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date: 19 November 2019

Targeted Prevention Approaches

Abstract and Keywords

This chapter reviews the current literature on targeted prevention approaches for adolescent alcohol and other drug (AOD) use. We open the chapter by examining both the historical and current use of the term “targeted prevention” in regard to teen AOD use. We then provide a review of existing targeted prevention work from a health communication perspective and offer recommendations for future areas for research on targeting in health campaigns. This is followed by a review of existing targeted prevention work from a clinical intervention perspective, with attention to both selective and indicated prevention strategies. This includes recommendations for future areas for research on targeting in early intervention programs. We conclude the chapter with a brief recapitulation of its contents.

Keywords: targeted prevention, message tailoring, selective prevention, indicated prevention, clinical intervention perspective

To be sure of hitting the target, shoot first, and call whatever you hit the target

Ashleigh Brilliant

Just what, exactly, do prevention scientists mean by targeted prevention for adolescent alcohol and other drug (AOD) use? It turns out there is no simple answer to this question. Despite more than half a century of rigorous attempts to reach consensus about what defines prevention, much semantic fuzziness remains (Kutash, Duchnowski, & Lynn, 2006; Mrazek & Haggerty, 1994). In the following introductory section, we review both the historical and current use of the term “targeted prevention.”

A Brief History of Attempts to Define the Term “Targeted Prevention”

The first prevention intervention definitional system was proposed by the Commission on Chronic Illness (1957), which distinguished three types of prevention: (1) primary prevention, which seeks to decrease the number of new cases of a disorder; (2) secondary prevention, which seeks to lower the rate of established cases of a disorder in the population (prevalence); and (3) tertiary prevention, which seeks to decrease the amount of disability associated with an existing disorder. It should be noted that the notion of “targeted prevention” was not included in the Commission on Chronic Illness’s prevention definitions.

Three decades later, Gordon (1987) proposed a prevention typology integrating a “risk-benefit” perspective, with an emphasis on specifying populations and subpopulations toward which different types of prevention should be directed. Gordon’s prevention classification included three types of preventive measures, with “measures” defined as actions applied to persons who are not suffering discomfort or disability due to the disorder being prevented. Essentially, defining measures in this way eliminates from consideration most of what was encompassed in the Commission on Chronic Illness’s tertiary (p. 656) prevention category. Gordon’s typology included (1) universal measures, which are desirable for everyone because the benefits outweigh the costs at the population level; (2) selective measures, which are desirable only for persons belonging to a subgroup at above-average risk of contracting a disorder; and (3) indicated measures, which are desirable only for persons already manifesting a disorder. The notion of targeted prevention, while never explicitly invoked by Gordon, is suggested in his use of the terms “selective” and “indicated.”

The next important development in refining prevention definitions came in the form of the Institute on Medicine’s (IOM) report on prevention research for mental disorders (Mrazek & Haggerty, 1994). The IOM used Gordon’s prevention classification system as its model, though improved upon it by (a) redefining “measures” as “prevention interventions” and (b) explicitly specifying targets for each class of preventive interventions. The IOM classification system included (1) universal preventive interventions, targeted to the general public or population, not based on individual risk, and desirable for everyone; (2) selective preventive interventions, targeted to individuals or a subgroup of the population at above-average imminent or lifetime risk for a disorder; and (3) indicated preventive interventions, targeted to individuals having minimal but detectable signs or symptoms foreshadowing meeting diagnostic criteria for a disorder. In the strictest sense, all three levels of intervention were targeted prevention approaches, distinguished from one another by their target recipients. However, universal approaches, just as in Gordon’s system, are designed for an entire population, whereas selected and indicated approaches target only subpopulations with elevated odds of developing a disorder.

A recent development in honing prevention definitions comes from Weisz, Sndler, Durlak, and Anton (2005), who (a) substituted the term “strategy” in place of Gordon’s “measure” and the IOM’s “intervention;” (b) made health promotion programs their own category of prevention strategies; and (c) reintroduced treatment (“tertiary prevention” as per the Commission on Chronic Illness) as its own category in the typology. Weisz et al.’s (2005) resulting five-tiered integrative classification included (1) health promotion/positive development strategies, targeted to enhance protective factors, or increase prospects for positive development, for an entire population; (2) universal prevention strategies, targeted to address risk factors for an entire population; (3) selective prevention strategies, targeted to address risk factors among specific subgroups at elevated risk for a disorder; (4) indicated prevention strategies, targeted to individuals with significant symptoms of a disorder, but not meeting diagnostic criteria for that disorder; and (5) treatment interventions, targeted to those presenting with diagnosable disorders. Like Gordon and the IOM, Weisz et al. define each level of prevention strategies by the specific (and increasingly narrow) subgroups for which they are appropriate. Weisz et al. also maintain the distinction between selected prevention strategies and indicated prevention strategies; such strategies have smaller subgroup targets than do health promotion/positive development or universal prevention strategies, and larger subgroup targets than do treatment interventions. Moreover, primary targets-for-change are risk factors for disorder in selected prevention strategies, versus symptoms of disorder as in indicated prevention strategies.

Current Use of the Term “Targeted Prevention” in the Adolescent Alcohol and Other Drug Use Literature

To delineate further what prevention scientists mean by the term “targeted prevention,” we conducted a literature search based on the term “targeted prevention.” We looked for the term in both titles and abstracts, concentrating on the publications from the past decades in order to gauge current usage. Our literature search identified a dozen evidence-based published articles that, in one way or another, reported on targeted AOD prevention approaches with teenagers. The operative phrase in the previous sentence is “in one way or another.” As it turns out, targeted prevention means different things to different people dedicated to preventing AOD use problems among adolescents. From our literature search, we identified seven different current specifications of what the adjective “targeted” means when applied to the noun “prevention” (see Table 30.1).

Koning et al. (2009) used “targeted” to describe broad demographic qualities of the intended recipients of a prevention program. For Koning et al., adolescents, their parents, or simultaneously adolescents and their parents were examples of three different prevention targets in their targeted prevention approach. From the Koning et al. perspective, a targeted prevention approach might include distributing informational pamphlets about positive parenting approaches to parents of middle school–aged (p. 657) children. This targets the broad demographic group of parents of middle school–aged children, but not the children themselves, despite having the goal of preventing AOD use problems among the children. Targeting in this case refers to whom the prevention program was applied.

Table 30.1 In regard to adolescent alcohol and drug use behavior, “targeted prevention” can mean targeted . . .

1.

to broad demographic qualities of the intended recipients (e.g., parents of teenagers)

2.

toward adolescents at elevated risk (e.g., eighth graders who report underage drinking)

3.

deployment of prevention program content (e.g., individualized content based on individual profiles)

4.

to affect mediators of program effects (e.g., social influences on underage drinking)

5.

to affect specific AOD outcomes (e.g., the initiation of smoking)

6.

toward a specific developmental cohort (e.g., eighth graders)

7.

toward a specific developmental transition (e.g., moving from high school to college)

Stewart et al. (2005) apply the label “targeted” only to AOD prevention programs geared toward adolescents at elevated risk for problematic substance use. Using Stewart et al.’s definition, teens who are early initiators of substance use, or teens with family histories of substance use problems, are examples of appropriate targets for a targeted prevention approach. Our own research group’s NIH-supported Adolescent Behavior and Lifestyle Evaluation (ABLE) projects, which evaluated the efficacy of school-based brief motivational intervention, conformed to Stewart et al.’s definition of a targeted prevention program. In the first ABLE randomized controlled trial (R01 AA013825; PI: Wagner), we targeted public high school sophomores and juniors who reported six or more drinking occasions during the past 90 days. In the second ABLE study (R01 DA025640; PI: Wagner), we targeted public high school sophomores who reported three or more marijuana use occasions during the past year. As these examples demonstrate, targeting in this case refers to directing prevention efforts toward subgroups of teenagers at above-average risk for using or abusing substances, commensurate with Selective and Indicated Prevention Strategies as defined by Weisz et al. (2005).

