A qualitative review of psychosocial risk factors associated with caffeinated alcohol use.

Experimental and Clinical Psychopharmacology 2014, Vol. 22, No. 2, 144 –153

© 2014 American Psychological Association 1064-1297/14/$12.00 DOI: 10.1037/a0036334

A Qualitative Review of Psychosocial Risk Factors Associated With Caffeinated Alcohol Use Ashley N. Linden and Cathy Lau-Barraco

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Old Dominion University Caffeinated alcoholic beverages (CABs) are increasingly popular among young adults. The use of such beverages is associated with negative consequences including heavy alcohol use, risky sexual and driving behaviors, as well as other drug use. The prevalence of CAB use and their related harms warrants greater focus into the factors that may explain why use is associated with negative outcomes or what factors may impact their association. Consequently, the present study reviewed existing research on CABs and highlighted constructs (i.e., drinking motives, substance expectancies, perceived drinking norms, risktaking propensity) that could act as mediators or moderators of CAB use and consequences. We proposed implications for practice and future research. Keywords: caffeinated alcohol, energy drinks, negative consequences, young adults

may underlie or influence CAB use. Furthermore, since publication of the existing reviews, new evidence has emerged on problems associated with CAB use that may be of importance, such as associations with other drug use and the potential risk for experiencing caffeine-related harms. Consequently, we sought to address the limitations of prior reviews by incorporating the most current research evidence and reviewing key psychosocial risk factors related to CAB consumption. We aimed also to contribute uniquely to prior reviews by offering a preliminary theoretical framework for examining CAB use outcomes, recommendations for advancing our knowledge on CAB consumption patterns, and suggestions for clinical interventions targeting CAB use. Thus, the goals of the present study were to (a) review literature on the harms associated with CAB consumption; (b) highlight key constructs (i.e., drinking motives, substance expectancies, perceived drinking norms, risk-taking propensity, gender) that may explain or impact CAB use; and (c) provide implications for theory, practice, and future research.

Caffeinated alcoholic beverages (CABs; e.g., Red Bull and vodka) are becoming increasingly popular among young adults (e.g., O’Brien, McCoy, Rhodes, Wagoner, & Wolfson, 2008) and adolescents (Kponee, Siegel, & Jernigan, in press) in the United States. This pattern also has been observed in other countries, including the Netherlands (de Haan, de Haan, van der Palen, Olivier, & Verster, 2012), Australia (Peacock, Bruno, & Martin, 2013), and Canada (Price, Hilchey, Darredeau, Fulton, & Barrett, 2010). Greater consumption of CABs is strongly associated with negative outcomes, such as heavy episodic drinking, risk behaviors (O’Brien et al., 2008), and drug use (Brache & Stockwell, 2011; Snipes & Benotsch, 2013). Given the popularity of CABs and the potential risks associated with its use, a greater and more in-depth understanding of the relevant underlying and influential factors associated with CAB consumption may be warranted. To date, there have been several qualitative reviews or commentaries on CAB use (Attwood, 2012; Howland, Rohsenow, Vehige Calise, MacKillop, & Metrik, 2011; Verster, Aufricht, & Alford, 2012). These previous articles, however, focused primarily on the prevalence and broad risks associated with CAB use (Attwood, 2012; Howland et al., 2011) or targeted specifically on experimental studies (Verster et al., 2012). Lacking from prior reviews is an emphasis on relevant psychosocial risk factors of CAB consumption. There is a growing body of research suggesting that individual differences related to motivations for drinking, outcome expectancies, drinking norms, and risk-taking propensity

CAB Use and Negative Consequences CAB use is associated with a host of negative outcomes above and beyond that of alcohol. Studies have found consistently that those who consume more CABs tend to be more frequent binge drinkers (e.g., Berger, Fendrich, Chen, Arria, & Cisler, 2011; Brache & Stockwell, 2011; O’Brien et al., 2008; Woolsey, Waigandt, & Beck, 2010). For instance, O’Brien and colleagues found that compared with nonusers, CAB users consumed more alcohol on any one occasion and had twice as many heavy drinking days and episodes of drunkenness. CAB users also are more likely to experience alcohol-related problems in general (Lau-Barraco, Milletich, & Linden, 2014) including getting hurt and needing medical attention (O’Brien et al., 2008), exhibiting aggression (Woolsey et al., 2010), and blacking out (Jones, Barrie, & Berry, 2012). CAB use is associated with engaging in a variety of risky behaviors. CAB users are more likely to exhibit risky driving behaviors than nonusers, such as driving home after drinking

Ashley N. Linden and Cathy Lau-Barraco, Department of Psychology, Old Dominion University. No financial support was provided for the present research. Both authors have participated in this research and/or the article preparation. We have no conflicts of interest that may have influenced this research. We would like to acknowledge and thank Dr. Michelle L. Kelley and Dr. James M. Henson for their feedback on earlier drafts of this article. Correspondence concerning this article should be addressed to Cathy Lau-Barraco, Old Dominion University, 244D Mills Godwin Building, Norfolk, VA, 23529-0267. E-mail: [email protected] 144


CAFFEINATED ALCOHOL USE: A REVIEW

(Brache & Stockwell, 2011) and being a passenger in a car with an intoxicated driver (Brache & Stockwell, 2011; O’Brien et al., 2008). One field study found that bar patrons who consumed CABs were at three times greater odds of leaving the bar intoxicated as well as four times more likely to intend to drive after drinking compared with those who did not consume CABs on that drinking occasion (Thombs et al., 2010). CAB users also are at greater odds of engaging in dangerous sexual behaviors, such as taking or being taken advantage of sexually than non-CAB users (O’Brien et al., 2008). CAB consumption is related to greater odds of engaging in casual sex, having sex while intoxicated (Miller, 2012), and having more frequent unprotected sex than non-CAB users (Snipes & Benotsch, 2013). These findings suggest that CAB use may pose an additional threat for engaging in risky behaviors beyond that of heavy alcohol use. CAB users are more likely to use a variety of substances including stimulants such as cocaine, amphetamines (e.g., diet pills, speed), (Brache & Stockwell, 2011), and cigarettes (Linden, 2012). CAB users also report greater marijuana and ecstasy use (Snipes & Benotsch, 2013) than nonusers. One potential explanation that CAB use is related to stimulant drugs may be due to users having a preference for stimulating substances in general (Brache & Stockwell, 2011; Snipes & Benotsch, 2013). Consequently, users may be drawn to CABs because of their perceived energizing properties. Another explanation could be that CAB users have a higher risk-taking propensity than nonusers and may therefore be more likely to engage in risky behaviors, such as using illegal substances. For instance, Brache and Stockwell (2011) found that after controlling for typical risky behavior, there was no difference between CAB users and nonusers in terms of drug use. Therefore, when examining future associations between CAB use and drug use, it may be useful to control for the user’s risk-taking propensity. Given the dearth of research on CABs and stimulant drugs, more research is needed to illuminate these associations. Beyond negative alcohol-related consequences, CAB users are at risk for experiencing caffeine-related harms. Broadly, those who typically consume more CABs experience greater caffeine use severity such as more caffeine dependence symptoms, drinking caffeine despite experiencing negative outcomes, and consuming caffeine to avoid withdrawal symptoms (Lau-Barraco et al., 2014). Furthermore, on drinking occasions in which individuals consume CABs as opposed to only alcohol, CAB users report greater odds of experiencing sleep difficulties, increased heart rate (Pennay & Lubman, 2012), heart palpitations (Jones et al., 2012), tremors, jolts, and crashes (Peacock, Bruno, & Martin, 2012). Overall, across studies of both alcohol- and caffeine-related problems, it appears that CAB consumption is strongly related to experiencing a host of unfavorable outcomes beyond that of regular alcohol use. It is, however, important to interpret these results with caution for two reasons. First, although CAB use shows consistent associations with negative consequences, such findings do not imply causality. That is, it remains unclear whether drinking CABs actually causes negative outcomes or whether these harms were present prior to initiation of CAB use. Only longitudinal or experimental methods are able to address the direction of the causal effect. Second, there is evidence to suggest that CAB use actually may be unrelated (Alford, Hamilton-Morris, & Verster, 2012; Peacock, Bruno, Martin, & Carr, 2013; Penning, de Haan, & Verster, 2011; Rohsenow et al., 2014) or negatively related to

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problematic drinking outcomes (Alford et al., 2012; de Haan et al., 2012; Peacock et al., 2012). One investigation, for example, found that CAB consumption did not produce increases in risk-taking behavior in an experimental context (e.g., Peacock et al., 2012). Other studies have found that CAB use is unrelated to hangover severity (Penning et al., 2011; Rohsenow et al., 2014) and related to decreases in alcohol use and negative consequences (de Haan et al., 2012). Thus, although the body of research on the consequences of CAB use generally supports the risky nature of its use, existing divergent findings highlight the need for further investigations to clarify these findings.

Influential and Underlying Mechanisms of CAB Use As reviewed above, the positive relationship between CAB consumption and negative consequences has been established. Few studies, however, have focused on factors that may explain or impact this association. Based on extant research, it appears that motivations for drinking, substance expectancies, perceived drinking norms, and risk-taking propensity could each act as important determinants of CAB drinking patterns.

Motivations for CAB Use Drinking motives, or one’s motivations for consuming alcohol, have been found to be strong determinants of alcohol use in general (e.g., Cooper, 1994; Cooper, Frone, Russell, & Mudar, 1995), yet only a few studies have identified motivations for consuming CABs specifically. Some studies have found that participants use CABs to be able to drink more, feel less tired while drinking, get intoxicated faster (Marczinski, 2011), and feel more energetic (Peacock et al., 2013). Therefore, users may be drinking CABs so that they will experience the stimulant effects of caffeine and not the sedative effects of alcohol. Additionally, qualitative research findings suggest that young adults often consume CABs in the context of pregaming (i.e., drinking prior to the main drinking event). They noted their motivation was to get more energy while partying to stay awake and thus continue to drink (Jones et al., 2012). Further, other researchers have found, more simply, that some use CABs to hide the flavor of alcohol (O’Brien et al., 2008). All together, these reports suggest that users are motivated to drink CABs generally to stay awake, drink more, and party longer. Reports of CAB users’ motivations for drinking are interesting given laboratory evidence suggesting that the effects of CABs are primarily a result of the subjective changes in the drinker. Specifically, experimental studies have suggested that one reason for the link between CAB use and negative consequences may be that drinking CABs can reduce an individual’s feelings of intoxication without actually reducing their level of drunkenness. That is, after consuming a CAB compared with other types of noncaffeinated alcohol, participants experience fewer perceived alcohol-related physiological symptoms such as headache, dry mouth, and motor coordination failures as opposed to consuming regular alcohol (Ferreira, De Mello, Pompéia, & de Souza-Formigoni, 2006; Marczinski & Fillmore, 2006). Furthermore, in comparison with noncaffeinated alcohol, participants exhibit less behavioral and impulse control (Marczinski, Fillmore, Bardgett, & Howard, 2011), greater likelihood to desire additional alcohol (Marczinski,


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Fillmore, Henges, Ramsey, & Young, 2013), and increased ability to drink for longer periods of time due to perceived arousal (Attwood, Rogers, Ataya, Adams, & Munafo, 2012) after drinking CABs. Moreover, given some laboratory evidence that one’s actual level of impairment does not differ based on the type of alcohol consumed (Alford et al., 2012; Ferreira et al., 2006; Marczinski, Fillmore, Henges, Ramsey, & Young, 2012), it seems that one of the differences between drinking noncaffeinated versus caffeinated alcohol may be the user’s lack of subjective feelings of intoxication (e.g., reduce mental fatigue, enhanced feelings of stimulation) rather than physiological changes produced by CABs (Marczinski et al., 2012). Even though users are reporting that drinking CABs makes them feel less intoxicated, experimental evidence suggests that it does not actually change their level of intoxication (Marczinski et al., 2012). As noted by Marczinski (2011), these experimental studies could indicate that users are unaware that CABs can produce fewer subjective sedative effects than alcohol. However, findings that individuals are reportedly using CABs in order to decrease a sedative effect (Jones et al., 2012; Marczinski, 2011; O’Brien et al., 2008) implies that the decision to use CABs is in part driven by their intentional motivation to feel less tired. It is important to note that although users reportedly are consuming CABs because it enables them to keep drinking without feeling sedated, they also report negative effects such as blacking out and consuming more alcohol than planned because they do not feel drunk (Jones et al., 2012). Furthermore, feeling fewer sedative effects of alcohol also may be one explanation for why some engage in risky behaviors, such as driving home after drinking (Brache & Stockwell, 2011; O’Brien et al., 2008). These findings could suggest that one’s motivations for consuming CABs could be contributing to hazardous drinking outcomes. For example, having greater motivations to drink CABs to feel less tired and continue drinking could be an underlying or influential factor in the relationship between CAB use and alcohol- and caffeinerelated harms. Research to date has not, however, examined such associations. Overall, these preliminary investigations signify the importance of assessing motivations for drinking CABs in order to gain a better conceptual understanding of CAB drinking patterns.

Substance Expectancies About the Effects of CABs Substance expectancies are defined as one’s beliefs about the effects of a substance, such as alcohol or caffeine (e.g., Brown, Goldman, Inn, & Anderson, 1980). Expectancies are a distinct and critical precursor to drinking motives (Cox & Klinger, 2004). Specifically, expectancies are the outcomes one anticipates from using a substance and motives are the value placed on the effects they want to attain, which motivate them to use a substance. Expectancies are strong predictors of alcohol use outcomes (see Jones, Corbin, & Fromme, 2001 for a review). That is, the more one perceives alcohol to have positive influences, the more likely they are to drink and to drink heavily. In relation to CAB use, because CABs have properties of both caffeine and alcohol, research has examined caffeine, alcohol, and CAB-specific expectancies. Caffeine expectancies. Research investigating caffeine expectancies has generally supported that having stronger beliefs about the favorable effects of caffeine (e.g., enhanced energy) is

predictive of greater frequency and quantity of caffeine use (Heinz, Kassel, & Smith, 2009; Huntley & Juliano, 2012). A handful of studies have examined how one’s beliefs about the effects of caffeine relate to CAB use. Greater CAB use is associated with increased withdrawal symptoms caffeine expectancies (Heinz et al., 2009; Lau-Barraco et al., 2014; Linden, D’Lima, & LauBarraco, 2012). That is, higher rates of CAB consumption are negatively reinforced by the perceptions that caffeine produces effects such as getting headaches and having trouble focusing if not consumed regularly. Moreover, having greater withdrawal symptoms expectancies were found to account for more variance in the association between CAB use frequency and negative outcomes than expectations of positive outcomes, such as feeling more energized and improving mood (Linden et al., 2012). Also, when examining expectancies based on CAB user classes, researchers found that the CAB class characterized by the highest CAB use quantity and frequency endorsed greater withdrawal symptoms caffeine expectancies (Lau-Barraco et al., 2014). These findings are particularly important given that having stronger withdrawal symptoms expectancies are associated with symptoms of caffeine dependence (Heinz et al., 2009). Other studies examining caffeine expectancies and CAB use have found associations between social/mood enhancement expectancies and CAB consumption (Huntley & Juliano, 2012; LauBarraco et al., 2014). These beliefs include perceptions that caffeine will enhance sociability, improve mood, and increase confidence. Heavier drinking CAB users reported stronger mood caffeine expectancies than those who consumed fewer CABs (LauBarraco et al., 2014). Importantly, having stronger positive reinforcement expectancies, such as mood expectancies, is associated with symptoms of caffeine dependence (Heinz et al., 2009; Huntley & Juliano, 2012). Heavier CAB users with greater mood caffeine expectancies were found to indicate more caffeine dependence and withdrawal symptoms than individuals who drank fewer CABs (Lau-Barraco et al., 2014). Overall, across all studies examining CAB use and caffeine expectancies, it appears that CAB users have a large range of beliefs about the effects of caffeine, which can, in turn, predict CAB use and negative outcomes. Alcohol expectancies. Only two studies to date have examined alcohol expectancies in relation to CAB consumption (LauBarraco & Linden, in press; Lau-Barraco et al., 2014). Consistent with research on heavy alcohol users in general, heavier CAB drinkers tend to have more positive beliefs about the effects of alcohol, such as enhanced sexual performance, tension reduction, social facilitation, and increased arousal and power (Lau-Barraco et al., 2014). Furthermore, one study examined the unique and combined contributions of both caffeine and alcohol expectancies on CAB outcomes (Lau-Barraco & Linden, in press). Here, it appeared that although both sets of expectancies accounted for variance in CAB use and consequences, alcohol expectancies were able to explain more of the variance in CAB-use quantity, frequency, and alcohol-related problems than caffeine expectancies. These findings suggest that although both caffeine and alcohol expectancies contribute to the prediction of CAB outcomes, alcohol expectancies are a stronger predictor across outcomes. CAB-specific expectancies. Recently, several studies examined CAB-specific expectancies. MacKillop et al. (2012) developed a measure assessing one’s beliefs about the effects of CAB use by deriving items from advertising claims and risks identified



in past research. They identified two primary classes of CAB expectancies: intoxication enhancement and avoidance of negative consequences. Perceptions of intoxication enhancement include having more energy to party and getting “buzzed” faster. Avoidance of negative consequences includes perceiving that CABs will allow one to drink more without feeling intoxicated and drive safer. Greater endorsement of intoxication enhancement expectancies was associated with more frequent CAB use, even after controlling for noncaffeinated alcohol use, but avoidance of negative consequences was not. This indicates that CAB use is driven more by the expectation that drinking CABs will enhance intoxication rather than help avoid negative outcomes. CAB-specific expectancies have been examined based on a drinker’s profile (Mallett, Marzell, Scaglione, Hultgren, & Turrisi, in press). Specifically, researchers examined three types of CAB expectancies that were similar MacKillop et al. (2012) intoxication enhancement subscale. It was found that heavier CAB users had stronger positive CAB-specific expectancies. Another study examined beliefs about the effects of CABs using four items (VarvilWeld, Marzell, Turrisi, Mallett, & Cleveland, 2013) comparable with both of MacKillop and colleagues’ subscales. Researchers found that profiles characterized by more positive CAB-related beliefs and attitudes were associated with greater CAB use and alcohol-related problems prospectively (Varvil-Weld et al., 2013). Although the literature regarding CAB use and expectancies is limited, all of the existing studies support substance expectancies as an important determinant of use and negative outcomes. However, given that multiple types of expectancies relate to CAB use, it may be useful to determine specifically which types of expectancies are the most salient. It also may be useful to determine the direct relationship of CAB-specific expectancies to not only CAB use but also to problem severity, as only one investigation (VarvilWeld et al., 2013) has examined this link specifically. Furthermore, it could be helpful for future research to consider incorporating all relevant expectancies in a single assessment instrument. Such a strategy may be able to more accurately measure the broad beliefs a user holds about the effects of CABs.

Normative Influences Some preliminary research has suggested that social influences, such as perceptions of peer use and approval of drinking CABs, may be a predictive factor of CAB use. A focus group conducted with young adult drinkers revealed that drinking CABs is perceived to be a social activity, such as counting down before consuming a Jager Bomb (Jones et al., 2012). Furthermore, respondents reported that drinking CABs indicates that they are popular to those outside their in-group. Support for such responses is provided by two studies that examined descriptive norms (i.e., perceptions of how much one’s friends are drinking) and injunctive norms (i.e., perceptions of how much one’s friends approve of certain drinking behaviors). Mallett, Marzell, Scaglione, Hultgren, and Turrisi (in press) found that heavier CAB users had stronger descriptive and injunctive norms regarding their friend’s views about drinking CABs compared with moderate CAB drinkers. Individuals with more positive attitudes about CABs and stronger perceptions of close friends’ CAB use consume heavier amounts of CABs each week than individuals who are categorized by lower drinking norms (Varvil-Weld et al., 2013). These preliminary

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findings suggest that the social aspect of consuming CABs could be a key determinant. As drinking norms are shown to be influential mechanisms in the general alcohol literature (Neighbors, Lee, Lewis, Fossos, & Larimer, 2007), additional research investigating associations between perceived drinking norms and CAB use is warranted.

Risk-Taking Propensity One’s typical level of risk-taking behavior may be another factor contributing to CAB-related harms. Some researchers have argued that an individual’s likelihood of experiencing negative consequences and engaging in risk behaviors is attributed to their typical risk-taking propensity rather than their CAB use (e.g., Howland & Rohsenow, 2013). Generally, because most studies have used between-groups comparisons (e.g., examining CAB users compared with non-CAB users) to identify differences in negative consequences, these analyses do not take into account individual differences such as proneness to risk-taking behaviors. That is, individuals who choose to consume CABs simply may be drawn toward engaging in risky behaviors in general, which may explain the reason for their experiencing more negative outcomes than nonusers. Some research has, in fact, supported that CAB users tend to be more impulsive and pleasure-seeking than nonusers (e.g., Heinz, de Wit, Lilje, & Kassel, 2013). For instance, Brache and Stockwell (2011) found that those who consumed CABs in the past month had greater typical risk-taking scores than those who did not consume CABs. One way to determine if CABs alone increase one’s likelihood of engaging in negative consequences is to conduct within-subjects designs, whereby participants report on both their experiences on days where they consumed CABs and occasions where they only consumed alcohol. Only a handful of studies have utilized such methodology and have produced mixed results. One study found that drinkers consumed fewer alcoholic beverages on days where they consumed CABs as opposed to occasions where they consumed noncaffeinated alcohol (de Haan et al., 2012). Woolsey, Waigandt, and Beck (2010) found similar results, such that when individuals drank only noncaffeinated alcohol, they consumed more drinks per occasion as compared to days where they consumed CABs (8.6 vs. 6.3). On the other hand, Woolsey and colleagues’ results also revealed that their sample reported experiencing more negative outcomes overall, such as having a rapid heartbeat, feeling nervous or jittery, and not sleeping well when drinking CABs. Also, Price, Hilchey, Darredeau, Fulton, and Barrett (2010) found that individuals consumed more alcohol on occasions when they consumed CABs as opposed to drinking alcohol only (8.6 vs. 4.7 drinks). Finally, Brache and Stockwell (2011) found that CAB users reported consuming more alcoholic beverages on occasions when they consumed CABs as opposed to occasions when they consumed noncaffeinated alcoholic beverages even after controlling for typical risk-taking propensity. Together, these findings present mixed reports regarding whether CABs pose a risk beyond that of alcohol or if using the substance is merely a reflection of the type of drinkers (e.g., impulsive, sensation-seekers, risk-takers) who choose to drink such a beverage. Alternatively, although within-subjects designs can help illuminate the influence of impulsivity and risk-taking propensity on the


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relationship between CAB use and negative outcomes, there may be other individual differences to take into account. For instance, the context in which an individual drinks may be a factor driving negative consequences. That is, given evidence that individuals are reportedly using CABs to get intoxicated faster and continue partying (Marczinski, 2011), it may be that individuals’ reasons for drinking CABs is a stronger predictor of alcohol-related problems than the CAB itself. A within-subjects design examining the strength of an individual’s motives on CAB drinking occasions compared with noncaffeinated alcohol drinking occasions could shed light on whether risky motives or CAB use is the more salient factor predicting negative outcomes. However, given the range of individual differences that could account for the association between consumption and outcomes, laboratory-based experimental studies may be a more advantageous solution to determine both a causal relationship between the type of alcohol consumed and negative consequences as well as the effect of context, drinking motives, and expectancies on CAB versus alcohol consumption.

Sex Differences Research focusing on differences between men and women pertaining to CAB use has been limited. Although existing studies do offer some insight, these findings have been mixed. Several studies suggest men consume more CABs than women (e.g., Miller, 2008; O’Brien et al., 2008) yet others indicate the prevalence is the same (e.g., Berger et al., 2011; Brache & Stockwell, 2011; Heinz et al., 2013). One potential reason for the discrepant findings may be attributed to the timeframe of typical use defined by each study. Some researchers compared past-month users with nonusers (e.g., O’Brien et al., 2008) whereas others measured use in the past year or past 3 months (e.g., Berger et al., 2011; Heinz et al., 2013). With regard to experiencing harms from CAB use, findings show that men and women may be at unique risk. One study indicated that for men, CAB consumption was associated with being sexual victimized including being coerced, threatened, physically forced, or incapacitated (Snipes, Green, Javier, Perrin, & Benotsch, in press). For women, however, CAB use was associated solely with being sexually victimized physically. Researchers explained these findings by suggesting that because energy drinks are advertised toward a masculine identity (Kuhns, Clodfelter, & Bersot, 2010), men might mix energy drinks with alcohol to reassert their masculinity after experiencing sexual victimization. Thus, men may have a distinctive association between CABs and sexual risk. Another issue relevant to differences between men and women is the type of CAB used as it may be related to differential risk for negative outcomes. It has been found that women are more likely to consume diet cola-caffeinated mixed drinks than men (Rossheim & Thombs, 2011). The consumption of artificially sweetened cola-caffeinated mixer drinks has been shown to produce the most pronounced effects on intoxication (Rossheim & Thombs, 2011) even though cola-caffeinated mixers (e.g., soda mixed with rum) and energy drink mixers (e.g., Red Bull and vodka) are associated with similar negative outcomes (e.g., driving while intoxicated; Thombs et al., 2011). Rossheim and Thombs (2011) found that those who drank diet cola-caffeinated drinks had the highest breath alcohol concentration (BrAC) reading as compared with alcohol-only drinks, regular cola-caffeinated mixed

drinks, and non-caffeinated mixed drinks consumed by patrons leaving a bar. Further supporting the potential harms of diet soft drinks is a study by Marczinski and Stamates (2013). Their experimental study found that diet soft drinks mixed with alcohol produced higher BrACs than regular soft drinks mixed with alcohol (i.e., mixed with a sugary beverage). Researchers suggested that this difference may be a result of the lack of sucrose in artificially sweetened drinks that normally slows the rate of gastric emptying of alcohol. Given both the rise in sugar-free caffeinated drinks and women’s increased susceptibility of alcohol-related diseases and other effects of alcohol (Baraona et al., 2001), understanding differential outcomes based on gender and type of CAB consumed is an important avenue for future research.

Future Directions A primary area for future work is to gain greater breadth regarding an individual’s cognitions for using CABs. As it relates specifically to drinking motivations, it is documented that most individuals are motivated to consume CABs in order to reduce the sedative effects of alcohol (Marczinski, 2011) as a means of continuing to party. However, it is unknown if stronger endorsement of this motive is predictive of greater CAB use. Furthermore, research has not investigated whether one’s motivations for drinking CABs may explain or impact the association between CAB consumption and negative consequences. Determining the salience of drinking motives in predicting CAB use outcomes indicate its relevance in incorporating CAB-specific motives as a component in existing alcohol interventions. Another avenue of future research is to gain greater insight into drinkers’ beliefs about the effects of CABs. One way to achieve this goal is to develop a more comprehensive measure of CABspecific expectancies. As mentioned, there currently exists one questionnaire of CAB expectancies: the Caffeine ⫹ Alcohol Combined Effects Questionnaire (CACEQ; MacKillop et al., 2012). Preliminary investigations of this measure’s efficacy in predicting CAB use and outcomes revealed that only one of its two factors (intoxication enhancement) was associated with CAB use frequency. Given the predictive utility of only one of those subscales, it may possible that there are other beliefs about CABs that were left untapped by this initial measure. Further, a future CAB expectancy questionnaire could include general caffeine and alcohol expectancies in addition to CAB-specific expectancies as to fully account for the range of expectations users perceive about CABs as well as each substance alone. Future research also is needed to determine the extent to which certain effects of CABs are due to expectations of the substance or pharmacological properties. To date, experimental research investigating the effects of CABs has largely relied on double-blind placebo-controlled designs (e.g., Ferreira et al., 2006; Marczinski & Fillmore, 2006). Within these designs, participants are often told that they may or may not ingest particular substances but are not told which specific beverage they will receive. Both the participants and the researcher are unaware of which type of substance is provided. Balanced placebo designs, on the other hand, provide participants with instructions on whether or not they will receive certain substances. For example, some participants may be told that they would receive alcohol with caffeine but they are actually given alcohol without caffeine. This design allows researchers to



determine if the effects produced are experiential in nature or actual pharmacological effects from the substance. One recent study suggested that some of the effects of CABs versus alcohol may, in part, be due to the expectation of receiving a CAB (Heinz et al., 2013). Such findings warrant additional research utilizing balanced placebo designs to discern the true basis of these effects among users. In addition to determining the pharmacological versus experiential effects of CAB use, laboratory-based experimental designs also could be used to better identify the causality of CABs and various types of negative consequences. Thus far, many experimental designs investigating the potential harms of use have used measures such as motor coordination and reaction time (RT) tasks in order to measure alcohol-related impairment (e.g., Marczinski & Fillmore, 2006; Marczinski et al., 2012). As with any experimental research design, study findings may be limited by the representativeness of these measures of alcohol-related impairment and may not model real life behaviors. Future experimental work could benefit from using performance measures that represent actual harms indicated in cross-sectional research. For instance, instead of using computer tasks to determine one’s RT after drinking CABs versus alcohol, perhaps more “real world” tasks of RT could be performed (e.g., driving simulation tasks). Alternative to lab-based studies, research methodologies that use event sampling may provide more insight regarding use in real contexts. For example, Thombs et al.’s (2011) field study found that bar patrons who consumed CABs were at greater likelihood of engaging in risky behaviors, such as intending to drive, than those who consumed other types of alcohol. Other field studies found that bar patrons who consumed alcohol mixed with energy drinks experienced similar levels of intoxication as those who consumed cola-caffeinated alcohol (Thombs et al., 2011); however, those who consume diet caffeinated alcoholic drinks may be at additional risk for intoxication (Rossheim & Thombs, 2011). Thus, the use of event sampling such as field studies may help provide greater external validity to existing retrospective, self-report-based survey studies as well as lab-based studies of CAB use. The use of event sampling also could contribute to knowledge regarding the potential effect of various cognitive factors to moderate or mediate CAB use in a real world environment. For example, bar patrons’ motivation or outcome expectancies for drinking may be assessed prior to entering the bar and their BAC assessed after leaving the bar; thus, providing data on the temporal relationships as to help determine the direction of influence. The current review also suggests implications for further examination of expectancies and motives in a single theoretical model. Thus far, research has demonstrated that many CAB drinkers hold certain expectancies (Lau-Barraco & Linden, in press; LauBarraco et al., 2014; MacKillop et al., 2012; Mallett et al., in press; Varvil-Weld et al., 2013) and motivations for drinking CABs (e.g., Marczinski, 2011) that are unique to this beverage. Consequently, it may be useful to integrate these constructs into one theoretical model. Currently established theories, such as alcohol expectancy theory (Brown et al., 1980) and the motivational model of alcohol use (Cooper et al., 1995; Cox & Klinger, 1988, 1990) offer explanations for alcohol outcomes. Alcohol expectancy theory posits that an individual’s beliefs about the effects of alcohol can influence alcohol use (Brown et al., 1980). The motivational model of alcohol use accounts for both expectancies and drinking moti-

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vations (Cooper et al., 1995), with expectancies conceptualized as antecedents to drinking motives and motives serving as the more proximal predictor of use. Within the above framework for understanding CAB drinking patterns, it may be useful to consider drinking motives and expectancies that are most relevant to CAB users. These expectancies may predict an individual’s motivations for drinking CABs specifically (e.g., stronger expectations that CABs have energizing properties may lead a drinker to be motivated to drink CABs to stay awake and continue partying). In turn, greater specific motives for drinking CABs may predict greater actual consumption and a range of negative outcomes, including risky driving and sexual behaviors, drug use, caffeine-related physiological problems, and general alcohol problems (e.g., getting injured, blacking out). Furthermore, according to the situational-specificity hypothesis where drinking behavior is theorized to vary based on context (Wall, McKee, & Hinson, 2000), individuals who are in a bar or other setting where CABs are commonly consumed may have stronger, positive expectancies about CABs and may subsequently drink more in those contexts. Consequently, drinking context may be a moderator or antecedent to the CAB-specific conceptual model. Additionally, psychosocial risk factors such as higher perceived drinking norms could be another moderator, with these individuals engaging in heavier CAB consumption due to inflated perceptions of other’s CAB use. Overall, although preliminary, integrating cognitions that are specific to CABs into a larger theoretical framework could provide a more comprehensive understanding of CAB use patterns. Although preliminary evidence suggests that norms could be a salient determinant of CAB drinking patterns (Mallett et al., in press; Varvil-Weld et al., 2013), more research is needed to understand how one’s perceptions of others’ drinking can affect their own drinking behavior. For example, research is needed to understand the degree to which CAB drinking norms may influence or impact the association between CAB use and negative outcomes. Given the salience of perceived drinking norms in the general alcohol literature (e.g., Neighbors et al., 2007) and limited research suggesting that stronger CAB drinking norms predict greater CAB use (Mallett et al., in press; Varvil-Weld et al., 2013), CAB drinking norms may act as a mediator or moderator of CAB consumption and negative consequences. Beyond examining descriptive norms, it could be useful to examine injunctive norms that are more inclusive than just approval of CAB use but to assess also norms regarding engagement in other types of risky behaviors associated with drinking CABs. Given the number of behaviors that are especially amplified by CAB use (e.g., driving a car after drinking), it may be useful to know the degree to which one sees their friends as approving of these behaviors may impact their own CAB use. Research methodologies beyond cross-sectional studies are needed in order to acquire more complete information regarding CAB use patterns. Longitudinal data collection would allow observation of the way CAB use may change over time, such as over the course of a year or throughout college. To date, only one study has examined some longitudinal aspects of CAB consumption (Varvil-Weld et al., 2013). Varvil-Weld and colleagues collected data across two semesters on use, perceptions of use, and negative consequences. Findings showed that individual risk profiles were associated with CAB use and negative outcomes one semester later. This study represents an important first step in determining


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the prospective association between one’s cognitions about CABs and their actual drinking patterns. However, research is still needed to assess how CAB use may change over time, which factors account for changes in use, and how consumption may be associated with negative outcomes for some individuals more than others (i.e., moderators). For example, research has indicated that many first-year college students are often heavier consumers of CABs (O’Brien et al., 2008) and college students’ drinking patterns established during one’s first year can predict future college drinking (Wechsler, Isaac, Grodstein, & Sellers, 1994). Consequently, studies are needed to determine how CAB use may persist or increase throughout college. Longitudinal research also could be used to identify trends of CAB use after college. Trends of noncaffeinated alcohol consumption suggest that many college students mature out of drinking as they transition into adulthood (Jackson, Sher, Gotham, & Wood, 2001; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996). However, because CAB use is related to problematic alcohol use behaviors, some heavier users may not experience such a decline. Longitudinal research could determine whether the trends of CAB use are similar to that of alcohol in general. Although longitudinal methods allow observations of CAB use over time, daily diary assessments may be used to gain more in-depth, ecologically valid information on the social and cognitive mechanisms influencing CAB consumption. Particularly, a daily methodology could assess the context (e.g., where, when, with whom, and how) of CABs use as to illuminate whether CABs are consumed in a setting that may increase one’s odds of engaging in risk behaviors. For example, given that many students report using CABs to stay awake and continue partying (Marczinski, 2011), it may be key to determine if individuals are coadministering CABs with other types of stimulant drugs, such as caffeine pills, to achieve similar effects. Furthermore, within-subjects comparisons may address some concerns as to whether the negative effects of CABs are due to the risk-taking proneness of the drinker as opposed to the type beverage consumed (de Haan et al., 2012). One key and important methodological issue in the CAB literature is the operational definition of “use.” When examining CAB consumption, many researchers examine users compared with nonusers based on either their reports of past-month or past-year consumption (e.g., Brache & Stockwell, 2011); others compare frequency or quantity of consumption during a typical drinking week (e.g., Lau-Barraco et al., 2014). It may be that using such broad information does not adequately assess the way in which CABs are most commonly used and how use may relate to alcohol problems. With varying definitions, it is difficult to make comparisons of CAB use across studies, impeding our ability to understand specific patterns of use. Furthermore, when only examining CAB users versus nonusers, we are unable to assess if the mere use of CABs is predictive of problems or if the heaviness of use is more salient. Finally, given findings that impulsivity can underlie the relationship between CAB use and certain negative consequences but not others (e.g., Brache & Stockwell, 2011), controlling for impulsivity or risk-taking propensity when examining such associations in future research would be useful. Overall, as the field of CAB research expands, a better sense of the way in which CABs are consumed is necessary to address the harms associated with these beverages.

Practice Implications The present review of the literature provides implications for the advancement of existing alcohol interventions, such as brief motivational interventions (BMIs). The Brief Alcohol Screening and Intervention for College Students (BASICS; Dimeff, Baer, Kivlahan, & Marlatt, 1999) is one current alcohol BMI that is empirically supported to reduce alcohol outcomes among college students (see Larimer & Cronce, 2007 for a review). One aspect of these interventions often includes delivering personalized feedback, or individually tailored information to the participant (e.g., Borsari & Carey, 2000), including information regarding their alcohol use, negative consequences, alcohol expectancies, perceived drinking norms, and risk levels. As mentioned, CAB use is driven by multiple factors including perceived drinking norms and substance expectancies. These factors are often targeted in BMIs (Larimer & Cronce, 2007) like BASICS, and thus, CAB users may benefit from this particular approach. Further, it may be helpful to adapt BASICS to include more beverage-specific information based on the type of beverage consumed. Preliminary investigations have demonstrated that delivering beverage-specific information may increase the efficacy of reducing the frequency of high-risk beverage use, such as malt liquor (Werch et al., 2005). Such investigations have not, however, been applied to CAB use. Given that some drinkers inaccurately perceive that drinking CABs can reduce negative outcomes (e.g., drinking CABs allows one to drive safer; MacKillop et al., 2012), education on CABs and potential harms associated with their use may be helpful to include in existing alcohol educational resources and interventions (Century Council, 2003). Education on CABs could be incorporated into prevention programs such as Alcohol 101 Plus (Century Council, 2003), an interactive program that aids in reducing alcohol-related harms among college students (e.g., Barnett, Murphy, Colby, & Monti, 2007). Although such programs are often found to be less efficacious in reducing alcohol outcomes than in-person interventions (e.g., Carey, Carey, Henson, Maisto, & DeMartini, 2011), research supports computerized interventions to be one viable and cost-effective way to reduce alcohol consumption (Elliott, Carey, & Bolles, 2008). Lastly, given the salience of substance expectancies in predicting CAB use (e.g., Lau-Barraco & Linden, in press; Lau-Barraco et al., 2014; Mallett et al., in press; Varvil-Weld et al., 2013), another intervention strategy may be to adapt an expectancy challenge for CAB drinkers. Expectancy challenges have been supported as one way to reduce alcohol consumption (e.g., Darkes & Goldman, 1993, 1998; Lau-Barraco & Dunn, 2008). The overall goal of an expectancy challenge is to reduce alcohol consumption through modifying the drinkers’ beliefs about the positive effects of alcohol. To apply this approach to CAB use, however, researchers must first determine the range and accuracy of users’ beliefs about the effects of CABs. As mentioned, researchers would need to determine which beliefs are true pharmacological versus subjective effects (e.g., whether or not staying alert for longer is an experientially learned behavior or if it is a pharmacological effect). Targets of the expectancy challenge could then focus on inaccurate CAB-related beliefs.



Conclusions Although many studies have investigated the potential negative consequences of CABs, only a few have determined factors that may explain or impact these outcomes. This review of the literature on CABs suggests a need for researchers to examine these relationships more in depth by investigating factors such as a CAB user’s drinking motives, substance expectancies, perceived drinking norms, and risk-taking propensity. Further, assessing these associations through daily diary and longitudinal methodologies could allow for a deeper understanding of CAB drinking patterns. Understanding the factors associated with hazardous CAB use could inform the advancement of more effective prevention and intervention programming.

References Alford, C., Hamilton-Morris, J., & Verster, J. C. (2012). The effects of energy drink in combination with alcohol on performance and subjective awareness. Psychopharmacology, 222, 519 –532. doi:10.1007/s00213012-2677-1 Attwood, A. S. (2012). Caffeinated alcohol beverages: A public health concern. Alcohol and Alcoholism, 47, 370 –371. doi:10.1093/alcalc/ ags062 Attwood, A. S., Rogers, P. J., Ataya, A. E., Adams, S., & Munafo, M. R. (2012). Effects of caffeine on alcohol-related changes in behavioral control and perceived intoxication in light caffeine consumers. Psychopharmacology, 221, 551–560. doi:10.1007/s00213-011-2601-0 Baraona, E., Abittan, C. S., Dohmen, K., Moretti, M., Pozzato, G., Chayes, Z. W., . . . Lieber, C. S. (2001). Gender differences in pharmacokinetics of alcohol. Alcoholism: Clinical and Experimental Research, 25, 502– 507. doi:10.1111/j.1530-0277.2001.tb02242.x Barnett, N. P., Murphy, J. G., Colby, S. M., & Monti, P. M. (2007). Efficacy and mediation of counselor vs. computer-delivered interventions with mandated college students. Addictive Behaviors, 32, 2529 – 2548. doi:10.1016/j.addbeh.2007.06.017 Berger, L. K., Fendrich, M., Chen, H.-Y., Arria, A. M., & Cisler, R. A. (2011). Sociodemographic correlates of energy drink consumption with and without alcohol: Results of a community survey. Addictive Behaviors, 36, 516 –519. doi:10.1016/j.addbeh.2010.12.027 Borsari, B., & Carey, K. B. (2000). Effects of a brief motivational intervention with college student drinkers. Journal of Consulting and Clinical Psychology, 68, 728 –733. doi:10.1037/0022-006X.68.4.728 Brache, K., & Stockwell, T. (2011). Drinking patterns and risk behaviors associated with combined alcohol and energy drink consumption in college drinkers. Addictive Behaviors, 36, 1133–1140. doi:10.1016/j .addbeh.2011.07.003 Brown, S. A., Goldman, M. S., Inn, A., & Anderson, L. R. (1980). Expectations of reinforcement from alcohol: Their domain and relation to drinking patterns. Journal of Consulting and Clinical Psychology, 48, 419 – 426. doi:10.1037/0022-006X.48.4.419 Carey, K. B., Carey, M. P., Henson, J. M., Maisto, S. A., & DeMartini, K. S. (2011). Brief alcohol interventions for mandated college students: Comparison of face-to-face counseling and computer-delivered interventions. Addiction, 106, 528 –537. doi:10.1111/j.1360-0443.2010 .03193.x Century Council. (2003). Alcohol 101 Plus [Computer software]. Retrieved from http://www.alcohol101plus.org/home.html Cooper, M. L. (1994). Motivations for alcohol use among adolescents: Development and validation of a four-factor model. Psychological Assessment, 6, 117–128. doi:10.1037/1040-3590.6.2.117 Cooper, M. L., Frone, M. R., Russell, M., & Mudar, P. (1995). Drinking to regulate positive and negative emotions: A motivational model of alco-

151

hol use. Journal of Personality and Social Psychology, 69, 990 –1005. doi:10.1037/0022-3514.69.5.990 Cox, W. M., & Klinger, E. (1988). A motivational model of alcohol use. Journal of Abnormal Psychology, 97, 168 –180. doi:10.1037/0021-843X .97.2.168 Cox, W. M., & Klinger, E. (1990). Incentive motivation, affective change, and alcohol use: A model. In W. M. Cox (Ed.), Why people drink: Parameters of alcohol as a reinforcer (pp. 291–314). New York, NY: Gardner Press, Inc. Cox, W. M., & Klinger, E. (2004). A motivational model of alcohol use: Determinants of use and change. In W. M. Cox & E. Klinger (Eds.), Handbook of motivational counseling: Concepts, approaches, and assessment (pp. 121–138). Chichester, UK: Wiley, Ltd. Darkes, J., & Goldman, M. S. (1993). Expectancy challenge and drinking reduction: Experimental evidence for a mediational process. Journal of Consulting and Clinical Psychology, 61, 344 –353. doi:10.1037/0022006X.61.2.344 Darkes, J., & Goldman, M. S. (1998). Expectancy challenge and drinking reduction: Process and structure in the alcohol expectancy network. Experimental and Clinical Psychopharmacology, 6, 64 –76. doi: 10.1037/1064-1297.6.1.64 de Haan, L., de Haan, H. A., van der Palen, H. A., Olivier, B., & Verster, J. C. (2012). Effects of consuming alcohol mixed with energy drinks versus consuming alcohol only on overall alcohol consumption and negative alcohol-related consequences. International Journal of General Medicine, 5, 953–960. doi:10.2147/IJGM.S38020 Dimeff, L. A., Baer, J. S., Kivlahan, D. R., & Marlatt, G. A. (1999). Brief alcohol screening and intervention for college students: A harm reduction approach. New York, NY: Guilford Press. Elliott, J. C., Carey, K. B., & Bolles, J. R. (2008). Computer-based interventions for college drinking: A qualitative review. Addictive Behaviors, 33, 994 –1005. doi:10.1016/j.addbeh.2008.03.006 Ferreira, S. E., De Mello, M. T., Pompéia, S., & Souza-Formigoni, M. L. (2006). Effects of energy drink ingestion on alcohol intoxication. Alcoholism: Clinical and Experimental Research, 30, 598 – 605. doi:10.1111/ j.1530-0277.2006.00070.x Heinz, A. J., de Wit, H., Lilje, T. C., & Kassel, J. D. (2013). The combined effects of alcohol, caffeine, and expectancies on subjective experience, impulsivity, and risk-taking. Experimental and Clinical Psychopharmacology, 21, 222–234. doi:10.1037/a0032337 Heinz, A. J., Kassel, J. D., & Smith, E. V. (2009). Caffeine expectancy: Instrument development in the Rasch measurement framework. Psychology of Addictive Behaviors, 23, 500 –511. doi:10.1037/a0016654 Howland, J., & Rohsenow, D. J. (2013). Risks of energy drinks mixed with alcohol. Journal of the American Medical Association, 309, 245–246. doi:10.1001/jama.2012.187978 Howland, J., Rohsenow, D. J., Vehige Calise, T., MacKillop, J., & Metrik, J. (2011). Caffeinated alcoholic beverages: An emerging public health problem. American Journal of Preventive Medicine, 40, 268 –271. doi: 10.1016/j.amepre.2010.10.026 Huntley, E. D., & Juliano, L. M. (2012). Caffeine expectancy questionnaire (CaffEQ): Construction, psychometric properties, and associations with caffeine use, caffeine dependence, and other related variables. Psychological Assessment, 24, 592– 607. doi:10.1037/a0026417 Jackson, K. M., Sher, K. J., Gotham, H. J., & Wood, P. K. (2001). Transitioning into and out of large-effect drinking in young adulthood. Journal of Abnormal Psychology, 110, 378 –391. doi:10.1037/0021843X.110.3.378 Jones, B. T., Corbin, W., & Fromme, K. (2001). A review of expectancy theory and alcohol consumption. Addiction, 96, 57–72. doi:10.1046/j .1360-0443.2001.961575.x Jones, S. C., Barrie, L., & Berry, N. (2012). Why (not) alcohol energy drinks? A qualitative study with Australian university students. Drug


152


and Alcohol Review, 31, 281–287. doi:10.1111/j.1465-3362.2011 .00319.x Kponee, K. Z., Siegel, M., & Jernigan, D. H. (in press). The use of caffeinated alcoholic beverages among underage drinkers: Results of a national survey. Addictive Behaviors. Kuhns, J. B., Clodfelter, T. A., & Bersot, H. Y. (2010). Examining and understanding the joint role of caffeine and alcohol in facilitating violent offending and victimization. Contemporary Drug Problems, 37, 267– 287. Larimer, M. E., & Cronce, J. M. (2007). Identification, prevention, and treatment revisited: Individual-focused college drinking prevention strategies 1999 –2006. Addictive Behaviors, 32, 2439 –2468. doi:10.1016/j .addbeh.2007.05.006 Lau-Barraco, C., & Dunn, M. E. (2008). Evaluation of a single-session expectancy challenge intervention to reduce alcohol use among college students. Psychology of Addictive Behaviors, 22, 168 –175. doi:10.1037/ 0893-164X.22.2.168 Lau-Barraco, C., & Linden, A. N. (in press). Caffeinated alcohol users’ expectancies for caffeine versus alcohol. Substance Use & Misuse. Lau-Barraco, C., Milletich, R. J., & Linden, A. N. (2014). Caffeinated alcohol consumption profiles and associations with use severity and outcome expectancies. Addictive Behaviors, 39, 308 –315. doi:10.1016/ j.addbeh.2013.10.017 Linden, A. N. (2012). Caffeinated alcohol use among college students with anxiety. Unpublished master’s thesis, Old Dominion University, Norfolk, VA. Linden, A. N., D’Lima, G., & Lau-Barraco, C. (2012, November). Caffeinated alcohol and negative consequences: Are caffeine expectancies a mediator? Poster presented at the annual meeting of the Association for Behavioral and Cognitive Therapies, National Harbor, MD. MacKillop, J., Howland, J., Rohsenow, D. J., Few, L. R., Amlung, M. T., Metrik, J., & Calise, T. V. (2012). Initial development of a measure of expectancies for combinations of alcohol and caffeine: The caffeine ⫹ alcohol combined questionnaire (CACEQ). Experimental and Clinical Psychopharmacology, 20, 466 – 472. doi:10.1037/a0030539 Mallett, K. A., Marzell, M., Scaglione, N., Hultgren, B., & Turrisi, R. (in press). Are all alcohol and energy drink users the same? Examining individual variation in relation to alcohol mixed with energy drink use, risky drinking, and consequences. Psychology of Addictive Behaviors. doi:10.1037/a0032203 Marczinski, C. A. (2011). Alcohol mixed with energy drinks: Consumption patterns and motivations for use in U.S. college students. International Journal of Environmental Research and Public Health, 8, 3232–3245. doi:10.3390/ijerph8083232 Marczinski, C. A., & Fillmore, M. T. (2006). Clubgoers and their trendy cocktails: Implications of mixing caffeine into alcohol on information processing and subjective reports of intoxication. Experimental and Clinical Psychopharmacology, 14, 450 – 458. doi:10.1037/1064-1297.14 .4.450 Marczinski, C. A., Fillmore, M. T., Bardgett, M. E., & Howard, M. A. (2011). Effects of energy drinks mixed with alcohol on behavioral control: Risks for college students consuming trendy cocktails. Alcoholism: Clinical and Experimental Research, 35, 1282–1292. doi:10.1111/ j.1530-0277.2011.01464.x Marczinski, C. A., Fillmore, M. T., Henges, A. L., Ramsey, M. A., & Young, C. R. (2012). Effects of energy drinks mixed with alcohol on information processing, motor coordination and subjective reports of intoxication. Experimental and Child Psychopharmacology, 20, 129 – 138. doi:10.1037/a0026136 Marczinski, C. A., Fillmore, M. T., Henges, A. L., Ramsey, M. A., & Young, C. R. (2013). Mixing an energy drink with an alcoholic beverage increases motivation for more alcohol in college students. Alcoholism: Clinical and Experimental Research, 37, 276 –283. doi:10.1111/j.15300277.2012.01868.x

