THE EFFECTS OF ON SUBJECTIVE LEVELS OF INTOXICATION IN A BLOOD DISCRIMINATION PARADIGM

Bryan G. Messina

A Thesis Submitted to the University of North Carolina Wilmington in Partial Fulfillment of the Requirements for the degree of Masters of Arts

Department of Psychology

University of North Carolina Wilmington

2012

Approved by

Advisory Committee

Raymond C. Pitts Nora Noel

Wendy Donlin-Washington Chair

Accepted by

Dean, Graduate School

TABLE OF CONTENTS

ABSTRACT ...... iv

LIST OF TABLES ......

LIST OF FIGURES ...... vi

INTRODUCTION ...... 1

Overview ...... 1

Alcohol Pharmacology...... 1

Alcohol Discrimination ...... 6

Caffeine Pharmacology ...... 21

Alcohol & Caffeine ...... 27

METHODS ...... 47

Participants ...... 47

Screening...... 48

Apparatus ...... 49

Procedure ...... 50

Data Analysis ...... 57

RESULTS ...... 59

Participant Overview ...... 59

Participant One...... 60

Participant Three ...... 62

Participant Six ...... 64

Participant Eight...... 66

DISCUSSION ...... 67

ii

Limitations ...... 75

LITERATURE CITED ...... 78

APPENDIX ...... 114

iii

ABSTRACT

The FDA (2011) recently cited that one of the primary dangers of caffeinated alcoholic beverages was the loss of subjective sensations of intoxication. The purpose of this study was to disrupt proficient alcohol discrimination. Participants were eight male age 21-24 university students. Four subjects were disqualified from participating, and four subjects completed the study. The four that completed acquired accurate blood alcohol concentration discrimination through a two day training procedure employing positive reinforcement and corrective feedback.

Subjects underwent another two sessions, receiving a counter balanced caffeine manipulation to examine how caffeine affected this skill. Blood alcohol concentration was measured by breathalyzer and estimations were reported using the same g/mL scale. Estimations and measured breathalyzer readings were graphed and area under the curve measurements was conducted. Disparity between the areas under each curve as well the number of reinforced estimations was examined to determine any differences. Overall, the evidence did not support caffeine disrupting the discrimination skill. The addition of caffeine to alcohol intoxication does not appear to disrupt subjective levels of intoxication, within this study.

iv

LIST OF TABLES

Table Page

1. Correlations of BAC with Degree of Intoxication ...... 86

2. Approximate Blood Alcohol Percentage in Males Based on Body Weight ...... 87

3. Approximate Blood Alcohol Percentage in Females Based on Body Weight ...... 88

4. Area Under the Curve (AUC) and Disparity Scores (%) for all Participants ...... 89

5. Ethanol (g/kg) and Caffeine (mg/kg) Doses for all Participants across Sessions ...... 90

6. Area Under the Curve (AUC) and Disparity Scores (%) for Rising, Peak, and Falling BAC: Participant One ...... 91

7. Area Under the Curve (AUC) and Disparity Scores (%) for Rising, Peak, and Falling BAC: Participant Three ...... 92

8. Area Under the Curve (AUC) and Disparity Scores (%) for Rising, Peak, and Falling BAC: Participant Six ...... 93

9. Area Under the Curve (AUC) and Disparity Scores (%) for Rising, Peak, and Falling BAC: Participant Eight ...... 94

v

LIST OF FIGURES

Figure Page

1. Hypothetical Participant’s BAC Discrimination Graph ...... 95

2. Participant Recruitment and Group Assignment ...... 96

3. Example of Scaled Scores and Trapezoids for Area Under the Curve Analysis ...... 97

4. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant One Session One ...... 98

5. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant One Session Two ...... 99

6. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant One Session Three ...... 100

7. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant One Session Four ...... 101

8. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Three Session One ...... 102

9. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Three Session Two ...... 103

10. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Three Session Three ...... 104

11. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Three Session Four ...... 105

12. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Six Session One ...... 106

13. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Six Session Two ...... 107

14. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Six Session Three ...... 108

15. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Six Session Four ...... 109

vi

16. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Eight Session One ...... 110

17. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Eight Session Two ...... 111

18. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Eight Session Three ...... 112

19. Actual and Estimate Blood Alcohol Concentration (BAC) Curves For Participant Eight Session Four ...... 113

vii

INTRODUCTION

The Effects of Caffeine on Subjective Levels of Intoxication in a

Blood Alcohol Discrimination Paradigm

Alcohol is one of the most readily available and pervasively used drugs in our society.

According to the 2009 SAHMSA report, over 130 million individuals aged 12 or older (51.9% of the population) reported being current drinkers, with approximately 60 million (23.7% of the population) binge drinkers (five or more alcoholic beverages within one session), and 17 million

(6.8% of the population) heavy drinkers (those that meet binge drinking criteria on five separate occasions during a thirty day period). Nutt, King, and Phillips (2010) named alcohol as one of the most dangerous drugs. This conclusion was reached as a result of a multi-criteria analysis of a drug’s impact on various aspects of not only the self (e.g. dependence, injury, drug-specific impairment of mental function), but on several societal factors as well (e.g. crime, environmental damage, economic cost, community). A series of experts rated a variety of drugs based on these criteria, with alcohol obtaining the highest overall score (followed by heroin and crack ).

Alcohol Pharmacology

“Ethyl alcohol is a simple two-carbon molecule (CH3CH2OH)” (Julien, Advokat, &

Comaty, 2008, 96). The main route of administration is orally through the ingestion of various beverages that contain ethanol, either through the process of fermentation or fortification (e.g. beer, wine, and ). Absorption is rapid, taking roughly 30-90 minutes. Speed of absorption is mediated by the time it takes alcohol to pass through the stomach. Twenty percent of alcohol absorption will take place in the stomach, with the remaining 80% taking place in the upper intestinal tract. Alcohol is both water and lipid soluble and diffuses across all membranes rapidly. Once alcohol has entered the blood stream, it has little trouble permeating the blood-

brain barrier, where it works as a general depressant on multiple neurotransmitter systems. This has led to a unitary hypothesis of action with regards to alcohol, stating that “the drug dissolves in nerve membranes, distorting, and disorganizing similar to the action of a general anesthetic”

(Julien et. al., 2008, 101).

Alcohol indirectly and non-specifically depresses neuronal functions, including but not limited to those of, glutamate receptors, GABA receptors, opioid receptors, serotonin receptors, adenosine receptors, and cannabinoid receptors (Ferre & O'Brien, 2011; Julien, et al., 2008). In the glutamate system alcohol acts as an inhibitor by depressing the responsiveness of NMDA receptors to release glutamate. This in turn causes the body to compensate by up-regulating

NMDA receptors, which sometimes leads to convulsions (Julien et al., 2008). Ethanol activates

GABA-mediated ion flows which result in neural inhibition. Ethanol consumption is generally seen to induce sedation, inhibition of cognitive and motor skills, as well as muscle relaxation. At the opioid receptor level, alcohol may induce opioid release, which in turn triggers dopamine release and provides neurochemical reinforcement for alcohol consumption.

Alcohol has a predictable set of both physiological and psychological effects within the body. Physiologically, at low doses alcohol stimulates respirations; at increased doses it has the converse effect (Julien et al., 2008). The depressant effect of alcohol on respiration is dose dependent, and can compound to eventually lead to death (Julien et al., 2008). Alcohol also acts as an anticonvulsant, though it has no relevant clinical application for this effect. Alcohol has a vasodilation effect in the circulatory system, manifesting as a warm flush coupled with a decrease in body temperature. This is a dangerous combination in that it gives the illusion that the participant is substantially warmer than their actual core body temperature, resulting in an increased risk of hypothermia (Julien et al., 2008). Long-term high dose usage of alcohol has

2 also been shown to be associated with heart disease, including heart failure. Conversely, low- dose usage of alcohol, particularly in those drinks that are high in antioxidants, has been shown to reduce the risk of coronary artery disease (Julien et al., 2008).

Alcohol is classified as a , which means it has dose dependent effects on behavior. Effects are mediated by the person involved, including their unique physiology, behavioral history, expectations, and the environment in which they are consuming. These factors are especially important at low doses, while at higher doses the effects become much more universal (Julien et al., 2008). For instance, in certain environments and with certain expectations an individual may become relaxed and euphoric; while in a different environment the some individual may become violent or withdrawn (Julien et al., 2008). Though the effects of alcohol depend largely on these individual and environmental characteristics, there are a set of effects one can expect with increased levels of intoxication (see Table 1).

Alcohol, like most other psychoactive drugs, can cause tolerance and dependence. The

DSM-IV defines dependence as “a maladaptive pattern of alcohol use, leading to clinically significant impairment or distress, as manifested by three or more of the following seven criteria, occurring at any time in the same 12-month period” (American Psychiatric Association [DSM-

IV-TR], 2000, 213). These seven criteria are clearly defined within the DSM-IV and manifested as follows:

If an individual develops a tolerance (as defined by a marked increase in amounts

of alcohol needed to achieve the desired effect)

Withdrawal

Taking alcohol in larger amounts or over longer periods than was intended

A persistent desire or unsuccessful efforts to cut down or control use

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Spending a great deal of time in activities necessary to obtain alcohol or use

alcohol to recover from the its effects

Important social, occupational, or recreational activities are given up or reduced,

Alcohol use is continues despite knowledge of having a persistent or recurrent

physical or psychological problem (American Psychiatric Association [DSM-IV-

TR], 2000, 192).

As previously stated, if an individual meets three of these seven criteria they are considered alcohol dependent. When this physical dependence develops, an individual is susceptible to withdrawal, and with alcohol, this is a particularly dangerous process. As stated earlier, alcohol is an anticonvulsant and through its depressant nature causes the brain to compensate by increasing neuronal activity. When one removes alcohol from their internal environment and starts going through withdrawal, they become extremely prone to seizures.

These are potentially life threatening, and one of the goals of detoxification is to control for these seizures.

Tolerance is generally developed in individuals who consume large amounts of alcohol on a frequent basis. The 2009 SAMHSA report defined binge drinking as consuming “5 or more standard alcoholic beverages in a single occasion” (SAMHSA, 2009). They also defined heavy use as meeting this binge drinking criteria on 5 or more occasions within the past 30 days

(SAMHSA, 2009). Those individuals who engage in binge drinking (5 or more standard alcoholic drinks on one drinking occasion) on a non-regular basis (only engaging in 1-4 binge drinking sessions within a 30 day period), as well as those who engage in more regular imbibing but with moderation are less likely to develop a tolerance.

4

Tolerance manifests itself as three different types with concern to alcohol. Metabolic tolerance is the physiological response of the body, specifically the liver, to large doses of alcohol consistently being in the system (Julien et al., 2008). The liver will increase amounts of alcohol dehydrogenase as a means of metabolizing the drug out of the system at a faster rate.

Also, neurons in the brain will adapt to the amount of drug present, in this case since we are examining a depressant, the neuronal activity will increase to compensate for the depressant action of alcohol on said process (Julien et al., 2008).

A second form of tolerance is environmental tolerance. A particular environment becomes associated with alcohol’s consumption via classical conditioning. The environment then theoretically acts as a conditioned stimulus for the imbiber which causes a reflexive action in the body as it prepares itself for alcohol consumption. If placed in a novel drinking environment, someone who has an environmental tolerance may often report feeling intoxicated with fewer beverages consumed (Shapiro & Nathan, 1986).

The final form of tolerance is called functional or behavioral tolerance. This form is a behavioral adaptation by the drinker where they do not exhibit the behavioral disruption of intoxication until they have attained much higher BACs (Julien et al., 2008). One must note however, that even those these functionally tolerant individuals are not exhibiting the effects of intoxication until higher BACs are attained, they still experience the cognitive and motor deficits of their less functionally tolerant counterparts, but their behavior will still appear reasonably normal. The distinction between a physiological and behavioral tolerance is an important foundational concept for the alcohol discrimination paradigm that will be discussed. This difference manifests predominantly in individuals with high behavioral tolerances, who may be physiologically impaired but do not exhibit the behavioral effects of low doses of alcohol.

5

Alcohol Discrimination

Individuals use many of the previously described physiological and psychological effects to self-monitor their intoxication level. However, because of many of the aforementioned mediating factors (e.g., setting, tolerance, etc.), individuals are not always completely accurate in judging their level of intoxication. Davies (1962) stated that individuals who exhibit drinking problems lose these implicit associations that normal drinkers have. That is to say, those individuals who have a drinking problem have essentially shifted normal behavioral impairments to higher blood alcohol concentrations. In essence, the associations that they originally made with low dose intoxication are now made with high dose intoxication. This leads to abusers imbibing more of the substance to feel the same effects while essentially being unaware of their overall shift in physiological and behavioral intoxication. To deal with this problem, discrimination training was originally proposed by Davies (1962), which laid the ground work for teaching alcoholics to control drinking by associating blood alcohol concentration (their physiological intoxication) and with their shifted behavioral intoxication

(Davies, 1962).

Blood alcohol concentration (BAC), measured in grams of alcohol contained in 100 mL of blood, is the most commonly used measure for objectively determining intoxication (Julien et al, 2008). For example, a blood alcohol concentration of 0.10 g/mL indicates that every 100 mL of blood should contain about 0.10 grams of ethanol. BAC is typically reported as a percentage, so 0.10 g/mL can also be written as a BAC of 0.10% (meaning 1/10th of 1% of the drinker’s blood is alcohol) (Julien et al., 2008). BACs can be measured two ways, a blood test or a breathalyzer. The more commonly used measure is the breathalyzer since they are highly accurate, portable, and provide a fast and accurate measurement of BAC. Breathalyzers analyze

6

BAC by measuring the amount of alcohol in exhaled air (Julien et al., 2008). Since 5% of alcohol metabolism is in the form of exhalation of unchanged alcohol through the lungs, a ratio of

1:2300 exists between the amount of alcohol exhaled and BAC (Julien et al., 2008). By using this ratio, breathalyzers are able to extrapolate what an individual’s BAC is without the need to draw blood. BAC has been shown to be a reliable means of measuring individual intoxication as it increases predictable even across gender and those of various weights (see Tables 2&3).

The study that first tested the feasibility of utilizing alcohol discrimination training as a means of teaching alcohol abusers normal drinking habits was Lovibond and Caddy (1970). The goal of this study was to combine BAC discrimination training and conditioned aversion to teach alcohol abusers how to self-titrate their intake to a normal BAC. In this study the researchers took 44 male participants and taught them to discriminate their blood alcohol concentrations by giving them pure alcohol mixed in fruit juice, and asking them to make BAC estimates every 15-

20 min (Lovibond & Caddy, 1970). Once the participants made their estimations, they were provided with immediate feedback of their actual BAC on an adjustable meter that was left in front of the participant. Participants were told to attend to their individual internal sensations and form associations with each BAC that they achieved. Participants were given a single session of this BAC discrimination training. The participants were then placed in a conditioned aversion session where they were given free access to their preferred alcoholic beverage, and told to drink until they believed they were at a BAC =0.065 g/mL (Lovibond & Caddy, 1970). If the participants exceeded this BAC, and then tried to continue consuming the beverage, they received a low level shock from two pads attached to the participant’s face. These shocks varied in their probability (only delivered 80% of the time), immediacy, duration, and intensity

(Lovibond & Caddy, 1970). During these conditioning sessions (participants received 6-12

7 depending on necessity); participants received external feedback of their actual BACs in addition to the aversive conditioning. The authors reported that out of the 28 participants that completed treatment, 21 were said to have achieved drinking in a controlled fashion with BAC excesses of

.07% rarely occurring (Lovibond & Caddy, 1970).

Caddy and Lovibond (1976) later did a study taking their original paradigm and comparing it with groups that emphasized different aspects of the treatment. Three groups were examined, one group received aversion plus self-regulation (i.e. BAC discrimination), a self- regulation-only group, and an aversion-only group. They came to the conclusion that the aversive therapy with an emphasis on BAC discrimination training resulted in a significant number of participants maintaining controlled drinking behaviors (Caddy & Lovibond, 1976).

The self-regulation-only group did respond better than the aversion group, but the combination of the two showed the best results. Though they sought to induce an aversion at the predetermined BAC, most often it was reported that the participants simply lost interest in imbibing after 3-4 drinks. Only about 20% of the participants actually developed a conditioned aversion (Lovibond & Caddy, 1970). More importantly, it was reported that participants who took part in only one session of BAC discrimination training were able to accurately discriminate what their BACs were using just internal cues (Lovibond & Caddy, 1970).

Though Lovibond and Caddy (1970) showed promising results using a combination of

BAC discrimination training and aversive conditioning, a number of flaws existed in their experiment. First, they had an external stimulus that informed the participants what BAC they had previously attained. This, combined with the lesson in alcohol absorption, provided the participants with a very small error probability. Participants were essentially given a baseline and were able to deduce that they had to be greater than that threshold, but within a very limited

8 range. Participants were also shown a series of BAC curves prior to administration which they could’ve used as means of calculating BACs instead of attending to internal sensations.

Though the reported results seemed promising, further research was required to see if internal or external stimuli were controlling the participant’s ability to discriminate their BAC.

Silverstein, Nathan, and Taylor (1974) sought to examine BAC discrimination training as a possible treatment for alcoholism while addressing some of these concerns. Unlike Lovibond and

Caddy (1970), the investigators in this study used a multiphasic and multiday discrimination training procedure to teach four male alcoholic inpatients BAC discrimination (Silverstein et al.,

1974). After attaining a baseline drinking and estimation rating from each of the participants, the participants moved on to the BAC discrimination training. Training took place in three two-day bouts designed to prolong the length of intoxication where the BAC would rise to 0.15% over the course of day one, remain there overnight, and slowly fall throughout the next day. The researchers set an error margin of 0.02% BAC for the participants to achieve 75% of the time to meet criteria for successful BAC discrimination (Silverstein et al., 1974). Each of the two-day bouts differed in the external feedback and reinforcement that was provided. For the first two- day bout feedback was given every time an estimation was made, for the second, feedback was given 50% of the time, and for the third participants were given reinforcement (points that could later be redeemed) contingent on their accuracy while receiving intermittent feedback

(Silverstein et al., 1974). As a result of this training 3 out of the 4 participants were able to accurately estimate their BACs within the margin of error previously stated by the experimenters both during training sessions and during post-training return to baseline. For the second phase of this study, the investigators examined whether the participants could utilize this training to go from 0.00% to a predetermined BAC (0.08%) and maintain this BAC. They also tested whether

9 or not a participant who was intentionally brought up to this target BAC could maintain this

BAC as well. During the first baseline phase of this control session, participants consistently remained below the target level. Once reinforcement and feedback became available again during the control training portion of this phase, participants were able to narrow in on their target BAC, and by the end of control training all the participants were able to reach their target

BAC (Silverstein et al., 1974).

Silverstein et al. (1974) found promising results as well as claimed to have taught discrimination based on internal states, though there existed a few confounds in this study. For one, participants were also placed on a reinforcement paradigm which was technically under the umbrella of external stimuli controlling and cueing the individual in to BAC recognition. Also, under this same umbrella of external stimuli control, participants were given frequent BAC feedback. This provides similar confounds to Lovibond and Caddy (1970) in that a definitive conclusion that accurate BAC estimates were completely controlled by internal states alone could not be stated.

To address this issue of external versus internal stimuli control, Lansky, Nathan, and

Lawson (1978), using two groups of alcohol abusers, designed treatments to investigate external and internal cues in these discrimination paradigms. The rationale, as previously stated, being that the literature up until then had either not utilized baseline performance measures (Lovibond

& Caddy, 1970; Vogler, Compton, & Weissbach, 1975) or a lack of generalization and retention once the external feedback was removed (Silverstein et al., 1974). The investigators used four participants diagnosed with heavy drinking problems and split them in to two groups; one taught internal stimulus control and the other external stimulus control. Unlike the Silverstein et al.

(1974), the participants in this study would undergo a three session pre-test, training, post-test

10 discrimination training procedure (Lansky et al., 1978). This three- session design came as a result of work done with BAC discrimination training in social drinkers and has been shown to be effective in training internal stimulus control for accurate BAC discrimination (Lansky et al.,

1978). Session one consisted of a pre-training baseline phase where participants consumed six cocktails (80-proof vodka & tomato juice) of varying strengths (0.5, 1.0, or 1.5 ounces of vodka) presented in a random order. Participants always had one of the 0.5 ounce concentration, two of the 1 ounce concentration, and three of the 1.5 ounce concentration (Lansky et al., 1978). Thus, even though the drinks that the participants consumed were randomized for concentration, participants always consumed 36 ounces of liquid, 7 ounces being vodka. Participants made four

BAC estimations utilizing a 0-150 point scale (with 0 representing stone cold sober and 150 representing as drunk as you’ve ever been) over the course of administration, completing a post- drink questionnaire following each estimation as a means of gauging the participants ability to discriminate the alcohol concentration in the drinks.

After establishing the participant’s baseline estimation accuracy they were split in to two groups. One group received external-cue training and one internal-cue training. For the external condition participants received an individually customized learning booklet explaining things such as BAC-dose relationship, a BAC chart customized for the participant, and individual absorption rates based on their baseline levels (Lansky et al., 1978). Participants were also taught the body’s metabolic rate for alcohol in terms of the point system used for making the BAC estimations, and received feedback of their actual BAC in the terms of the point system. The internal training group began by listening to a relaxation tape designed to attune individuals to specific sensations and muscle groups, and afterward, participants filled out several questionnaires designed to focus individuals on specific body sensations and moods (Lansky et

11 al., 1978). Prior to each estimation, participants completed the questionnaires again. Post estimation participants received feedback of actual BAC. This sequence was repeated for each drink administration and participants reporting on the same 0-150 point scale as the external group. The groups both then had a third session testing phase which was procedurally identical to the first session, but with two distinct changes. First, participants were required to use the anesthetic mouthwash prior to each drink, and second, the participants were told the alcohol content of their drinks in order to standardize the testing conditions (Lansky et al., 1978). After data analysis, Lansky et al. (1978) discovered that individuals trained with external cue training were better able to discriminate their BAC when compared to internal trained group individuals.

Though both groups showed marked increases in BAC estimation accuracy during Session two, the participants in the internal training group showed an increase in error scores during the post- test sessions, while participants in the external training group continued to improve during the post-test.

The authors note several methodological factors that may have influenced the outcomes of this study, including the fact that external participants were given objective information concerning the relationship between BAC and external cues while internal participants were given a less concrete base of cues with which to make their estimations (Lansky et al., 1978).

Combining this with the fact that authors informed the participants during the post-test session of the concentration of alcohol within each dosage and it’s possible that the accuracy of the externally trained individuals was derived by their ability to objectively calculate their estimated

BACs, while the internal group was grounded in a less distinct set of cues to rely upon.

Many of the studies that utilized BAC discrimination training in an alcoholic population during this time were met with mixed results, some being successful at training while others

12 were unable to attain sufficient accuracy (Foy, Nunn, & Rychtarik, 1984; Miller, Becker, Foy, &

Wooten, 1976; Shapiro, Nathan, Hay, & Lipscomb, 1980). At the same time this research in to the efficacy of using BAC discrimination training as a treatment was underway, researchers were also looking at this phenomenon in individuals who would be classified as social drinkers. This research was undertaken as a way to give a proof of concept for BAC discrimination training.

These researchers were interested in whether or not someone who has not developed a tolerance for alcohol could accurately discriminate their blood alcohol concentration. If those that have not developed a tolerance could not learn these discriminations, then the likelihood of an individual who has developed a tolerance (i.e. abuse/dependent criteria individuals) learning these discriminations would be greatly reduced.

In an effort to provide a proof of concept for this treatment, Bois and Vogel-Sprott (1974) sought to train social-drinkers to not only discriminate low blood alcohol levels, but also to self- titrate their drinking to achieve a specific BAC. The investigators used nine male participants in this study, all of which were classified as social drinkers based on empirical measures which reported the frequency of social drinking occasions, the amount consumed during said occasions, and the length of time that the participant had been drinking alcohol (Bois & Vogel-Sprott,

1974). The participants were given general frames of reference for their estimates as they were told that their target BAC of interest would be no higher than a 0.15% and that consumption of two martinis would yield a reading within the 0.05%-0.08% range (Bois & Vogel-Sprott, 1974).

Given the importance of symptom reports, as the notion behind BAC discrimination training is that one can associate a specific experience with a particular BAC (Lovibond & Caddy, 1970), participants were given sensations lists developed to encourage the widest expression of sensations possible. This list of “example” symptoms was read to each participant prior to

13 drinking administrations. The list was then removed during administrations to promote participants utilizing their own internal sensations, and to prevent participants from just picking from the list provided. These sensations were categorized as physical sensation (e.g., tight forehead, warm stomach), feeling states (e.g. relaxed, happy), and “autistic” experiences (e.g. out of myself, melting with the room) (Bois & Vogel-Sprott, 1974).

For sessions 1-3 (the sessions designed to teach BAC discrimination) participants received a cocktail tailored to have to participant’s peak BAC be a 0.08%. Participants received and drank 1/4th of the cocktail over a 10 minute period, were given a 10 minute rest, and then reported their experienced symptoms, provided a BAC estimate, and then provided a breath sample. This was repeated every 20 minutes until the entire cocktail had been consumed.

Participants continued to report symptoms and BAC estimates for an additional six estimations for a total of 10 BAC estimations (Bois & Vogel-Sprott, 1974). These estimations corresponded with various points on the BAC curve, with four estimations during the rising BAC, two during peak BAC, and an additional four during the falling BAC. It is important to note that participants were given feedback on their actual BAC during every 5th estimation; the rationale was since this was the start of the falling part of the BAC curve participants should know what their peak BAC was in order to control for their BAC knowledge at the start of the study. That is, participants came in aware that they were at a 0.00% BAC during the start of estimations, so the authors sought to control for this floor effect by informing participants of their peak BAC. All three of these sessions were conducted in an identical manner, the only difference being participants received feedback of their actual BAC during session two (the training session) after each estimation (Bois & Vogel-Sprott, 1974). Sessions 4-6 were dedicated to participants self-titrating their BACs to a particular low level (within the 0.04%-0.06% range). During session four

14 participants received a drink that varied in alcohol content (0-100%), was in a novel glass, and were directed to consume the beverage at the rate they desired, while for sessions five and six participants were given their preferred alcoholic beverage.

Analysis of the results of this study was based on the mean absolute error in estimations of the %BAC for the participants. The researchers determined that participants showed a statistically significant improvement in accuracy from session one to session two, but showed no such statistically significant improvement from session two to session three. However, accuracy in both sessions two and three were significantly better than session one (Bois & Vogel-Sprott,

1974). High error estimates at the outset of the experiment (session one) were essentially eliminated by supplying feedback of actual BAC after each estimate, and this performance was not significantly impaired once feedback was removed during the subsequent sessions (Bois &

Vogel-Sprott, 1974). For sessions 4-6 (where self-titration was required) participants showed interesting results. Accuracy for BAC estimation decreased during session four, most likely due to the novel drinking situation, but over time returned to the higher accuracy estimations. This resulted in the accuracy improvement from session four to session six being statistically significant (Bois & Vogel-Sprott, 1974). Sessions 4-6 also give support for the associations of internal sensations with particular BACs. When constant environmental factors were removed a slight increase in error was observed but regular improvement was seen over the course of the sessions. This suggests that BAC estimates can occur without constant environmental factors, and remain accurate under internal stimulus control. One additional interesting result the authors found was that symptom reports did not correlate with specific BACs, that is, participants would report different symptoms for each BAC across sessions. The authors posit that this could be because discrimination of BACs may be based on sensations and experiences that are not

15 commonly verbalized, but instead fall under the blanket terminology of “intoxication” (Bois &

Vogel-Sprott, 1974).

The results of this study lend support to social drinkers’ ability to discriminate their

BACs through a three session training paradigm, and maintain that accuracy across novel situations. This effect was later replicated and expanded upon by Ogurzsoff and Vogel-Sprott

(1976). The study was essentially an expansion of the Bois and Vogel-Sprott (1974) using an identical training and testing paradigm, though they examined additional correlates such as drinking frequency and doses imbibed during sessions. The results of this study were also congruent with Bois and Vogel-Sprott (1974). Frequency of drinking, as well as doses imbibed during those sessions did not have an effect on participants ability to learn accurate BAC discrimination, as well as their ability to self-titrate their drinking given a novel beverage and environment (Ogurzsoff & Vogel-Sprott, 1976). Key aspects showed that participants may be attending to internal sensations that are not normally verbalized, with the possibility that the external feedback from the breathalyzer is essential (Ogurzsoff & Vogel-Sprott, 1976).

The Bois and Vogel-Sprott (1974) and Ogurzoff and Vogel-Sprott (1976) studies provided promising results for the BAC discrimination literature, however, the research group of

Huber, Karlin, and Nathan (1976) were quick to point out that their still had been no definitive empirical support for these discriminations being made based on internal cues alone (Huber et al., 1976). The researchers state that it was unclear if participants used the external cues of drink strength and familiarity with the temporal parameters of the drink sessions, or if they made their estimations purely on internal subjective cues in Bois and Vogel-Sprott (1974) (Huber et al.,

1976). To examine the efficacy of internal versus external cue control over social drinkers’ ability to discriminate their BACs Huber et al. (1976) recruited a group of 36 male drinkers to

16 test external, internal, and combined cue training. The researchers emulated Bois & Vogel-

Sprott’s three session training paradigm. During baseline (session one) participants were given six drinks, one of which was consumed every half hour (Huber et al., 1976). These drinks varied in alcohol content and frequency in order to control for participants possibly using drink strength to make BAC estimates. Participants were then split in to three experimental groups, one receiving internal cue training, one receiving external cue training, and one that received both internal and external training. Estimates were made on a scale ranging from 0-50 which was analogous to a 0.00%-0.10% BAC (Huber et al., 1976). External training was comprised of each participant receiving an individualized packet explaining BAC-dose relationships for their specific weight, and the absorption and metabolic rate of the alcohol for that specific person.

Internal groups listened to a standard relaxation tape to increase their awareness of various muscle groups and the sensations that arise during tension and relaxation, they utilized this to tune in to various sensations they experienced in muscles, feelings, emotions, and clarity during the drinking phase (Huber et al., 1976). Participants in the internal group also filled out a mood adjective checklist and body sensations checklist prior to each estimate. The last experimental group received all the training previously described. It is important to note that during training all participants, regardless of group, received feedback of their actual BAC from the breathalyzer converted to the point scale previously described.

For the post test phase the groups were split in half again, with half of each of the experimental groups being told the amount of alcohol in each drink and the other half remaining unaware of the alcohol concentration. The results of this study indicated that none of the groups differed significantly from the other in their ability to estimate their BACs accurately, the only significant difference seen was when the known versus unknown groups were compared (Huber

17 et al., 1976). Additionally, participants with internal cues training showed an increased sensitivity to lower BACs when compared to their external counterparts, a trend that translated in to internal cue individual’s overestimating while external cue individuals underestimated at

BACS below 0.04%. Based on this study, it can be concluded that participants can use internal subjective cues to estimate BAC accurately. This study also found that the removal of BAC feedback didn’t diminish estimation accuracy of nonalcoholic participants. The conclusions found in this study give support to the theory that participants who remain sensitive to low levels of blood alcohol concentration (i.e. social drinkers) are able to accurately estimate these levels based on internal cues alone after receiving training.

Shortt and Vogel-Sprott (1978) examined social drinkers’ ability to naturally discriminate to a specific BAC. Fifteen social drinkers took part in a four once per week session paradigm. A week prior to the experiment, participants were introduced to and practiced giving breathalyzer samples, and were informed that they would receive two different alcoholic beverages; one that was indicated as preferred by the participant, and the other an unfamiliar drink. Participants convened for four weekly drinking sessions, where half of the participants were served their preferred drink and the other half served an unfamiliar drink that was presented in a variety of unusual glasses as well as irregular-serving sizes (Shortt & Vogel-Sprott, 1978). Participants received the opposite beverage in the second session from the one they received at the first, and this alternation continues through the remainder of the sessions. Sessions one and two required participants to judge when they reached a level of intoxication where they would normally stop drinking, while sessions three and four participants were asked to reproduce their previously chosen stopping point with each beverage. Participants in this study were able to reproduce the same drug states in both drink conditions with a mean absolute BAC error of 0.013% (Shortt &

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Vogel-Sprott, 1978). This indicates that participants were able to reproduce a previous BAC in both a familiar and novel beverage within plus or minus one standard alcoholic beverage.

Additionally, the results from this study support that social drinkers have a natural ability to discriminate their drug states with a relatively high degree of accuracy. The authors posit that

BACs which differ by 0.013% may produce indistinguishable drug states in a normal social drinker, and that specialized training (i.e. BAC discrimination training) may hone social drinkers discrimination accuracy (Shortt & Vogel-Sprott, 1978). This effect was also seen in a study done by Lansky, Nathan, Ersner-Hershfield, & Lipscomb (1978) where they measured the pre-training accuracy of both alcoholics and social drinkers. The results of this study also supported social drinkers’ ability to discriminate their BAC within a degree of accuracy; something the alcoholic group was unable to do (Lansky et al., 1978).

A more recent study done by Martin, Rose, and Obrenski (1991) examined the effects of behavioral impairment, self-reported subjective levels of intoxication, and family history of alcohol dependence on a participants ability to estimate their BAC. Thirty-nine male participants completed a two session experimental paradigm. A body sway task, which used a computer to analyze how much a person sways, was used as their measure of behavioral impairment (Martin et al., 1991). Family history of alcoholism was determined by participants filling out a Michigan

Alcoholism Screening Test (MAST) for themselves as well as their biological parents, as well as undergoing a Family History Research Diagnostic Criteria interview (Martin et al., 1991).

Session one consisted of the participant familiarizing themselves with the experimental space, filling out all questionnaires/measures, providing a baseline for the body sway task, and receiving information about how alcohol affects the body for their specific gender and weight.

Session two was where participants consumed an alcohol dose of 0.75 g/kg via two mixed drinks

19 that were to be consumed within five minutes of receiving them. BAC estimates and subjective intoxication ratings were collected at 11 time points: twice on the ascending limb, twice at the peak, twice on the descending limb, and 5 more times at 5-minute intervals as BACs descended

(Martin et al., 1991).

Statistical analysis showed that family history had no effect on a participant’s accuracy for BAC estimation, and participants who reported lower levels of subjective intoxication underestimated their BACs more than participants who reported higher levels. Additionally, participants who showed less behavioral impairment also underestimated their BACs more than those who showed greater behavioral impairment on the ascending limb. Lastly, accuracy was better on the ascending and descending limb when compared to peak estimations, but descending limb accuracy decreased over time (Martin et al., 1991). Results of this study suggest that individuals who experience the more salient effects of intoxication (i.e. higher ratings of subjective intoxication, higher levels of behavioral impairment) are more accurate at estimating their BAC when compared to individuals who do not experience these more salient cues. These cues also dissipate over the course of the descending limb of the BAC curve, causing greater inaccuracy with BAC estimations (Martin et al., 1991).

These studies support that social drinkers under controlled conditions, where alcohol is consumed at regular intervals, and BAC feedback is received can learn how to discriminate their BACs. However, the precise mechanism behind this discrimination has yet to be determined, showing that there may be numerous factors that inform these inferences.

Additionally, alcohol tends to be consumed in chaotic environments (e.g. bars, night clubs etc.), comes in numerous novel forms (e.g. different types, various degrees of strength), and is often mixed with other chemicals (e.g. caffeine, nicotine). Though a few studies have sought to control

20 for novelty in both the environment and drink type, only some research has looked in to the effects of dual drug administration (Bois & Vogel-Sprott, 1974; Ogurzsoff & Vogel-Sprott,

1976). Caffeine, for example, is a drug that is becoming increasingly popular to mix with alcohol in various forms. Though caffeine may seem innocuous, given its wide consumption, it can interact with alcohol in a complex manner.

Caffeine Pharmacology

Caffeine is one of the most commonly used psychoactive drugs, with roughly 80% of the adult population in the reporting daily consumption (Julien et al., 2008). Caffeine is consumed via numerous beverages (e.g. coffee, tea, and soda) and food items (e.g. chocolate, cocoa). On average, a cup of coffee contains around 100mg of caffeine in a 5oz dose (Reissig,

Strain, & Griffiths, 2008). In recent years, “energy supplements” fortified with caffeine have been marketed to individuals as an alternative to traditional vehicles for caffeine consumption.

These supplements vary greatly in the amount of caffeine they each contain. These supplements can have caffeine concentrations that range from as little as 50mg per can/bottle (roughly equivalent to what one would receive in most sodas) to as high as 505mg per can/bottle (the equivalent of 5 cups of coffee) (Reissig et al., 2008). Since energy drinks are qualified as supplements, they do not have to adhere to the same caffeine content restrictions that other caffeinated beverages must (Duchan, Patel, & Feucht, 2010; Ishak, Ugochukwu, Bagot, Khalili,

& Zaky, 2012; Rath, 2012; Seifert, Schaechter, Hershorin, & Lipshultz, 2011). sales have increased exponentially since their introduction in the U.S., and continue to do so today (Reissig et al., 2008). The energy drink market is projected to reach $9 billion in revenue in 2011, with half of the market consisting of children, adolescents, and young adults (Seifert et al., 2011).

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Caffeine is generally consumed orally, and is absorbed rapidly in the system (complete absorption taking place within 90 min) with peaks occurring at around two hours post ingestion

(Julien et al., 2008). It is a water soluble drug, found in equal concentrations across the body and brain. Caffeine is metabolized in the liver, and has a half-life between 2.5 and 10 hours and 10% of the drug remains unchanged upon excretion (Julien et al., 2008). Lastly, two of the three metabolites of caffeine (theophylline & parazanathine) behave similarly to caffeine which result in caffeine’s extended effects in the body (Julien et al., 2008, 481).

Caffeine is an adenosine antagonist; binding to the same receptors as adenosine, thus blocking adenosine (Julien et al., 2008). Adenosine is a neuromodulator, coaxing the release of several other neurotransmitters. The adenosine neurons form a diffuse system that causes a sedative effect in the central nervous system, decreasing the discharge rate of a number of central neurons and increasing the activity of numerous depressant neurotransmitters (Julien et al., 2008,

484). Thus, a blockade of adenosine receptors prevents stimulation of GABAnergic neurons which causes an inverse effect, typically seen as stimulating. Not only does caffeine act as a

CNS-stimulant, it also stimulates cardiac and respiratory systems (Julien et al., 2008). Typically caffeine is ingested for these stimulant effects which are coupled with a series of behavioral effects including increased mental alertness, wakefulness, reduced fatigue, delay of need for sleep, and increased concentration (Julien et al., 2008). Consumption of 1-3mg/kg or 12.5-

100mg/day has shown improved exercise endurance, cognition (concentration and memory), mood elevation, increased alertness, reaction time, and less sleepiness in adults (Ishak et al.,

2012; Seifert et al., 2011). Heavy caffeine consumption is associated with agitation, anxiety, tremors, and insomnia. Symptoms of caffeine intoxication include nervousness, anxiety,

22 restlessness, insomnia, GI issues, tremors, tachycardia, and psychomotor agitation (Rath, 2012).

Caffeine overdose can cause liver damage, kidney failure, respiratory disorders, agitation, seizures, psychotic conditions, tachycardia, cardiac dysrhythmias, hypertension, heart failure, and death (Seifert et al., 2011).Like many psychoactive drugs, caffeine also has the risk for the development of tolerance and dependence. Regular usage of caffeine at doses as low as 100mg can produce habituation, tolerance, and a low-grade withdrawal if removed (Julien et al., 2008,

487). Withdrawal symptoms generally begin slowly, maximizing after 1-2 days and typically include headaches, drowsiness, and fatigue.

A novel and increasingly popular way for caffeine consumption is in the form of energy drink supplements. These supplements often contain large caffeine doses as well as several other chemical compounds meant to stimulate those that consume. Two such compounds are and taurine. Guarana is a plant derivative which contains caffeine, theophylline and theobromine with caffeine being a main by product (Duchan et al., 2010; Ishak et al., 2012; Rath 2012;

Seifert, et al., 2011). Each gram of guarana can contain between 40 and 80 mg of caffeine such that 3.4g of guarana provides up to 250mg of caffeine (Ishak et al., 2012; Rath 2012; Seifert et al., 2011). Guarana has a longer half-life than caffeine because of potential interactions with other plan compounds (Seifert et al., 2011). Taurine is an amino acid found in animal tissue, and is thought to increase the effects of caffeine as well as alleviate muscle fatigue (Duchan et al.,

2010; Rath, 2012). An average diet contains between 20-200 mg of taurine, while the average energy drink dose ranges between 600-100 mg (Duchan et al., 2010; Rath, 2012). It is considered essential for normal development and growth, and is therefore typically added to baby formula

(Rath, 2012; Siegel, 2011).

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Energy drinks are often advertised towards children, adolescents, and young adults (Ishak et al., 2012; Rath, 2012; Seifert et al., 2011). On average, adolescents consume 60-70mg of caffeine per day (often from soda products), but this can range up to 800mg (Seifert et al., 2011).

Recent studies have revealed that 28% of 12-14 year olds, 31% of 12-17 year olds, and 34% of

18-24 year olds consume energy drinks on a regular basis (Seifert et al., 2011). In 10-12 year olds, 31% of girls and 50% of boys reported trying energy drinks, with 5% of girls and 23% of boys reporting drinking at least one can per week (Seifert et al., 2011). Several potential problems can emerge from overconsumption of energy drinks at these young ages including exacerbation of cardiac conditions, potential complications with ADHD medications, numerous nutritional issues (eating disorder, caloric intake, childhood obesity), as well as diabetes (Rath,

2012; Seifert et al., 2011).

College students and athletes are two additional populations that have been shown to consume energy drinks on a regular basis. In a review done by Seifert et al., (2011) 51% of college students consumed an energy drink at least once per month, with men twice as likely to consume energy drinks when compared to women. The most common reasons cited for using these energy drinks was to antagonize insufficient sleep and to increase energy (Seifert et al.,

2011). In addition, 13% of athletes reported consuming energy drinks in order to obtain caffeine, citing the desire for caffeine’s ergogenic effects as well as to improve exercise performance as reasons for consuming (Duchan et al., 2010).

To examine caffeine consumption, Griffiths et al., (1986) examined how several manipulations to coffee affected nine heavy coffee drinkers’ quantity and frequency of coffee consumption. In this multi-experiment paper, participants were housed in a research unit under

24 hour observation where a baseline of their coffee consumption behavior (i.e. when each cup

24 was consumed, how many cups, at what intervals) was taken. The experimenters then examined how manipulating coffee concentration, coffee concentration, and caffeine dose, caffeine dose alone, and preloading the participants with caffeine, affected the aforementioned variables

(Griffiths et al., 1986). During experiment four, consumption behavior of three participants was examined in relation to multiple caffeine concentrations (0 mg, 25 mg, 50 mg, 100 mg, 200 mg, and 400 mg) per cup of coffee. The experimenters examined the effects of these doses on total cups per day, total caffeine, bitterness, liking, and arm tremors. What the experimenters found was that caffeine dosage was highly detectable by the three participants as caffeine concentration was inversely related to the amount of cups per day consumed. Cup consumption was increased at the 25mg and 50mg dosages, while dropping off at the higher dosages, giving some evidence for the reinforcing/punishing effects of caffeine. Participants also reported a dose-related relationship between the caffeine concentration and bitterness, even at the lowest dosages

(Griffiths et al., 1986). These results provide some evidence as to the high discriminability that caffeine has, even at low doses.

Two additional studies were done by Griffiths and colleagues to examine volunteers’ ability to discriminate caffeine. The first study done by Evans and Griffiths (1991) examined whether or not humans can discriminate caffeine versus a placebo, and which subjective effects were the important indicators for determining which was which (Evans & Griffiths, 1991).

During this experiment, all participants were taught to discriminate between a 300 mg dose of caffeine and a placebo capsule. Once all participants acquired discrimination between placebo and the 300mg dose of caffeine they entered a testing phase. The testing phase consisted of caffeine doses ranging from placebo, 50 mg, 100 mg, 200 mg, 300 mg, 400 mg, and 600 mg administered in a pseudorandom order (Evans & Griffiths, 1991). Participants were again asked

25 to tell whether or not they had received caffeine or a placebo. The results of this study showed a dose dependent orderly relation between increases in caffeine concentration and caffeine identification. That is, participants were able to discriminate the presence of caffeine far more often with higher doses. The study also showed that participants were unable to discriminate at low doses (50 mg), and didn’t perform above chance levels until 100 mg or higher. The prominent subjective effects noted for caffeine were feeling jittery/nervous/anxious as well as alert/active while the placebo was characterized by tired and/or headache and absence of the drug effect (Evans & Griffiths, 1991).

To test the lowest dose of caffeine that would be discriminable by a human participant,

Silverman and Griffiths (1992) trained eighteen volunteers on a caffeine discrimination paradigm. Using a 178 mg caffeine dose versus a placebo in capsule form, the experimenters were able to successfully teach nine of the eighteen participants to discriminate between the two conditions at a high level of accuracy (Silverman & Griffiths, 1992). The experimenters then proceeded to provide doses of 10 mg, 18 mg, 32 mg, 56 mg, 100 mg, and 178 mg capsules of caffeine, in descending order, to the nine participants in order examine how low a dose would be detectable. The results of the study indicate that the majority of participants were able to detect caffeine doses as low as 100 mg. three of the nine participants were able to discriminate the presence of caffeine at the 56 and 32 mg dose, while one participant was able to detect caffeine at the low dose of 18mg (Silverman & Griffiths, 1992). This study provided evidence that people could detect caffeine at much lower levels than previously thought.

These three studies support that not only is the taste of caffeine highly detectable in a human participant, but also the physiological effects can be detected accurately at low doses when caffeine is consumed in a form that eliminates the participant’s ability to taste the drug.

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Though the participants in both the Evans and Griffiths (1991) and Silverman and Griffiths

(1992) studies were trained to discriminate the presence of caffeine, the results of these studies suggest that participants can feel the physiological effects of caffeine at low levels. This is important in the context of alcohol and caffeine consumption for several reasons. First, low dose caffeine can act as a reinforcer for increased consumption of the caffeinated beverage (Griffiths et al., 1986). Given that one of the main forms of alcohol and caffeine consumption comes in the form of mixed beverages (e.g. Irish coffee, and vodka, jack and coke) this reinforcing effect of low dose caffeine can increase the probability of consuming these beverages.

Additionally, given the findings of Silverman & Griffiths (1992), individuals may also be experiencing the physiological effects of caffeine at these low doses. Given the high taste detectability of caffeine our study will be emulating the capsule administration procedure utilized in Evans and Griffiths (1991) and Silverman and Griffiths (1992).

Alcohol & Caffeine

The combination of the two psychoactive substances, alcohol and caffeine, is not a novel one. Drinks such as an Irish coffee or a rum and cola have provided drinkers with alcohol combined with low doses of caffeine for some time. Given that a 12-ounce Coca-Cola has roughly 34.5 mg of caffeine while an average 5 ozs of coffee has a range of 75-100 mg of caffeine, these mixed drinks would provide a relatively low dose of caffeine per their consumption (Reissig et al., 2008). However, since the introduction of Red Bull© (80 mg of caffeine per 8 oz can) in 1997, the market for energy drinks fortified with high amounts of caffeine has grown increasingly (Reissig et al., 2008). These energy supplements have a range of caffeine concentration from 50 mg-505 mg per can/bottle (Reissig et al., 2008). It has become increasingly popular to mix these highly caffeinated energy supplements with alcohol (e.g., Red

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Bull and Vodka). O’Brien, McCoy, Rhodes, Wagoner, and Wolfson(2008) sent a web-based survey to students at 10 different universities in the state of North Carolina with the explicit purpose of analyzing demographic variables, attitudes about alcohol consumption, drinking behavior, other substance use, consequences experienced from one’s own drinking, and consequences experienced by other’s drinking. Of the 4,271 respondents to this survey, 2,886

(68%) of them reported drinking alcohol at least once in the past 30 days (O'Brien et al., 2008).

Out of the 2,886 reported alcohol consumers 697 (24%) reported having consumed alcohol mixed with an energy drink. A bivariate analysis revealed that individuals who were younger, white, male, intramural athletes, or fraternity or sorority pledges or members were at an increased likelihood of using alcohol mixed with an energy drink (Berger, Fendrich, Chen, Arria,

& Cisler, 2011; O'Brien et al., 2008). When queried as to why they mixed alcohol and energy drinks, 51% stated it was to mask the flavor of alcohol, 15% mixed in order to drink more and not feel drunk, and 5% reported mixing in order to drink more and not look as drunk.

Additionally, those that engaged in alcohol/energy drink consumption were also more likely to engage in high risk drinking behavior. For example, participants who drank alcohol and energy drinks were more likely to drink more during a drinking session, had a higher frequency of heavy drinking days in the past 30 days, and reported twice as many episodes of weekly intoxication when compared to regular drinkers (O'Brien et al., 2008). Those that mixed alcohol and energy drinks also experienced a higher frequency of alcohol-related consequences such as likelihood of being taken advantage of sexually, taking advantage of someone else sexually, riding with a driver who is under the influence, being hurt or injured, or requiring medical treatment (O'Brien et al., 2008). Finally, individuals who consumed alcohol and energy drinks showed an increased likelihood of driving under the influence, but it is important to note this difference was only seen

28 for individuals who imbibed in a low number of drinks (between 1 and 7) per single episode

(O'Brien et al., 2008). The results of this study not only shed light on the high frequency of alcohol and energy drink consumption (24% of drinkers reporting consumption) but also the increased likelihood of negative outcomes associated with consuming these substances in tandem.

Though the O’Brien et al.,(2008) study provided vital information on the prevalence of alcohol/energy drink consumption as well as the increased likelihood for negative outcomes, it was a retrospective survey, where survey takers were asked to describe a typical night of drinking and were not queried during an actual drinking session. With this in mind, Thombs et al., (2010) performed an event-level analysis on bar patrons to examine whether or not consuming alcohol and caffeine would increase the likelihood one would leave a bar highly intoxicated (defined by the authors as a BAC ≥ 0.08 g/210 L) as well as an increase the intention to drive upon leaving the bar. Interviews, self-report surveys, and BAC data were collected between 10:00pm and 3:00am on a street with bars frequented by a college population (Thombs et al., 2010). Recruiters approached every third person exiting the drinking establishments to participate in the study, any person who was not approached but expressed interest was also recruited. Individuals who agreed to participate underwent an interview which asked demographic information, energy drink consumption over the past 12 hours, consumption of energy drinks not mixed with alcohol, time spent drinking, type, size, and number of alcoholic drinks that night. Participants were then given a survey that asked intended mode of transportation for leaving the bar district as well questions pertaining to their drinking behavior over the last 12 months. Data collected from 802 participants indicated that individuals who consumed energy mixed with alcohol were three times more likely to leave a bar highly

29 intoxicated (a BAC ≥ 0.08 g/210 L) when compared with individuals who did not drink alcohol mixed with energy drinks (Thombs, et al., 2010). Participants who consumed alcohol mixed with energy drinks were also four times more likely to intend on driving when leaving the bar district when compared to those who did not combine the two (Thombs et al., 2010). This study provides evidence for one of the more cited concerns with this trend of alcohol and energy drink mixing.

That is, those individuals who imbibe both of these substances are experiencing some loss of the subjective cues that would indicate to a drinker their level of intoxication. As seen in the results

Thombs et al., (2010), the individuals who imbibe in both these substances during a drinking session had higher BACs and showed an increased intention to drive. These findings support the hypothesis that the stimulating effect of caffeine may be masking these high levels of intoxication as well as the level of impairment the individual should be experiencing.

An additional study done by Barche and Stockwell (2011) also found similar results.

Researchers examined risk taking behaviors associated with those who consume alcohol mixed with energy drinks (AmED), while controlling for risk taking propensity. They provided a web based survey to university students in western (n=465) where 23% (n-105) reported consuming an AmED within the last 30 days (Brache & Stockwell, 2011). AmED users had twice the odds of experiencing one or more negative consequences and were heavier drinkers when compared to the non-AmED group. The authors stated that AmED consumers are at a higher risk for harm than their counterparts, and should be classified has “high risk drinking”

(Brache & Stockwell, 2011). In addition, those who consumed AmEDs had also engaged in more stimulant drug use (cocaine & amphetamine) over the past 12 months when compared with non-

AmED groups, and were more likely to have ridden home with a driver who had been drinking, driven home after drinking, and been hurt or injured.

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Alcoholic energy drinks have also been the culprit in several emergency room cases.

Cleary, Levine, and Hoffman (2012) examined 11 cases from a metropolitan hospital during a 4- month period for patients 25 or younger admitted intoxicated under alcoholic energy drink use.

The median age of cases was 16, with 90% of cases under the legal drinking age, and 64% being male (Cleary et al., 2012). Patients admitted for acute intoxication under alcoholic energy drink use were often under the legal drinking age, found in high risk situations (subway tracks, parks, public buildings), often had to be admitted to the hospital, and often times had to arrive via EMS.

The authors advocate that usage is due to the desire for what they call the “wide-awake drunk” where individuals perceive themselves to be wide awake though not really having enhanced behavioral differences compared to alcohol alone (Cleary et al., 2012).

High energy drink consumption has also been linked to increased risk of meeting alcohol dependence criteria. Arria et al., (2011) conducted personal interviews of one thousand and ninety seven fourth year college students to examine if a link between energy drink usage and risk for alcohol dependence exists. The authors split participants up in to groups based on their frequency of energy drink consumption; those that did not consume (38.6% of the sample), low frequency users (defined as having consumed an energy drink 1 to 51 days out of the last year)

(51.3%), and high frequency users (consumed energy drinks ≥52 days out of the last year)

(10.1%) (Arria et al., 2011). The investigators found that those in the high-frequency group drank alcohol more frequently and in higher quantities. In addition, high-frequency consumers were also at a significantly greater risk for alcohol dependence relative to both non-users and low frequency users, with low frequency and non-users not differing for risk of alcohol dependence

(Arria et al., 2011). These differences were seen after controlling for several mediating variables

(e.g. typical alcohol consumption, depressive symptoms, parental history of alcohol/drug

31 problems, etc.). The authors advocate for a strong association between energy drink consumption and risk for alcohol dependence, and that energy drink consumption on a weekly or daily basis represents a specific population that may need to be targeted for intervention (Arria et al., 2011).

The hypothesis that caffeine is attenuating several of the depressive aspects of alcohol has been examined in physiological and behavioral paradigms as well as in both rodent and human models. Two studies done in rodents have examined the effects of caffeine and alcohol. A study done by Ferreira et al., (2004) examined whether or not an energy drink could attenuate the depressant effects of alcohol on locomotor activity. The experimenters used 80 male swiss albino mice in this study which examined the effects of four doses of Red Bull ( 3.57 ml/kg, 10.71 ml/kg, 17.86 ml/kg, and control) and four doses of ethanol (0.5 g/kg, 1.0 g/kg, 1.5 g/kg, and 2.5 g/kg) (Ferreira et al., 2004). The doses of Red Bull were determined by defining one dose of an energy drink as the equivalent of a 250 ml can ingested by a 70 kg individual, this would make the 3.57 ml/kg dose equivocal to 1 can, 10.71 ml/kg 3 cans, and 17.86 ml/kg would be equivalent to 5 cans. After testing these four doses in the locomotor activity chamber, the investigators determined that all doses of the energy drink increased locomotion in the animals when compared to controls. Peak locomotor activity was determined to occur during the 10.71 ml/kg dose, making it the optimal choice for the caffeine/alcohol co-administration condition

(Ferreira et al., 2004). No effect for locomotion was found at the low dose ethanol (0.5 g/kg, 1.0 g/kg, and 1.5 g/kg) alone or in combination with the 10.71 ml/kg of energy drink. However, the

2.5 g/kg dose of ethanol showed a reduction in activity that was then attenuated by the 10.71 ml/kg dose of the energy drink (Ferreira et al., 2004).

Gulick and Gould (2009) examined the effects of alcohol, caffeine, and a combination of the two in an elevated plus maze discriminated avoidance task. The plus-maze avoidance task

32 measures anxiety, locomotion, and learning by comparing the time spent in the various arms of the plus maze (Gulick & Gould, 2009). This is done by having two open arms, and two closed arms with one of the closed arm given an aversive stimulus during training while the other is not.

