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College Students Consumption of Alcohol Mixed with Energy Drinks.


Research on substance abuse and misuse among college students has typically focused on alcohol consumption, illicit drug use and their sequelae. Yet, energy drink consumption can also be considered a form of substance use and abuse, although research in this area is in its infancy. Energy drinks are caffeinated beverages that claim to stimulate the body mentally and physically using a mixture of caffeine, plant-based stimulants, sugars, herbs, amino acids, metabolites and vitamins (O'Brien, McCoy, Rhodes, Wagoner, & Wolfson, 2008). Examples include products such as Red Bull[R], Monster[R], Rock Star[R], Amp[R], 5-hr Energy[R] and other similar beverages, but does not include beverages such as coffee, tea, or soda. Nonetheless, energy drinks typically contain around the same amount of sugar as a regular soda and around the same amount of caffeine as a cup of coffee, though they are not nearly as strictly regulated by the FDA as both coffee and soda. They often contain taurine, riboflavin and other substances whose effects have not been thoroughly studied. Furthermore, while the FDA sets limits on the amount of caffeine in soda, by claiming their products as natural dietary supplements, energy drink manufacturers are able to bypass such regulations (Seifert, Schaechter, Hershorin, & Lipshultz, 2012).

Energy drinks debuted in the United States in 1997, and consumption and popularity of the beverages have increased steadily in recent years, evidenced by the wide variety (300+) of energy drinks available and the variety of areas (including gas stations, grocery stores and campus vending machines, to name a few) from which a consumer can purchase an energy drink (Heckman, Sherry, & Gonzalez De Mejia, 2010). The increase in energy drinks and their use may be due, in part, to aggressive marketing strategies that promise improved mood, increased physical and mental performance and prolonged concentration time. Consequently, from 2008 to 2012, the energy drink market grew by nearly two-thirds, with an estimated U.S. market of $32 Billion by 2025 (Natural Product Insider, 2013; PR Newswire, 2017).

College students represent a significant segment of the energy drink market, with researchers reporting 30-day consumption patterns ranging from 22% to 70% depending on the year and geographical location of the respective studies (Gallucci, Martin, Hackman, & Hutcheson, 2016; Pettit & DeBarr, 2011). Further, 82% of college students reported consuming an energy drink at least once in their lifetime, and about a third (36.4%) had consumed energy drinks in the past two weeks (Marczinski, 2011). Perhaps the popularity of these products among this demographic relates to advertisements, many of which are geared toward people 18 to 34 years of age who have busy schedules and hectic lifestyles (Heckman, Sherry, & Gonzalez De Mejia, 2010).

The upsurge of energy drink consumption is not without safety concerns, especially when the product is pre-mixed with alcohol (e.g., Four Loko[R]). Indeed, the U.S. Food and Drug Administration (FDA) effectively banned the manufacturing and sale of alcoholic energy drinks in 2010 (FDA, 2010). While this legislation represents a healthy direction, alcohol mixed with energy drinks (AmEDs) (e.g., manually prepared cocktails such as RedBull[R] and vodka or Jagerbombs [Jagermeister[R] and Red Bull[R]]) nonetheless remain common among young adults. Approximately a third of college students reported consuming AmEDs during the past month and two-thirds indicated drinking them within the last year (Berger, Fendrich, Fuhrmann & 2013; Patrick, Macuada, & Maggs, 2016). Energy drinks alone accounted for roughly 12,000 emergency department visits in 2011, and another 2,600 were caused by their combination with alcohol (SAMSHA, 2013). Additionally, 8% of emergency department visits for energy drinks mixed with alcohol or other drugs resulted in hospitalization of the patient (SAMSHA, 2014).

Individuals who consume AmEDs are likelier to engage in binge-drinking behaviors and report specific reasons for their doing so. Of those who tried or regularly consumed AmEDs, 60% report binge drinking, compared to 28% of non-AmED consumers who binge drink (Marczinski, 2011). This disparity helps explicate previous findings, in which researchers found that over half of energy drink users consume them with alcohol while partying (Malinauskas, 2007). Moreover, Marczinski (2011) discovered that the motivation to consume AmEDs included the assumed benefits that AmEDs help one "hold [their] liquor," that the consumer can drink more alcohol by consuming AmEDs, and that AmEDs allow a person to get drunk faster (p. 3240).

Furthermore, the consumption of AmEDs has been linked with other risky behaviors and consequences, including greater risk for dependence tendencies. According to Berger (2013), hazardous drinkers--a term that refers to those who suffer from alcohol-related consequences--who have consumed AmEDs are more likely to drive with a BAC of > .08 than hazardous drinkers who did not consume AmEDs, and AmED users have been shown to drive while impaired at more than twice the rate of non-AmED users (Woolsey, 2015). Additionally, past-month and past-week analyses indicate those who consumed AmEDs had higher rates of drug use--including marijuana, ecstasy and ketamine--compared to alcohol-only users and non-drinkers (Snipes, 2015). AmED users were also associated with having more unprotected sex after having "too much to drink" (Snipes & Benotsch, 2013, p. 1420). Lastly, AmED users were found to be more likely than non-AmED users to be sexually assaulted or to commit sexual assault (O'Brien et al., 2008; Snipes, 2014).

While numerous studies on AmED have been conducted, to date the literature related to AmED in the U.S. college student population has not been critically reviewed and synthesized. Thus, the purpose of this study was to summarize the research on college students and AmEDs and identify potential gaps in the literature. More specifically, this systematic review explores the prevalence of energy drink consumption among college students, the association between AmED consumption and deleterious health outcomes and the link between AmED consumption and academics. A final focal point of this inquiry includes a critical analysis of the level of scientific rigor that was used in conducting previous research on AmEDs.


Articles included in the present review were limited to those in which data were collected from United States college students between 18 and 25 years old. In addition, articles published prior to 2006 were not included unless they were deemed seminal studies, mainly due to the lack of research on the topic prior to 2006. International studies were also excluded from this analysis, as laws, restrictions and cultural values related to energy drinks in other countries likely differ from those in the United States--differences that, if present, might significantly influence consumption rates and perceptions.

After determining the inclusion and exclusion criteria, the research team identified key search terms and specified appropriate databases from which to gather data. Keywords included energy drinks, college students, alcohol, AmED, as well as other iterations of these terms and terms in combination. Databases searched included Academic Search Complete, Education Resources Information Center (ERIC), MEDLINE, ProQuest Dissertations, PsycINFO, PubMed, Web of Science and Google Scholar. In addition, the reference lists of each selected article were reviewed to determine what other studies might be included in the review.

We first conducted an initial assessment of all study titles to determine eligibility for inclusion. If a study was considered eligible, a member of the team reviewed the abstract, and if information in the abstract aligned with the focus of the present study, we obtained and reviewed the full-text article. Eligible studies were then entered into a table that included author and year, purpose, study design, location, number of sites at which data were collected (e.g., universities), sample size, methods, reliability, validity, main results and limitations. To augment the search and to ensure interrater reliability, the co-authors reviewed each article for inclusion and exclusion criteria and corroborated their critiques of the various articles.


Figure 1 displays the study selection process. Of the 164 articles identified in the literature search, 60 were excluded by title review. Following the abstract review, 61 articles were excluded because they either did not include U.S. college undergraduate students between 18 and 25 or were not relevant. Forty-three full-text articles were reviewed, eight of which were removed from final analysis because non-college students were sampled, or the topic was not relevant to the focus of the study. The final number of articles included for systematic analysis was 35.

Purpose, design, location, target population, sample size, response rate and validity and reliability measures among the included studies are depicted in Table 1. The 35 articles included a fairly homogeneous trend of study designs: 29 were cross-sectional (83%), five were longitudinal (14%) and one was experimental (3%). Researchers from the various studies used the following sampling techniques: 19 used convenience sampling (54%), nine featured random sampling (26%), three employed stratified random sampling (8%), two utilized cluster sampling (6%), one included probability sampling (3%) and another used a combination of a convenience and stratified random sampling (3%). Data collection methods were mainly survey-based, with 19 utilizing web-based questionnaires (54%) and nine using pencil-paper questionnaires (26%). Three studies reported a daily diary format (9%), two included both observation and survey (6%), one used personal interviews (3%) and one other reported use of a web-based questionnaire, interviews and hair and fingernail collection (3%).

Response rates were reported in under half of the studies (n = 17, 46%). Response rates ranged from 19.7% to 98.1%, with an average response rate of 58% in the 17 applicable studies. Web-based response rates were on average 23% lower than response rates of studies conducted in-person. Authors mentioned they used standardized instruments in 13 of the studies, and Cronbaeh's alpha levels were reported in 17 of the studies; however, researchers did not always include reliability measures for all variables, and in two studies, no reliability information was included whatsoever. Only one study reported on instrument stability by including Wilcoxon signed-rank test (3%), and the remaining articles reported no test-retest measures. Lastly, few studies made any mention of face, construct, or content validity.

Locations for the included studies varied, with six from the Northeast (17%), six from the Midwest (17%), six from the East Coast (17%), five from the Mid-Atlantic (14%), three from the Southeast (9%), three from the Southwest (9%), one from the West Coast (3%) and five that did not list a location (14%). University or college size was fairly homogeneous: 22 of 35 were classified as large (at least 10,000 students), seven were midsized, one was a combination of large and small size, one did not list size (but stated the universities were NCAA Division III) and four others did not delineate any size parameters (The Carnegie Classifications of Institutions of Higher Education, 2014). There were 14 public institutions, one private institution and one other that included both a public and a private institution; 19 others did not list public or private classifications.

Target populations in the studies varied, from ten with undergraduate students in general (28%), seven with first year or freshman students (20%), four that included athletes (11%) and three with college students in general (9%). Additionally, three studies included college students who used alcohol and caffeine and experienced heavy episodic drinking in the past month (9%), two featured undergraduate AmED users (6%), one reported on college students with past year alcohol use and typical weekly use of Caffeinated Alcoholic Beverages (CABs, a pre- or self-mixed drink containing any caffeine and alcohol) (3%), one included college drinkers (3%), one had college students who experienced heavy episodic drinking twice in the past month (3%), one sampled non-smoking females undergraduates who were 21 or older (3%) and one other featured freshman alcohol drinkers (3%). Study populations ranged from as few as 27 participants in the experimental study, to as many as 4,271 students, with an average of 633 participants per study.

Regarding funding, 19 studies were federally funded, and two received internal funding from the researchers' respective universities, totaling 21 studies (60%) that were funded in some way. The National Institute of Alcohol Abuse and Alcoholism (NIAAA) (n = 15), the National Institute of Drug Abuse (NIDA) (n = 6), the National Institutes of Health (NIH) (n = 1) and the National Science Foundation (NSF) (n = 1) each funded studies included in this review, with six studies receiving funding from multiple sources. Collectively, funded studies were conducted with more rigor, evidenced by higher average sample sizes and a greater percentage of studies that reported response rates and psychometric measures. However, funded studies had a 20% greater prevalence of utilizing a convenience sample than did non-funded studies.

Measured Outcomes

Table 2 illustrates the measured outcomes, main results and limitations associated with the studies. Twenty-one of the 35 studies included AmED use or use patterns; another seven measured CAB use, 24 measured alcohol use or alcohol use patterns and 12 measured energy drink or caffeine use. Risky behaviors were accounted for in 15 studies, including topics of drunk or high-risk driving, drug/substance use, misuse of prescription drugs, tobacco use, heavy episodic/binge drinking, location of drinking, risky drinking activities, sexual behavior and general risk taking. Alcohol consequences were measured in 18 studies, which included short- and long-term consequences, including alcohol dependence and spring break-related problems. Consequences of AmED and energy drink use were measured in nine studies and included sleep problems, general side effects, sexual victimization, effects of AmED use on alcohol behaviors and general short- and long-term consequences and outcomes. Personality traits were assessed in 12 studies and included depression symptoms, athlete status, social context of drinking, impulsivity, AmED attitudes, AmED perceived norms, AmED beliefs, "jock" identity, masculine norms, activity involvement, living situation, academic demands, Theory of Planned Behavior variables and general personality traits. Personal motives were measured in 12 studies and featured reasons for CAB use, motivations and reasons for energy drink use, alcohol expectancies, caffeine expectancies, AmED expectancies, CAB expectancies, motivations and reasons for AmED use and alcohol motives. Finally, neuropsychologic assessment was measured in one study, and nutrition knowledge was measured in another.

Key Findings

Much of the conducted research was focused on epidemiologic findings, particularly the association between energy drink consumption and the related consequences. Past 30-day AmED consumption ranged from as low as 11% to 32% (Poulos & Pasch, 2016; Snipes et al., 2015). Further, lifetime AmED consumption ranged from just under 40% to 75% (Berger et al., 2013; Woolsey, 2010a). Men appeared to be as much as twice as likely to consume AmEDs than women (Miller, 2008; Poulos & Pasch, 2016; Velazquez et al., 2012; Woolsey et al., 2015). Moreover, white individuals held significantly higher rates of AmED consumption than individuals of other race/ethnicities (O'Brien et al., 2008; Velazquez et al., 2012). Alcohol users and energy drink users also had higher likelihood to consume AmEDs, and the rates of energy drink users who consumed AmEDs in the past year were as high 71% (Arria et al., 2016). Further, those who consumed energy drinks in the past month were nearly two times as likely to consume AmEDs than those who did not consume energy drinks (Velazquez et al., 2012). Rates of alcohol users who consumed AmEDs in the past month ranged from 24% to 30%, with one study reporting 78% of alcohol users having tried or regularly using AmEDs (Marczinski, 2011; Obrien et al., 2008; Snipes & Benotsch, 2013).

AmED users were more likely than alcohol-only users to participate in high-risk drinking (Arria et al., 2016; Marzell et al., 2014; Patrick et al., 2014). High-risk drinking measures included past 30-day number of days drinking, number of days "drunk," and number of heavy drinking episodes, among others (Woolsey et al., 2015). More specifically, 70% of those who have tried or who currently consume AmEDs reported high-risk drinking, compared to 28% of non-AmED users (Marczinski, 2011). Past-year hazardous drinkers who consumed AmEDs were more likely to drive a car above the legal alcohol consumption limit of BAC > .08 than those who were hazardous drinkers and did not consume AmEDs (Berger et al., 2013). AmED consumers were more likely than non-AmED consumers to drive while knowing they have had too much to drink, to believe they were over the legal limit and to ride with a driver who was under the influence (O'Brien et al., 2008; Woolsey et al., 2015).

Aside from binge drinking or drunk driving, AmED use is also commonly associated with risk-taking behaviors, including sexual, physical and academic consequences (Haas et al., 2017; Marzell et al., 2014; Miller, 2008; Woolsey, 2010a). For instance, past month and past week AmED users had higher rates of drug use--including marijuana, ecstasy and ketamine--than alcohol-only users and non-drinkers (Snipes et al., 2013). Further, AmED consumers were more likely than non-AmED consumers to be sexually assaulted, commit sexual assault, be hurt or injured, or require medical treatment (O'Brien et al., 2008). Lastly, AmED users were more likely than non-AmED or low frequency AmED users to report academic consequences (Haas et al., 2017; Marzell etal., 2014).

Reasons for energy drink consumption varied, with a common theme among ED users to drink with alcohol while partying (Hardy et al., 2017; Malinauskas et al., 2007). Some other common reasons for consuming AmEDs were that it tastes good, to hide the flavor of alcohol, to drink less and get drunk, to feel less drunk and to provide an energy boost (Cobb et al., 2015; O'Brien et al., 2008; Poulos & Pasch, 2016). When comparing men with women, men were more likely to report other reasons, including "I can party longer," "relaxation," and "1 can drink more alcohol and not feel as drunk" (Poulos & Pasch, 2016, p. 324).

While some researchers analyzed motivations and personality traits linked to energy-drinking consumption, only a few utilized theoretical constructs to further examine the behavior. Of note, students whose alcohol use contained a high proportion of AmEDs held more positive expectancies than those who did not use AmEDs as often and those who held negative beliefs in regard to AmEDs (Mallett et al., 2014; Varvil-Weld et al., 2013). Furthermore, following the examination of the "avoidance of negative consequences" (Linden-Carmichael el al., 2015, p. 39) expectancy, researchers found that students consumed energy drinks thinking it would keep them more alert (e.g., harm reduction); however, it turns out they were less likely to employ other protective strategies, more likely to consume more CABs and more likely to experience negative consequences. In the same study, researchers found, somewhat unexpectedly, that the expectancy of intoxication enhancement (i.e., more intense euphoric feeling), was not found to be a significant mediator between CAB use and experiencing harm (Linden-Carmichael et al., 2015). In another study, the constructs of behavioral intention and attitude from the Theory of Planned Behavior (leek, 1991) were significant predictors of AmED use (Reddy, 2014).


Energy drinks have become more prevalent since their introduction to the U.S. market two decades ago. Since that time, there have been numerous emergency room visits and hospitalizations due to energy drink and AmED consumption. While literature reviews exist on AmEDs, none were focused exclusively on college students. It is important to summarize research conducted specifically on college students due to the popularity of AmEDs with this group and the aggressive marketing practices directed at them (Heckman, Sherry, & Gonzalez De Mejia, 2010). The search criteria for the present review yielded 35 articles examining college students and AmEDs, the associated behaviors of using AmEDs, motivations of using AmEDs and consequences of using AmEDs. The earliest study was published in 2007, with a large majority of the studies published from 2013 onward. The increasing number of studies may be indicative of the growing concern of AmED consumption and the related consequences college students experience. The studies' purposes, designs, locations, target populations, sample sizes, response rates, psychometric measures, measured outcomes, main results and limitations were queried. Additionally, studies were analyzed based on scientific rigor and categorized topically.

Topical areas of the assessed articles were widespread. While the majority of the articles included information on consumption patterns of AmEDs, energy drinks, and alcoholic drinks, a variety of other variables were studied. For example, researchers examined risky behaviors associated with consuming energy drinks, such as the co-consumption of alcohol and drugs, drunk driving, as well as the risk factors for becoming dependent on psychotropic substances. Further, self-reported side-effects of use of energy drinks and AmEDs were studied by several researchers, including measures of anxiety, sleep disturbance and sexual victimization. To a lesser degree, personality traits--including "jock" identity, reasons or motivations for use and impulsivity and cravingwere also examined. Gaps in the literature exist and begin with a lack of qualitative research focused on the reasons for consumption. Equally important, more research is needed exploring longitudinal and experimental designs with a specific focus on interventions aimed at lowering AmED consumption. Lastly, theoretical constructs, for the most part, were not utilized widely. Theory-based research can help answer questions specific to attitudes, assumptions and motivations that might impact AmED use. These antecedents may be vital in impacting use and progressing with prevention strategies.

Results from the present review show prevalence rates varied, with past 30-day AmED use ranging from 11% to almost a third (Poulos & Pasch, 2016; Snipes et al., 2015). Additionally, being male, white, a current energy drink user, or a current alcohol user were each associated with higher rates of energy drink consumption (Arria et al., 2016; Marczinski, 2011; Miller, 2008; O'Brien et al., 2008; Poulos & Pasch, 2016; Snipes & Benotsch, 2013; Velazquez et al., 2012; Woolsey et al., 2015). AmED users were more likely than alcohol-only users to participate in high-risk drinking behaviors, with more than double the rate of AmED users reporting binge drinking than non-AmED users (Arria et al., 2016; Marczinski, 2011; Marzell et al., 2014; Patrick etal., 2014). AmED use was also associated with drunk driving, drug use, more negative academic consequences and a higher likelihood of being injured or requiring medical attention than non-AmED users (Berger et al., 2013; Haas et al., 2017; Marzell et al., 2014; O'Brien et al., 2008; Snipes et al., 2015; Woolsey et al., 2015). The most common reasons for consuming AmEDs included taste, to feel less drunk, to provide an energy boost, or to "party" longer (Cobb et al., 2015; O'Brien et al., 2008; Poulos & Pasch, 2016).