Coffman, Patrick, Palen, Rhoades, and Ventura (2007) offer a third variation on defining targeted prevention. These investigators used “targeted” to describe the tailored deployment of prevention program content such that it is “molded to each participant’s needs based on tailoring variables and individual characteristics” (p. 246). From the Coffman et al. perspective, targeting means tailoring program content so as to best fit a particular participant’s presenting or baseline profile; this profile is derived from one or more tailoring variables. For Coffman et al., adolescents’ motivations for drinking (i.e., Experimentation, Thrill-seeking, Multi-reasons, & Relaxing) are an especially promising tailoring variable for targeted prevention. In this case, targeting involves systematically varying prevention program content according to motivations for drinking particular to individual adolescents (or according to other tailoring variables, singularly or in combination with one another), with the ultimate goal of improving the effectiveness of preventive efforts.

Stephens et al. (2009) used “targeted” to describe prevention programs that attempt to directly affect constructs associated with adolescent AOD initiation and use. The constructs may be mediators of the association between program participation and AOD outcomes (i.e., mechanisms of change) or the AOD outcomes themselves. For example, Stephens and colleagues examined the effectiveness of a universal prevention approach that targeted the constructs of social influences and competence, both of which are thought to account for program effectiveness. These targets were selected because of their (a) significant associations with the initiation and use of substances by youth and (b) amenability to intervention. To make matters a bit more complicated, Stephens et al. also use targeted to describe prevention approaches designed for specific drugs. In this case, a universal prevention approach specifically focusing on adolescent cigarette smoking could be defined as a targeted prevention approach. Thus, targeting in Stephens et al.’s sense involves orienting prevention efforts toward specific skills and competencies believed to be protective against the use of specific drugs.

(p. 658)

A final variation on the definition of targeted prevention is developmental in nature. Prevention approaches may define “targeted” as (a) designed for a specific developmental cohort (e.g., middle school students) or (b) focused on a specific developmental transition (e.g., the transition from high school to college). For example, Stephens et al. point out that universal school-based substance abuse prevention programs are generally administered in middle school and the first 2 years of high school. In this case, targeting involves directing prevention efforts toward a specific developmental cohort experiencing a specific (and normative) developmental transition. Both the targeted developmental cohort (e.g., 8th graders) and the targeted developmental transition (e.g., moving from middle to senior high school) were selected because of their association with risk for AOD initiation and use. This risk is not subgroup specific; rather, it is population-level risk based on developmental level, making it commensurate with Universal Prevention Strategies as defined by Weisz et al. (2005). This developmental angle is yet another semantic slant to the term “targeted prevention” as currently used in the adolescent AOD prevention literature.

Our Use of the Term “Targeted Prevention”: Differences Between Disciplines

So . . . what, exactly, do the authors of this chapter mean by targeted prevention for adolescent AOD use? Both of us have spent considerable time working on the targeted prevention of adolescent AOD use, and we had assumed that our definitions of the term would line up. However, that was not the case. N. L., who is trained in health communication, applies the term “targeted prevention” to media campaigns aimed at influencing secular trends in the amount and veracity of information available on a given topic, with the ultimate goal of health promotion. For her, targeted prevention involves the tailored deployment of prevention messages so as to best fit a particular participant’s presenting or baseline profile, similar to Coffman et al.’s (2007) use of the term. E. F. W., who is trained as a clinical psychologist, defines targeted prevention as interventions designed for adolescents at elevated risk for substance use problems, similar to Stewart et al.’s (2005) use of the term. In speaking with our colleagues, we realized that they too were roughly equally divided in defining targeted prevention as either (a) message targeting or tailoring in health campaigns or (b) providing specialized intervention to select high-risk individuals. Agreeing to disagree, we chose for the purposes of this chapter to use both definitions. The next section reviews the empirical literature on targeted prevention as defined as the tailored deployment of AOD prevention messages to youth, which represents the health communication perspective on targeted prevention. The section after that reviews the empirical literature on targeted prevention as defined as intervening with youth at elevated risk for AOD use problems, which represents the clinical intervention perspective on targeted prevention.

Targeted Prevention: Health Communication Perspective

Mass media campaigns, because of their wide reach, appeal, and cost-effectiveness, have been major tools in health promotion and are often the primary or sole component in a variety of public health campaigns (Backer, Rogers, & Sopory, 1992; Flay, 1987; Randolph & Viswanath, 2004; Rice & Atkin, 1989). In particular, campaigns directed at the prevention of substance abuse and other risk behaviors have often relied on the mass media as the primary vehicle for disseminating prevention messages (Flay & Sobel, 1983; Rogers & Storey, 1987; Schilling & McAllister, 1990). Mass media campaigns have been described as exercises in information control (Viswanath, Finnegan, Hannan, & Luepker, 1991). Targeted interventions utilizing mass media channels usually are aimed at influencing the secular trend in the amount of information available on a given topic in a system. This occurs through attempting to increase the amount of information available on a particular topic, or by also redefining or framing the issue as a public health problem to make it salient, engage the attention of the target audience, and suggest a solution to resolving a problem (Randolph & Viswanath, 2004; Viswanath et al., 1991; Wallack & Dorfman, 1996). Typical campaigns have placed messages in media that reach large audiences, most frequently via television or radio but also outdoor media (billboards and posters) and print media (magazines and newspapers) (Wakefield, Loken, & Hornik, 2010). One of the benefits of mass media campaigns lies in their ability to disseminate well-defined, behaviorally focused messages to large audiences repeatedly, over time, in a cost-effective manner (Wakefield et al., 2010).

Mass media campaigns can work through direct and indirect pathways to change the behavior of a (p. 659) population (Hornik & Yanovitzky, 2003). Media campaigns can influence behavior through three paths—institutional diffusion, social diffusion, and the individual path, which involves direct exposure of individuals to the persuasive messages generated by the campaign, whether through ads placed in the media, educational programs, or other forms of messages (Hornik & Yanovitzky, 2003). The individual path of media effects is the path of effects most commonly conceptualized and tested in the design and evaluation of many targeted health communication campaigns. This path is derived directly from influential theories of health behavior change. A great deal of evidence exists of successful campaigns that applied guiding theoretical frameworks such as social learning theory, diffusion of innovations, the theory of reasoned action, the health belief model, the elaboration likelihood model, and protection motivation theory (Flora, Maibach, & Maccoby, 1989; Maibach & Parrott, 1995; McAlister, Ramirez, Galavotti, & Gallion, 1989; Petty, Baker, & Gleicher, 1991; Rogers, 1995; Rosenstock, 1990; Schilling & McAllister, 1990; Zimmerman & Vernberg, 1994).

Using Behavioral Theory in Substance Abuse Prevention Campaigns

Theory can be used to carefully identify a set of determinants that influence cognitions, affect, and behaviors. For example, the Integrated Model of Behavior Change (Fishbein, 2000; Fishbein, Cappella, et al., 2002; Fishbein & Ajzen, 2010) provides guidance for message strategies. According to this approach, if the target population has formed intentions to perform the behavior, for example, to reduce their alcohol intake, but are not acting on it, possibly due to a lack of skills or the presence of environmental constraints, the strategy will be directed at skill building or at helping people to overcome environmental constraints (Fishbein & Yzer, 2003). In contrast, if formative research indicates that the target population has not formed strong intentions to reduce their alcohol intake, a message might address one or more of the primary determinants of intention—attitude toward performing the behavior, perceived norms concerning performing the behavior, and self-efficacy with respect to performing the behavior (Fishbein & Yzer, 2003, p. 167). The message strategy will depend both upon the behavior and the population being targeted. Once a researcher determines, in a given population, which factors determine a particular behavior, they can identify specific beliefs that discriminate between people who do or do not engage in the behavior in question, that is, beliefs that are highly correlated with behavioral intention (Fishbein & Yzer, 2003, p. 172). Beliefs that are most amenable to change are those for which most of the population do not already hold the belief in question. For example, if the targeted belief is that “ alcohol does not make you feel good,” an individual’s own direct experience with alcohol may make this belief more difficult to change than other less common beliefs.

Message Effects Theories

Although the application of behavioral theory and exposure to the message is a critical element in the success of any media-based campaign, the content and format of a message is also an important factor. Message effects theories can be used to design messages for target audience, which can enhance the probability of a campaign’s success. There are a number of studies which provide evidence that theoretically based messages addressing the beliefs and values of a specific population can significantly change behavior (CDC AIDS Community Demonstration Projects Research Group, 1999; Fishbein, 1997; Fishbein et al., 1997; Kamb et al., 1998, among others). For example, theory on the use of narratives in persuasion could suggest whether a message in the form of a narrative may be more persuasive for a particular population, compared with an informational message (Berger, 2013; Green & Brock, 2000). The Extended-ELM (E-ELM: Slater, 2002; Slater & Rouner, 1997, 2002) is a useful theoretical framework with which to examine the effects of narrative and nonnarrative (expository) messages. The E-ELM proposes that interest in the plot (transportation) and identification with protagonists facilitate persuasion in the context of narrative messages (Cohen, 2001; Green & Brock, 2000; Slater & Rouner, 1997, 2002). Among audiences who are likely to resist overt attempts at persuasion, such as high-risk adolescents, the use of narratives may help overcome various kinds of resistance to persuasion, particularly through reducing message counterarguing, a key obstacle to persuasion and attitude change (Petty & Cacioppo, 1986; Slater & Rouner, 2002). A substance abuse prevention campaign focused on prevention of alcohol abuse among a high-risk population should, according to this approach, adopt narrative messages emphasizing the benefits of reducing alcohol intake or illustrating the risks of alcohol abuse. Message effects theories such as (p. 660) the E-ELM (Slater, 2002; Slater & Rouner, 1997, 2002) can be applied to encourage a new viewpoint or to alter the current view of a public health issue among the target audience. Messages designed to address substance abuse can be conceptualized in an infinite number of different ways. In this section we will review a number of approaches to media-based substance abuse prevention messages, highlighting examples from recent campaigns.

Targeted Substance Abuse Prevention Campaigns: A Review

Although media-based substance abuse campaigns play an important role by providing information and education, generally, these and other public information campaigns have not been found to be effective in reducing alcohol-related outcomes (Babor et al., 2003) and have shown modest or mixed effects on drug-related outcomes (Block, Morwitz, Putsis, & Sen, 2002; Fishbein, Hall-Jamieson, Zimmer, Haefter, & Nabi, 2002). It is difficult for a campaign to lead to sustained changes in behavior in an environment in which individuals are surrounded by many competing messages in the form of marketing and social norms supporting drinking, and in which alcohol and other substances are readily acceptable (Anderson, Chisholm, & Fuhr, 2009).

However, when properly designed, public information media campaigns can be effective in changing beliefs, attitudes, intentions, and even behaviors (e.g., Beck et al., 1990; Flay, 1987; Flynn, Worden, Seeker-Walker, Badger, & Geller, 1995; Hornik, 2002; McDivitt, Zimick, & Hornik, 1997). Derzon and Lipsey (2002) conducted a meta-analysis of 72 antidrug media campaigns targeting young people and found that effects, while small overall, were larger for campaigns using a series of ads rather than a “one shot” treatment, and they were larger for campaigns that were supplemented with other activities (e.g., peer advocacy or community-based programs) to reinforce and provide greater depth to the media messages (Longshore, Ghosh-Dastidar, & Ellickson, 2006).

Public health campaign literature outlines four important principles that underscore campaign success: (a) interventions based on well-tested social and behavioral theories are more likely to be successful (Donohew, Lorch, & Palmgreen, 1991; Flay, 1987; Maibach & Parrott, 1995; McAlister et al., 1989; Rogers, 1995; Rosenstock, 1990; Schilling & McAllister, 1990; Zimmerman & Vernberg, 1994, among others); (b) obtaining sufficient (i.e., widespread, frequent, and prolonged) exposure to messages to achieve measurable impact is vital to campaign success (Flay, 1987; Hornik, 2002); (c) audience segmentation is required to target messages to at-risk audiences (Backer et al., 1992; Slater, 1996); and (d) formative research should be employed throughout the audience segmentation, message design, and channel selection phases (Atkin & Freimuth, 1989), in particular identifying message strategies that can achieve the desired impact given adequate exposure (Pechmann, Zhao, Goldberg, & Reibling, 2003; Worden, 1999).

Some substance abuse prevention campaigns have applied these principles, but others have failed to do so. There have been relatively few controlled mass-media based campaigns which have applied these principles and can establish campaign exposure effects on behavioral outcomes. One campaign that successfully did so was a two-city antismoking advertising campaign conducted by Palmgreen, Donohew, Lorch, Hoyle, and Stephenson (2001). This prevention campaign illustrates the critical roles of careful message construction and choice of messages reflecting an underlying model or theory about how communication is to influence behavior (Hornik, 2002). The campaign also successfully employed audience targeting, which employs variables kinked both to the behavior of interests and to the communication channels and message styles most preferred by the target audience (Slater, 1996).

Palmgreen and colleagues’ prevention approach was called SENTAR (sensation-seeking targeting). The SENTAR approach can be summarized by four principles: (a) employ sensation seeking as a major audience segmentation variable; (b) conduct formative research with high sensation-seeking members of the target audience; (c) design prevention messages high in sensation value; and (d) place campaign messages in high–sensation value contexts (e.g., TV programs). Sensation seeking is a personality trait with a relatively high degree of temporal stability, and it is associated both with drug risk as well as with the need for novel, complex, ambiguous, and emotionally intense stimuli and the willingness to take risks to obtain such stimulation (Zuckerman, 1979, 1994). Sensation seeking is a moderate to strong predictor of drug use and earlier onset of use, with high sensation seekers at greater risk than low sensation seekers (Kilpatrick, Sutker, & Smith, 1976; Pedersen, 1991; Segal, Huba, & Singer, 1980; Zuckerman, 1979, 1994). High sensation seekers have distinct preferences for certain types of message characteristics based on (p. 661) their need for messages that are novel, usual and intense (Donohew et al., 1991; Zuckerman, 1994). Messages that appeal to this group elicit strong sensory, affective, and arousal responses (Palmgreen et al., 1991) and tend to be novel, dramatic, emotionally powerful or physically arousing, graphic or explicit, somewhat ambiguous, unconventional, fast-paced, or suspenseful (Palmgreen, Donohew, Lorch, Hoyle, & Stephenson, 2002).

Focus groups of 8th through 12th graders, scoring above and below the median on a sensation-seeking scale for adolescents, expressed opinions on existing antidrug public service announcements varying in sensation value and discussed salient risks and consequences associated with marijuana use (Palmgreen et al., 2002). Feedback from these focus groups guided the creation of five 30-second TV ads, which were shown together with several public service announcements provided by the Partnership for a Drug-Free America (PDFA), which were judged high in sensation value. These ads were aired on local TV station and cable companies, and had high exposure among the target audience; at least 70% of the targeted age group was exposed to a minimum of three campaign ads per week.

The campaign was evaluated using a 32-month controlled, interrupted time-series with switching replications (Cook & Campbell, 1979) in Fayette County, Kentucky, and Knox County, Tennessee, in 1997 and 1998. Interviews were conducted using systematic random sampling of 32 monthly pools of potential respondents from 7th to 10th graders attending public schools in 1996. The campaign showed declines in marijuana use among high sensation seekers with each wave of their campaign (Hornik, 2002; Palmgreen et al., 2002). The results indicate that mass media–based substance abuse campaigns can affect drug behavior, but only in the context of carefully targeted campaigns that achieve high levels of reach and frequency, and with messages designed specifically for the target audience on the basis of social scientific theory and formative research (Palmgreen et al., 2002, p. 52).

Another successful media-based substance abuse prevention campaign applied social marketing principles, in combination with a participatory, community-based media effort, to reduce marijuana, alcohol, and tobacco use among middle school students (Slater et al., 2006). This program was developed over 5 years of formative research and testing (Kelly, Swaim, & Wayman, 1996; Kelly, Stanley, & Edwards, 2000; Kelly, Comello, & Slater, 2006). This was a randomized controlled trial (RCT) conducted in 16 middle schools, with 8 schools randomly assigned to media treatment and 8 schools serving as controls. Eight communities received the in-school and community media campaign and eight did not. Within each community, two middle schools were recruited, one of which received a classroom-based intervention and one that did not (Slater, Kelly, Lawrence, Stanley, & Comello, 2011). The media program was conducted through several channels (print, posters, T-shirts, book covers, water bottles) with the positive theme of “Be Under Your Own Influence” in combination with related community activities. This theme was intended to emphasize the inconsistency of substance use (and, to a lesser extent, smoking), with the aspirations of an adolescent to attain greater independence and autonomy (Oman et al., 2004). The campaign aimed to reframe substance use as a choice that impairs rather than enhances personal autonomy (Williams, Cox, Kouides, & Deci, 1999). In addition, the campaign messages incorporated images that were intended to be appealing to risk-oriented, sensation-seeking youth, similar to the approach used by Palmgreen et al. (2001).

Results of the evaluation of this campaign provided support for the effectiveness of in-school media efforts combined with participatory communication efforts at the community level. The intervention was found to reduce uptake trajectories for several substance outcomes, and in particular for marijuana and alcohol use (Slater et al., 2006). Compared with the control group, youth in the intervention communities had fewer users at final posttest for marijuana (OR = .50, p < .05), alcohol (OR = .40, p < .01), and cigarettes (OR = .49, p < .05). These results suggest that the focus on autonomy and personal aspirations was seen as applicable to both substances (Slater et al., 2006, p. 164). The “Be Under Your Own Influence” campaign’s success illustrates the importance of aligning substance abuse prevention measures with developmentally appropriate goals (Slater et al., 2011; Wagner, Brown, Monti, Myers, & Waldron, 1999).

In contrast to these examples of successful media-based campaigns, the Office of National Drug Control Policy’s (ONDCP) 5-year, $2 billion National Youth Anti-Drug Media Campaign is an example of a media-based substance abuse campaign for which there was high hopes, but which failed to show positive effects, and even, it is argued, led to negative effects on some outcomes among high-risk youth. The National Youth Anti-Drug Media Campaign was the largest drug abuse prevention (p. 662) effort in history. The campaign was a multimedia effort that tried to stimulate community-based programs, but its central component was the targeted dissemination of televised antidrug ads and public service announcements (Palmgreen et al., 2002). The messages reflected three themes of the campaign’s community strategy—resistance self-efficacy, antidrug norms, and negative consequences of use—and were aired in paid and donated advertising on a full range of media (Longshore et al., 2006). The 5-year campaign, initiated in 1998, was designed to be a comprehensive social marketing effort that aimed antidrug messages through a range of media channels at youths aged 9 to 18 years, their parents, and other influential adults. In addition, the campaign established partnerships with civic, professional, and community groups and outreach programs with the media, entertainment, and sports industries (Hornik, Jacobsohn, Orwin, Piesse, & Kalton, 2008).

The evaluation of this campaign was supervised by the National Institute on Drug Abuse and undertaken by Westat and the Annenberg School for Communication at the University of Pennsylvania. The primary evaluation tool was the National Survey of Parents and Youth (NSPY), an in-home survey of youths and their parents living in households in the United States (Hornik et al., 2008). Results showed substantial exposure to antidrug advertising, but no change in prevalence of marijuana use among those aged 12.5 to 18 years between 2000 and 2004, and no association between exposure to antidrug ads and any of the outcomes, after adjusting for confounders. In lagged analyses, results indicated the possible presence of pro-marijuana effects of ad exposure on intentions to use marijuana; examination of the 80 subgroup analyses reveals 20 significant effects, with 19 of those in a pro-marijuana direction, an overriding pattern of unfavorable lagged exposure effects.

One explanation for these findings is that youth who saw the campaign perceived that their peers were using marijuana. In turn, those who came to believe that their peers were using marijuana were more likely to initiate use themselves (Hornik et al., 2008). Evidence consistent with this explanation is that more ad exposure was associated with the belief that other youths were marijuana users, and this belief was predictive of subsequent initiation of marijuana use (Hornik et al., 2008; Orwin et al., 2006). Jacobsohn (2007) studied possible explanations for the campaign’s boomerang effect. Findings from the research pointed to mass communication’s role in influencing perceived norms; results suggested that the campaign ads cumulatively delivered an implicit “meta-message” that marijuana use was widespread among youth, which in turn, negatively affected youth behavior. Future substance abuse campaigns would be well advised to empirically test the effects of ads among members of the target population prior to launching a full-scale campaign in order to avoid undesirable boomerang effects such as appear to have occurred in the ONDCP campaign.

Following public release of the negative findings for the “My Anti-drug” campaign, the ONDCP campaign was rebid and the contract was assigned to a new advertising firm, which launched a rebranded marijuana prevention campaign, “Above the Influence,” in 2005 (Slater et al., 2011). No formal external evaluation of the rebranded campaign was funded. However, internal rolling cross-sectional surveys conducted weekly over the course of the campaign show significant positive associations between exposure to the new campaign and antidrug attitudes (White, 2008).

