E., Campbell, F., Schlesinger, C., . . . Burnand, B. (2007). Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database of Systematic Reviews, 2.Find this resource:

Kaner, E., Bland, M., Cassidy, P., Coulton, S., Dale, V., Deluca, P., . . . Drummond, C. (2013). Effectiveness of screening and brief alcohol intervention in primary care (SIPS trial): Pragmatic cluster randomised controlled trial. BMJ, 346, e8501.Find this resource:

Kazdin, A. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings. 2d ed. New York: Oxford University Press.Find this resource:

Khadjesari, Z., Freemantle, N., Linke, S., Hunter, R., & Murray, E. (2014). Health on the Web: Randomised controlled trial of online screening and brief alcohol intervention delivered in a workplace setting. PLoS One, 9(11), e112553.Find this resource:

Khadjesari, Z., Murray, E., Hewitt, C., Hartley, S., & Godfrey, C. (2011). Can stand-alone computer-based interventions reduce alcohol consumption? A systematic review. Addiction, 106, 267–282.Find this resource:

Khadjesari, Z., Newbury-Birch, D., Murray, E., Shenker, D., Marston, L., & Kaner, E. (2015). Online health check for reducing alcohol intake among employees: A feasibility study in six workplaces across England. PLoS One, 10(3), e0121174.Find this resource:

Khadjesari, Z., Stevenson, F., Godfrey, C., & Murray, E. (2015). Negotiating the “grey area between normal social drinking and being a smelly tramp”: A qualitative study of people searching for help online to reduce their drinking. Health Expectations, 18(6), 2011–2020.Find this resource:

Kypri, K., Hallett, J., Howat, P., McManus, A., Maycock, B., Bowe, S., & Horton, N. J. (2009). Randomized controlled trial of proactive Web-based alcohol screening and brief intervention for university students. Archives of Internal Medicine, 169, 1508–1514.Find this resource:

Kypri, K., Langley, J. D., Saunders, J. B., Cashell-Smith, M. L., & Herbison, P. (2008). Randomized controlled trial of Web-based alcohol screening and brief intervention in primary care. Archives of Internal Medicine, 168, 530–536.Find this resource:

Linke, S., Brown, A., & Wallace, P. (2004). Down Your Drink: A Web-based intervention for people with excessive alcohol consumption. Alcohol and Alcoholism, 39, 29–32.Find this resource:

Linke, S., Harrison, R., & Wallace, P. (2005). A Web-based intervention used in general practice for people with excessive alcohol consumption. Journal of Telemedicine and Telecare, 11(Suppl. 1), 39–41.Find this resource:

Linke, S., McCambridge, J., Khadjesari, Z., Wallace, P., & Murray, E. (2008). Development of a psychologically enhanced interactive online intervention for hazardous drinking. Alcohol and Alcoholism, 43, 669–674.Find this resource:

Linke, S., Murray, E., Butler, C., & Wallace, P. (2007). Internet-based interactive health intervention for the promotion of sensible drinking: Patterns of use and potential impact on members of the general public. Journal of Medical Internet Reearch, 9(2), e10.Find this resource:

Lopez-Pelayo, H., Wallace, P., Segura, L., Miquel, L., Diaz, E., Teixido, L., . . . Gual, A. (2014). A randomised controlled non-inferiority trial of primary care-based facilitated access to an alcohol reduction website (EFAR Spain): The study protocol. BMJ Open, 4(12), e007130.Find this resource:

Lygidakis, C., Wallace, P., Tersar, C., Marcatto, F., Ferrante, D., Della Vedova, R., . . . Struzzo, P. (2016). Download Your Doctor: Implementation of a digitally mediated personal physician presence to enhance patient engagement with a health-promoting Internet application. JMIR Research Protocols, 5(1), e36.Find this resource:

Marlatt, G., & Gordon, J. (1985). Relapse prevention: A self-control strategy for the maintenance of behavior change. New York: Guilford.Find this resource:

McCambridge, J., Butor-Bhavsar, K., Witton, J., & Elbourne, D. (2011). Can research assessments themselves cause bias in behavior change trials? A systematic review of evidence from Solomon 4-group studies. PLoS One, 6(10), e25223.Find this resource:

McCambridge, J., O’Donnell, O., Godfrey, C., Khadjesari, Z., Linke, S., Murray, E., & Wallace, P. (2010). How big is the elephant in the room? Estimated and actual IT costs in an online behavior change trial. BMC Research Notes, 3, 172.Find this resource:

McNamee, P., Murray, E., Kelly, M. P., Bojke, L., Chilcott, J., Fischer, A., . . . Yardley, L. (2016). Designing and undertaking a health economics study of digital health interventions. American Journal of Preventive Medicine, 51, 852–860.Find this resource:

Michie, S., van Stralen, M. M., & West, R. (2011). The behavior change wheel: A new method for characterising and designing behavior change interventions. Implementation Science, 6, 42.Find this resource:

Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change (Vol. 2). New York: Guilford.Find this resource:

Murray, E., Hekler, E. B., Andersson, G., Collins, L. M., Doherty, A., Hollis, C., . . . Wyatt, J. C. (2016). Evaluating digital health interventions: Key questions and approaches. American Journal of Preventive Medicine, 51, 843–851.Find this resource:

Murray, E., Khadjesari, Z., White, I. R., Kalaitzaki, E., Godfrey, C., McCambridge, J., . . . Wallace, P. (2009). Methodological challenges in online trials. Journal of Medical Internet Research, 11(2), e9.Find this resource:

Murray, E., Linke, S., Harwood, E., Conroy, S., Stevenson, F., & Godfrey, C. (2012). Widening access to treatment for alcohol misuse: Description and formative evaluation of an innovative Web-based service in one primary care trust. Alcohol and Alcoholism, 47, 697–701.Find this resource:

National Institute for Health and Care Excellence. (2010, June). Alcohol-use disorders: Preventing harmful drinking. Retrieved from www.nice.org.uk/guidance/PH24.

Newbury-Birch, D., Coulton, S., Bland, M., Cassidy, P., Dale, V., Deluca, P., . . . Drummond, C. (2014). Alcohol screening and brief interventions for offenders in the probation setting (SIPS Trial): A pragmatic multicentre cluster randomized controlled trial. Alcohol and Alcoholism, 49, 540–548.Find this resource:

Nowinski, J., Baker, S., & Carroll, K. M. (1992). Twelve-step facilitation therapy manual: A clinical research guide for therapists treating individuals with alcohol abuse and dependence (Vol. 1). [Project MATCH Monograph Series]. National Institute on Alcohol Abuse and Alcoholism, Rockville, MD.Find this resource:

Office for National Statistics. (2016). Adult drinking habits in Great Britain: 2014. Retrieved from https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/drugusealcoholandsmoking/bulletins/opinionsandlifestylesurveyadultdrinkinghabitsingreatbritain/2014.

Pemberton, M. R., Williams, J., Herman-Stahl, M., Calvin, S. L., Bradshaw, M. R., Bray, R. M., . . . Mitchell, G. M. (2011). Evaluation of two Web-based alcohol interventions in the U.S. military. Journal of Studies on Alcohol and Drugs, 72, 480–489.Find this resource:

Prince, M. A., Connors, G. J., Maisto, S. A., & Dearing, R.L. (2016). Within treatment therapeutic alliance ratings profiles predict posttreatment frequency of alcohol use. Psychology of Addictive Behaviors, 30(2), 184–193.Find this resource:

Prochaska, J. O., & DiClemente, C. C. (1994). The transtheoretical approach: Crossing traditional boundaries of therapy. Malabar, FL: Krieger.Find this resource:

Project MATCH Research Group. (1998). Matching alcoholism treatments to client heterogeneity: Project MATCH three-year drinking outcomes. Alcoholism, Clinical and Experimental Research, 22, 1300–1311.Find this resource:

Riper, H., Kramer, J., Keuken, M., Smit, F., Schippers, G., & Cuijpers, P. (2008). Predicting successful treatment outcome of Web-based self-help for problem drinkers: Secondary analysis from a randomized controlled trial. Journal of Medical Internet Research, 10(4), e46.Find this resource:

Riper, H., Kramer, J., Smit, F., Conijn, B., Schippers, G., & Cuijpers, P. (2008). Web-based self-help for problem drinkers: A pragmatic randomized trial. Addiction, 103, 218–227.Find this resource:

Riper, H., Spek, V., Boon, B., Conijn, B., Kramer, J., Martin-Abello, K., & Smit, F. (2011). Effectiveness of e-self-help interventions for curbing adult problem drinking: A meta-analysis. Journal of Medical Internet Research, 13(2), e42.Find this resource:

Schinke, S. P., Schwinn, T. M., Di Noia, J., & Cole, K. C. (2004). Reducing the risks of alcohol use among urban youth: Three-year effects of a computer-based intervention with and without parent involvement. Journal of Studies on Alcohol, 65, 443–449.Find this resource:

Slade, T., Chapman, C., Swift, W., Keyes, K., Tonks, Z., & Teesson, M. (2016). Birth cohort trends in the global epidemiology of alcohol use and alcohol-related harms in men and women: Systematic review and metaregression. BMJ Open, 6(10), e011827.Find this resource:

Sobell, L. C., & Sobell, M. B. (1992). Timeline follow-back. In R. Z. Litten & J. P. Allen (Eds.), Measuring alcohol consumption: Psychosocial and biochemical methods (pp. 41–72). Totowa, NJ: Humana.Find this resource:

Struzzo, P., Scafato, E., McGregor, R., Della Vedova, R., Verbano, L., Lygidakis, C., . . . Wallace, P. (2013). A randomised controlled non-inferiority trial of primary care-based facilitated access to an alcohol reduction website (EFAR-FVG): The study protocol. BMJ Open, 3(2).Find this resource:

Substance Abuse and Mental Health Services Administration. (2014). Results from the 2013 National Survey on Drug Use and Health: Summary of national findings. NSDUH Series H-48, HHS Publication No. (SMA), 14-4863, Rockville, MD.Find this resource:

Sundstrom, C., Blankers, M., & Khadjesari, Z. (2016). Computer-based interventions for problematic alcohol use: A review of systematic reviews. International Journal of Behavioral Medicine, 24, 646.Find this resource:

Telecommunication Development Bureau (2017), “ICT Facts and Figures 2005, 2010, 2016” International Telecommunication Union (ITU).

Ubhi, H. K., Michie, S., Kotz, D., van Schayck, O. C., Selladurai, A., & West, R. (2016). Characterising smoking cessation smartphone applications in terms of behavior change techniques, engagement and ease-of-use features. Translational Behavioral Medicine, 6, 410–417.Find this resource:

UKATT Research Team. (2005). Effectiveness of treatment for alcohol problems: Findings of the randomised UK alcohol treatment trial (UKATT). BMJ, 331.Find this resource:

Wallace, P., Murray, E., McCambridge, J., Khadjesari, Z., White, I. R., Thompson, S. G., . . . Linke, S. (2011). On-line randomized controlled trial of an Internet-based psychologically enhanced intervention for people with hazardous alcohol consumption. PLoS One, 6, e14740.Find this resource:

Waller, G. (2009). Evidence-based treatment and therapist drift. Behaviour Research and Therapy, 47(2), 119–127.Find this resource:

West, R., & Brown, J. (2013). Theory of addiction: John Wiley & Sons.Find this resource:

White, A., Kavanagh, D., Stallman, H., Klein, B., Kay-Lambkin, F., Proudfoot, J., . . . Young, R. (2010). Online alcohol interventions: A systematic review. Journal of Medical Internet Research, 12(5), e62.Find this resource:

World Health Organization. (2014). Global status report on alcohol and health. Geneva, Switzerland: World Health Organization.Find this resource:

Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D., Curtis, K., . . . Blandford, A. (2016). Understanding and promoting engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51(5), 833–842.Find this resource: