- Research article
- Open Access
Use of theory in computer-based interventions to reduce alcohol use among adolescents and young adults: a systematic review
BMC Public Health volume 16, Article number: 517 (2016)
Alcohol use and binge drinking among adolescents and young adults remain frequent causes of preventable injuries, disease, and death, and there has been growing attention to computer-based modes of intervention delivery to prevent/reduce alcohol use. Research suggests that health interventions grounded in established theory are more effective than those with no theoretical basis. The goal of this study was to conduct a literature review of computer-based interventions (CBIs) designed to address alcohol use among adolescents and young adults (aged 12–21 years) and examine the extent to which CBIs use theories of behavior change in their development and evaluations. This study also provides an update on extant CBIs addressing alcohol use among youth and their effectiveness.
Between November and December of 2014, a literature review of CBIs aimed at preventing or reducing alcohol in PsychINFO, PubMed, and Google Scholar was conducted. The use of theory in each CBI was examined using a modified version of the classification system developed by Painter et al. (Ann Behav Med 35:358–362, 2008).
The search yielded 600 unique articles, 500 were excluded because they did not meet the inclusion criteria. The 100 remaining articles were retained for analyses. Many articles were written about a single intervention; thus, the search revealed a total of 42 unique CBIs. In examining the use of theory, 22 CBIs (52 %) explicitly named one or more theoretical frameworks. Primary theories mentioned were social cognitive theory, transtheoretical model, theory of planned behavior and reasoned action, and health belief model. Less than half (48 %), did not use theory, but mentioned either use of a theoretical construct (such as self-efficacy) or an intervention technique (e.g., manipulating social norms). Only a few articles provided detailed information about how the theory was applied to the CBI; the vast majority included little to no information.
Given the importance of theory in guiding interventions, greater emphasis on the selection and application of theory is needed. The classification system used in this review offers a guiding framework for reporting how theory based principles can be applied to computer based interventions.
Alcohol use and binge drinking in youth aged 12 to 21 are frequent causes of accidents and injuries, preventable death, disease and psychosocial problems . Though past-month binge drinkingFootnote 1 and alcohol use among adolescents and young adults in the United States have declined over the past decade, rates remain high: 23 % report current alcohol use and 14 % binge drinking . Over the past several decades, there have been extensive efforts to address alcohol use among young people. Some interventions have focused on environmental factors (to address youth access)  while others have been individual or group level interventions aimed at improving knowledge and attitudes, and reducing alcohol use . These have been primarily face-to-face interventions delivered in structured school or community-based settings.
The application of theory is widely recognized as a crucial component of behavior change interventions. Theories help explain the pathways that lead to or predict behavior and in doing so, provide guidance on how to influence or change behavior. Interventions, that clearly articulate their use of theories, can contribute to a greater understanding of not just what interventions work, but why they work. While the interventions targeting alcohol use among youth have resulted in mixed findings, this vast body of work has contributed to the evidence base for what constitutes effective interventions [5, 6]. Interventions that are grounded in established theories of behavior change, and include approaches that address social norms, build self-efficacy and enhance skills to resist pressure to use alcohol, have been found to be more effective than those lacking a theoretical framework .
As the field of preventing/reducing alcohol use among adolescents and young adults is evolving, there has been growing attention to the development and use of computer-based modes of intervention delivery . Computer-based interventions (CBIs) have a number of advantages over traditional face-to-face interventions. They are more likely to be implemented with fidelity because they do not rely on the skills, motivation, or time of the facilitator; and they provide a standardized approach to delivering the intervention content . In addition, recent technology innovations enable CBIs to be interactive, provide individually tailored messages and simulate experiences where adolescents can learn and practice skills in convenient and private settings [10, 11]. CBIs also have the potential to be more cost effective than face-to-face interventions . Additionally, computers have become widely accessible and are especially popular among adolescents and young adults .
CBIs provide a promising approach to addressing alcohol use among adolescents and young adults. Over the last decade, there have been five literature reviews that have examined the nascent field of digital interventions for alcohol use prevention targeting adolescents and young adults [14–18]. Overall, many of the CBIs have been shown to improve knowledge, attitudes, and reduce alcohol use in the short-term. Three of the five literature reviews examined interventions for college students [14–16]. One review found that CBIs were more effective than no treatment and assessment-only controls, and approximately equivalent to various non-computerized interventions . Another review found that CBIs, when compared to non-CBIs, were more likely to reduce alcohol use . The third review found that CBIs reduced short-term alcohol use compared to assessment-only controls, but not compared to face-to-face interventions .
In addition to the reviews focused on alcohol use among young adults, there were two reviews of CBIs targeting younger adolescents. One demonstrated that CBIs delivered in middle or secondary schools effectively reduced alcohol, cannabis and/or tobacco use . The other review was a metanalysis focused on computer games to prevent alcohol and drug use among adolescents and concluded that the games improved knowledge, but it did not find sufficient evidence that these games changed substance use attitudes or behaviors . While these reviews suggest that CBIs have the potential to be efficacious, the mechanisms that contribute to improvements in attitudes and behaviors are not well understood. Use of a theoretical framework helps to explain the mechanisms of change by informing the causal pathways between specific intervention components and behavioral outcomes. Understanding these mechanisms improves our understanding of how and why a particular intervention works.
There has been little attention as to how theoretical frameworks have informed the development of CBIs focused on alcohol use among adolescents and young adults. Only two of the five aforementioned literature reviews covering CBIs for alcohol use in youth examined the underlying theoretical basis of the CBIs [17, 18]. In both of these reviews, the names of the theory and/or specific theoretical constructs were mentioned; however, there was little examination of how the theories were applied to the CBIs. In addition to the reviews focused specifically on adolescent and young adult substance use, there was an additional systematic review that examined the relationship between the use of theory and the effect sizes of internet-based interventions. This study found that extensive use of theory was associated with greater increases in the effect size of behavioral outcomes . They also found that interventions that utilized multiple techniques to change behavior change tended to have larger effect sizes compared to those using fewer techniques. This review builds on prior work demonstrating that health interventions grounded in established theory are more effective than those with no theoretical basis [7, 21–25]. However, this review did not exclusively focus on alcohol use or adolescents specifically. It is therefore important to build upon this knowledge base and focus on the application of theory in CBIs to address adolescent/young adult alcohol use.
The primary goal of this study is to conduct a review of how theory is integrated into CBIs that target alcohol use among adolescents and young adults. Specifically, this study examines which CBIs are guided by a theoretical framework, the extent to which theory is applied in the CBIs and what if any measures associated with the theoretical framework are included in the CBI’s evaluation. A secondary goal is to provide an update of CBIs addressing alcohol use among youth in order to expand our understanding of their effectiveness.
The methods follow the guidelines developed and recommended by the PRISMA group.
PsychINFO and PubMed (electronic databases) were searched to identify peer-reviewed journal articles on computer-based interventions aimed at preventing or reducing alcohol. The search included previous reviews of CBIs. In addition, Google Scholar was searched to identify additional articles/abstracts that may have been published. The reference lists of all the identified articles were also reviewed. The search, which used both Medical Subject Headings [MeSH] and non-MeSH terms, used the search terms: “alcohol abuse prevention,” “alcohol,” “alcohol drinking/prevention and control,” “computer,” “internet,” “web,” “computer software,” “computer games,” and “intervention.” The search was conducted between November and December of 2014.
Inclusion and exclusion criteria
To be included in this review, the main component of the intervention was required to be delivered via computer, tablet or smartphone. Interventions could include a video game, computer program, or online module. In addition, the intervention needed to target alcohol use among adolescents and young adults between the ages of 12 to 21 years. While adolescence covers a wide range, we chose this age range because there is general consensus that it has begun by age 12, and we included youth up to age 21 since that is the legal drinking age in the U.S. Studies whose participants’ had a mean age between 12 and 21 years were included even when individual study’s participants’ ages extended outside this age range. Interventions intended to treat a substance use disorder were excluded. Non-English language articles, research protocols, and intervention studies that did not report outcomes were also excluded from analyses.
Data extraction and synthesis
Once eligible studies were identified, the characteristics of the intervention, the context of the intervention, the population targeted, intervention dosage, study author, year and outcomes were entered into a spreadsheet for analyses. Duplicate articles were deleted and journal articles which discussed the same intervention were grouped together. When there was more than one unique article for any given CBI, the CBI was counted only once. In some cases, a given CBI existed in several editions, was modified, or was applied to a different study population. These variations of the CBI were grouped together.
Painter et al.’s classification system was used to categorize the use of theory in each of the CBIs . Consistent with this system, first a CBI was examined to see if an established, broad theory was mentioned in any of the corresponding articles for a given CBI. If so, the CBI was classified as “mentioned”. Second, articles were reviewed to see if they provided any information about how the CBI used theory to inform the intervention. If any of the articles associated with a given CBI provided any information about the use of theory, the CBI was classified as “applied. For our third category, we used “measured” to classify CBIs if any associated article included at least one specific measure of a construct within the theoretical framework. This third category is a slight departure from Painter’s typology which classifies interventions as “tested” if over half of the constructs in the theory are measured in the evaluation of the intervention. We opted for “measured” because testing theories is a complex process and not a common practice of CBIs. We did not use Painter’s 4th category, “building or creating theory” because this was not applicable for any of these interventions.
For all articles reporting on effects of the intervention on alcohol use, attitudes, or knowledge on an included CBI, the effectiveness of the CBI on these outcomes was also examined.
Two senior health research scientists (a counseling/health psychologist and developmental psychologist), with advanced training in theories of behavior change, oversaw the classifications system and addressed questions about the application of a theory/theoretical constructs. The review was conducted by a trained research associate with a master’s degree in public health. A spread sheet was created that included each classification, a description of how the theory was applied, and a list of relevant constructs that were measured.
The search strategy yielded a total of 600 unique articles published between 1999 and 2014, including 15 articles identified through hand searches and reviews of previous literature reviews. Of these, 500 were excluded because they did not meet the study inclusion criteria. The final sample consisted of 100 articles of 42 unique CBIs. There were more articles than interventions because multiple articles were published on any one CBI intervention. See Fig. 1 for a more full explanation of the articles excluded and yielded during the search process. The list of the 42 interventions and corresponding articles associated with the intervention are provided in Tables 1 and 2. Of the interventions reviewed for this study, 50 % were not included in previous review articles. Of the 42 CBIs in this study, 33 were delivered in school settings and the remaining CBIs were administered in home or in clinic settings.
Summary of included studies
Tables 1 and 2 summarize the included studies. The interventions were largely studied exclusively in the United States (30/42). The remaining interventions were studied in Australia (n = 3), New Zealand (n = 3), the Netherlands (n = 2), the United Kingdom (n = 2), Sweden (n = 1) and in both the United States and Canada (n = 1). Study sample sizes ranged widely. Included studies had between 59 and 20,150 participants. The number of study participants was less than 200 in 26 % of included studies, from 200 to 1,000 in 53 % of studies, and over 1,000 in 21 % of studies. Nearly all interventions had at least one study that measured alcohol use as a primary outcome (n = 37). Other common primary outcomes included binge drinking (n = 17), perceived alcohol norms (n = 14), consequences of alcohol use (n = 14), alcohol-related attitudes (n = 8), and alcohol-related knowledge (n = 6).
Classification of CBIs
Table 1 provides a list of the CBIs (and corresponding articles) and how theory was used according to the classifications of “mentioned”, “applied” or “measured”. In addition, if the theory was applied to the intervention, a brief description of its application is provided. Similarly if it was classified as “measured” the measure of the theoretical construct was also listed. The CBIs in Table 1 all indicated use of a broad theoretical framework. Broad theories specify the relationship between a number of constructs and associated variables that explain or predict behaviors. Broad theories of behavior change take into account a number of complex contextual factors (e.g. social, cultural, economic, etc.) and inter-related sets of constructs that influence behaviors. CBIs that did not mention use of a broad theoretical framework are listed in Table 2. These CBIs typically mentioned use of a specific theoretical construct without reference to a broader theory, or intervention technique. In addition, sometimes a specific construct or intervention technique can be associated with more than one theory. For example, several of these CBIs mentioned that the goal of the intervention was to improve “self-efficacy”, a specific construct that is most often associated with Social Cognitive Theory , but is also incorporated within other theories such as the Theory of Reasoned Action . We applied the same classification system to these CBIs with regard to mention, application and measure for the construct and/or techniques. For each CBI listed in Tables 1 and 2, the use of the theory or construct/technique are classified as (1) mentioned, (2) applied, or (3) measured (using at least one of the theoretical constructs).
Theory mentioned in CBIs
Half of the CBIs (21) were affiliated with at least one article that explicitly mentioned use of a broad, overarching theoretical framework in the development of the CBI (see Table 1). Eleven of these mentioned drawing from more than one broad theoretical framework. The primary theories mentioned were Social Cognitive Theory  and its predecessor Social Learning Theory  (n = 10); the Theory of Planned Behavior  and the Reasoned Action  and the Health Belief Model (n = 5) ; Social Norms Theory (n = 4) ; and the Transtheoretical Model (sometimes referred to as Stages of Change Theory) (n = 3) .
The other half of the CBIs did not mention use of a broad/overarching theoretical framework; however, all but one of these mentioned use of a specific theoretical construct and/or evidence-based intervention technique (see Table 2). Of the 20 CBIs that mentioned a specific construct/technique, personalized normative feedback was mentioned in 18 CBIs, followed by motivational interviewing (mentioned in 5 CBIs), self-efficacy (mentioned twice) and manipulating subjective norms (once).
Application of theory in CBIs
As noted above, a CBI was classified as “applied” if any one of the associated articles provided some description of how the theory/construct was used in the CBI. Of the 21 CBIs that mentioned use of a broad theory, all provided at least some information about how the theory was applied to the intervention (see Table 1). However, the quality of the description explaining how the theory was applied varied considerably across the CBIs. Tables 1 provides a brief summary of how the articles, associated with each CBI, applied theory. There were a number of articles that provided a strong description of how the theory was applied to the intervention (e.g. Alcohol Edu [33–41], Michigan Prevention and Alcohol Safety for Students [42–44] and a mother-daughter intervention for black and Hispanic girls ). Another intervention, the Life Skills Training CD-ROM , was derived from an evidence-based comprehensive in-person curriculum with a strong basis in Social Learning/Cognitive Theory. The Life Skills Training CD-ROM, like the original face-to face curriculum, contains a number of modules that articulate the specific linkages between theory and intervention approaches. Other articles described how one or two aspects of the theory were applied to the CBI, but not the overall theoretical pathway that would inform behavior change (e.g. PAS [47, 48] and a emergency department-based laptop intervention [49, 50]) In contrast, the majority of articles lacked sufficient information to understand how theory informed the development of the intervention.
For the CBIs listed that did not mention use of a broad theory (those listed in Table 2), but mentioned using a specific construct or technique, all provided a description of how it was applied in the intervention (see Table 2); however the amount and quality of information provided about the application of the construct/techniques varied considerable across this group of CBIs.
Measurement of theoretical constructs
Of the 21 CBIs that mentioned use/application of theory (in Table 1), all but two included at least one measure of a construct associated with the theory. If a CBI mentioned use of a theory, it was more likely to include a measure of specific constructs associated with the theory compared to CBIs that did not mention use of a broad theory. Specifically, of the CBIs, that did not explicitly mention use of a theory, but did include a specific construct, only five included corresponding measures of the theoretical construct (see Table 2). Tables 1 and 2 lists the classification of each CBI and provides a list of the measure(s) associated with the theory, construct or intervention technique.
Effectiveness of CBIs
The effectiveness of the CBI was also examined. Tables 3 and 4 provides information about the 83 articles associated with an includedCBI that reported study outcomes: the setting, participants, a brief description of the intervention, comparators and the primary outcome measures that were used to evaluate the effectiveness of the CBI. The measures listed in Table 3 and 4 are primary outcome measures and, in many cases, are different from those listed in Tables 1 and 2 which lists the measures of theoretical constructs which were often secondary rather than primary outcomes. For the outcomes listed in Tables 3 and 4, an asterisk denotes statistical significance (at the level of p ≤ 0.05) indicating that the intervention showed more favorable results than the comparator (e.g., lower alcohol use or frequency of binge drinking, greater negative expectancies related to alcohol use, etc.) Of the 42 CBIs, all but one  demonstrated improvements in alcohol knowledge and/or attitudes. In addition to these knowledge or attitude outcomes, the majority (62 %) of the CBIs showed significant reductions in alcohol related behaviors. The proportion of CBIs reporting significant behavioral outcomes was greater among those that used a broad theoretical framework (71 %) compared to those that targeted a specific theoretical construct and/or intervention technique (51 %).
This study identified 100 unique articles covering 42 unique computer-based interventions (CBIs) aimed at preventing or reducing alcohol use among adolescents and young adults. Half of these CBIs have not been included in previous reviews. Thus, this review includes a total of 21 new CBIs and 43 new articles.
This review is the first to provide an in-depth examination of how CBI’s integrate theories of behavior change to address alcohol use among adolescents and young adults. While theories of behavior change are a critical component of effective interventions that have been developed and evaluated over the past several decades [51, 52], attention to the application of theory in CBIs has been limited. We utilized a simple classification system to examine if theories were mentioned, applied or measured in any of the publications that corresponded with the CBIs.
Only half of the CBIs reviewed mentioned use of an overarching, established theory of behavior change. The other half mentioned used of a single construct and/or intervention technique but did not state use of a broader theory. CBIs that were based on a broad theoretical framework were more likely to include measures of constructs associated with the theory than those that used a discrete construct or intervention technique. However, greater attention to what theory was used, articulating how theory informed the intervention and including measures of the theoretical constructs is critical to assess and understand the causal pathways between intervention components/mechanisms and behavioral outcomes (that would be predicted according to the theory). When mentioning the use of a theory or construct, almost all provided at least some description of how it informed the CBI; however, the amount and quality of information about how the theory was applied to the intervention varied considerably. Greater attention to what is inside the “black box” is critical in order to improve our understanding of not only what works, but why it works. While a few articles provided detailed information about the application of theory, the majority included limited information to examine the pathway between intervention approach and outcomes.
There are a number of reasons why there may be limited information on the use of theory in CBIs. Some researchers/intervention developers may not fully appreciate how theory can be used to inform intervention approaches. There is an emphasis on outcomes/effectiveness of interventions and less attention is placed on their development. In addition, to our knowledge, there are no publication guidelines/standards for describing the use of theoretical frameworks in intervention studies and the inclusion of this information is often up to individual authors and reviewers. Given the importance of theory in guiding interventions, greater emphasis on the selection and application of theory is needed in publications. The classification system used in this review (and originally developed by Painter , can serve as a simple framework for intervention developers, authors and journal reviewers so that there is greater consistency in the information provided on how theories are mentioned applied and measured in CBIs.
While there was considerable variation in how theory or constructs were applied to the CBIs, almost all (26) provided some form of personalized normative feedback and applied it relatively consistently across the CBIs. Personalized normative feedback is designed to correct misperceptions about the frequency and acceptability of alcohol use among peers. It typically involves an assessment of a youth’s perceptions of peer norms around alcohol attitudes and use followed by tailored information about actual norms . In addition, some interventions have recently incorporated personal feedback to address individual’s motivations to change through assessing and providing feedback on drinking motives  or in decisional balance exercises . The widespread use of personalized normative feedback in CBIs may be because it has been widely documented as an effective strategy and because it lends itself readily to an interactive, personalized computer-based intervention. Motivational interviewing was also used in several of the CBIs and is an effective face-to-face counseling technique . In contrast, this technique was applied to CBIs in a number of different ways, such as exercises designed to clarify goals and values, making both the description of how it was applied even more essential to examine differential effectiveness across various CBIs.
This study builds on the growing evidence supporting the use of CBIs as a promising intervention approach. We found most of the CBIs improved knowledge, attitudes and reduced alcohol use among adolescents and young adults. In addition, this study suggests CBIs that use overarching theories more frequently reported significant behavioral outcomes than those that use just one specific construct or intervention technique (in isolation from a broader theory). This finding is consistent with prior studies examining the use of theory in face-to-face interventions targeting alcohol use in adolescents . However, it is important to acknowledge the wide variation across the CBIs not only in their use of theory, but in scope, the targeted populations, duration/dosage, and measured outcomes. It is encouraging that even brief/targeted CBIs demonstrated some effectiveness and thus can play an important role in improving knowledge and attitudes, which are important contributors to changes in behavior.
There are limitations to this study. As discussed previously, many articles did not explicitly describe how theory was applied in the CBI. It is therefore possible that the theoretical pathways for the intervention were further developed than we have noted, and possibly included in other documents, such as logic models and/or funding applications; however, such information is not readily accessible and was outside the scope of this review. Thus, lack of mention of the name of a theory or construct or its application does not mean that the intervention did not integrate the theory in the intervention, only that the article did not provide information about its application. Thus, due to variations in the described use of theory along with the wide range of CBIs, it was not possible to draw comparisons about the relative effectiveness of CBIs according to the theory used. The ability to make such comparisons is further limited by the wide time frame in which CBIs were developed. This review spanned articles published between 1995 and 2014. During this period, CBIs to address health issues have been rapidly evolving due to major advancements in technological innovations (e.g., touch screen capabilities, mobile computing, improved graphics and user interfaces, and adaptive interface technologies features, etc.). These advancements coupled with greater interest and investments from federal agencies and philanthropic foundations. Over time one would expect these factors to further contribute to the effectiveness of CBIs.
This study points to the promise of CBIs for reducing alcohol use, as well as gaps in the use and application of theory in the development and testing of these interventions. This study provides a useful framework for articulating explanatory pathways leading to behavioral outcomes. Unlike traditional curriculum-based, face-to-face interventions, CBIs offer a great deal of flexibility with regards to when and where they can be delivered. Across the 42 CBIs in this study, some (33) were delivered in schools, but many were used at home or in a clinic setting. However, CBIs are often stand-alone interventions that have not been integrated into broader intervention delivery systems (e.g., schools or health care systems), potentially limiting the impact of the CBI. Future research should explore how CBIs can be integrated into broader intervention efforts that take place in schools, clinics, and other community-based settings, while ensuring the privacy and confidentiality of adolescents’ sensitive health .
Defined as consuming four or more alcoholic drinks per occasion for women and five or more for men .
Hingson R, White A. New research findings since the 2007 Surgeon General’s call to action to prevent and reduce underage drinking: A review. J Stud Alcohol Drugs. 2014;75(1):158–69.
Chen CM, Yi H-y, Faden VB. Surveillance Report# 101: Trends in Underage Drinking in the United States, 1991–2013. Rockville: NIAAA, Division of Epidemiology and Prevention Research, Alcohol Epidemiologic Data System; 2015.
Toomey TL, Lenk KM. A review of environmental-based community interventions. Alcohol Res Health. 2011;34(2):163.
Hennessy EA, Tanner-Smith EE. Effectiveness of brief school-based interventions for adolescents: A meta-analysis of alcohol use prevention programs. Prev Sci. 2015;16(3):463–74.
Spoth R, Greenberg M, Turrisi R. Preventive interventions addressing underage drinking: State of the evidence and steps toward public health impact. Pediatrics. 2008;121(Supplement 4):S311–36.
Tripodi SJ, Bender K, Litschge C, Vaughn MG. Interventions for reducing adolescent alcohol abuse: a meta-analytic review. Arch Pediatr Adolesc Med. 2010;164(1):85–91.
Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health. 2010;31:399–418.
Copeland J. Application of technology in the prevention and treatment of substance use disorders and related problems: opportunities and challenges. Subst Use Misuse. 2011;46(1):112–3.
Johnson M, Jackson R, Guillaume L, Meier P, Goyder E. Barriers and facilitators to implementing screening and brief intervention for alcohol misuse: a systematic review of qualitative evidence. J Public Health. 2010;33(3):412–21. doi:10.1093/pubmed/fdq095.
Khadjesari Z, Murray E, Hewitt C, Hartley S, Godfrey C. Can stand‐alone computer‐based interventions reduce alcohol consumption? A systematic review. Addiction. 2010;106(2):267–82.
Copeland J, Martin G. Web-based interventions for substance use disorders: A qualitative review. J Subst Abus Treat. 2004;26(2):109–16.
Rooke S, Thorsteinsson E, Karpin A, Copeland J, Allsop D. Computer‐delivered interventions for alcohol and tobacco use: a meta‐analysis. Addiction. 2010;105(8):1381–90.
Madden M, Lenhart A, Duggan M, Cortesi S, Gasser U. Teens and Technology 2013. Pew Internet & American Life Project. Retrieved December 2013.
Elliott JC, Carey KB, Bolles JR. Computer-based interventions for college drinking: a qualitative review. Addict Behav. 2008;33(8):994–1005.
Carey KB, Carey MP, Henson JM, Maisto SA, DeMartini KS. Brief alcohol interventions for mandated college students: Comparison of face‐to‐face counseling and computer‐delivered interventions. Addiction. 2011;106(3):528–37.
Carey KB, Scott‐Sheldon LA, Elliott JC, Bolles JR, Carey MP. Computer‐delivered interventions to reduce college student drinking: A meta‐analysis. Addiction. 2009;104(11):1807–19.
Champion KE, Newton NC, Barrett EL, Teesson M. A systematic review of school‐based alcohol and other drug prevention programs facilitated by computers or the Internet. Drug Alcohol Rev. 2013;32(2):115–23.
Rodriguez DM, Teesson M, Newton NC. A systematic review of computerised serious educational games about alcohol and other drugs for adolescents. Drug Alcohol Rev. 2014;33(2):129–35.
Carey KB, Scott-Sheldon LA, Elliott JC, Garey L, Carey MP. Face-to-face versus computer-delivered alcohol interventions for college drinkers: a meta-analytic review, 1998 to 2010. Clin Psychol Rev. 2012;32(8):690–703.
Webb T, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4.
Taylor N, Conner M, Lawton R. The impact of theory on the effectiveness of worksite physical activity interventions: a meta-analysis and meta-regression. Health Psychology Review. 2012;6(1):33–73.
Painter JE, Borba CP, Hynes M, Mays D, Glanz K. The use of theory in health behavior research from 2000 to 2005: a systematic review. Ann Behav Med. 2008;35(3):358–62.
Marteau T, Dieppe P, Foy R, Kinmonth A-L, Schneiderman N. Behavioural medicine: changing our behaviour: A growing body of evidence shows how to make behavioural interventions effective. BMJ. 2006;332(7539):437.
Michie S, Sheeran P, Rothman A. Advancing the science of behaviour change. Invited editorial. Psychol Health. 2007;22(3):249–53.
Rothman AJ. International journal of behavioral nutrition and physical activity. Int J Behav Nutr Phys Act. 2004;1:11.
Bandura A, McClelland DC. Social learning theory. 1977.
Fishbein M. A theory of reasoned action: some applications and implications. 1979.
Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998;13(4):623–49.
Ajzen I. From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.) Action control: From cognition to behavior. Berlin, Heidelber, New York: Springer-Verlag; 1985. pp. 11-39.
Janz NK, Becker MH. The health belief model: A decade later. Health Educ Behav. 1984;11(1):1–47.
Prentice DA, Miller DT. Pluralistic ignorance and alcohol use on campus: some consequences of misperceiving the social norm. J Pers Soc Psychol. 1993;64(2):243.
DiClemente CC, Prochaska JO. Toward a comprehensive, transtheoretical model of change: Stages of change and addictive behaviors. In: Miller WR & Heather N, editors. Treating addictive behaviors (2nd ed) New York: Plenum Press; 1988. pp. 3-24.
Croom K, Lewis D, Marchell T, Lesser ML, Reyna VF, Kubicki-Bedford L, Feffer M, Staiano-Coico L. Impact of an online alcohol education course on behavior and harm for incoming first-year college students: short-term evaluation of a randomized trial. J Am Coll Heal. 2009;57(4):445–54.
Hustad JT, Barnett NP, Borsari B, Jackson KM. Web-based alcohol prevention for incoming college students: a randomized controlled trial. Addict Behav. 2010;35(3):183–9.
Lovecchio CP, Wyatt TM, DeJong W. Reductions in drinking and alcohol-related harms reported by first-year college students taking an online alcohol education course: a randomized trial. J Health Commun. 2010;15(7):805–19.
Paschall MJ, Antin T, Ringwalt CL, Saltz RF. Evaluation of an Internet-based alcohol misuse prevention course for college freshmen: findings of a randomized multi-campus trial. Am J Prev Med. 2011;41(3):300–8.
Paschall MJ, Antin T, Ringwalt CL, Saltz RF. Effects of AlcoholEdu for college on alcohol-related problems among freshmen: a randomized multicampus trial. J Stud Alcohol Drugs. 2011;72(4):642–50.
Paschall MJ, Ringwalt C, Wyatt T, Dejong W. Effects of an online alcohol education course among college freshmen: an investigation of potential mediators. J Health Commun. 2014;19(4):392–412.
Wyatt TM, Dejong W, Dixon E. Population-level administration of AlcoholEdu for college: an ARIMA time-series analysis. J Health Commun. 2013;18(8):898–912.
Wall AF. Evaluating a health education web site: the case of AlcoholEdu. NASPA J. 2008;44(4):692–714.
Wall AF. On-line alcohol health education curriculum evaluation: harm reduction findings among fraternity and sorority members. Oracle. 2006;2(1):29–45.
Barretto AI, Bingham CR, Goh KN, Shope JT. Developing a web-based health promotion intervention: a case study from a brief motivational alcohol program. Health Promot Pract. 2011;12(2):193–201.
Bingham CR, Barretto AI, Walton MA, Bryant CM, Shope JT, Raghunathan TE. Efficacy of a web-based, tailored, alcohol prevention/intervention program for college students: initial findings. J Am Coll Heal. 2010;58(4):349–56.
Bingham CR, Barretto AI, Walton MA, Bryant CM, Shope JT, Raghunathan TE. Efficacy of a web-based, tailored, alcohol prevention/intervention program for college students: 3-month follow-up. J Drug Educ. 2011;41(4):405–30.
Schinke SP, Fang L, Cole KC, Cohen-Cutler S. Preventing substance use among Black and Hispanic adolescent girls: Results from a computer-delivered, mother–daughter intervention approach. Subst Use Misuse. 2011;46(1):35–45.
Williams C, Griffin KW, Macaulay AP, West TL, Gronewold E. Efficacy of a Drug Prevention CD-ROM Intervention for Adolescents. Subst Use Misuse. 2005;40(6):869–78.
Koning IM, van den Eijnden RJ, Engels RC, Verdurmen JE, Vollebergh WA. Why target early adolescents and parents in alcohol prevention? The mediating effects of self‐control, rules and attitudes about alcohol use. Addiction. 2011;106(3):538–46.
Koning IM, Vollebergh WA, Smit F, Verdurmen JE, Van Den Eijnden RJ, Ter Bogt TF, Stattin H, Engels RC. Preventing heavy alcohol use in adolescents (PAS): cluster randomized trial of a parent and student intervention offered separately and simultaneously. Addiction. 2009;104(10):1669–78.
Gregor MA, Shope JT, Blow FC, Maio RF, Weber JE, Nypaver MM. Feasibility of using an interactive laptop program in the emergency department to prevent alcohol misuse among adolescents. Ann Emerg Med. 2003;42(2):276–84.
Maio RF, Shope JT, Blow FC, Gregor MA, Zakrajsek JS, Weber JE, Nypaver MM. A randomized controlled trial of an emergency department-based interactive computer program to prevent alcohol misuse among injured adolescents. Ann Emerg Med. 2005;45(4):420–9.
Foxcroft DR, Tsertsvadze A. Cochrane Review: Universal school-based prevention programs for alcohol misuse in young people. Evid Based Child Health. 2012;7(2):450–575.
Foxcroft DR, Tsertsvadze, A. Universal family-based prevention programs for alcohol misuse in young people. Cochrane Database Syst Rev, 9. 2011. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD009308/abstract
Perkins HW. Social norms and the prevention of alcohol misuse in collegiate contexts. J Stud Alcohol Suppl. 2002;14:164–72.
Canale N, Vieno A, Santinello M, Chieco F, Andriolo S. The efficacy of computerized alcohol intervention tailored to drinking motives among college students: a quasi-experimental pilot study. Am J Drug Alcohol Abuse. 2015;41(2):183–7.
Collins SE, Kirouac M, Lewis MA, Witkiewitz K, Carey KB. Randomized controlled trial of web-based decisional balance feedback and personalized normative feedback for college drinkers. J Stud Alcohol Drugs. 2014;75(6):982–92.
Rubak S, Sandbæk A, Lauritzen T, Christensen B. Motivational interviewing: a systematic review and meta-analysis. Br J Gen Pract. 2005;55(513):305–12.
Stigler MH, Neusel E, Perry CL. School-based programs to prevent and reduce alcohol use among youth. Alcohol Res Health. 2011;34(2):157.
Gray SH, Pasternak RH, Gooding HC, Woodward K, Hawkins K, Sawyer S, Anoshiravani A. Recommendations for electronic health record use for delivery of adolescent health care. J Adolesc Health. 2014;54(4):487–90.
Miller JW, Naimi TS, Brewer RD, Jones SE. Binge drinking and associated health risk behaviors among high school students. Pediatrics. 2007;119(1):76–85.
Botvin GJ, Kantor LW. Preventing alcohol and tobacco use through life skills training. Alcohol Res Health. 2000;24(4):250–7.
Primary support provided by the National Science Foundation under grants IIS-1344803 & IIS-1344670, and by the Maternal and Child Health Bureau, Health Resources and Services Administration, USDHHS through a Cooperative Agreement (UA6MC27378) and through the Leadership in Adolescent Health Training Program (T71MC00003). The funding bodies had no role in the design of the study; collection, analysis, or interpretation of data; or writing the manuscript.
Availability of data and materials
EO and KT conceived of the study. EO, KT, and RE drafted the manuscript. EO, KT, MB, CJ and JL participated in the study’s design and coordination, edited the manuscript, and read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
About this article
Cite this article
Tebb, K.P., Erenrich, R.K., Jasik, C.B. et al. Use of theory in computer-based interventions to reduce alcohol use among adolescents and young adults: a systematic review. BMC Public Health 16, 517 (2016). https://doi.org/10.1186/s12889-016-3183-x
- Young adult
- Alcohol drinking
- Alcohol prevention
- Theoretical models
- Computer systems
- Computer-based interventions
- Systematic review