Fear appeals are a widely used message strategy in media-based substance abuse prevention campaigns. A review by Witte and Allen (2000) suggested that strong fear appeals increase perception of susceptibility, and that, combined with messages suggestive of skills and actions, fear appeals can be an effective way to change behavior. However, when strong fear appeals are not accompanied by self-efficacy information, the effects of messages on substance abuse outcomes can be undermined. One recent example of the challenges posed by using this form of appeal is the Montana Meth Project (MMP), a large-scale methamphetamine prevention program in Montana in 2005. The campaign used extremely graphic ads featuring the effects of methamphetamine use, portraying users as unhygienic, dangerous, untrustworthy, and exploitive (Erceg-Hurn, 2008). Users are depicted killing their parents, being raped, and prostituting themselves to support their drug habit (Erceg-Hurn, 2008). The project was lauded as a resounding success by the nonprofit organization MMP, media, and politicians, who have argued that the campaign has dramatically increased antimethamphetamine attitudes and reduced drug use in Montana. However, these claims have been called into question by Erceg-Hurn (2008), who reviews the evidence for effects and finds that the empirical support for the campaign is weak, and not supported by data. Erceg-Hurn (2008) further argues that the MMP misrepresented campaign data, allegedly selecting data for presentation that (p. 663) is consistent with its claims of positive campaign effects. Erceg-Hurn’s (2008) argument is that teens exposed to these ads are likely to believe that the risks of methamphetamine use are exaggerated, and that the use of scary, graphic images in public health campaigns such as the Montana Meth Project is an ineffective strategy to bring about behavior change, based on existing research into this approach (Ringold, 2002; Ruiter, Abraham, & Kok, 2001; Witte & Allen, 2000).

Future Directions in Research on Targeted Health Communication Campaigns

In recent years, with advances in communication technologies, the transformation of the public communication environment has led to a fragmentation of what was formerly perceived as a mass public, to increasingly distinct subgroups, each with a varied pattern of media use. This change has made it more challenging to reach large numbers of people with effective messages. Given that exposure to a message is vital to a campaign’s success (Hornik, 2002; McGuire, 1989), this can present a significant obstacle for practitioners and theorists working in the area of substance abuse interventions.

In response to audience fragmentation, researchers have begun to use audience segmentation and message targeting and tailoring in behavior change interventions. Segmentation and targeting practices focus on identifying group-level similarities and designing messages that are hypothesized to resonate with the particular group or subgroup (Noar, Harrington, & Aldrich, 2009). Audience segments are homogenous subgroups that are internally similar yet differ from one another. The rationale for audience segmentation is that, when audiences are divided into groups with more similar than different members, research suggests that they react similarly (and positively) to a campaign message designed for the segment (Noar et al., 2009). Audience segmentation can be done on an almost infinite number of variables, including demographic, geographic, psychographic, attitudinal, cultural, and behavioral characteristics (Albrecht & Bryant, 1996; Hornikx & O’Keefe, 2009; Slater, 1996).

Recent research has examined engagement with drug-related information as a potential indicator of increased risk of substance use, and thus a potentially important variable for segmentation in targeted substance use prevention programs. This project, supported by the European Union (Marie Curie Career Integration Grant 333605; PI: Lewis), investigates the role of active and passive (scanning) drug-related information seeking among young adults in transition to college, and their effects in shaping drug use trajectories (Lewis, Martinez, Agbarya, & Piatok-Vaisman, 2016). Results of online surveys show direct associations between information seeking and (nonmedical) marijuana use intention among young adults in the United States and in Israel. Furthermore, drug-related information seeking was shown to indirectly impact intentions through changes in attitude and perceived norms (Martinez & Lewis, 2016). These preliminary findings offer evidence to suggest that information seeking may serve as an earlier indicator of drug-use risk, which may provide a tool for earlier identification of subgroups of young adults who are at greater need of targeted intervention.

Message Tailoring

Message tailoring refers to the practice of designing messages at the individual level (see Kreuter, Farrell, Olevitch, & Brennan, 2000; Kreuter & Skinner, 2000; Kreuter & Wray, 2003). Messages that are tailored are customized to fit the interests and values of the individual target, enhancing the perceived relevance of the message for the recipient, and consequently the likelihood that the message will be attended to and processed, necessary conditions for long-term persuasion. The elaboration likelihood model (ELM; Petty & Cacioppo, 1981) provides the most common explanation for the mechanism of effects. The ELM suggests that tailored messages are perceived as personally relevant more often than generic ones, thus increasing the chance that central processing of the message will occur, and that the result will be attitude and/or behavior change (Noar et al., 2009). The majority of reviews of tailored interventions support this claim; participants perceive tailored messages as more relevant, and they are also more likely to read and recall such messages (Noar et al., 2009). Tailored messages also have been shown to be more effective at influencing health behavior change as compared with targeted interventions or no-treatment control conditions (see meta-analyses by Noar et al., 2009; Sohl & Moyer, 2007, and Kreuter et al., 2000; Kroeze, Werkman, & Brug, 2006; Richards et al., 2007; Rimer & Glassman, 1999; Skinner, Campbell, Rimer, Curry, & Prochaska, 1999). This practice has the potential for stronger direct effects of media-based health communication campaigns, for example, sending personal reminders to moderate alcohol intake on one’s birthday or prior to a holiday party. Tailoring has been used successfully (p. 664) in commercial operations like Amazon.com and Netflix, where suggestions for products are based on past preferences or matches to related items. The Internet lends itself very well to tailored messaging, as does the smart phone. However, message tailoring also raises concerns about intrusion and privacy (Cohen-Almagor, 2007), and the exploitation of personal information for persuasion purposes through database marketing (McAllister & Turow, 2002).

Research on tailored health interventions is currently being developed to test the efficacy of matching health messages to preassessed characteristics of the target population. For example, a team of researchers from the University of Pennsylvania’s Annenberg and Engineering schools have recently been awarded a $1 million Exceptional Unconventional Research Enabling Knowledge Acceleration (EUREKA) grant from the National Institutes of Health (NIH) to develop a new way of evaluating effective antismoking appeals. The team at the University of Pennsylvania, headed by Joseph Cappella, from the Annenberg School for Communication, and Michael Kearns from the School of Engineering and Applied Science, Department of Computer and Information Science, are developing descriptors for a large number of smoking cessation advertisements, and compiling preference data from smokers to develop and test algorithms that can be used to recommend antismoking appeals that are effective for individual smokers. A similar approach could be applied to substance abuse interventions, and it has been applied in a few studies (Neumann et al., 2006; Simon-Arndt, Hurtado, & Patriarca-Troyk, 2006; Weitzel, Bernhardt, Usdan, Mays, & Glanz, 2007; Werch et al., 2005). As noted earlier in the chapter, adolescents’ motivations for drinking (i.e., Experimentation, Thrill-seeking, Multi-reasons, & Relaxing) may be particularly helpful in tailoring alcohol prevention messages in order to make them more effective.

Targeted Prevention: Clinical Intervention Perspective

In regard to adolescent AOD use, universal prevention typically involves educating a youth population about the harms associated with substance use. The underlying premise is that better educated youth (i.e., those who come to know more about the negative consequences of AOD use) will make better decisions (i.e., not initiate AOD use). While well intentioned, most universal prevention programs have not fared well when subjected to rigorous evaluation (Foxcroft, Ireland, Lister-Sharp, Lowe, & Breen, 2003; Monti et al., 1999; Stewart et al., 2005; Wagner et al., 1999). To date, the only universal programs showing any promise in preventing adolescent AOD are multifaceted, multiyear, school-based programs, which involve skills training for teachers, parents, and young (preteen) children, and which require ongoing professional and peer support (Conrod, Stewart, Comeau, & Maclean, 2006). This state of affairs has led many researchers and practitioners to advocate instead for targeted prevention approaches, which by definition prioritize the allocation of limited prevention intervention resources to those who need it most. Targeted prevention programs need only one half to one third of the usual number of students that universal prevention programs need to produce a significant preventive effect on adolescent drinking behavior (Conrod et al., 2006).