Overall, the studies were conducted with varying degrees of rigor. Most researchers used cross-sectional designs (83%) to conduct their studies, designs that do not allow for examination of cause and effect relationships involving AmEDs. Convenience samples were used in approximately half of the studies (54%), and none drew participants from a national sample, thus limiting generalizability of the results. While response rates were typically over 50%, only half of the articles included such information. Incidentally, 80% of the web-based questionnaires yielded responses in which fewer than half of the sample participated. Indeed, questionnaires administered in-person had an average response rate approximately 23% higher than those administered online, findings consistent with past research showing that web-based surveys generally elicit a lower response rate than those administered in person (Nulty, 2008). Validity and reliability testing were reported in more than half of the studies (53%), and two-fifths (41%) used a standardized instrument. Overall, the sample sizes obtained by the researchers were adequate, with more than three-quarters of the studies containing more than 300 participants. A total of 21 studies were funded at the federal level (54%) or internally with money from the university (6%). In general, researchers who received funding conducted their research with more scientific rigor than those who did not; however, more funding is required to conduct a study that encompasses a national data set.

Nonetheless, the findings from this systematic review should be considered with attention to a few limitations. Despite the extensive review strategy, some research may have been inadvertently missed, though the research team minimized this possibility by employing a diverse search strategy using web-based search engines with multiple search terms as well as reviewing reference sections of each article. Unpublished studies (e.g., gray literature) might also exist and could include insightful information, yet assessing these sorts of publications was not feasible for this particular sort of inquiry. Moreover, the heterogeneity of the methods (study population, campus size, response rates and reliability and validity) used in the included studies makes it difficult to provide definitive conclusions regarding the research conducted in this area. Lastly, the consuming of energy drinks while drinking alcohol (i.e., non-mixing of the two) may not have been delineated or controlled in some studies. In other words, the authors in some studies did not discuss--or perhaps track--whether participants consumed alcohol while also drinking an energy drink (non-alcoholic) versus mixing an energy drink with alcohol (e.g., "Red Bull[R] & vodka"). While this differentiation may be a moot point, as alcohol and energy drinks are digested essentially the same either way, the different ways drinks are marketed towards consumers might nonetheless influence consumption patterns.

While energy drinks have only been in the US for a relatively short period of time, and AmEDs even less so, there are serious negative consequences associated with their use, including higher rates of binge drinking, more frequent alcohol use and increased rates of drunk driving. Based on the results from this literature review, future research needs to be executed with more scientific rigor. Areas of potential focus include utilizing randomized sampling methods, collecting and reporting of response rates and reporting reliability and validity measures. Furthermore, intervention research needs to be performed utilizing longitudinal research rather than simply describing the problem through the use of cross-sectional surveys. Finally, qualitative research with college students would allow researchers to further investigate use patterns, reasons for use and the assumptions surrounding energy drink and AmED use. Journal editors, reviewers and grant funders must uphold high research standards to ensure confidence in the results of the various studies.

In sum, the field would benefit from a national data source that allows researchers to more accurately assess the prevalence of AmED use and related issues. Perhaps energy drink survey items could be added to nationally recognize standardized instruments such as the National College Health Assessment II or the Core Alcohol and Drug Survey so universities can make comparisons regarding the prevalence of AmEDs and energy drink consumption (American College Health Association, 2016; Southern Illinois University--Carbondale & Core Institute, 2017). Additional research should also be conducted to further parse out the motivations or expectancies of AmED use and associations with other substance use among college students. As evidenced by the results of this review, funding positively impacts the scientific rigor researchers employ with their studies; therefore, increased funding should be directed at examining AmED use to ensure more sophisticated study designs.

With regard to policy, advocacy for changes at the state level should be initiated, some of which might include new regulations requiring energy drink cans or bottles to have warning labels that deter mixing of alcohol with energy drinks. Moreover, limiting availability--more specifically, implementing policies that restrict the consumption of AmEDs at bars and restaurants--represent a fundamental step in addressing this issue. Within higher education, college students need additional education concerning health hazards associated with mixing alcohol with energy drinks, a task that could be accomplished via presentations during campus orientation and/or first year experience courses as well as through health communication campaigns. Finally, AmED use and the severity of the associated consequences warrant greater recognition and attention by university officials. Equipping practitioners with the knowledge and skills necessary to help prevent this behavior will enhance wellness and perhaps academic outcomes among college students.

Correspondence concerning this article should be addressed to: Aaron C. Luneke, Ph.D., Evidence-based Prevention and Intervention Support Center (EPISCenter), Edna Bennett Pierce Prevention Research Center, College of Health and Human Development, The Pennsylvania State University, 206 Towers Building, University Park, PA 16802


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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-relatedconsequences among college students. Academic Emergency Medicine, 15(5), 453-460. doi:10.1111/j.l553-2712.2008.00085.x

Patrick, M. E., Evans-Polce, R. J., & Maggs, J. L. (2014). Use of alcohol mixed with energy drinks as a predictor of alcohol-related consequences two years later. Journal of Studies on Alcohol & Drugs, 75(5), 753-757.

Patrick, M. E., Macuada, C, & Maggs, J. L. (2016). Who uses alcohol mixed with energy drinks? Characteristics of college student users. Journal of American College Health, 64(\), 74-79.

Pettit, M. L., & DeBarr, K. A. (2011). Perceived stress, energy drink consumption, and academic performance among college students. Journal of American College Health, 59(5), 335-341.

Poulos, N. S., & Pasch, K. E. (2016). Socio-demographic differences in energy drink consumption and reasons for consumption among US college students. Health Education Journal, 75(3), 318-330.

Reddy, S. G. (2014). The examination of mixing alcohol and energy drinks among college undergraduates using the theory of planned behavior. (75), ProQuest Information & Learning, U.S. Retrieved from Available from EBSCOhost psyh database.

Seifert, S. M., Schaechter, J. L., Hershorin, E. R., Lipshultz, S. E. (2011). Health effects of energy drinks on children, adolescents, and young adults. Pediatrics, 127(3), 511-528.

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, 35(1), 1418-1423. doi:10.1016/j.addbch.2012.07.011

Snipes, D. J., Green, B. A., Javier, S. J., Perrin, P. B., & Benotsch, E. G. (2014). The use of alcohol mixed with energy drinks and experiences of sexual victimization among male and female college students. Addictive Behaviors, 39(1), 259-264. doi:

Snipes, D. J., Jeffers, A. J., Green, B. A., & Benotsch, E. G. (2015). Alcohol mixed with energy drinks are robustly associated with patterns of problematic alcohol consumption among young adult college students. Addictive Behaviors, 41, 136-141. doi:10.1016/j.addbeh.2014.10.010

Southern Illinois University--Carbondale & Core Institute. (2017). Surveys: Long form. Retrieved from

Stamates, A. L., & Lau-Barraco, C. (2017). Environmental context effects on craving among consumers of caffeinated alcohol beverages: Associations with aspects of impulsivity. Experimental and Clinical Psychopharmacology, 25(6), 503-511.doi:10.1037/pha0000160

Substance Abuse and Mental Health Services Administration. (2013). The DAWN Report: Update on emergency department visits involving energy drinks: A continuing public health concern. Retrieved from

Substance Abuse and Mental Health Services Administration. (2014). The DAWN Report: 1 in 10 energy drink-related emergency department visits results in hospitalization. Retrieved from 124-energy-drinks-2014.pdf

United States Food & Drug Administration. (2010). Caffeinated Alcoholic Beverages. Retrieved from

University of Maryland. (2014). Energy drinks: Fact sheet. Retrieved from Sheet.pdf

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(8), 1410-1416.

Velazquez, C. E., Poulos, N. S., Latimer, L. A., & Pasch, K. E. (2012). Associations between energy drink consumption and alcohol use behaviors among college students. Drug and Alcohol Dependence, 123, 167-172.

Woolsey, C. (2010). Energy drink cocktails: A dangerous combination for athletes and beyond. Journal of Alcohol and Drug Education, 54(3), 41-68.

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(1), 65-71. doi:10.1080/10413200903403224

Woolsey, C. L., Williams, R. D., Housman, J. M., Barry, A. E., Jacobson, B. H., & Evans, M. W. (2015). Combined use of alcohol and energy drinks increases participation in high-risk drinking and driving behaviors among college students. Journal of Studies on Alcohol and Drugs, 76(4), 615-619. doi:10.15288/jsad.2015.76.615

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Study Design and Characteristics

Author & Year  Purpose                  Study Design

Arria, 2016    1) Describe patterns     * 10-ycar prospective
               of ED consumption.       longitudinal.
               recusing on
               estimating the
               prevalence of            * Interview.
               Ml) consumption
               with and without
               alcohol,                 * Convenience sample.
               2) Examine how
               ED consumption
               patterns explained
               the variance in          * Data captured in year 6.
               drunk driving
               behavior after
               taking alcohol use
               patterns and
               several background
               risk factors
               into account.

Berger, 2013   1) Estimate AmED         * Cross-sectional:Web-based
               use prevalence,          survey.
               AmED use, before
               the FDA ban on           * Interview.
               pre-mixed AmEDs.
               2) Examine the
               associations             * Hair and fingernail collection
               among AmED use.          for a direct alcohol biomarker.
               hazardous drinking
               and multiple
               consequences.            * Probability sample.

Bonar, 2017    Examine links            * Cross-sectional:Web-based
               among AmED               survey.
               use, depression
               symptoms and sleep
               problems among           * Convenience sampling.
               college student
Cobb, 2015     1) Track the use         * Cross-sectional:Web-based
               patterns of alcohol      survey.
               combined with
               different caffeine
               additives.               * Convenience sample.
               2) Compare CAB
               users and those
               who drank alcohol
               3) Compare drinking
               and reasons for
               CAB use based on
               the type of beverage
Curry, 2009    Examine the              * Double-blind, placebo
               neuropsychological       controlled experimental.
               after consuming a
               beverage containing
               caffeine and             * Observation and survey.
                                        * Random sample.
Gallucci,      1) Explore the           * Cross-sectional:Survey.
2016           differences in ED
               consumption and
               motivations of
               ED use, based on         * Cluster sample.
               athlete status.
               2) Identify
               potential ED
Haas, 2017     Understand how           * Cross-sectional: Web-based
               AmEDs impact             and paper-pencil survey.
               problematic              * Convenience and a cluster
               drinking.                sample.
Hardy, 201 7   Identity the             * Cross-sectional:Web-based
               relationships            survey.
               among ED
               knowledge and            * Cluster sample.
               among Division
Lau-Barraco,   1) Examine how           * Cross-sectional: Survey.
2014a          predictive caffeine
               and alcohol
               expectancies are
               in regard to CAB         * Convenience sample.
               use outcomes
               including CAB
               use quantity and
               frequency, and
               2) Explore and
               compare the
               influence of
               versus caffeine
               expectancies in
               explaining CAB
Lau-Barraco,   1) Identify              * Cross-sectional: Survey.
2014b          categories of CAB
               2) Examine
               between                  * Convenience sample.
               categories on
               measures that
               include the
               severity of
               alcohol and
               caffeine use.
               3) Examine
               between categories
               with regards to
               and alcohol
Linden-        To test different        * Cross-sectional: Survey.
Carmichael,    types of CAB-specific
2015           expectancies
               as mediators
               in a model that          * Convenience sample.
               include CAB use.
               expectancies, use
               of Protective
Linden-        1) Identify the          * Cross-sectional:Web-based
Carmichael,    prevalence of            survey.
2016           Spring Break CAB
               2) Determine how         * Convenience sample.
               college students'
               CAB use dilfers
               from their normal
               use and whether
               they vacationed
               during Spring
               3) Examine the
               relationship between
               CABS used during
               Spring Break and
Linden-        1) Examine               * Cross-sectional.
Carmichael,    the social and
2017a          environmental            * 14-day daily diary.
               characteristics of
               AmED use.
               comparing days
               where they
               used AmEDs               * Convenience sample.
               and days they did
               not use AmEDs.
               2) Examine
               the impact of
               drinking contexts
               assoeialed with
               AmED use.
Linden-        1) Compare days          * Cross-sectional.
Carmichael,    mixing any type
2017b          of caffeine with         * 14-day daily diary.
               alcohol was
               2) Test the
               impact of the            * Convenience sample.
               caffeine mixer by
               comparing mixed
               and non-mixed
               days on drinking
Linden-        1) Determine             * Cross-sectional.
Carmichael,    which motives
2017c          were associated          * 14-day daily diary.
               with the odds of
               using AmEDs.
               2) Compare the
               relationships            * Convenience sample.
               between drinking
               motives measured
               daily and at
               baseline predicting
               daily AmED
               use, in order to
               determine which
               motives were
               more noticeable in
               predicting AmKD
Malinauskas,   1) Determine HI)         * Cross-sectional: Survey.
2007           use patterns.
               2) Determine
               prevalence and           * Random sample.
               frequency of ED
               use for different
               situations or
               reasons, including
               insufficient sleep,
               to increase energy.
               while studying,
               driving for long
               periods, drinking
               with alcohol while
               partying and to
               treat a hangover.
               3) Determine the
               prevalence of
               adverse side-effects
               and ED use
Mallett, 2014  1) Identify specific     * Cross-sectional:Web-based
               risk-profiles using      survey.
               both AmED use
               and alcohol-only
               use.                     * Random sample.

               2) Compare the
               AmED risk-pro-files
               with regard to
               AmED expectancies,
               attitudes and
               3) Compare the
               AmED risk-profiles
               with regard
               to the amount and
               different types of
Mullett, 2015  1) Examine differences   * Longitudinal: Web-based
               in AmED                  survey.
               use patterns
               between the first
               and second years         * 6-monlb follow-up.
               of college.
               2) Examine
               differences in the       * Random sample.
               rates of high-risk
               drinking behaviors
               and consequences.
Marczinksi,    Examine consumption      * Cross-sectional:Web-based
2011           habits and motivations   survey.
               for using AmEDs.         * Convenience sample.
Mar/ell.       Examine differences      * Longitudinal: Web-based
2014           in attitudes.            survey.
               beliefs, and normative
               of AmEDs among
               students who use         * Random sample.
               AmEDs compared
               to students who
               consume only
Miller, 2008   Examine gender           * Cross-sectional: Survey.
               links among
               sport-related identity,
               endorsement              * Convenience sample.
               ol masculine
               norms, risk-taking
               behaviors and
               ED use.
O'Brien.       Investigate the          * Cross-sectional:Web-based
2008           relationship between     survey.
               ED use. high-risk
               drinking behavior
               and alcohol-related
               consequences.            * Stratified, random sample.

Patrick, 2014  Investigate the          * Longitudinal: Web-based
               association              survey.
               between past-month
               AmED, alcohol-related    * Stratified, random sample.
               problems symptoms, and
               accidents for two
Patrick, 2016  Examine the              * Cross-sectional:Web-based
               characteristics          survey.
               of AmED use.
               activity involvement,    * Random sample.
               academic demand, and
               drinking motives.
Poulos, 2016   Examine                  * Cross-sectional:Web-based
               characteristics ot       survey.
               current ED users and
               AmED users to
               better understand        * Random sample.
               ED use behaviors
Reddy,2014     1) Examine which         * Cross-sectional: Survey.
               constructs from
               the Theory of
               Planned Behavior
               predict alcohol          * Convenience sample.
               mixed with energy
               drink (AmED)
               2) Estimate the
               prevalence and
               explain correlates
               of AmED consumption,
Snipes, 2013   1) Examine the           * Cross-sectional:Web-based
               relationship between     survey.
               AmED use and
               drug use.
               2) Examine the           * Convenience.
               association between
               AmED use and
               high-risk sexual
Snipes, 2014   1) Examine               * Cross-sectional:Survey.
               the association
               between AmED
               use and sexual           * Convenience sample.
Snipes, 2015   Describe associations    * Cross-sectional:Web-based
               between                  survey.
               AmED use and alcohol
               patterns.                * Convenience sample.

Stamates,      1) Determine             * Cross-sectional
2017           the impact of
               context on craving       * Observational.
               ratings for alcohol
               and CABs.                * Convenience sample.
               2) Test inhibitory
               control as a
               mediator between
               the association
               of context and
               3) Examine associations
               between impulsivity and
               craving for alcohol
               and CABs.
Varvil-Weld,   1) Identify              * Longitudinal: Web-based
2013           risk-profiles of         survey.
               college students
               based on AmED
               constructs including     * 6-month follow-up.
               attitudes, and
               2) Examine the           * Random sample.
               associations between
               AmED use.
               alcohol use, and
Velazquez,     Explore associations     * Cross-sectional:Web-based
2012           between ED use and       survey.
               alcohol use.             * Randomsample.
Woolsey,       1) Examine               * Cross-sectional:Survey.
2010a          patterns of alcohol
               use. combined use.
               and ED use among         * Random sample.
               student athletes.
               2) Investigate the
               differences within
               combined users
               on risk taking
               and consequences
               when comparing
               alcohol-only to
               combined use.
Woolsey,       1) Measure               * Cross-sectional: Survey.
2010b          alcohol. ED and
               combined use of
               student athletes.        * Random sample.
               2) Compare reported
               and consequences,
               alcohol-only and
               combined use.
Woolsey.       Compare risky            * Cross-sectional:Web-based
2015           driving behaviors        survey.
               and high-risk
               drinking behaviors
               between AmED             * Convenience sample.
               users and alcohol-only

Author & Year  Study Location    Target Population     Sample

Arria, 2016    * Large public    * 6th year following    1.000
               university.       students since
                                 freshman year.
               * Mid-Atlantic.
                                 * Ages 23-25
Berger, 2013   * Large public-   * Undergraduate       600
               university.       students.
               * Urban           * Ages 18-25.
               metropolis in
               the Midwest.
Bonar, 2017    * Large, public   * Undergraduate       560
               university.       students.
               * Midwest.        * 18 and older.
Cobb, 2015     * Large, urban    * 1 Indergraduate       1.174
               university.       students.
               * Mid-Atlantic.   * 18 and older.
Curry, 2009    * N/A             * Non-smoking.         27
                                 * Female college
                                 * 21 years or older.
Gallucci,      * Large, private  * Undergraduate       205 athletes.
2016           university.       students.
                                                       487 teles.
               * Southwest.      * Varsity athletes
                                 and non-athletes.
                                 * Ages 18-25.
Haas, 2017     * Two             * Undergraduate       458
               independent       students.
               California        * Reported AmED
               universities.     use in the past
                                 three months.
               * Site I, large,
               urban, public
               * Site 2, small,
Hardy, 201 7   * 5 NCAA D-III    * Undergraduate       194
               colleges and      varsity athletes.
                                 * 18 and older.
               * Upper Midwest.
Lau-Barraco,   * Mid-sized,      * College students.   419
2014a          urban             18 to 25.
                                 * <1 alcoholic
               * East coast.     beverage per
                                 * <1 caffeinated
                                 beverage per
Lau-Barraco,   * Mid-sized.      * College students.   583
2014b          university.
                                 * 18 to 25.
               * East coast.
                                 * Alcohol
                                 consumption at
                                 least once in the
                                 past 12 months.
                                 * Consume CABs
                                 at least once
                                 during a typical
Linden-        * Mid-sized.      * College drinkers.   322
Carmichael,    university.
               * East coast.
Linden-        * Mid-sized.      * College students.    95
Carmichael,    university.
2016                             * 18 to 25.
               * East coast.
                                 * Heavy episodic
                                 drinking at least
                                 twice in the past
Linden-        * Mid-sized,      * College students.   122
Carmichael,    public
2017a          university.       * 18 to 25.
               * East coast.
                                 * Consumed
                                 caffeine mixed
                                 with alcohol In
                                 past week.
                                 * Heavy episodic
                                 drinking in past
                                 * Daily internet
Linden-        * Public-         * College students.   122
Carmichael,    university.
2017b                            * 18 to 25.
               * Bast coast.
                                 * Consumed
                                 caffeine mixed
                                 with alcohol in
                                 past week.
                                 * Heavy episodic
                                 drinking in past
                                 * Daily internet
Linden-        N/A               * College students.   122
Carmichael,                      18 to 25.
                                 * Consumed
                                 caffeine mixed
                                 with alcohol in
                                 past week.
                                 * Heavy episodic
                                 drinking in past
                                 * Daily internet
Malinauskas,   * Rural, public   * College students.     4%
2007           university.
               * Mid-Atlantic.
Mallett, 2014  * Large           * Undergraduate       195
               university.       students.             AmED
               * Northeast.
Mullett, 2015  * Large, public   * Freshman college      1.710
               university.       students.
               * Northeast.      * Alcohol users.
Marczinksi,    * Public          * I 'ndergraduate     706
2011           university.       students.
               * Southeast.
               * 12.000
Mar/ell.       * Large.          * first year college  386
2014           university.       students.
               * Northeast.
Miller, 2008   * Large, public   * Undergraduate       795
               university.       students.
               * Northeast.
O'Brien.       * 10              * Undergraduate       4271:
2008           universities.     students.             Target
                                                       of 365
               * North                                 per
               * Southeast.
Patrick, 2014  * Large, public,  * Freshman            544-620
               university.       college
Patrick, 2016  * Large,          * Freshman            614
               university.       college
               * Northeast.
                                 * Under 21 years
Poulos, 2016   * Large, public   * First year          603
               university.       college
               * Southwest.
Reddy,2014     * Large, public   * Undergraduate       605
               university.       students.
               * Southeast.      * Ages 18-24.
Snipes, 2013   * University.     * Undergraduate       549-624
               * Southeast.
Snipes, 2014   * University.     * College             798
               * Mid-Atlantic.
Snipes, 2015   * University.     * Undergraduate       757
               * Mid-Atlantic.
                                 * Ages 18-25.
Stamates,      * Mid-sized,      * Undergraduate       143
2017           university.       students.
               * East coast.     * 18-25.
                                 * Having
                                 consumed any
                                 alcohol in the
                                 past 30 days.
                                 * No history of
                                 * Normal color
                                 vision, and
                                 to normal vision.
Varvil-Weld,   * large, public   * Incoming            387
2013           university.       freshman
               * Northeast.
Velazquez,     * Large, public-  * Incoming            603
2012           universily.       freshman
               * Southwest.      students.
Woolsey,       * Large. D-l      * College             401
2010a          university.       athletes.
               * Midwest.
Woolsey,       * Large, D-l      * College             401
2010b          university.       athletes.
               * Midwest.
Woolsey.       * Large.          * College             549
2015           university.       students.
               * Midwest.

Author & Year  Response        Validity &      Notes
               Rate            Reliability

Arria, 2016    80.00%          N/A             No validity or
                                               reliability was reported;
                                                however, the measures
                                               used were cited from
                                               previous research.

Berger, 2013   54.00%          N/A             No validity or
                                               reliability was reported;
                                               however, the measures
                                               used were cited from
                                               previous research.

Bonar, 2017    N/A             N/A             No validity or
                                               reliability was reported;
                                               however, the measures
                                               used were cited from
                                               previous research and
                                               were reviewed by content

Cobb, 2015     N/A             N/A             No validity or
                                               reliability was reported;
                                               however, the measures
                                               used were cited from
                                               previous research.

Curry, 2009    N/A             RBANS:          N/A
                               .84. Delayed
                               memory .84.
                               .77. Attention
                               .84. and
                               Language .75.

Gallucci,      98.10%          N/A             No validity or
2016                                           reliability was reported;
                                               however, the measures
                                               used were cited from
                                               previous research and
                                               were reviewed by content
                                               and survey design
                                               experts, as well as pilot
                                               tested by a small sample
                                               of college students.

Haas, 2017     N/A             Young Adult     No validity or
                               Alcohol         reliability was reported
                               Consequences    for other measures;
                               Questionnaire,  however, they were cited
                               Cronbach's      by previous research.
                               Drinking: .79
                               Control: .70
                               .44 Risky
                               .69 Social
                               in Self Care:
                               Alcohol Use
                               Test (AUDIT):

Hardy, 201 7   19.70%          Cronbach        N/A
                               alpha, .74 for
                               overall score

Lau-Barraco,   N/A             Comprehensive   No validity or
2014a                          Effects         reliability was repoted
                               of Alcohol:     for the Daily Drinking
                               IC, .95         Questionnaire; however,
                               Caffeine        it is cited by previous
                               Expectancy      research.
                               IC, .93
                               Young Adult
                               IC, .93

Lau-Barraco,   N/A             Alcohol         No validity or
2014b                          E.xpectancy     reliability was repoted
                               Question-       for the Daily Drinking
                               naire: IC. .66  Questionnaire; however,
                               to .86          it is cited by previous
                               Caffeine        research.
                               IC, .84
                               to .91
                               Alcohol Use
                               lest: .82
                               .83 and

Linden-        N/A             Caffeine        No validity or
Carmichael,                    plus Alcohol    reliability was repoted
2015                           Combined        for the Daily Drinking
                               Effects         Questionnaire; however,
                               Questionnaire:  it is cited by previous
                               Intoxication    research.
                               .80 and
                               of negative
                               Survey. .85
                               Young Adult

Linden-         N/A            Brief Young     No validity or
Carmichael,                    Adult Alcohol   reliability was repoted
2016                           Consequences    for the Daily Drinking
                               Questionnaire,  Questionnaire; however,
                                .89            it is cited by previous

Linden-        N/A             N/A

Linden-        N/A             The Barrett     N/A
Carmichael,                    Impulsiveness
2017b                          Scale,
                               of .85.
                               Brief Young
                               Adult Alcohol
                               of .90.

Linden-        51.50%          N/A             No validity or
Carmichael,                                    reliability was reported;
2017c                                          however, the measures
                                               used were cited by
                                               previous research.

Malinauskas,   N/A             N/A             Questionnaire was
2007                                           designed after focus
                                               groups totaling 32
                                               students, as well as
                                               being field tested by ten
                                               other students. No
                                               validity or reliability

Mallett, 2014  51.00%          AmED            No validity or
                               Expectancies,   reliability was reported;
                               Cronbach's,     however, the measures
                               .71             used were cited by
                               AmED            previous research.
                               Young Adult

Mullett, 2015  N/A             Daily Drinking  N/A
                               Time 1: .76
                               lime 2: .77
                               AmED DDQ
                               Time 1: .79
                               lime 2: .77
                               Young Adult
Marczinksi,    58.00%          N/A             No validity or
2011                                           reliability was reported;
                                               however, the motivation
                                               items were fueled by a
                                               pilot study, and measures
                                               cited by previous

Mar/ell.       75.40%          Beliefs,        No validity or
2014                           Cronhach's,     reliability was reported;
                               ,78             however, the measures
                               Alcohol         used were cited by
                               related         previous research.
                               .60 to .75

Miller, 2008   53.00%          Jock Identity:  N/A
                               subscale; .81
                               subscale: .69
                               10-item risk
                               scale: .57
                               subscale, .81
                               Norms scale,
O'Brien.       N/A             N/A             No validity or
2008                                           reliability was reported;
                                               however, the measures
                                               used were cited by
                                               previous research.

Patrick, 2014  66.00%          Rutgers         No validity or
                               Alcohol         reliability was reported;
                               Problem         however, the measures
                               Index: .90      used were cited by
                                               previous research.

Patrick, 2016  N/A             Importance of   No validity or
                               Consequences    reliability was reported;
                               of Drinking     however, the measures
                               (ICOD)          used were cited by
                               short form,     previous research.
                               Cronbach, .87
                               to .92

Poulos, 2016   20.30%          N/A             No validity or
                                               reliability was reported;
                                               however, the measures
                                               used were cited by
                                               previous research.

Reddy,2014     43% of          Cronbach's
               instruc-        for Theory
               tors            of Planned
               agreed (19/44)  Behavior
                               norms .733.

Snipes, 2013   45.60%          N/A             No validity or
                                               reliability was reported;
                                               however, the measures
                                               used were cited by
                                               previous research.

Snipes, 2014   N/A             N/A             No validity or
                                               reliability was reported;
                                               however, the measures
                                               used were cited by
                                               previous research.

Snipes, 2015   N/A             Substance       No validity or
                               Use Risk        reliability was reported;
                               Profile Scale   however, the measures
                               (SURPS)         used were cited by
                               subscales       previous research.
                               .84, Anxiety
                               .70, and
                               seeking .68.

Stamates,      N/A             Daily           No validity or
2017                           Drinking        reliability was reported;
                               Questionnaire   however, the measures
                               (DDQ): .78.     used were cited by
                               Barratt         previous research.
                               Scale: .73.
                               Alcohol Urge
                               .89 for
                               craving and
                               .84 CAB
                               craving at
                               .85 for
                               craving and
                               .84 for CAB
                               craving at
                               second time

Varvil-Weld,   75.40%          AmED            No validity or
2013                           Expectancies:   reliability was reported;
                               .90.and         however, the measures
                               AmED            used were cited by
                               Attitudes;      previous research.
Velazquez,     20.30%          N/A             No validity or
2012                                           reliability was reported;
                                               however, the measures
                                               used were cited by
                                               previous research.

Woolsey,       87.90%          Cronbach's,     No validity or
2010a                          Risk taking     reliability was reported;
                               .726. and       however, the measures
                               Consequences    used were cited by
                               .737.           previous research.

Woolsey,       87.90%          N/A             No validity or
2010b                                          reliability was reported;
                                               however, the measures
                                               used were cited by
                                               previous research.

Woolsey.       N/A             Wilcoxon        No validity or
2015                           signcd-rank     reliability was reported
                               lest. QDS       for other measures;
                               - .92.          however, they were cited
                                               by previous research.

Outcomes, Results, & Limitations

Author & Year     Measured Outcome

Arria, 2016       * Drunk driving frequency.
                  * ED consumption patterns.
                  * Alcohol use patterns.
                  * Caffeine consumption.
                  * Suspected risk factors for drunk driving.
Berger, 2013      * AmED Use.
                  * Alcohol Use.
                  * Alcohol-related negative consequences.
Bonar, 2017       * AmED Use.
                  * Alcohol Use.
                  * Alcohol Use Disorders Identification
                  Test-Consumption (AUDIT-C).
                  * Drug Abuse Screening Test(DAST-IO).
                  * Patient Health Questionnaire (PHQ-9) for past
                  two-week depression symptoms.
                  * Sleep Problems Scale (SPS).
Cobb, 2015        * Caffeine consumption.
                  * Alcohol consumption.
                  * Caffeinatsd Alcohol Beverage (CAB) consumption.
                  * Reasons for CAB consumption.
Curry, 2009       * Repeatable Battery for the Assessment of
                  Neuropsychological Status
                  (KHANS): Immediate memory, delayed memory,
                  visuospatial/ constructional, attention and language.
Galium, 2016      * ED use.
                  * Athlete status.
                  * Misuse of prescription stimulants
                  * Tobacco use.
                  * Heavy episodic drinking.
                  * Motivations for ED consumption.
Haas, 2017        * AmED Use.
                  * Alcohol Use.
                  * Heavy Episodic Drinking.
                  * Alcohol Consequences.
                  * Alcohol-related Problems.
Hardy, 2017       * General Nutrition
                  Knowledge Questionnaire
                  * ED use.
                  * Reasons for consuming EDs.
                  * ED side-effects,
Lau-Barraco,      * Alcohol expectancies.
2014a             * Caffeine expectancies.
                  * Alcohol use.
                  * Caffeine use.
                  * CAB use.
                  * Alcohol-related problems.
Lau-Barraco,      * Alcohol expectancies
2014b             * Caffeine expectancies
                  * Alcohol use.
                  * CAB use.
                  * Alcohol use problems.
                  * Caffeine use dependence and withdrawal.
Linden            * Alcohol use.
Carmichael,       * CAB use.
2015              * CAB-specific expectancies.
                  * Protective behavioral strategies.
                  * Alcohol-related problems.
Linden            * Alcohol use.
Carmichael,       * CAB use.
2016              * Spring Break alcohol and CAB consumption.
                  * Spring Break alcohol-related problems.
                  * Spring Break plans.
Linden            * AmED Use
Carmichael,       * Alcohol-only use.
2017a             * Location of drinking.
                  * Risky drinking activities.
                  * Social context of drinking.
Linden            * Impulsivity.
Carmichael,       * Alcohol use.
2017b             * CAB use.
                  * Drinking-related outcomes.
Linden            * Drinking motives.
Carmichael,       * AmED use.
Malinauskas,      * ED use.
2007              * Situations of ED use.
                  * Side effects of maximum number of EDs consumed.
Mallett, 2014     * Alcohol use.
                  * AmED use.
                  * AmED Expectancies.
                  * AmED Attitudes.
                  * AmED Norms,
                  * Alcohol-related consequences.
Mallett, 2015     * Drinking behaviors.
                  * Alcohol use.
                  * AmED use.
                  * Alcohol-related consequences.
                  * Patterns of AmED use.
                  * Effects of AmED use on drinking behaviors and
                  alcohol-related consequences over time.
Marczinksi, 2011  * ED consumption patterns.
                  * Alcohol consumption patterns.
                  * AmED consumption patterns.
                  * AmED motivations.
Marzell, 2014     * AmED Attitudes.
                  * AmED Beliefs.
                  * AmED perceived peer norms.
                  * Heavy drinking.
                  * AmED consumption,
                  * Alcohol-related consequences.
Miller, 2008      * ED use.
                  * AmED use.
                  * Jock identity.
                  * Masculine norms.
                  * Risk-laking behavior.
O'Brien, 2008     * AmED use.
                  * Alcohol use.
                  * Alcohol-related consequences.
Patrick, 2014     * AmED use.
                  * Alcohol consequences.
                  * Hazardous alcohol use.
                  * Serious alcohol problems.
                  * Alcohol-related accidents.
Patrick, 2016     * AmED consumption.
                  * Activity involvement.
                  * Living situation.
                  * Academic demands.
                  * Drinking motives.
                  * Binge drinking.
Poulos, 2016      * ED consumption.
                  * Side effects of ED consumption.
                  * AmED consumption.
                  * Reasons for AmED consumption.
Reddy, 2014       * AmED consumption and prevalence.
                  * ED consumption and prevalence.
                  * Alcohol consumption and prevalence.
                  * Theory of Planned Behavior variables including
                  attitudes, subjective norms, behavioral intentions
                  and perceived behavioral control associated with
Snipes, 2013      * Drug use.
                  * Alcohol consumption.
                  * AmED consumption.
                  * Sexual behavior.
Snipes, 2014      * Drug use.
                  * Alcohol consumption.
                  * AmED consumption.
                  * Sexual victimization.
Snipes, 2015      * Substance use.
                  * Personality trails.
                  * Alcohol consumption.
                  * AmED consumption.
                  * Alcohol dependence patterns.
Stamates, 2017    * Alcohol use.
                  * Caffeinated alcohol use.
                  * Trait impulsivity.
                  * Inhibitory control.
                  * Craving.
Varvil-Weld,      * AmED-specific latent profile indicators.
                  * AmED Expectancies.
                  * AmED attitudes.
                  * AmED normative beliefs.
                  * Longitudinal AmED-related outcomes.
                  * Longitudinal drinking-related outcomes.
                  * AmED use.
                  * Heavy episodic drinking.
                  * Alcohol-related consequences.
Velazquez, 2012   * ED use behaviors.
                  * Alcohol use behaviors.
                  * AmED consumption.
Woolsey, 2010a    * Alcohol consumption.
                  * AmED consumption.
                  * Risk taking,
                  * Alcohol Consequences.
                  * AmED Consequences.
                  * Brief Comprehensive Effects of Alcohol (B-CEOA)
                  * Brief Comprehensive Effects of Combined Use
Woolsey, 2010b    * Alcohol consumption.
                  * AmED consumption.
                  * Risk taking.
                  * B-CEOA
                  * Comprehensive Effects of Combined Use
                  * Alcohol consequences.
                  * AmED consequences.
Woolsey, 2015     * Alcohol consumption.
                  * AmED consumption.
                  * High-risk driving.
                  * High-risk drinking.

Author & Year     Main Results

Arria, 2016       * 57% lifetime energy drink (ED) consumption.
                  * 40% lifetime alcohol mixed with energy drink (AmED)
                  * 71% of KD users drank AmEDs.
                  * Drunk driving frequency correlated with ED
                  consumption (with and without alcohol), and
                  quantity/frequency of alcohol use.
                  * Indirect path from AmED use to drunk driving, with
                  alcohol quantity as the mediator.
Berger, 2013      * 75% lifetime AmED use (mixed and pre-mixed).
                  * 65% past-year AmED use.
                  * Majority of lifetime and past-year AmED use were
                  mixed (e.g.. Red Bull[R] and vodka) versus pre-mixed
                  (e.g.. Four Loko[R]).
                  * Hazardous drinkers, who used AmEDs. were more
                  likely to drive a car under the influence or have
                  unprotected sex. than hazardous drinkers who do not
                  use AmEDs.
                  * Hazardous drinkers, who consumed AmEDs, had the
                  highest AUDIT-C scores.
Bonar, 2017       * 32.4% of drinkers used AmEDs in the past three
                  * 81 % of students who used AmEDs reported they
                  usually consumed one ED (although not necessarily
                  mixed). 14.5% consumed two and 4.5% consumed three or
                  * AmED users had higher scores of sleep problems and
                  depressive symptoms than that of non-users.
                  * AmED users reported higher alcohol use severity
                  scores and drug severity scores compared to non-users.
Cobb, 2015        * Of past 30-day alcohol/caffeine consumers, 68.8%
                  drank either ready to drink AmEDs (50.3%) or
                  self-mixed AmEDs (18.5%) compared to 26.4% who drank
                  caffeinaled sodas self-mixed with alcohol.
                  * Most common reason for mixing with caffeinated
                  beverages was to "hide the flavor of alcohol.'" "drink
                  less and get drunk," "only mixer available." or,
                  "stay alert while drinking."
Curry, 2009       * Consumers of energy drink plus alcohol (ED+A)
                  scored lower on total RBANS cognitive status scores.
                  visuospatial/construction scores. and language scores
                  than the control group.
                  * ED* A scored significantly lower on overall
                  neuropsychological status; specifically, visuospatial
                  judgments and semantic fluency.
Galium, 2016      * Low-use and high-use non-student athletes reported,
                  "to prolong the effects of alcohol or other
                  substances" as a motivation for consuming EDs.
Haas, 2017        * 38% of overall sample used AmEDs in the past three
                  * High-proportion AmED groups (75% or more of all
                  drinking events involved Am ED use), compared to low
                  and moderate, stored higher on all five consequence
                  areas (social consequences, risky behaviors, academic
                  difficulties, physiological dependence and impaired
Hardy, 2017       * 18% of ED users' reasoning for ED consumption was,
                  "as a mixer tor alcoholic beverages."
Lau-Barraco,      * Caffeine and alcohol expectancies variance account
2014a             for 12% in quantity. 8% in frequency, and 16% in
                  * Alcohol expectancies accounted for 10-11% of the
                  variance of CAB use quantity, caffeine expectancies
                  accounted for 6%.
                  * CAB frequency was 8% alcohol expectancies and 4%
                  * Alcohol expectancies accounted for 12-14% of the
                  variance, caffeine expectancies accounted for 4-6% of
                  variance in alcohol related harms.
Lau-Barraco,      * Four classes of drinkers were found:
2014b             High Alcohol/High CAB (6.0%), High Alcohol/Moderate
                  CAB (5.15%), High Alcohol/Low CAB (22.9%), and Low
                  Alcohol/Low CAB (65.8%).
                  * Low Alcohol/Low CAB class reported the lowest
                  levels of caffeine withdrawal, Caffeine dependence
                  symptoms, heavy episodic drinking frequency, and
                  alcohol use problems.
                  * CAB users in the High Alcohol/Low CAB class held
                  more positive alcohol expectancies than those in the
                  Low Alcohol/Low CAB class.
                  * High/Alcohol/High CAB class reported withdrawal
                  symptom caffeine expectancies that were stronger than
                  all other classes.
Linden            * The developed model accounted for 31% of the
Carmichael,       variance in negative consequences.
2015              * Avoiding Negative Consequences expectancies were
                  significantly associated with less application of
                  protective behavioral strategies and greater
                  experiences of harms, while Intoxication Enhancement
                  expectancies was not significantly associated with
                  * CAB use was associated with stronger expectations
                  that drinking CABs can help avoid negative
                  * These assumptions are linked to negative
                  consequences, including failing to protect themselves
                  from harm and previously experiencing alcohol-related
                  problems. Therefore, IE expectancies were not a
                  significant factor in the relationship of CAB use
                  and harms.
Linden            * 54% of students used CABs during Spring Break
Carmichael,       * CAB use frequency during Spring Break predicted
2016              negative consequences, with controlling for other
                  alcohol consumed and whether the participant was on
                  vacation for Spring Break.
Linden            * Individuals are more likely to consume AmEDs if
Carmichael,       they are drinking at home, in a bar or club, or while
2017A             pre-gaming, but not when playing drinking games.
Linden            * AmEDs were consumed on 39.6% of CAB drinking days.
Carmichael,       * Alcohol and soda pop on 57.43% of CAB drinking days.
2017b             * AmED use was associated with heavier alcohol
                  consumption (after controlling for impulsivity) and
                  more alcohol-related harms (after controlling for
                  number of drinks consumed and impulsivity).
                  * Regardless of whether the mixer was soda or EDs,
                  participants drank more heavily on days when they
                  mixed with caffeine.
Linden            * Daily conformity motives (e.g., lo fit in with
Carmichael,       people you like) were positively associated with odds
2017c             of using AmEDs.
                  * Daily enhancement motives (e.g.. because it gives
                  you a feeling you like) were negatively associated to
                  odds of using AmEDs.
                  * None of the drinking motives measured at baseline
                  were associated with AmED use.
Malinauskas,      * 54% of ED users consumed EDs to mix with alcohol
2007              while partying.
                  * 16% of females and 18% of males consumed EDs lo
                  treat a hangover.
                  * Using 3 or more EDs in one sitting was a more
                  common practice among those who used them to drink
                  with alcohol (49% of users).
Mallett, 2014     * Those whose alcohol use contained higher proportions
                  of AmEDs had significantly higher AniED-relatcd
                  expectancy scores than those with lower proportions
                  of use.
                  * Higher proportion AmED users perceived their
                  friends to drink significantly more AmEDs than lower
                  proportion users.
                  * Moderate drinkers with high proportions of AmED use
                  perceived their friends to be more accepting of AmED
                  use than moderate drinkers with low proportions of
                  AmED use.
                  * High-proportion AmED users reported more
                  alcohol-related consequences than moderate drinkers
                  with low proportions of AmED use.
                  * Heavier AmED users had more favorable cognitive
                  factors related to AmEDs.
                  * Moderate drinkers who use more AmEDs are at a
                  greater risk for more alcohol-related consequences
                  than those who use less AmEDs.
Mallett, 2015     * 39.6% of students used AmEDs at some point during
                  the study.
                  * 12.4% initiated use during the Fall semester of
                  their sophomore year (T2) 15.6% discontinued use at
                  T2 and 11.6% use continuously.
                  * In the Spring semester of their freshman year
                  (T1) discontinuers drank more and had a higher
                  frequency of drunkenness than initiators - there was
                  an inverse relationship of this at T2.
                  * Continuous users consumed more AmEDs in a typical
                  week at T1 and T2.
                  * Continuous users reported the highest alcohol
                  consumption  and alcohol consequences, followed by
                  initiators, then discontinuous, then non-drinkers,
                  at all time points.
                  * From T1 to T2, initiators showed an increase in
                  alcohol consequences and drinking patterns.
                  Discontinuers, increased their drinking patterns
                  as well, but had significantly less alcohol
                  consequences than initiators.
Marczinksi, 2011  * Of the entire sample, 44% had tried or were regular
                  consumers of AmEDs.
                  * 70% of those who have tried or are current AmED
                  users also reported hinge drinking.
                  * 78% of alcohol users have tried AmEDs or use them
                  * Most common motivations of regular consumers
                  included, "feel less tired", "get drunk faster",
                  "can drink more", "it is a common drink", "to hold
                  your liquor better", "to socialize", "to get drunk",
                  "to celebrate", "to get work done" and they like the
Marzell, 2014     * 27% past 30-day AmED consumption.
                  * AmED users had more positive attitudes and
                  beliefs related to AmEDa than non-users.
                  * AmED users had higher perceived norms of
                  acceptance by their friends.
                  * AmED users had stronger intentions of use in the
                  future than non-users.
                  * AmED users reported higher AmED use. heavy alcohol
                  use. sexual, academic and physical consequences
                  than alcohol only users.
                  * AmED altitude measured during the Spring semester
                  of their freshman year (T1) was associated with
                  academic consequences measured during the fall
                  semester of their sophomore year (T2).
                  * AmED use at Tl is associated with alcohol-related
                  consequences at T2.
Miller, 2008      * 26% 30-day AmED consumption, men reported on
                  average twice as many days than women.
                  * Those with higher scores of jock identity,
                  conforming to masculine norms and risk-taking
                  behaviors were more likely to consume and reported
                  greater frequency of consuming AmEDs in the past
O'Brien, 2008     * 24% past 30-day AmED use.
                  * Those with higher odds of AmED use were male,
                  white, intramural athletes, fraternity or sorority
                  members and younger persons.
                  * AmED users bad more drinking days during their
                  last year of high school than non-AmED users.
                  * 55% of AmED users did so to hide the flavor or
                  alcohol, 15% to not feel as drunk, 7% to not gel a
                  hangover, 5% to not look as drunk and to drink
                  more alcohol, 41% had other reasons such as it was
                  being served at a party, only mixer available and to
                  make Jagerbombs.
                  * AmED users drank more drinks in a typical session,
                  twice as many heavy episodic drinking days in the
                  past month, twice as many weekly drunkenness episodes
                  and the greatest number of drinks in a single setting
                  was 36% higher than non-AmED users.
                  * AmED users had a greater prevalence of being taken
                  advantage of sexually, taking advantage of someone
                  else sexually, riding with a driver under the
                  influence, being hurt or injured, requiring medical
                  treatment, and were more likely to drive drunk at
                  lower amounts of alcoholic drinks than non-AmED users.
Patrick, 2014     * 26% past 30-day AmED use, 29% of them were frequent
                  users. 30% of heavy episodic drinkers consumed
                  AmEDs infrequently and 13% used AmEDs frequently.
                  * Infrequent and frequent AmED use was associated
                  with higher reporting of negative consequences and
                  greater odds of hazardous alcohol use.
                  * Frequent AmED use was associated with greater odds
                  of serious alcohol problems and having an
                  alcohol-related accident in the following two years.
Patrick, 2016     * 27% past 30-day AmED use by 2nd year students.
                  * Belonging to a fraternity /sorority, participating
                  in athletics, or residing off-campus had greater odds
                  of using AmEDs.
                  * Inverse relationship between number of early
                  morning classes and odds of using AmEDs.
                  * Honors program students had lower odds of AmED
                  * Students who held greater fun/social, relaxation
                  and image motives for drinking had higher odds of
                  using AmEDs.
                  * Those who held greater physical/behavioral motives
                  not to drink had lower odds of using AmEDs.
                  * Binge drinking was a strong predictor of AmED use
                  in multivariate models.
Poulos, 2016      * 31 .9% of past year AmED users consumed AmEDs
                  in the past month.
                  * Men were more likely than women to use AmEDs.
                  * Popular reasons for AmED use included good taste,
                  masking the taste of alcohol and to provide an
                  energy boost.
                  * Men were more likely to report "partying longer,"
                  "drinking more without looking drunk and relaxation
                  as reasons for AmED use.
Reddy, 2014       * Theory of Planned Behavior constructs of
                  behavioral intention and attitude were the only
                  significant predictors of AmED use.
                  * Class year was a significant predictor of AmED use,
                  when analyzed by logistic regression.
                  * Behavioral intention was a full mediator for the
                  effect of subjective norms on past 30-day AmED use
                  and is also a partial mediator for the effect of
                  attitudes on past 30-day AmED use.
Snipes, 2013      * 19.4% past month AmED use.
                  * 29.7% of alcohol users used AmEDs in the past month.
                  * AmED users were more likely to use marijuana,
                  ecstasy and cocaine.
                  * Having unprotected sex, sex after having too much
                  to drink, sex after drug use and more sexual partners
                  in the past 3 months were associated to AmED use.
                  * AmED use predicted unprotected sex, sex after too
                  much to drink and sex after drug use, over various
                  factors depending on the relationship.
Snipes, 2014      * AmED consumption was associated with each type of
                  sexual victimization (coerced, threatened, physically
                  forced, incapacitated) for men, but only for
                  physically forced victimization for women.
                  * AmED consumption was associated with male sexual
                  victimization, but not female sexual victimization.
Snipes, 2015      * 11.6% past-month AmED use.
                  * 9.7% past-week AmED use.
                  * Past-month and past-week AmED users are more likely
                  to report AUDIT scores greater than or equal to 8
                  (alcohol dependence). ED consumption, lower anxiety
                  and greater impulsivity when compared to alcohol
                  users who do not use AmEDs.
                  * AmED use predicted patterns of alcohol dependence
                  more so than demographics, drug use. personality
                  factors (e.g., hopelessness, anxiety, impulsivity
                  and sensation seeking), ED use and alcohol use.
Stamates, 2017    * Greater subjective craving for alcohol and CABs
                  for those in the simulated bar group than those in
                  the neutral group.
                  * Inhibitory control did not mediate the association
                  between alcohol craving and the respective context.
                  * Impulsivity was positively associated with alcohol
                  and CAB-specific craving.
Varvil-Weld,      * 53.7% of the sample were in the Occasional AmED
2013              group. This group had neutral expectancies, attitudes
                  and injunctive normative beliefs regarding AmED use.
                  Those in this group also believed friends used small
                  numbers of AmEDs each week.
                  * 30.5% of the sample were in the Anti-AmED group.
                  This group had highly negative expectancies,
                  attitudes and injunctive normative beliefs regarding