Marczinski, C. A., & Stamates, A. L. (2013). Artificial sweeteners versus regular mixers increase breath alcohol concentrations in male and female social drinkers. Alcoholism: Clinical and Experimental Research, 37, 696 –702. doi:10.1111/acer.12039 Miller, K. E. (2008). Energy drinks, race, and problem behaviors among college students. Journal of Adolescent Health, 43, 490 – 497. doi: 10.1016/j.jadohealth.2008.03.003 Miller, K. E. (2012). Alcohol mixed with energy drink use and sexual risk-taking: Casual, intoxicated, and unprotected sex. Journal of Caffeine Research, 2, 62– 69. doi:10.1089/jcr.2012.0015 Neighbors, C., Lee, C. M., Lewis, M. A., Fossos, N., & Larimer, M. E. (2007). Are social norms the best predictor of outcomes among heavydrinking college students? Journal of Studies on Alcohol and Drugs, 68, 556 –565. O’Brien, M. C., McCoy, T. P., Rhodes, S. D., Wagoner, A., & Wolfson, M. (2008). Caffeinated cocktails: Energy drink consumption, high-risk drinking, and alcohol-related consequences among college students. Academic Emergency Medicine, 15, 453– 460. doi:10.1111/j.1553-2712 .2008.00085.x Peacock, A., Bruno, R., & Martin, F. H. (2012). The subjective physiological, psychological, and behavioral risk-taking consequences of alcohol and energy drink co-ingestion. Alcoholism: Clinical and Experimental Research, 36, 2008 –2015. doi:10.1111/j.1530-0277.2012.01820.x Peacock, A., Bruno, R., & Martin, F. H. (2013). Patterns of use and motivations for consuming alcohol mixed with energy drinks. Psychology of Addictive Behaviors, 27, 202–206. doi:10.1037/a0029985 Peacock, A., Bruno, R., Martin, F. H., & Carr, A. (2013). The impact of alcohol and energy drink consumption on intoxication and risk-taking behavior. Alcoholism: Clinical and Experimental Research, 37, 1234 – 1242. doi:10.1111/acer.12086 Pennay, A., & Lubman, D. I. (2012). Alcohol and energy drinks: A pilot study exploring patterns of consumption, social contexts, benefits and harms. BMC Research Notes, 5, 369. doi:10.1186/1756-0500-5-369 Penning, R., de Haan, L., & Verster, J. C. (2011). Caffeinated drinks, alcohol consumption, and hangover severity. The Open Neuropsychopharmacology Journal, 4, 36 –39. doi:10.2174/1876523801104010036 Price, S. R., Hilchey, C. A., Darredeau, C., Fulton, H. G., & Barrett, S. P. (2010). Energy drink co-administration is associated with increased reported alcohol ingestion. Drug and Alcohol Review, 29, 331–333. doi:10.1111/j.1465-3362.2009.00163.x Rohsenow, D. J., Howland, J., Alvarez, L., Nelson, K., Langlois, B., Verster, J. C., . . . Arnedt, J. T. (2014). Effects of caffeinated vs. non-caffeinated alcoholic beverage on next-day hangover incidence and severity, perceived sleep quality, and alterness. Addictive Behaviors, 39, 329 –332. doi:10.1016/j.addbeh.2013.09.008 Rossheim, M. E., & Thombs, D. L. (2011). Artificial sweeteners, caffeine, and alcohol intoxication in bar patrons. Alcoholism: Clinical and Experimental Research, 35, 1891–1896. doi:10.1111/j.1530-0277.2011 .01534.x Schulenberg, J., O’Malley, P. M., Bachman, J. G., Wadsworth, K. N., & Johnston, L. D. (1996). Getting drunk and growing up: Trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol, 57, 289 –304. Snipes, D. J., & Benotsch, E. G. (2013). High-risk cocktails and high-risk sex: Examining the relation between alcohol mixed with energy drink consumption, sexual behavior, and drug use in college students. Addictive Behaviors, 38, 1418 –1423. doi:10.1016/j.addbeh.2012.07.011 Snipes, D. J., Green, B. A., Javier, S. J., Perrin, P. B., & Benotsch, E. G. (in press). The use of alcohol mixed with energy drinks and experiences of sexual victimization among male and female college students. Addictive Behaviors. Thombs, D. L., O’Mara, R. J., Tsukamoto, M., Rossheim, M. E., Weiler, R. M., Merves, M. L., & Goldberger, B. A. (2010). Event-level analyses


CAFFEINATED ALCOHOL USE: A REVIEW of energy drink consumption and alcohol intoxication in bar patrons. Addictive Behaviors, 35, 325–330. doi:10.1016/j.addbeh.2009.11.004 Thombs, D., Rossheim, M., Barnett, T. E., Weiler, R. M., Moorhouse, M. D., & Coleman, B. N. (2011). Is there a misplaced focus on AmED? Associations between caffeine mixers and bar patron intoxication. Drug and Alcohol Dependence, 116, 31–36. doi:10.1016/j.drugalcdep.2010.11 .014 Varvil-Weld, L., Marzell, M., Turrisi, R., Mallett, K. A., & Cleveland, M. J. (2013). Examining the relationship between alcohol-energy drink risk profiles and high-risk drinking behaviors. Alcoholism: Clinical and Experimental Research, 37, 1410 –1416. doi:10.1111/acer.12102 Verster, J. C., Aufricht, C., & Alford, C. (2012). Energy drinks mixed with alcohol: Misconceptions, myths, and facts. International Journal of General Medicine, 5, 187–198. doi:10.2147/IJGM.S29313 Wall, A., McKee, S. A., & Hinson, R. E. (2000). Assessing variation in alcohol outcome expectancies across environmental context: An exam-

153

ination of the situational-specificity hypothesis. Psychology of Addictive Behaviors, 14, 367–375. doi:10.1037/0893-164X.14.4.367 Wechsler, H., Isaac, N. E., Grodstein, F., & Sellers, D. E. (1994). Continuation and initiation of alcohol use from the first to the second year of college. Journal of Studies on Alcohol, 55, 41– 45. Werch, C. C., Jobli, E., Moore, M. J., DiClemente, C. C., Dore, H., & Brown, C. H. (2005). A brief experimental alcohol beverage-tailored program for adolescents. Journal of Studies on Alcohol, 66, 284 –290. Woolsey, C., Waigandt, A., & Beck, N. C. (2010). Athletes and energy drinks: Reported risk-taking and consequences from the combined use of alcohol and energy drinks. Journal of Applied Sport Psychology, 22, 65–71. doi:10.1080/10413200903403224

Received September 5, 2013 Revision received February 3, 2014 Accepted February 5, 2014 䡲

A Preliminary Investigation of Caffeinated Alcohol Use During Spring Break.

Caffeinated Alcohol, Sensation Seeking, and Injury Risk.

Caffeinated alcohol use and expectancies for caffeine versus alcohol.

Caffeinated and non-caffeinated alcohol use and indirect aggression: The impact of self-regulation.

Psychosocial Factors Associated With Adolescent Electronic Cigarette and Cigarette Use.

Psychosocial Factors and Health-Risk Behaviors Associated with Hookah use among College Students.

Identifying maternal risk factors associated with Fetal Alcohol Spectrum Disorders: a systematic review.

A comprehensive review of the effects of mixing caffeinated energy drinks with alcohol.

Risk Factors Associated With Hookah Use.

Correction: Risk Factors Associated With Epidural Use.

Alcohol and Cigarette Use Among Warsaw Adolescents: Factors Associated With Risk and Resilience.

Parental factors associated with mexican american adolescent alcohol use.

Risk factors associated with sacral stress fractures: a systematic review.

A systematic review of psychosocial factors associated with emotional adjustment in in vitro fertilization patients.

Factors associated with metabolic syndrome in a mediterranean population: role of caffeinated beverages.

Testing a model of caffeinated alcohol-specific expectancies.

Are caffeinated beverages risk factors for delayed conception?

Qualitative studies of psychosocial interventions for dementia: a systematic review.

Caffeinated alcohol consumption profiles and associations with use severity and outcome expectancies.

Higher Caffeinated Coffee Intake Is Associated with Reduced Malignant Melanoma Risk: A Meta-Analysis Study.

Binge drinking: a pattern associated with a risk of problems of alcohol use among university students.

Using Online Respondent Driven Sampling for Vietnamese Youths' Alcohol Use and Associated Risk Factors.

Adverse psychosocial outcomes associated with drug use among US high school seniors: a comparison of alcohol and marijuana.

Examination of Several Physiological and Psychosocial Factors Potentially Associated With Masked Hypertension Among Low-Risk Adults.