Anxiety is measured by measuring time spent in the open arms of the maze compared with the other arms, locomotion is measured by total entries in to all arms, and learning is measured as time spent in the aversive closed arm compared to time in the non-aversive closed arm (Gulick &

Gould, 2009). All participants underwent a training session which measured the amount of time spent in each arm, and included the aversive stimuli (a light and noise) for the conditioned aversive arm. Participants were either given one of four doses of caffeine (5 mg/kg, 10 mg/kg, 20 mg/kg, or 40 mg/kg) or a placebo during training in order to develop a dose dependent curve for the effects of caffeine on discriminated avoidance. To test whether the effects of caffeine on learning were dose dependent, they compared the effects of the highest dose of caffeine with saline on before training or before training and during testing. Finally, to examine the interaction between ethanol and caffeine they co-administered doses of both drugs, examining how ethanol doses of 1.0 g/kg and 1.4 g/kg alone affected time in each arm, and how a combination of ethanol and caffeine doses of 20 mg/kg or 40 mg/kg affected time in each arm (Gulick & Gould,

2009). Ethanol produced a dose-dependent decrease in anxiety and learning, but increased locomotion, while caffeine produced a dose dependent increase in anxiety but decreases in both locomotion and learning (Gulick & Gould, 2009). Caffeine also failed to attenuate any ethanol induced learning deficits, but the 1.4 g/kg dose of ethanol did attenuate the anxiogenic effects of caffeine (Gulick & Gould, 2009). These studies both support that physiologically, caffeine does not attenuate the effects of alcohol in a rodent model.

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Several studies in the literature use a human model to examine the effects of alcohol and caffeine. Rush, Higgins, Hughes and Bickel (1993) examined the acute behavioral and physiological effects of alcohol and caffeine alone and in combination. Participants received either one of two doses of ethanol (0.5 g/kg or 1.0 g/kg), one of two doses of caffeine (250 mg/70 kg or 500 mg/70 kg), a placebo, or a combination of any of the above (Rush et al., 1993).

The behavioral tasks used to measure behavioral impairment were the digit symbol substitution task, and the repeated acquisition and performance procedure. Physiological measures of heart rate and blood pressure were taken, as well as ratings of subjective levels of intoxication.

Alcohol alone disrupted responding in both the digit-symbol substitution task and repeated acquisition task, increased heart rate and subjective rating of intoxication, and decreased blood pressure. Caffeine attenuated some of the learning deficits seen in the behavioral task across the length of the experimental session, increased blood pressure, and showed a high rating of subjective drug strength (Rush et al., 1993). The combination of the drugs showed that caffeine partially attenuated some of the disruptive behavioral effects of alcohol. The drug combination did not have an effect on the participant rated drug effects (Rush et al., 1993). This study is one of the earliest that provides evidence of caffeine’s ability to attenuate certain behavioral disruptions that alcohol produces.

Some more recent studies have examined the effects that the drug combination has on various physiological measures. These studies focused on tasks such as a maximal effort, psychomotor speed and accuracy, and reaction time to see whether caffeine can attenuate the depressant effects of alcohol. Ferreira, de Mello, Rossi, and Souza-Formigoni (2004) examined how co-administration of these two drugs affected participants on a maximal effort task.

Fourteen participants completed four sessions where they received a control, alcohol (1.0 g/kg),

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Red Bull (3.57 ml/kg), or a combination of both alcohol and Red Bull (Ferreira et al., 2004).

Sixty minutes after drug ingestion, participants underwent a maximal effort task using a cycle ergometer while their oxygen consumption, ventilation, and respiration were measured. The experimenters found no significant differences between alcohol and alcohol plus the energy drink across the physiological and biochemical parameters that were tested. However both conditions varied significantly from control (Ferreira et al., 2004). This suggests that energy drinks at this dose do not alter performance changes induced by alcohol consumption on a physical task. However, given that caffeine has a dose dependent effect, a higher dose may show a different effect.

To test how alcohol and caffeine affects psychomotor speed and accuracy Mackay,

Tiplady, and Scholey (2002) used both a four choice reaction time task and a digit symbol substitution task. Sixty four participants received either ethanol (0.66 g/kg), caffeine (110-120 mg), both, or neither (Mackay et al., 2002). Participants began testing 50 minutes after drug consumption. The results indicated that alcohol, caffeine, or the combination of the two produced no effects in the reaction time task (Mackay et al., 2002). However, caffeine did attenuate the alcohol induced slowing in the digit symbol substitution task, bringing the number of responses back to control levels (Mackay et al., 2002). The authors do note that the findings for the reaction time task were atypical in comparison to the prior literature and that the task may have been relatively insensitive to changes in speed as evidenced by the variability in error rating (Mackay et al., 2002).

In comparison, Martin and Garfield (2006) examined the effects of a placebo, alcohol, caffeine, and the combination of alcohol and caffeine on reaction time. Sixteen participants were examined across all four conditions using both a choice and simple reaction time task (Martin &

35

Garfield, 2006). The simple reaction time task consisted of the participants responding on a key whenever a stimulus was presented, while the choice reaction time task had the participants respond on a specific key that appeared on the screen (e.g. when A was presented press the A key). Alcohol significantly increased the decision time (the interval between stimulus presentation and response) on the reaction tasks, while caffeine decreased the reaction time but only in the choice task (Martin & Garfield, 2006). When alcohol and caffeine was taken together, no significant difference was found between placebo and alcohol/caffeine on reaction time, suggesting that the stimulatory effects of caffeine were able to attenuate the depressive effects of alcohol in these reaction time tasks (Martin & Garfield, 2006). A more recent study examined the effects of alcohol and caffeine on several tasks showed a similar outcome, where alcohol and caffeine showed no significant differences from placebo, but also showed no significant difference to alcohol only in a simple reaction time task (Attwood, Rogers, Ataya, Adams, &

Munafo, 2011).

In a similar study Marczinski and Fillmore (2003) examined the effects of alcohol/caffeine co-administration on how responses are executed or inhibited. The experimenters tested this using 12 social drinkers performing a cued go/no-go task. The cued go/no-go task used two separate cues that either cued a response emission or that the participant was to inhibit responding. Participants received a dose of alcohol (0.0 g/kg and 0.65 g/kg) in combination with the doses of caffeine (0.0 mg/kg, 2.0 mg/kg, and 4.0 mg/kg) across the experimental sessions. When alcohol was received without caffeine it impaired both inhibition and response emission (Marczinski & Fillmore, 2003). When alcohol and caffeine were taken in tandem participants showed an increase in response emission, but showed no attenuation of the inhibitory control (Marczinski & Fillmore, 2003). That is, caffeine was able to antagonize

36 alcohol’s effects on response execution, but had no effect on the participants’ ability to inhibit responses. This was not a dose-dependent effect since caffeine attenuated alcohol’s effects on response execution across all doses equally.

In an effort to replicate some of the findings on alcohol/caffeine interactions, Attwood et al. (2011) examined some of the frequently used tasks deemed sensitive to this interaction. In a repeated measures design, twenty eight participants underwent a battery of tasks frequently used to examine alcohol/caffeine interactions. These tasks included, a simple reaction time task, a go/no-go task, a stop-signal task, and a stroop task (Attwood et al., 2011). All participants underwent all conditions; placebo, caffeine only (2.0 mg/kg), alcohol only (0.6 g/kg), and alcohol/caffeine (Attwood, et al., 2011). For the simple reaction time task, go/no go task, stroop task, and stop-signal task a main effect of drink was observed, where the alcohol condition differed significantly from plcaebo. However, there were no significant differences between the alcohol/caffeine condition and alcohol, as well as alcohol/caffeine and placebo. This indicated that though reaction time may have decreased when alcohol and caffeine were consumed together, it did not return reaction time to the levels observed in the no-drug condition (Attwood et al., 2011). This study showed no evidence that the alcohol/caffeine interaction showed a significant difference from alcohol or placebo in the go/no-go task, which is contrary to the study done by Marczinski and Fillmore (2003).

This attenuation of some reaction time deficits but retention of inaccuracy provides evidence for caffeine masking some of the behavioral effects of low dose alcohol administration.

In essence, since individuals were not experiencing some of the more salient low dose alcohol cues (e.g. psychomotor impairment) they may assume that they are less intoxicated. Furthermore, since caffeine had no effect on accuracy ratings in this task the potential for high risk drinking

37 and driving exists. As stated earlier Thombs et al. (2010) found that participants who ingested both alcohol and caffeine during a drinking session were four times more likely to intend on driving home. This, coupled with the still impaired accuracy rating, provides evidence for a high risk drinking and driving scenario, where a driver who is over the legal limit intends to drive but is still deficient in their ability to drive.

Burns and Moskowitz (1989) examined the effects of alcohol and caffeine consumption on several tasks meant to be analogous to driving. Participants underwent a test battery which included a compensatory tracking task, divided attention task, visual backward masking task, and critical tracking task (Burns & Moskowitz, 1989). Each of these tasks was chosen as a representation of various aspects of driving. For example, the divided attention task measured a participant’s ability to perform two tasks simultaneously, which was considered analogous to the multiple tasks driving imposes on a driver (e.g. being aware of multiple cars around you, traffic lights etc.,). Twelve male participants were given alcohol (0.58 g/kg), caffeine (4.4 mg/kg), a combination of the two, or placebos across multiple experimental sessions. The alcohol dose alone produced deficits across all the tasks, while caffeine taken alone improved performance on the majority of tasks. When alcohol and caffeine were administered together, caffeine counteracted the effects of alcohol on all the tasks except the visual backward masking task. To test this effect across a range of doses, the experimenters expanded the experiment to include a

0.99 g /kg dose of alcohol and a 5.87 mg/kg dose of caffeine (Burns & Moskowitz, 1989). Thirty six experimentally naive participants underwent the same test battery as the original experiment, with all participants receiving caffeine at all levels and alcohol at one of the levels. The results of this study replicated the original, but showed that higher doses of caffeine did not significantly improve performance across either alcohol condition and that the high alcohol dose (0.99 g/kg)

38 negated any of the compensatory effects of the caffeine (Burns & Moskowitz, 1989). The authors posit that one of the reasons caffeine attenuated the effects of low dose alcohol (0.58 g/kg) may be due to the BACs produced by this dose (Burns & Moskowitz, 1989). The average

BAC for the low dose alcohol group was 0.047% (roughly 2 alcoholic beverages in an average male). Given that at this lower BAC the effects of alcohol are somewhat mild it’s not surprising that caffeine can attenuate some of alcohols low dose effects. However, in the high alcohol dose

(0.99 g/kg) participants reached an average peak BAC of 0.11% well above the legal limit. These participants showed performance deficits that persisted across all caffeine doses, providing evidence that grossly impaired individuals (BACs ≥ 0.08%) show no performance improvements when caffeine is given in conjunction with alcohol (Burns & Moskowitz, 1989). This supports the hypothesis that caffeine does not attenuate the effects of alcohol at legally intoxicated levels.

Additional support for caffeine not attenuated alcohol effects can be seen in Liguori and

Robinson (2001), where an examination of the effects of alcohol, caffeine, and a combination of the two was examined using a driving simulator. This simulator tested the speed at which a participant could apply a car brake at highway speeds when an obstacle randomly appeared

(Liguori & Robinson, 2001). Fifteen participants ingested all combinations of the two doses of alcohol (0.0 g/kg, 0.6 g/kg) and the three doses of caffeine (0 mg, 200 mg, and 400 mg) across multiple experimental sessions. When alcohol was given without caffeine participants showed a deficit in their reaction time to brake, and caffeine had no effect on reaction time. When alcohol and caffeine were taken together, participants showed a statistically significant improvement in reaction time compared to the alcohol condition, but this improvement did not return participants to their baseline levels (Liguori & Robinson, 2001). This study provides additional evidence for caffeine’s ability to attenuate alcohol induced reaction time deficits. The authors do note that

39 caffeine only provided a 9% increase in participants brake response time, and given that the car was traveling 84 feet per second (55-60 miles per hour) this would result in only a total of 59 additional feet before the brake is applied (Liguori & Robinson, 2001). At these highway speeds, caffeine would does not attenuate the reaction time deficits of alcohol enough to provide any additional safety (Liguori & Robinson, 2001).

Howland, Rohsenow, Damaris, and Arnedt (2011) also examined whether or not caffeine could attenuate alcohol induced driving deficits in a driving simulator. The driving simulator task measured an individual’s ability to complete 30 minutes of driving on a simulated highway

(Howland et al., 2011). In the simulation, participants were placed on a two-lane highway and were instructed to stay in the center of the right lane and to follow a fixed speed limit unless signs indicated otherwise. Speed deviation, lane position deviation, and crashes were the dependent variables (Howland et al., 2011). One-hundred and twenty-seven participants were placed in one of four conditions; caffeinated beer, non-caffeinated beer, caffeinated non- alcoholic beer, or non-caffeinated non-alcoholic beer. Participants in the alcoholic beer conditions consumed enough alcohol to reach a target BAC of 0.12 g/ml, and individuals in the active caffeine conditions consumed an average of 383mg of anhydrogonous caffeine (Howland et al., 2011). Results of this study showed that when alcohol was given it significantly impaired driving as well as sustained attention and reaction times, while no main or interaction effects were found under the caffeine conditions (Howland et al., 2011). The authors conclude that caffeine does not reduce the alcohol induced impairment. This study provides additional evidence to show that at higher BACs, caffeine fails to attenuate alcohol induced deficits, and that caffeine has been shown to have no benefit to driving performance for acute intoxication.

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As the previous studies show, caffeine will attenuate some of the reaction time deficits seen at moderate blood alcohol levels, but does not attenuate many of the other effects. One of the primary reasons for the consumption of alcohol and caffeine, given by those that drink this combination, is that they wish to drink more as well as not look/feel as intoxicated (O'Brien et al., 2008). Evidence supports that caffeine does not provide these effects physiologically

(O'Brien et al., 2008). Since behaviorally, caffeine is only mildly attenuating some of the effects of alcohol in an experiment where the participant is blind to drug administration, would someone who expects caffeine to attenuate the effects of alcohol experience a placebo like effect with this drug co-administration? Fillmore and Vogel-Sprott (1995) examined the effects expectations have on the behavioral effects of this drug combination. Fifty male social drinkers were split in to one of four treatment groups: expected to receive caffeine and received it, expected to receive caffeine and received placebo, did not expect to receive caffeine and received it, didn’t expect caffeine and received a placebo dose. All participants received a dose of alcohol (0.56 g/kg) and half the groups received caffeine (4.4 mg/kg). After a 25 minute absorption period participants performed a pursuit rotor task, where participants had to use a computer mouse to follow a target that was moving clockwise at 23 rotations per minute. The percentage of time the participant remained on the target was the dependent variable (Fillmore & Vogel-Sprott, 1995). As the investigators hypothesized, participants’ individual expectations predicted participant’s performance on the pursuit rotor task when they expected to receive caffeine with alcohol

(Fillmore & Vogel-Sprott, 1995). Those who expected the most impairment from receiving both drugs performed the worst regardless of whether or not caffeine was actually administered

(Fillmore & Vogel-Sprott, 1995).

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A study done by Fillmore, Roach, and Rice (2002) examined what effects expectancy would have on participant performance if a participant was led to believe caffeine would antagonize alcohol-induced impairment compared to a participant who was told caffeine would have no antagonizing effect. Forty two participants were split in to six groups, two groups received alcohol or placebo alcohol and were treated as the controls as they received no caffeine.

The remaining four received a dose of alcohol (0.65 g/kg), either received caffeine (4.0 mg/kg) or placebo caffeine, and were told that the caffeine would or would not antagonize the effects of alcohol (Fillmore et al., 2002). The experimenters also used the pursuit rotor task as their dependent variable. The results of this study showed the participants who were led to believe that caffeine would antagonize the effects of alcohol performed worse than those who were told caffeine would provide no antagonistic effect. In addition, caffeine had no significant antagonist effects when the caffeine versus placebo groups were compared. The authors theorize that individuals who expected caffeine to antagonize the effects of alcohol performed worse because they were not compensating for the effects of alcohol due to their expectation of caffeine’s antagonistic effects (Fillmore et al., 2002). Participants who were told that caffeine would have no antagonizing effect focused on compensating for the effects of alcohol (Fillmore et al., 2002).

Conclusions from this study illuminate a dangerous potential scenario, where individuals believe that caffeine is compensating for the effects of alcohol more so than it is and thusly do not compensate behaviorally themselves, creating a situation where they are exhibiting even more impairment.

Another aspect of intoxication examined by many of these studies is subjective levels of intoxication. By utilizing various self-report measures while the participants are under the influence of alcohol, caffeine, or both of alcohol/caffeine provides a basis of comparison of

42 subjective level of intoxication. As discussed in the alcohol discrimination section, individuals have shown the ability to discriminate their blood alcohol content within a certain degree of accuracy prior to training (Bois & Vogel-Sprott, 1974; Davies, 1962; Lovibond & Caddy, 1970;

Ogurzsoff & Vogel-Sprott, 1976). The effect caffeine has on this ability to subjectively discriminate one’s level of intoxication has been a secondary measure in a handful of studies.

These studies all use different means of measuring these subjective ratings, making comparisons across studies difficult. Participants in Liguori and Robinson (2001) reported their subjective level of intoxication across drug conditions using the Profile of Mood States confusion subscale.

Participants provided ratings for caffeine only, alcohol only, and alcohol/caffeine conditions

(Liguori & Robinson, 2001). When the alcohol/caffeine condition results were compared with a baseline comprised of the alcohol only condition, no significant differences were found (Liguori

& Robinson, 2001). Fillmore et al. (2002) had their participants rate their subjective level of intoxication by drawing a dash through a 100mm line, where 0mm indicated sobriety and

100mm indicated high levels of intoxication. Participants in the active alcohol conditions reported significantly greater subjective intoxication compared with placebo (Fillmore et al.,

2002). Administration of both alcohol and caffeine did not reduce ratings of intoxication, with those receiving caffeine actually reporting greater subjective levels of intoxication when compared to participants who received placebo caffeine (Fillmore et al., 2002).

Marczinski and Fillmore (2003) had participants complete a Biphasic Alcohol Effects

Scale and Beverage Rating Scale across the alcohol and caffeine conditions. The Biphasic

Alcohol Effects scale is a 14-adjective rating scale where participants provide their subjective ratings of stimulation and sedation (Marczinski & Fillmore, 2003). The Beverage Rating Scale asked participants to estimate the alcohol content of the dose received in relation to how many

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5% alcohol by volume bottles of beer they believe their drink was equivalent to (Marczinski &

Fillmore, 2003). Results from the Biphasic Alcohol Effects scale indicated that caffeine did not significantly affect subjective ratings for the participants across the sedation and stimulation scales (Marczinski & Fillmore, 2003). Sedation ratings did show an inverse trend with increased caffeine dosages, while stimulation rating showed a direct trend with increased caffeine.

Beverage ratings also indicated a main effect of alcohol, but no significant effect of alcohol and caffeine. However, in another study done by Marczinski & Fillmore (2006) participants were asked to rate their level of intoxication using the same Beverage Rating scale. Participants who received alcohol and caffeine reported a statistically significant decrease in subjective intoxication under the 2.0 mg/kg caffeine dose condition, but not under the 4.0 mg/kg caffeine condition. (Marczinski & Fillmore, 2006).

Ferreira, de Mello, Pompeia, and de Souza-Formigoni (2006) examined subjective levels of intoxication using a Visual Analog Scale of Somatic Symptoms (VASSS). Participants were to draw a dash through a 100 mm line to indicate the intensity which the participant felt the symptom (Ferreira et al., 2006). The scale included 18 items to be rated (Ferreira, et al., 2006).

Participants in this study were split in to two groups based on alcohol dose (0.6 g/kg or 1.0 g/kg).

Each group was split in half, where half the group received alcohol and an energy drink and the other just alcohol. Participants reported a significant decrease in the headache, weakness, dry mouth, and impairment of motor coordination subscales when they consumed alcohol and caffeine in comparison to alcohol alone (Ferreira et al., 2006). Attwood et al., 2011 also used a

Visual Analogue Scale, as well as the Biphasic Alcohol Effects Scale (adapted to a Visual

Analogue Scale as well), Spielberg State Trait Anxiety Inventory state sub-scale, and Alcohol

Urges Questionnaire to examine subjective intoxication shifts in a repeated measures design.

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Main effects for time were seen for the sedation subscale, as well as decreases in anxiety, drowsiness and state anxiety (Attwood et al., 2011). The only significant time by drink interactions were found for the stimulation sub-scale, with participants during the alcohol and placebo conditions indicated decreased stimulation over time while in the alcohol/caffeine condition stimulation increased, and for the anxiety sub-scale on the VAS with anxiety decreasing over time in both alcohol conditions when compared to placebo (Attwood et al.,

2011).

In a more recent study, Marczinski, Fillmore, Bardgett, and Howard (2011) examined whether AmED consumption altered neurocognitive and subjective measures of intoxication when compared to alcohol alone. Investigators took 56 participants and randomized them in to one of four conditions (0.65 g/kg alcohol, 3.57 mL/kg energy drink, AmED, or placebo).

Participants performed an initial baseline test on a cued go/no-go task. Upon completion of the task participants also filled out questionnaires designed to assess impulsivity, attention, ratings of stimulation and sedation, subjective effects, and intoxication ratings (Marczinski et al., 2011).

After baseline testing the participant received and consumed their assigned beverage. After 45 minutes passed the participants again performed the cued go/no go task as well as completed the same battery of questionnaires. Results showed the reaction time in the cued go/no go task was slowed and response inhibition failures increased in the alcohol condition when compared to the non-alcohol condition (Marczinski et al., 2011). In addition, reaction time decreased from baseline in the energy drink condition compared to the no energy drink condition. AmEDs attenuated some of the reaction time differences but not response inhibition. Ratings of stimulation, feeling the drink, liking the drink, impairment, level of intoxication, and rating of

45 ability to drive were not significantly different between the alcohol and AmED conditions with the only significantly different rating was self-reported stimulation (Marczinski et al., 2011).

As one can see, the literature examining the effects of alcohol and caffeine co- administration on subjective levels of intoxication has conflicting findings. Studies such as

Liguori and Robinson (2001) and Marczinski and Fillmore (2003) show no difference in subjective rating comparisons, some show an increase in subjective rating of intoxication

(Fillmore et al., 2002), while other studies show that subjective rating of intoxication decreases when caffeine is co-administered (Ferreira et al., 2006; Marczinski & Fillmore, 2006). One reason why conflicting findings exist may be due to the lack of uniformity in methodology. Each of these studies takes one of several different methods for measuring these subjective sensations which may or may not be sensitive to this interaction. Each study also used one of several different doses for both alcohol and caffeine. Finally, the administration of both alcohol and caffeine to participants was different across many of these studies.

Given that these studies are using a variety of self-report measures as well as numerous different dosages of alcohol and caffeine one can see why there is such variety in outcomes. Our current study sought to examine the effects alcohol and caffeine co-administration has on subjective intoxication using a BAC discrimination paradigm as our dependent variable. A BAC discrimination paradigm was chosen because it theoretically provided an objective means of examining the effects caffeine has on subjective intoxication. Our study was modeled based on the three session training paradigm outlined in several of studies mentioned previously (Bois &

Vogel-Sprott, 1974; Lansky et al., 1978; Ogurzsoff & Vogel-Sprott, 1976). Our study was a modified version of this paradigm featuring four sessions; the first two as training, and a test/caffeine manipulation counterbalance phase for sessions three and four. Four sessions was

46 chosen based on the blood alcohol discrimination training literature, which indicates that participants can obtain accurate discrimination of their BAC within one to two sessions of alcohol consumption while receiving feedback of their BAC (Bois & Vogel-Sprott, 1974;

Lansky et al., 1978; Ogurzsoff & Vogel-Sprott, 1976).

Our two training sessions featured a counter-balanced alcohol placebo to control for participants making estimates by counting the number of beverages they had ingested instead of based on internal cues. Additionally, participants also received a counter balanced caffeine placebo. For half of the participants the caffeine manipulation was received on the third session, and the other half received it on the fourth in order to control for the effects of skill degradation.

Capsules were used as the route for caffeine administration based on prior research showing that caffeine is highly discriminable by participants even at low doses (Evans & Griffiths, 1991;

Griffiths et al., 1986; Silverman & Griffiths, 1992). The participants chosen for this experiment were classified as social drinkers. This population was chosen because prior evidence has shown that social drinkers can not only naturally discriminate their BACs, but with training, can do so with a high degree of accuracy (Bois & Vogel-Sprott, 1974; Ogurzsoff & Vogel-Sprott, 1976;

Shortt & Vogel-Sprott, 1978). It was hypothesized that participants’ who acquire high accuracy in discriminating their BACs when alcohol was administered alone would consistently underestimate their current BAC when alcohol was co-administered with caffeine.

METHOD

Participants

Eight male undergraduate students were recruited for this study (n=8), the average age was 22 years old. 75% of participants identified as Caucasian, 12.5% as African-American, and

12.5% as biracial. Of the eight participants recruited, four were disqualified from participating in

47 the study. Reasons for participants being disqualified included a score in the problematic range on the alcohol screening measures, a family history of alcoholism in direct relations, and a history of substance abuse. One participant failed to meet mastery criteria for session three, with only 31% of estimations meeting reward criteria, and thus this participant’s data was not continued. Four participants made up the overall sample analyzed, with all participants undergoing all conditions.

Screening

Screening

All participants were screened carefully for potential substance abuse issues, substance use history, potential psychiatric or physiological problems as well as any other factors that would deem them unsafe to participate in the study. All volunteers were provided with an informed consent prior to participation (see Appendix A). After signing the consent, volunteers were screened using a number of questionnaires designed to detect individuals that exhibit characteristics that would make it unsafe for them to participate in this study. The Quantity

Frequency Index (QFI) (see Appendix B) and the Rutgers Collegiate Substance Abuse

Screening Test (RCSAST) (see Appendix C) were initially used to assess for potential alcohol and/or drug problems, drug use, and psychological and/or physiological problems that would make alcohol and caffeine consumption dangerous (Bennett, McCrady, Frankenstein, &

Laitman, 1993; Hagman, Clifford, Noel, Davis, & Cramond, 2007). The first six participants were screened using the RSCAST and it was determined that the RCSAST was too conservative of a measure. The remaining two participants were screened using the Rutgers Alcohol Problem

Index (RAPI) (see Appendix F). The RAPI is a 23-item measure designed to detect problematic drinking in young adults (White & Labouvie, 1989). Participants were also given the Caffeine

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Consumption Questionnaire (CCQ) (see Appendix D) in order to gauge the amount of caffeine they consume on a typical weekday/weekend as well as the Caffeine Expectancy Questionnaire

(CEQ) (see Appendix E) to measure the individual outcomes participants expect when consuming caffeine (Heinz, Kassel, & Smith, 2009; Heaton, 2012).

Individuals that scored five points or higher on the RCSAST were disqualified from participating in the study, as this is indicative of problem alcohol usage (Bennett et al., 1993).

Subsequently, individuals that scored within the 21-25 point range on the RAPI were also considered to have problematic alcohol usage and were disqualified (White & Labouvie, 1989).

Individuals that indicated their naivety to, or infrequent use of, either alcohol on the QFI, or caffeine, on the CCQ, were disqualified from participating in the study. Those excluded were individuals who either have no or infrequent (i.e. once a month or less) experience with either substance under investigation. Additionally, any participants that indicated usage of currently prescribed medications detailed in the QFI, regular black outs, withdrawal symptoms, head trauma, CNS injuries, or have sought or are currently seeking substance abuse treatment were disqualified from participation.

Apparatus

All participants were tested in the Substance Abuse and Nicotine Dependence Laboratory in the Department of Psychology at the University of North Carolina Wilmington. The alcohol used in this study was 80 proof Skyy© Vodka (Skyy Spirits, San Francisco, CA, USA). All alcohol dose calculations were done by entering the participants’ weight in to a Microsoft

Excel© (Microsoft, Redmond, Washington, USA) spreadsheet which automatically calculated how many milliliters of alcohol to mix with the vehicle. The computer available was a Dell©

OptiPlex 745 (Dell Corporation, Round Rock, TX). The alcohol dosage was measured using a

49 graduated cylinder, and given to the participant in a 12oz highball glass. Participants were given a choice between orange juice and cranberry juice as their vehicle. All breathalyzer readings were taken using the AlcoHAWK Pro© (Q3 Innovations, Independence, IA) or BACTRACK

S80 Pro© (KHN Solutions Inc., San Francisco, CA) breathalyzers.

The caffeine used was Spectrum Caffeine© (Spectrum Chemical Mfg. Corp., New

Brunswick, NJ), while the placebo was the non-nutritive sweetener Splenda© (McNeil

Nutritionals, Fort Washington, PA). Each dose of placebo or caffeine was calculated via a

Microsoft Excel© (Microsoft, Redmond, Washington, USA) spreadsheet and measured using the

Acculab VI-1mg© (Acculab, Huntington Valley, PA) scale. Once measured, placebo or caffeine was placed in to The Capsule Machine© (Capsule Connection, Prescott, AZ) so that they could be administered in capsule form. Our prize tickets consisted of standard raffle tickets. The number on each ticket corresponded with one of three possible outcomes; “good job” (50% probability), “small prize” (49.5% probability), or “large prize” (0.5% probability) that were chosen out of a fish bowl. The “small prize” included such items as shaving cream, books, water bottles, gum, and pens while a “large prize” included such things as computer speakers, computer mice, tool kits, and Bluetooth headsets.

Procedure

Participants were recruited through class announcements, flyers, and word of mouth..

Volunteers who did not qualify due to one of the aforementioned disqualification criteria were asked to inform any friends that could potentially qualify for the study. Students interested in participating were asked to contact the researchers via email.

All sessions took place in the Substance Abuse and Nicotine Dependence Laboratory in the Department of Psychology at the University of North Carolina with all sessions beginning

50 between 12 pm and 6 pm. Participants were informed that experimental sessions can take between 3-5 hours and that the scheduled time should be one that can be adhered to throughout the course of the experiment. Participants were also informed that the purpose of the study was to study alcohol and caffeine. Prior to each experimental session, participants were asked to fast for 4 hrs, avoid caffeine for 8 hrs, and abstain from alcohol or illicit drug use for 24 hrs. Prior to all testing, participants had their BAC of 0.0% confirmed by a breathalyzer. The AlcoHAWK

Pro© Breathalyzer (Q3 Innovations, Independence, IA) and BACTRACK S80 Pro© (KHN

Solutions Inc., San Francisco, CA) each measure grams of alcohol per 100 milliliters of blood and coverts it in to a percentage, such that 0.01 g per 100mL is displayed as 0.01%. Participants were asked to participate for up to four sessions. Additionally, two researchers were present at all sessions. One was either the primary investigator or the graduate research assistant, who was responsible for mixing any drinks, preparing capsules (both placebo and caffeine), and for the actual administration, while the other researcher was an undergraduate research assistant there to assist as needed.

Session 1: Participants met two researchers at the laboratory. Upon arrival, participants completed a packet consisting of an informed consent, the RCSAST or RAPI, a modified version of the QFI, the Caffeine Expectancy Questionnaire, and the Caffeine Consumption

Questionnaire. Participants were asked for their age (verified by driver’s license inspection), date of birth, primary ethnic background, marital status, education level, and current university status.

The RCSAST, RAPI, QFI, and the CCQ were scored by either the PI or graduate researcher.

Participants who indicated significant psychiatric or medical problems, or stated that they have a current physical condition that contraindicates the use of alcohol or caffeine (e.g. medication) were disqualified from the study, three of the eight individuals recruited for this

51 study did not meet criteria to participate. Participants that qualified for the study were then given a breathalyzer reading, to confirm a 0.0% BAC, were weighed, and asked when was the last time they consumed any food, alcohol, or caffeine. Weight was taken and entered into a spreadsheet which automatically calculated how many milliliters of alcohol based on a 0.33mL per kilogram of body weight dose to add to the juice without the risk of human error during calculation.

Participants that met criteria for the study then took part in the first training session on the same day. All participants were asked to take a capsule with water. For this first session, the capsule was an inert (placebo condition) non-nutritive sweetener. Capsules were chosen as the route of caffeine administration based on prior research supporting the high detectability of the taste of caffeine by participants (Evans & Griffiths, 1991; Griffiths, et al., 1986; Griffiths, Evans,

Heishman, & Preston, 1990). After capsule consumption, the participant was given access to a movie, book, or magazine. The experimenter gave the participant a general frame of reference by explaining that the alcohol levels of interest will not exceed 0.12%. The participant was then shown a chart of how BAC is affected by alcohol in someone of their gender and weight. The experimenter then left the participant with the research assistant and went behind a screen to prepare the participant’s first beverage.

The participants were given a choice of cranberry juice or orange juice as their mixer. For half of the participants, this first drink was a placebo while the other half received an alcoholic beverage. The placebo had the rim of the glass wiped with vodka and the liquid volume within the glass equaled that of an active dose. This gave off an aroma sufficient enough to mask the placebo nature of the drink from the participant. The non-placebo participants also received a choice of juice mixed in a three to one ratio with 0.33 mL of ethanol per kg of body weight to produce a BAC of 0.02-0.05 g/mL within 30 min. After participants in both conditions receive

52 their drink they were asked to consume it within a five minute time period. During this time, participants were given a pair of head phones as they listened to a guided relaxation tape. The guided relaxation script was meant to help direct the participants to systematically relax and attend to various parts of their body, and should have made the physiological effects of alcohol more salient. This allowed our participants to experience the physiological sensations of alcohol without the risk of injury from loss of motor function. Additionally, there is research that suggests that drinkers experience numerous individual symptoms that indicate intoxication but are often unspoken, instead falling under the umbrella term of intoxicated (Huber, et al., 1976).

By guiding participants, but never directly telling the participant sensations they should be feeling, we hoped to utilize these individual sensations to increase discrimination accuracy.

Ten minutes after the end of consumption, participants were asked to give their first BAC estimation, give a breathalyzer sample, and then shown their actual BAC. If the participant’s estimate was ±0.01% of their actual BAC they received an opportunity to draw from a prize bowl. For these prize draws, participants were allowed to choose a ticket out of a large jar. The number on the ticket corresponded with one of three outcomes. Fifty percent of these tickets were “good job” and resulted in no prize being received. For 49.5% of tickets participants received a “small prize.” This prize consisted of an item whose monetary value is less than $5

(e.g. books, small sporting equipment, water bottles, t-shirts etc.). The remaining 0.5% of tickets were “large prize” winners. These jackpot tickets comprise of prizes that range from $30-$50 in value (e.g. computer speakers, Bluetooth headsets, etc.).The opportunity to attain prizes based on accurate performance acted as a positive reinforcer for accuracy, strengthening the acquisition of this new skill.

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For Session 1, a prize draw was always earned if a participant’s estimation was within

±0.01% accuracy of the actual BAC. Also, participants that earn less than one prize draw per hour were allowed one prize draw for every hour they participated in the session; these draws were given at the end of the session. After an additional 10 minutes passed, participants once again were asked to estimate their BAC, give a breathalyzer sample, and receive feedback as to what their actual BAC is in relation to their feedback.

After this second estimation, participants were given their second drink of the study. This occurred within 20 minutes of consuming the first drink. For this second drink, all participants received a mixture of 0.33 mL of ethanol per kg of body weight. Participants were again given five minutes to consume their beverage while listening to the relaxation tape. BAC estimations occurred at the same 10 and 20 minutes after consumption as with the first drink. The procedure of making the estimation, providing a breathalyzer reading, receiving feedback, and receiving prize draws for the ±0.01% accuracy occurred just as in the first drink. The final drink was a counterbalanced placebo or 0.33 mL/kg of ethanol. That is, any individual that had not receive a placebo at the beginning of the session now received one, while those who received an initial placebo now received the alcoholic beverage. This placebo served as an experimental control for individuals “counting drinks.” This helped ensure that participants were making estimations based on their own internal cue associations that were being reinforced, and not just making estimations based on how many drinks they believe they have had. This resulted in an overall cumulative dose of 0.66 mL of ethanol per kg of body weight for each participant. The third drink followed just like the first two, with estimations having occurred at 10 minute intervals until the participant reaches a BAC of 0.00%. During the consumption period for the third drink, participants were given the option to listen to the relaxation tape for the third time.

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Once a BAC of 0.0% was attained participants were allowed to leave the laboratory. As discussed with the participants in their informed consent, any attempt to leave the laboratory before a confirmed BAC of 0.0% was attained would result in a telephone call to the police. If the participant had earned less than one prize draw per hour of participation, they were allowed one drawing per each hour they did not earn a draw at this time. At this time participants were also scheduled for their second experimental session and reminded to abstain from alcohol for 24 hrs, caffeine for 8 hrs, and food for 4 hrs prior to this session.

Session 2: The second session occurred at least 3 days following session one. Upon arriving at the laboratory, participants were again weighed, provided a confirmed 0.0% BAC, and asked when was the last time they consumed any food, alcohol, or caffeine. Once their 0.0%

BAC was confirmed, participants again consumed a placebo capsule containing a non-nutritive sweetener. Session 2 was conducted in almost an identical manner to session one, with a few exceptions. No screening tests were given. The participant was weighed for alcohol calculation purposes. For those participants who received alcohol as their first drink in the first session, they instead received a placebo as their first drink, and vice versa. Otherwise, all drinking protocols previously outlined remained the same including prize draw criteria.

In order to determine mastery of the BAC discrimination, at the end of session two a simple calculation to determine participant’s ability to discriminate their BAC was conducted. If participants were within a ±0.01% for their estimation in relation to their actual BAC it was scored as a hit. Anything greater or less than ±0.01% was scored as a miss. A participant was said to have attained mastery if 80% of their estimations fell within the correct category, and all participants who made it to the end of session two met mastery criteria. Participants were then scheduled for a third session to occur at their earliest convenience.

55

Session 3: Participants that have been invited to the third session again had their weight taken, a confirmed 0.0% BAC, and were asked when was the last time they consumed any food, alcohol, or caffeine upon arrival to the laboratory. For this portion of the experiment, half of the participants received their caffeine manipulation during their third session, while the other half would receive it on the fourth. This counter-balance of the caffeine manipulation was to control for the effects of skill degradation over time. The caffeine dose was based on the participant’s weight, such that each participant received 3.0 mg of caffeine per kg of body weight. This dose was chosen as it roughly equates to 7.5 ozs of coffee in a 150 lbs male. For this session, all participants received alcohol in all three drinks consumed. In order to maintain a constant level of alcohol consumed across sessions, for session three, all drinks were a mixture of 0.22 mL of ethanol per kg of body weight. This resulted in a total consumption of 0.66 mL of ethanol per kg of body weight, which was identical to the previous two sessions. For this session, participants received their first drink and were given 5 minutes for consumption while listening to the relaxation tape. Ten minutes after consuming the drink, participants gave a BAC estimation and provided a breathalyzer reading. However, participants did not receive feedback for these estimations. The drinking procedure continued as in sessions one and two, with estimations occurring 10 and 20 minutes after each drink consumed, but participants received no feedback on their accuracy. Again, as in the previous two sessions, estimations were collected every 10 minutes after the last drink until a BAC of 0.0% is attained. All participants received one prize draw per hour of participation during session three. Initially, participants underwent an additional calculation of their discrimination accuracy. This calculation was conducted for the first three participants, and resulted in one participant being disqualified for not meeting criteria. The caffeine/placebo counterbalance was initiated after this participant. Mastery criterion was no

56 longer used as a disqualifying variable for this session, since half of the participants received their caffeine manipulation on this day.

Session 4: Upon arrival to the laboratory, participants were weighed, had a confirmed

0.0% BAC taken, and asked when was the last time they consumed any food, alcohol, or caffeine. For this session, participants that had ingested a placebo capsule the previous session would now consume a caffeine capsule and vice versa. The caffeine dose was calculated based on the participant’s weight, such that each participant received 3.0 mg of caffeine per kg of body weight. Ten minutes after ingesting the capsule, participants underwent the same drinking procedure as outlined in session three. Each of the three drinks contained 0.22 mL of alcohol per kg of body weight, and each were consumed 20 minutes after the prior drink. Participants again listened to the relaxation tape during drink consumption, underwent a 5 minute absorption period, provided a BAC estimation, and a breathalyzer reading. Just as in session three, no feedback was given to the participant about their actual BAC. After consumption of the last drink, BAC estimations occurred at 10 minute intervals until the participant reached a confirmed

BAC of 0.02%. Once this BAC was reached, participants were debriefed as to the nature of the study and were allowed one prize draw per each hour they spent in the laboratory. Participants provided additional breathalyzer readings until a BAC of 0.0% was attained, at which point they were allowed to leave the laboratory.

Data Analysis

Data analysis utilized area under the curve calculations in order to quantitatively determine the disparities between actual and estimated BAC curves. BAC was measured via breathalyzer on as grams/milliliter (g/ml). The breathalyzer used had an error margin of 0.01 g/ml and all data reported was measured on this scale. Each participant had their session data

57 plotted on a scatter plot graph, with the X-axis displaying minute across session and the y-axis set at BAC (see Figure 4). Area under the curve (AUC) analysis utilizes the equation:

(X2-X1)[(Y1-Y2)/2) to split a curve into a series of trapezoids composed of two congruent points on the X and Y axis

(Myerson, Green, & Warusawitharana, 2001). This is first done by scaling the X any Y axis parameters into a 1:1 ratio. The conversion is done by simple equation, where the data points for each axis are divided by a common denominator. For the purposes of this study the Y-axis data points were divided by the denominator of 0.12. This was chosen because it was predicted to be the highest possible BAC achievable based on the dose of alcohol received. The X-axis data points were divided by 300 minutes, the longest amount of time that was hypothesized to take to complete a single session in this study. The scaling of the X and Y axis allows us to calculate the area of the graph at to be one (see Figure 3). After the scores are scaled, corresponding points on the X and Y axis were entered in to the equation. These calculations were completed for each subsequent trapezoid along both curves (see Figure 3).

Once the areas of each of the trapezoids were calculated, they were summated. These summations resulted in total AUC for the participants actual BAC (AUCA) and for the participants estimated BAC (AUCE) (Myerson et al., 2001). These summated scores are the percent of the total graph area that is beneath each curve (see Table 4). These percentages allowed for direct quantitative comparisons between a participant’s actual and estimated BACs.

A disparity score was also calculated for each session. This score consisted of subtracting AUCE by AUCA. These disparity scores yield the percent difference in AUC (see Table 4). For the purposes of this study, a negative disparity score indicated that the participant’s estimated BAC curve was lower than their actual BAC (indicating that the participant was underestimating their

58

BAC), while a positive score indicated that the estimated BAC curve was higher than the actual

(indicating that the participant was overestimating their BAC). Disparity scores approaching zero indicated that the two curves closely overlapped.