                  AmED use. Those in this group also believed friends
                  used small number of AmEDs each week.
                  * 5.2% of the sample were in (be ProAmED group. This
                  group had the most positive attitudes and injunctive
                  normative beliefs.
                  * 10.6% of the sample were in the Strong Peer
                  Influence group. This group held moderately negative
                  expectancies and attitudes, moderately positive
                  injunctive normative beliefs and perceived their
                  friends consumed a high number of AmEDs per week.
                  * Pro-AmED and Strong Peer Influence reported
                  higher weekly AmED use than Anti-AmED.
                  * Anti-AmED reported fewer consequences than Pro-AmED.
                  Strong Peer Influence reported fewer consequences
                  than Pro-AmED,
                  * AmED use was associated with AmED profiles, after
                  controlling for heavy and typical weekly drinking.
Velazquez, 2012   * 14.9% past-month AmED use.
                  * For every one unit increase in past-month ED
                  consumption, the likelihood of consuming AmEDs
                  increased by 90%.
                  * Eor every one unit increase in past-week ED
                  consumption, the likelihood of consuming AmEDs
                  increased by 140%.
Woolsey, 2010a    * 48% of college athlete drinkers used AmEDs in the
                  past year.
                  * 78.6% of athletes used alcohol.
                  * 48.4% used energy drinks not with alcohol.
                  * Binge drinking occurred in 98.1% and 97.5% of male
                  and female AmED users, respectively.
                  * AmEDs are associated to a significant increase in
                  risk-taking, compared to alcohol-only consumption.
                  * Compared to females, males reported they would
                  enjoy sex more, act more aggressively, be more likely
                  to drive a vehicle and be more likely to fight when
                  using AmEDs.
Woolsey, 2010b    * 37.4% past-year AmED use.
                  * AmED users consumed more alcohol, had riskier
                  drinking habits, greater overall risk-taking and
                  greater consequences than those who used alcohol but
                  not AmEDs.
                  * AmED users drank more alcohol per occasion, binge
                  drank more often, drank more often, drank more
                  alcohol on one occasion and used more than double the
                  amount of alcohol the previous year, than alcohol
                  users who did not use AmEDs.
Woolsey, 2015     * 48.8% past-year AmED use by alcohol users.
                  * AmED users were more likely than alcohol user who
                  do not use AmEDs to report driving even though they
                  knew they had too much alcohol, thinking they were
                  driving over the legal limit and riding with a driver
                  they knew had too much alcohol to drive safely.
                  * AmED users had greater rates of high-risk drinking
                  behaviors for all measures

Author & Year     Limitations

Arria, 2016       * Not generalizable.
                  * Self-reported data.
                  * Cross-sectional design.
                  * Did not ask participants to   explain "drove while
                  drunk."   From this, participants may   not have
                  mentioned times   they drove while "tipsy"   or
                  "buzzed" but were not   subjectively drunk.
                  * Did not ask participants about   consuming EDs
                  when they   drove drunk, Cannot draw   conclusions
                  about whether   ED consumption contributed   to them
                  driving drunk.
Berger, 2013      * Cross-sectional design.
                  * Not generalizable.
                  * Partner type was not assessed in terms of
                  unprotected sex (e.g., casual partner).
Bonar, 2017       * Cross-sectional design.
                  * Retrospective, self-reported data.
                  * Convenience sample.
                  * Not generalizable
                  * AmED use was asked about   in general, rather than
                  a   specific time. This may not   capture current or
                  future   patterns in use.
                  * Newly developed AmED use measures needs
                  psychometric evaluation.
Cobb, 2015        * Cross-sectional design.
                  * Study was conducted in the fall semester, could
                  have resulted in biased measurements of alcohol
                  drinking behaviors.
                  * Not generalizable.
                  * May not generalize to alcohol-caffeine users who
                  are no longer able to purchase ready-to-drink AMEDs.
                  * Self-reported data.
                  * Social desirability bias.
Curry, 2009       * Large amount of within-group variance from pre- to
                  post-test in the control group.
                  * There was not an alcohol-only group.
                  * Sample was limited to undergraduate women.
                  * Not generalizable.
                  * Drinks used were proprietary blends of premixed
                  ingredients; these ingredients between the two drinks
                  were similar, but not identical.
Galium, 2016      * Convenience sample from one private university.
                  * Self-reported data.
                  * Cross-sectional design.
                  * Not generalizable.
                  * The sport in which the participants participated in
                  was not listed.
                  * Only consumption of ED was asked, did not account
                  for the different types of EDs, varying levels of
                  caffeine in each ED, nor the consumption of other
                  caffeinated beverages.
Haas, 2017        * Constrained, urban, geographical area.
                  * Convenience sample.
                  * Not generalizable.
                  * Number of days of AmED use was not measured.
                  * Restricted to AmED use and alcohol-related problems.
Hardy, 2017       * Not generalizable.
                  * Cross-sectional design.
                  * Self-reported data.
Lau-Barraco,      * Cross-sectional design.
2014a             * Did not assess the role of CAB-specific
                  expectancies, as the instrument does not exist.
                  * Self-reported data.
                  * No clear, operational, or widely adopted
                  definitions of (Alls or AMEDs exist.
                  * Homogeneous sample.
                  * Not generalizable.
Lau-Barraco,      * Cross-sectional design.
2014b             * Homogeneous sample.
                  * Not generalizable.
                  * Did not assess CAB-specific expectancies.
Linden-           * Cross-sectional design.
Carmichael, 2015  * Self-reported data.
                  * Sample was mostly female.
                  limits generalizability lo
                  * Did not control for
Linden-           * Small sample size, due to being a preliminary study.
Carmichael, 2016  * CAB use was based on co-use of alcohol and any
                  caffeine mixers, not just EDs.
                  * Majority of the sample was female.
                  * Limited generalizability
                  * Cross-sectional research
Linden-           * Cross-sectional design.
Carmichael,       * Sample was mostly women.
2017a             * Limited generalizability.
                  * Limited number of days in which AmEDs were
                  * AmED use was examined dichotomously.
                  * Social context was measured in a way that
                  limited the researchers' ability to make
                  conclusions about whether participants drank AmEDs
                  around others.
Linden-           * Cross-sectional research
Carmichael,       design over two-week period.
2017b             * Limited to examine several consequences of drinking.
                  * Limited comprehensiveness of measuring sexual
                  behavior: did not ask about sexual victimization.
                  * Low response rate.
                  * Not generalizable.
                  * The BYAACQ used in this study is not standardized
                  for use in a daily diary study.
Linden-           * The study included only a
Carmichael,       few AmED days.
2017c             * Self-reported data
                  * Recall of previous night's drinking motives.
                  * Not generalizable.
Malinauskas,      * Cross-sectional design.
2007              * Limited demographic information.
                  * Rural state university and
                  a fairly homogeneous sample.
                  * Not generalizable.
                  * Self-reported data.
Mallett, 2014     * Only looked at the negative effects associated with
                  alcohol, and not caffeine. when examining AmEDs.
                  * Not generalizable.
                  * Self-reported, retrospective data.
                  * When AmED use in a drinking session may influence
                  the likelihood of adverse consequences, for
                  instance, whether they are used early or later in the
                  * Did not assess whether non-alcoholic ED use may
                  be done prior to alcohol use, which could serve impact
                  alcohol use or high-risk drinking.
Mallett, 2015     * Not generalizable.
Marczinksi, 2011  * Not generalizable.
                  * Cross-sectional design.
                  * List of motivations was not exhaustive and may have
                  been too general for specific interpretation for
Marzell, 2014     * Longitudinal design, but only two data collection
                  * Self-reported and retrospective data.
                  * Not generalizable.
                  * Alphas for the version of the YAAPST were low to
Miller, 2008      * Sample was from one Northeastern public university.
                  * Cross-sectional design.
                  * Self-reported data,
                  * Three scale measures. CMNI Dominance. Masculine
                  Norms and Risk-laking Behavior fell short
                  acceptable reliability (0.7).
O'Brien, 2008     * Cross-sectional design.
                  * Possible selection bias.
                  * Not generalizable.
                  * Self-reported data.
Patrick, 2014     * Sample from one university.
                  * AmED measure does not account for ED and alcohol
                  consumption in separate containers during the same
                  * Did not collect information on dietary intake or
                  other forms of caffeine consumption.
                  * Not generalizable.
Patrick, 2016     * Single university sample.
                  * Not generalizable.
                  * Cross-sectional design.
                  * Past 30-day AmED use measurement was dichotomous.
Poulos, 2016      * Cross-sectional design.
                  * Sample was from a single, large U.S. university.
                  * Not generalizable.
                  * No direct measure for the number of EDs consumed in
                  an entire day.
                  * Low response rate (20.3%).
Reddy, 2014       * Self-reported data.
                  * Cross-sectional design.
                  * Not generalizable.
Snipes, 2013      * Not generalizable.
                  * Self-reported data.
                  * Risk-taking tendencies was not measured, which could
                  explain some associations.
                  * AmED consumption was computed and served as
                  an estimate of AmED consumption which might not
                  be accurate.
Snipes, 2014      * Preliminary data.
                  * Cross-sectional design.
                  * Small sample of students reported being sexually
                  victimized, which creates a power issue.
                  * Retrospective, self-reported data.
                  * Not generalizable.
Snipes, 2015      * Not generalizable.
                  * Self-reported data.
                  * Cross-sectional design.
Stamates, 2017    * Not generalizable.
                  * Baseline performance of impulsivity was not
                  * The neutral context was not outfitted with stimuli
                  like that of the bar (e.g.. signs, advertisements,
                  or bottles) that could have influenced the
                  * The study did not assess other variables, such as
                  depression or student schedule, that could have
                  influenced the findings.
                  * Definitions and measurements of impulsivity
                  were specific to alcohol-related cues and subjective
                  reports of craving, rather than including other
                  measures for impulsivity.
Varvil-Weld,      * Not generalizable.
2013              * Two data collection periods, but did not follow
                  students past their second year of college.
                  * Retrospective data.
                  * Self-reported data.
Velazquez, 2012   * Low response rate (20.3%).
                  * Cross-sectional design.
                  * Not generalizable.
                  * Self-reported data.
                  * Retrospective data.
Woolsey, 2010a    * No limitations reported in the article.
Woolsey, 2010b    * No limitations reported in the article.
Woolsey, 2015     * Not generalizable.
                  * Cross-sectional data.
                  * Self-reported data.
                  * Could not assess the number of EDs when
                  participants drove impaired or after they knew they
                  had drunk too much alcohol.
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Author:Luneke, Aaron C.; Glassman, Tavis J.; Dake, Joseph A.; Thompson, Amy J.; Blavos, Alexis A.; Diehr, A
Publication:Journal of Alcohol & Drug Education
Date:Aug 1, 2019
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