Targeted prevention involves intervening with youth subpopulations at above-average risk for using or abusing substances. The targeted subpopulations may be “selected” based on risk factor profiles, or “indicated” based on disorder symptom profiles. Therapeutic processes (e.g., changing distorted perceptions about drinking or drugging norms, increasing self-confidence to resist substance use, increasing motivation to change), rather than increased knowledge, are responsible for preventive effects. Meta-analyses of school-based AOD prevention studies find targeted prevention programs generally produce stronger effects than do universal prevention programs (Gottfredson & Wilson, 2003). Although some existing selective prevention programs have proved effective, rigorous empirical studies of their efficacy are rare, and the full range of putative intervention targets (e.g., underlying motivations for alcohol misuse among teenagers who are at greatest risk) has not been explored (Conrod, Castellanos, & Mackie, 2008; Stewart et al., 2005). Several other studies have examined the efficacy of indicated prevention strategies targeting teenage alcohol and/or marijuana users. Particularly in regard to motivational interviewing (Miller & Rollnick, 2013), indicated prevention strategies appear to yield clinically meaningful reductions with teen substance users. The following sections review targeted prevention from a clinical intervention perspective, focusing first on selective prevention strategies and next on indicated prevention strategies.

(p. 665) Targeted Prevention: Selective Prevention Strategies

For more than a decade, Patricia Conrod and colleagues have been systematically investigating the efficacy of a school-based selective prevention program targeting personality risk factors for adolescent alcohol use problems. As a selective prevention strategy, their approach targets precursors to alcohol use problems, rather than symptoms of alcohol use problems. Their manualized, personality-matched intervention strategies are designed to target anxiety sensitivity, hopelessness, or sensation seeking, and have been tested in three recent randomized controlled trials (RCTs) described next.

Conrod and colleagues (2006) conducted an RCT of their personality-matched intervention strategies with 297 Canadian high school students (56% girls; mean age, 16 years). Participants were selected through school-wide screenings; selection was based on (a) reporting at least one episode of underage drinking during the previous 4 months and (b) scoring at least one standard deviation above the mean on measures of sensation seeking, anxiety, and/or hopelessness. Participants were assigned to either personality-matched interventions or a no-treatment control group, and they were assessed at preintervention and at 4 months postintervention. Interventions were conducted with groups of two to seven adolescents during two 90-minute sessions spread across 2 weeks, with a between-session homework exercise. Interventions (a) relied on motivational and cognitive-behavioral behavior change principles, (b) had main three components (psychoeducation, behavioral coping skills training, and cognitive coping skills training), and (c) throughout incorporated personality-specific content (i.e., sensation seeking, anxious, or hopeless). Participants were matched to interventions based on the personality variables assessed during screening. It was hypothesized that students in the personality-targeted intervention condition would show reductions in overall drinking levels, binge drinking, and drinking problems relative to students in the no-treatment condition.

In regard to 4-month follow-up abstinence rates, there was a trend for a greater proportion of the intervention group to report being abstinent relative to the control group (22% vs. 14%; p < .08). Rates of binge drinking at the 4-month follow-up were significantly lower for the intervention group relative to the control group (42% vs. 60%; p < .01). For drinking quantity, the intervention group demonstrated lower levels of alcohol consumption at follow-up relative to the control group (3–4 drinks per drinking occasion vs. 5–6 drinks per drinking occasion; p < .05). The frequency of drinking, however, did not differ between groups at follow-up. Finally, problem drinking symptoms at the 4-month follow-up were significantly less common among the intervention group relative to the control group (63% vs. 78%; p < .01). In sum, relative to no intervention, the personality-matched interventions were shown to facilitate abstinence and significantly reduce binge drinking rates, drinking quantity, and alcohol problems in selected groups of high-risk youth.

Conrod and colleagues (2008) conducted a second RCT of their personality-matched intervention strategies with 368 English secondary school students (68% girls; median age, 14 years). The study’s procedures and design paralleled those used in Conrod and colleagues (2006), with three notable exceptions: (1) the targeting of negative thinking as a fourth personality risk factor for adolescent alcohol use problems; (2) the use of 6- and 12-month follow-up assessments; and (3) the omission of a measure of drinking problems. It was hypothesized that students in the personality-targeted intervention condition would show reductions in overall drinking levels and binge drinking relative to students in the no-treatment condition.

Conrod and colleagues (2008) found abstinence rates did not differ between the intervention and control groups at either the 6- or 12-month follow-up assessment. Rates of binge drinking at the 6-month follow-up were significantly lower for the intervention group relative to the control group (41.1% vs. 64.6%; p = .009), though by the 12-month follow-up, these differences had disappeared. Finally, a significant personality by intervention interaction effect was documented for sensation seeking. Sensation-seeking drinkers in the intervention group were 45% less likely to report drinking at the 6-month follow-up, and 50% less likely to report binge drinking at the 12-month follow-up, compared to sensation-seeking drinkers in the control group.

Findings from a third RCT of Conrad and colleagues’ personality-matched intervention strategies were recently published. Conrod and colleagues (2013) studied the long-term effectiveness of their targeted prevention approach with 1,210 high-risk and 1,433 low-risk English students enrolled in the ninth grade (unspecified gender distribution, mean age = 13.7 years). High-risk youth were assigned to (p. 666) personality-matched interventions or treatment as usual (i.e., the regular national statutory drug education curriculum provided in England). Low-risk youth received no intervention—they were included in the study in order to assess for secondary school–level “herd effects” from the interventions.

Procedures and design were generally the same as Conrod and colleagues (2008), though in this study, impulsivity, rather than negative thinking, was used as a fourth personality risk factor for adolescent alcohol use problems. Also, school personnel (teachers, mentors, counselors, and educational specialists), rather than master’s-level therapists with cofacilitators, were trained in and administered the personality-matched intervention strategies. Finally, participants were assessed every 6 months over the course of 24 months of follow-up. It was hypothesized that the personality-matched intervention strategies would prevent the growth and severity of alcohol misuse among the targeted high-risk youth.

Latent growth modeling analyses indicated significant long-term effects of the intervention on abstinence ( p = .03), binge drinking ( p = .03), and drinking quantity ( p = .04). The personality-matched interventions also significantly slowed growth over 24 months in binge drinking ( p = .009), binge drinking frequency ( p = .047), drinking quantity ( p = .02), and problem drinking ( p = .02) for high-risk youth. Among low-risk youth in schools with active intervention, some mild herd effects were observed. These youth demonstrated higher abstinence rates ( p = .049) and slower growth of binge drinking ( p = .001) during the 24-month follow-up relative to low-risk youth in schools in the treatment-as-usual condition. These findings offer additional support for the long-term benefits of Conrod et al.’s personality-targeted approach to alcohol. High-risk youth who received the intervention reported 29% reduced odds of drinking, 43% reduced odds of binge drinking, and 29% reduced odds of problem drinking relative to high-risk youth receiving only treatment as usual.

Targeted Prevention: Indicated Prevention Strategies

Recently, several investigators have begun to systematically investigate the efficacy of brief motivational interventions for preventing substance use problems among teenagers already in the early stages of developing problems. As indicated prevention strategies, these prevention programs target individuals with significant symptoms of a disorder, but not meeting diagnostic criteria for that disorder. Recently, motivational interviewing (Miller & Rollnick, 2013) has been an especially popular choice in RCTs of indicated prevention strategies targeting youth with early indications of substance use problems. This literature is reviewed next in separate sections devoted to alcohol use and to marijuana use.

Motivational Interviewing and Adolescent Alcohol Use Problems

A small empirical literature exists concerning the effectiveness of motivational interviewing with adolescent drinkers. Marlatt et al. (1998) conducted a randomized controlled clinical trial comparing the effectiveness of a brief motivational intervention (assessment plus a subsequent motivational interview feedback session) with assessment-only for reducing heavy drinking among college students reporting high-risk drinking behavior ( n = 348). The feedback included attention to (a) self-reported drinking rate vis-à-vis college averages, (b) perceived current and future risks of drinking, (c) beliefs about real and imagined alcohol effects, (d) the biphasic effects of alcohol, and (e) methods for risk reduction. Each motivational interview participant left the interview with a printed personalized summary concerning self-reported drinking, along with a generic tips page concerning the biphasic effects of alcohol, imagined (placebo) alcohol effects, and suggestions for alcohol risk reduction.

At 6-month postintervention follow-up, motivational interview participants demonstrated significantly lower drinking frequency, quantity, and peak quantity than did assessment-only participants (standardized effect size of .15 on a composite drinking pattern score). These differences remained through the 24-month postintervention follow-up, with effect sizes for individual drinking outcomes ranging from .14 to .20. Putative moderator variables, including gender, parental history of alcoholism, and conduct disorder history, did not interact with treatment condition, indicating the motivational interview was effective independent of individual risk factors. Roberts, Neal, Kivlahan, Baer, and Marlatt (2000) reanalyzed these findings using clinical significance methodology and confirmed the previous results. Motivational interview participants were more likely to improve and less likely to worsen than assessment-only participants over the 2 years of follow-up. Finally, Burke, Arkowitz, and Menchola’s (2003) reanalysis of the Marlatt et al. (1998) study using unit-free, bias-corrected effect size calculations documented (p. 667) effects of .23 for drinking frequency at 26 weeks posttreatment, .34 for drinking consequences at 52 weeks posttreatment, and .28 for drinking consequences at 208 weeks posttreatment.

Borsari and Carey (2000) conducted a randomized controlled clinical trial comparing the effectiveness of a brief motivational intervention with assessment-only for reducing alcohol use and related consequences among college student binge drinkers ( n = 60). The motivational interviewing intervention consisted of five components: (1) self-reported drinking rate vis-à-vis campus and national averages; (2) personal negative consequences of drinking; (3) the influence of positive and negative expectancies on personal alcohol use including perceived risks and benefits; (4) accurate information about alcohol and its effects; and (5) options for decreasing drinking and avoiding high-risk drinking situations. At 6 weeks postintervention follow-up, motivational interviewing participants reported fewer drinks consumed per week (ES = .21), fewer alcohol use occasions per month (ES = .28), and fewer binge drinking episodes (ES = .12). Burke et al.’s (2003) reanalysis of the Bosari and Carey study documented a unit-free, bias-corrected effect size of .57 for standard ethanol content consumed per week at 6 weeks posttreatment.

Larimer et al. (2001) conducted an RCT comparing the effectiveness of a brief motivational intervention (assessment plus a subsequent 1-hour individually tailored feedback session) with assessment-only for reducing drinking and drinking-related consequences among first-year college student fraternity members ( n = 120). The feedback session included attention to (a) individual drinking patterns, (b) training in estimating blood alcohol concentration, (c) comparing typical alcohol use patterns and perceived norms to actual college-wide norms, (d) the biphasic effects of alcohol, (e) identifying and challenging alcohol-related expectancies, (f) personalized review of drinking-related consequences, and (g) reviewing strategies to encourage moderate drinking. At their fraternity houses, participants in both conditions received a 1-hour didactic group presentation about drinking and drinking-related problems; this presentation included motivational feedback in the intervention condition but not in the control condition. At 1-year postintervention follow-up, motivational interview participants demonstrated significant reductions in average drinks per week (ES = .42) and typical peak blood alcohol content (ES = .38), but not in quantity per occasion, frequency of alcohol consumption, or alcohol-related negative consequences. Putative moderator variables, including family history of alcoholism, motivation to change alcohol use, and desire to avoid the risks associated with drinking, did not interact with treatment condition, indicating the motivational interview was effective independent of individual risk factors.

Monti, Colby, et al. (1999) conducted an RCT comparing the effectiveness of a brief motivational intervention (assessment followed immediately by a motivational interviewing session) with assessment-only for reducing alcohol use and related consequences among adolescents treated in an emergency room following an alcohol-related event. The motivational interviewing session included five sections: (1) introduction and review of event circumstances; (2) exploration of motivation to change (e.g., pros and cons); (3) personalized and computerized assessment feedback; (4) imagining the future; and (5) establishing goals. Participants in both the motivational interviewing and assessment-only conditions received a handout on avoiding drinking and driving and a list of local treatment agencies. At a 6-month postintervention follow-up, motivational interviewing participants were significantly less likely to report drinking and driving (OR = 3.92), alcohol-related injuries (OR = 3.94), and alcohol-related problems (standardized effect size [ES] of .23). Moreover, putative moderator variables including gender and stage of change did not interact with treatment condition, indicating the motivational interview was effective independent of individual risk factors. Burke et al.’s (2003) reanalysis of the Monti et al. (1999) study documented a unit-free, bias-corrected effect size of .43 for alcohol-related problems at 52 weeks posttreatment.

Motivational Interviewing and Adolescent Marijuana Use

Clinical trials concerning the effectiveness of motivational interviewing with adolescent marijuana users have been conducted in Australia, Great Britain, and the United States. Martin, Copeland, and Swift (2005) conducted a nonrandomized pretest/posttest feasibility study of 73 young Australian cannabis users (aged 14–19 years; mean age = 16.4 years; 81% Australian nonindigenous). These investigators’ four-session Adolescent Cannabis check-up (ACCU) was adapted from Stephens et al.’s (2004) Marijuana Checkup (MCU) for adults, and it included (1) an initial session with (p. 668) concerned others (e.g., parents), (2) a second session devoted to assessment, (3) a third session focused on personalized feedback, and (4) a final optional session addressing pragmatic strategies for reducing cannabis use. Personalized feedback covered the topics of amount of cannabis used, comparison of each individual’s cannabis use with age specific normative data, the pros and cons of using cannabis, and perceived interactions between cannabis use and individual goals. Results provided preliminary support for this adolescent-focused adaptation of the MCU. Days using cannabis in the past 90 days declined from the mean of 56.6 days at baseline to 42.6 days at 3 months, and amount of cannabis used declined from 512.5 cones at baseline to 358.3 cones at 3 months. Moreover, three quarters (77.7%) of the follow-up sample reported voluntarily reducing or stopping their cannabis use at some time during the follow-up period. In addition, high levels of consumer satisfaction with the ACCU were reported, including lengths of the session (68% satisfied, 28% neutral), the clinician (98.5% moderately or very satisfied; 96.9% described the clinician as moderately or extremely helpful), and receiving feedback on cannabis use and its consequences (86.9% believed it was helpful; 10.8% were neutral). Martin et al.’s study indicated that the ACCU was both feasible and effective, though these researchers caution “[a] more rigorous design and a larger sample size are required to demonstrate that participation in the check-up is causally related to reductions in quantity and frequency of cannabis use and cannabis-related problems.”

McCambridge and Strang (2004) randomized a sample of 200 young British drug users (aged 16–20 years; mean age = 17.6 years; 88% non-Hispanic White or Black) to either (a) a single 60-minute session of motivational interviewing or (b) “education as usual.” Those randomized to motivational interviewing reduced their use of cigarettes, alcohol, and cannabis (effect sizes of 0.37, 0.34, and 0.75, respectively), mainly through moderation of ongoing drug use rather than cessation. Three months postintervention, the mean frequency of cannabis use declined by 66% in the MI group (from 15.7 to 5.4 times per week); in contrast, there was a cannabis use increase of 27% in the education-as-usual group (from 13.3 to 16.9 times per week). In analyses restricted to ongoing cannabis smokers only, the mean weekly frequency of cannabis use in the intervention group reduced from 18.0 to 6.6 times per week, while the control group increased from 13.9 to 18.2 times per week. In a subsequent report, McCambridge and Strang (2005) describe 12-month outcomes, many of which no longer differed between the MI and control groups. These researchers ruled out selective attrition as explaining their dissipated results, given no differences between their original sample and the 81% successfully contacted at 1-year follow-up. Instead, they suggest two additional explanations: assessment reactivity or simple deterioration of effects over time.

A dissemination study of McCambridge and Strang’s intervention also has been conducted. Gray, McCambridge, and Strang (2005) trained college-based youth work practitioners in the MI intervention and examined its effectiveness in a nonrandomized comparison of 59 intervention recipients and 103 nonintervention controls. Over the 3-month study period, there was no change in the prevalence of current cannabis smoking in either group. Moreover, the groups did not differ in attempts to cut down or stop smoking cannabis. Finally, McCambridge and Strang (2004) examined the ability of practitioner ratings to predict outcome among the MI participants from McCambridge and Strang (2004, 2005). Clinician reports of (a) discussing readiness-to-change-related issues including the pros and cons of using and making changes in use, (b) being directive, and (c) conducting intervention in college interview rooms versus more informal settings (e.g., cafés) were associated with greater participant improvement.

Stein et al. (2006) assigned 105 American substance-using incarcerated adolescents (72.4% non-Hispanic White or Black; 89.5% male) to either motivational interviewing (MI) or relaxation training (RT) and examined how depressed mood might moderate treatment response. Key outcomes included DUI (alcohol and/or marijuana) and being a passenger with a driver under the influence (PUI). Adolescents who received MI had lower rates of drinking and driving, and being a passenger in a car with someone who had been drinking; MI and RT participants did not differ on any of the marijuana-related outcomes. There was a significant treatment by depressive symptom interaction for driving under the influence of marijuana ( η2 = .043). Post-hoc analyses revealed this resulted from high-depressive adolescents responding better to RT. Stein et al.’s results suggest that among incarcerated teenagers marijuana-related risk behaviors may be more resistant to MI intervention than are alcohol-related risk behaviors.

(p. 669) Walker, Roffman, Stephens, Berghuis, and Kim (2006) examined the effectiveness of motivational interviewing with adolescent marijuana abusers in Washington State. Walker et al. (2006) randomized 97 adolescent marijuana users (mean age = 15.6 years, 5% Hispanic/Latino, 52% female), recruited through classroom presentations, advertisements, and referrals, to either an immediate two-session MI intervention (i.e., an assessment session followed 1 week later by a personalized feedback session) or to a 3-month delay condition. The MI intervention was called the Teen Marijuana Check-Up (TMCU), derived directly from Stephens et al.’s (2004) Marijuana Check-up (MCU), and took place entirely at school. Over the 3-month study period, both groups significantly reduced their marijuana use (e.g., past 60 days mean of 38.2 days at baseline to 32.0 days at 3 months), but did not differ from one other in amount of change. The investigators speculated that assessment reactivity may have influenced their findings and cautioned that their findings were preliminary, based on a small sample, and in need of replication.

D’Amico, Miles, Stern, and Meredith (2008) published a small pilot study evaluating a very brief MI intervention in a primary care clinic with teens ( n = 42; 85.7% Hispanic/Latino; 48% male). Patients between 12 and 18 years of age who scored positively on a substance abuse screening questionnaire were randomly assigned to usual care or MI. The 15-minute MI session focused on assessing motivation to change, enhancing motivation for change, and making a plan. At the 3-month follow-up, adolescents assigned to MI reported less marijuana use (standardized ES = .79), lower perceived prevalence of marijuana use (ES = .79), fewer friends who used marijuana (ES = .61), and lower intentions to use marijuana in the next 6 months (ES = .86), as compared to adolescents assigned to usual care. This preliminary works suggests that even very brief MI interventions, in specialized contexts like primary care clinics, can positively impact marijuana use outcomes among teenagers.

Future Directions in Targeted Selective and Indicated Prevention

Conrod and colleagues’ work supports the effectiveness of personality-matched interventions as targeted prevention strategies for adolescents at risk for the development of substance use problems. The effects they documented were larger than effect sizes obtained in other effective prevention and early intervention programs; effect sizes for the group at greatest risk for binge drinking, sensation-seeking drinkers, were found to be double that typically seen in prevention and early intervention programs (Conrod et al., 2008). Spurred on by their success, these researchers have identified several paths down which future research in personality-matched indicated prevention strategies should go.

First, RCTs need to compare the personality-based approach to an attention-only control; thus far, only no-treatment or treatment-as-usual comparison conditions have been used. Second, the relative efficacy of the personality-based approach should be compared with established effective prevention programs. In making such comparisons, both behavioral outcomes and cost-effectiveness of the programs need to be taken into account. Third, research needs to explicitly test hypotheses about the role of matching in the efficacy of personality-based approaches; to this end, Conrod and colleagues suggest future studies include nonmatched intervention comparison groups in which youth are provided interventions that target a personality dimension irrelevant to their own personality profile. Fourth, studies need to begin to investigate putative mechanisms of intervention effects by measuring potential mediators of intervention impact such as changes in drinking motives and coping strategies. Finally, in regard to the herd effects documented in Conrod et al. (2013), social network analyses may help to test whether intervention effects on high-risk youth prospectively influenced the behavior of low-risk youth attending the same school at the same time intervention is taking place.

A growing empirical literature also supports the effectiveness of motivational interviewing (MI) as targeted prevention strategies for adolescents with indications of alcohol or marijuana use problems. Relatively large intervention effects, sustained as much as 208 weeks postintervention, have been documented. Treatment effects have been independent of putative moderator variables (i.e., amenability to treatment variables), including gender, parental history of alcoholism, history of conduct disorder, and stage of change. However, RCTs of target prevention strategies that have relied on MI have the following limitations: (a) limited diversity of samples (to date, studies have included predominantly non-Hispanic White, older adolescents), (b) no direct examination of putative mechanisms of change, (c) absence of no-assessment (or minimal assessment) control conditions, which prevents the estimation of reactivity-to-assessment effects, and (d) absence of research designs (p. 670) incorporating booster sessions. All four of these areas remain fertile and important topics for additional inquiry.

Conclusion

Targeted prevention strategies offer great promise in efforts to reduce substance use problems among adolescents. They appear to be more clinically effective and more cost-effective than nontargeted universal prevention strategies, which require intensive labor and resources and at best produce only modest AOD prevention effects. Conceptual fuzziness about what exactly is meant by “targeted prevention” has slowed development of the field, but things are gradually improving. Currently, two definitions of targeted prevention in regard to adolescent AOD use predominate: (1) message tailoring in health campaigns (the health communication definition), and (2) specialized intervention to select high- risk individuals (the clinical intervention perspective). Recent empirical work from both of these perspectives has supported the efficacy of targeting prevention, and it argues persuasively for increased research devoted to refining and testing targeted prevention strategies for adolescent substance use.

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