AUC was calculated for the three parts of the curve; rise, peak, and fall to examine where the greatest disparity score occurs within-session (Bois & Vogel-Sprott, 1974). The rise is defined as the data points on the curve up to the highest BAC achieved, and was calculated by summating the area under the curve from start of session to peak BAC (see Figure 3). The peak of the BAC curve was defined as the data point of the highest achieved actual BAC. Peak AUC was calculated by summating the area from one point prior to the peak to the first subsequent data point after peak (see Figure 3). The falling section of the curve was defined as the area under the curve derived from all the points from peak BAC to the end of the session, and was calculated by summating the area underneath these data points. Lastly, the percentage of estimations that fell within reward criteria was also calculated for each session.

RESULTS

The mean age for all participants was 22 (SD=1.3) years old with, a mean age of those who completed the study of 21.3 (SD=0.58). Average scores for the drinking measures were as follows: the RSCAST had an average of 4 (SD=2.5) for the sample with an average of for 2.7

(SD=2) for those that completed the study, for the RAPI the average score was for 13 (SD=7.1).

Overall participants averaged 34 (SD=23) drinking days out of the last ninety, with those that completed the study averaging 27.5 (SD=12). Average caffeine consumption for a typical weekday was 6 (SD=3) for the entire sample, and an average of 6.8 (SD=2.9) caffeinated items for those who completed the study. A typical weekend day averaged 4.6 (SD=2.2) for the sample and 5 (SD=0.8) caffeinated items for those that completed the study. All participants that

59 completed the study reached a maximum BAC in the range of 0.38 g/ml-0.86 g/ml. Participants took an average of 232 (SD=31.6) minutes to complete a session, with training sessions (M=232

(SD=39.7)) and challenge sessions (M=233 (SD=223)) taking almost the same amount of time.

Participants had an average of 12 (SD=3.3) with a range of 5-25 days between sessions.

Participant 1

Participant one drank two alcoholic drinks of 0.33 g/kg followed by an alcohol placebo for the first session, and received a placebo caffeine capsule (see Table 5). Active doses provided a controlled BAC rise that peaked at 0.086 g/ml at 42 minutes after the first drink administration, and showed a predicted inverted U shape curve; active dose administration points are symbolized by a triangle (see Figure 4). The participant met reward criteria for 86% of estimations. AUC was calculated for both curves; resulting in an AUCA of 24.3% and an AUCE of 20.3% revealing a disparity score of -4%, showing that the participant was underestimating their BAC

(see Table 4). For the rising portion of the curve AUCA and AUCE showed a disparity score of

-1.7% (see Table 6). Peak AUCA and peak AUCE resulted in a disparity score of 0.18% (see

Table 6). AUCA falling and AUCE falling calculations resulted in a disparity score of -2.31%

(see Table 6). These disparity scores indicate that the participant was most inaccurate during falling BAC, and showed the highest level of accuracy during peak BAC (see Figure 4).

For the second session, which occurred 25 days after, the participant received the counterbalanced placebo with his first drink, as well as a placebo capsule (see Table 6). Active doses also provided a controlled BAC rise for this session, resulting in a peak BAC of 0.075 g/ml at 79 minutes (see Figure 5). The participant met reward criteria for 93% of estimations during this session. The actual BAC curve showed an inverted U shape, no rise during placebo, sharp increases during alcohol administration, and a systematic decrease over time (see Figure 5).

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AUCA was calculated at 21.2% while AUCE was 21.7% resulting in a disparity score of 0.5%

(see Table 4). AUCA and AUCE for the rising portion of the curve had a disparity score of

0.08% (see Table 6). Peak AUCA and peak AUCE resulted in a disparity score of 0.08% (see

Table 6). Falling AUCA and AUCE had a disparity of -0.29% (see Table 6).

For the third session, which occurred 14 days after session two, the participant received three 0.22 g/kg doses of alcohol and a placebo caffeine capsule (see Table 5). A peak BAC of

0.043 g/ml was observed 80 minutes after session commencement (see Figure 6). The actual

BAC measurements resulted in an inverted u-shaped curve (see Figure 6). The participant met reward criteria for 81% of estimations during this session. His AUCA was calculated at 14.4% while the AUCE was 17.3% resulting in a disparity score of 2.8% (Table 4). AUCA and AUCE for the rising portion of the curves netted a disparity score of 3.91% (see Table 6). This disparity score indicates over estimation of the participant’s BAC during the rising porting of the curve.

Peak AUCA and peak AUCE showed a disparity of 0.29% (see Table 6). The falling AUCA and

AUCE indicated a disparity score of -1.03% (see Table 6).

Session four was participant one’s caffeine manipulation phase, where the participant received a 3.0 mg/kg caffeine dose in the capsule form and three 0.22 g/kg alcohol doses (see

Table 5). This session occurred ten days after session three. A peak BAC of 0.063 g/ml occurred at 79 minutes after the first drink administration (see Figure 7). The graphs again show an inverted u-shape, with overestimations of BAC consistently across the session (see Figure 7).

AUC scores also continued the shift from underestimation to overestimation with AUCA at

20.7% while AUCE was 25.2% resulting in a disparity score of 4.5% (see Table 4). AUCA and

AUCE rising scores resulted in a disparity of 1.03% (see Table 6). Peak AUCA and AUCE showed a disparity score of 1.9% (see Table 6). Falling AUCA and AUCE also revealed

61 overestimation with a disparity score of 3.43% (see Table 6). The number of estimations that met reinforcement criteria dropped to 6%, substantially lower than the prior session.

Participant 3

Participant three was randomized to receive a placebo alcohol dose first, followed by two alcohol doses, as well as a caffeine placebo (see Table 5). Active doses provided a controlled

BAC rise that peaked at 0.046 g/ml 76 minutes after the first drink was administered (see Figure

8). The curve had a particular irregularity in that after the second drink the participants BAC spiked, then returned back to zero (see Figure 8). Multiple measurements of this BAC confirmed a zero, but the possibility of a breathalyzer error cannot be ruled out; the rest of the curve showed the predicted inverted u-shape. The participant met reward criteria for 75% of estimations. AUC was calculated for both curves; resulting in an AUCA of 7.86% and an AUCE of 9.94% revealing a disparity score of 2.08% showing that the participant was overestimating their BAC

(see Table 4). For the rising portion of the curve AUCA and AUCE netted a disparity score of

2.69% (see Table 7). This large disparity existed, in part, due to the aforementioned breathalyzer error. Peak AUCA and a peak AUCE resulted in a disparity score of 0.84% (see Table 7). AUCA and AUCE falling had a disparity score of -0.61% (see Table 7).

For the second session, which occurred 14 days after session one, the participant received the counterbalanced placebo, with his first two drinks containing 0.33 g/kg of alcohol and his third an alcohol placebo (see Table 5). A placebo capsule was also ingested (see Table 5). The active doses provided a controlled BAC rise, resulting in a peak BAC of 0.055 g/ml at 43 minutes (see Figure 9). The participant met reward criteria for 81% of estimations during this session. The actual BAC curve showed an inverted U shape, with a peak after the second drink administration, and a systematic decrease over time (see Figure 9). AUCA was calculated at

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14.8% while AUCE was 13.4% resulting in a disparity score of -1.4% (see Table 4). AUCA and

AUCE for the rising portion of the curve had a disparity score of -0.72% (see Table 7). Peak

AUCA and peak AUCE resulted in a disparity score of -0.61% (see Table 7). Falling AUCA and

AUCE had a disparity of -0.69% (see Table 7).

For the third session the participant received three .022 g/kg of alcohol and a placebo caffeine capsule (see Table 5). This session occurred five days after session three. A peak BAC

0.039 g/ml at 69 minutes post first drink administration was observed (see Figure 10). The actual

BAC measurements resulted in an inverted u-shape curve similar to the prior two sessions, though lacking in a sharp peak (see Figure 10). The participant met reward criteria for 81% of estimations during this session. Their AUCA was calculated at 14.7% while their AUCE was

16.4% resulting in a disparity score of 1.7% (Table 4). AUCA and AUCE for the rising portion of the curves netted a disparity score of 0.41% (see Table 7). Peak AUCA and peak AUCE had a disparity of 1.23% (see Table 7). The falling AUCA and AUCE indicated a disparity score of

1.25% (see Table 7).

Session four was the participant’s caffeine manipulation phase, where the participant received 3.0 mg/kg of caffeine in the capsule form as well as three 0.22 g/kg alcohol doses (see

Table 5). Session four occurred 17 days after session three. A peak BAC of 0.038 g/ml was observed at 71 minutes (see Figure 11). The actual BAC graph again showed an inverted u-shape with steep increases coinciding with alcohol administration and a steady fall (see Figure 11). The participant met reinforcement criteria for 43% of BAC estimations. AUC scores indicated a shift to overestimation with an AUCA at 10.8% and an AUCE of 17.1% resulting in a disparity score of 6.4% (see Table 4). AUCA and AUCE rising scores resulted in a disparity of 2.44% (see

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Table 7). Peak AUCA and AUCE showed a disparity score of 1.9% (see Table 7). Falling AUCA and AUCE had a disparity score of 3.94% (see Table 7).

Participant 6

Participant six received two 0.33 g/kg alcohol doses, followed by a placebo alcohol dose, as well as a placebo caffeine capsule for his first session (see Table 5). These doses provided a controlled BAC rise that peaked at 0.072 g/ml at 43 minutes (see Figure 12). Actual BAC curve showed sharp increases with each active dose, and a long metabolic period (see Figure 12). The participant met reward criteria for 83% of estimations. AUC was calculated for both curves; resulting in an AUCA of 25.7% and an AUCE of 24% revealing a disparity score of -1.7% showing underestimation (see Table 4). For rising AUCA and AUCE there was a disparity score of 0.06% (see Table 8). Peak AUCA and AUCE had a disparity score of 0.47% (see Table 8).

AUCA and AUCE falling resulted in a disparity score of -1.73% (see Table 8).

For the second session, which occurred seven days after session one, the participant received the counterbalanced placebo with his first drink, two 0.33 g/kg doses of alcohol, as well as a placebo capsule (see Table 6). A peak BAC of 0.058 g/ml was observed at 71 minutes (see

Figure 12). The participant met reward criteria for 90% of estimations. The curve showed an inverted U shape, with a peak and long systematic decrease over time indicative of this particular participant (see Figure 13). AUCA was calculated at 25.6% while AUCE was 23.5% resulting in a disparity score of -2.1% (see Table 4). AUCA and AUCE for the rising portion of the curve had a disparity score of 0.16% (see Table 8). Peak AUCA and peak AUCE resulted in a disparity score of -0.53% (see Table 8). Falling AUCA and AUCE had a disparity of -2.29% (see Table

8).

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For the third session the participant received three 0.22 g/kg alcohol doses as well as a

3.0 mg/kg caffeine dose in capsule form (see Table 5). The third session occurred seven days after session two. Peak BAC was 0.046 g/ml at 82 minutes (see Figure 14). The actual BAC curve resembled an inverted u-shape curve, with sharp rises during alcohol administrations, followed by a peak and then descending (see Figure 14). It is important to note that for the actual

BAC curve, an irregularity during the falling portion occurred in the form of several unpredicted increases in BAC (see Figure 14). These occasional spikes post drug administration are attributed to the inherent error margin in breathalyzer readings. The participant met reward criteria for

83% of estimations. Their AUCA was calculated at 20.7% while their AUCE was 17.9% resulting in a disparity score of -2.8% (see Table 4). AUCA and AUCE for the rising netted a disparity score of -0.68% (see Table 8). Peak AUCA and AUCE had a disparity of zero (see

Table 8). The falling AUCA and AUCE resulted in a disparity score of -2.17% (see Table 8).

For session four, which occurred ten days after session three, the participant received thee alcohol doses as well as placebo caffeine capsule (see Table 5). A peak BAC of 0.05 g/ml was recorded 71 minutes (see Figure 15). The actual BAC curve shows an inverted u-shape with step increases coinciding with alcohol administration and a steady elongated fall (see Figure 15). The participant met reinforcement criteria for 89% of estimations. AUC scores showed a trend for underestimation with an AUCA at 23% and an AUCE of 18.8% resulting in a disparity score of -

4.2% (see Table 4). AUCA and AUCE rising scores resulted in a disparity score of -0.64% (see

Table 8). Peak AUCA and AUCE had a disparity score of 0.06% (see Table 8). Falling AUCA and AUCE indicated a disparity of -3.49% between curves (see Table 8).

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Participant 8

Participant eight received an alcohol placebo followed by two 0.33 g/kg alcohol doses, and a placebo caffeine capsule (see Table 5). A peak BAC of 0.06 g/ml was observed at 70 minutes (see Figure 16). The actual BAC showed an inverted U shape, with no rise following placebo administration; sharp rises post alcohol consumption, a peak, and steady decrease over time (see Figure 16). One irregularity occurred on the ninth measurement where BAC rose slightly 40 minutes after the last active alcohol dose this increase is most likely due to the inherent error margin in breathalyzer readings (see Figure 16). The participant met reward criteria for 95% of estimations. AUC was calculated for both curves; resulting in an AUCA of

23.8% and an AUCE of 22% revealing a disparity score of -1.8% (see Table 4). Rising AUCA and AUCE resulted in a disparity score of -0.31% (see Table 9). Peak AUCA and AUCE had a disparity score of -0.35% (see Table 9). AUCA and AUCE falling indicated a disparity score of -

1.5% (see Table 9).

For the second session, which occurred 29 days after session one, the participant received the counterbalanced alcohol placebo. He also received two active alcohol doses; followed by a placebo alcohol dose as well as a placebo caffeine capsule (see Table 5). A peak BAC of 0.07 g/ml at 43 minutes was observed (see Figure 17). The participant met reward criteria for 94% of estimations during this session. The curve showed an inverted u-shape, with an increase after the first and second drinks, peak, and then relatively steady metabolism over time (see Figure 17).

AUCA was calculated at 23.3% while AUCE was 22.6% resulting in a disparity score of -0.7%

(see Table 4). AUCA and AUCE for the rising portion of the curve had a disparity score of -

0.27% (see Table 9). Peak AUCA and AUCE resulted in a disparity score of 0.28% (see Table

9). Falling AUCA and AUCE showed a disparity of -0.46% (see Table 9).

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For the third session the participant received three active alcohol doses and an active caffeine dose in a capsule (see Table 5). This session occurred seven days after session two. A peak BAC of 0.053 g/ml was observed at 83 minutes (see Figure 18). The actual BAC curve resembled an inverted u-shape, exhibiting a gradual rise, distinct peak, and fall (see Figure 18).

The participant met reward criteria for 82% of estimations. AUCA was calculated at 20.3% while AUCE was 18.3% resulting in a disparity score of -2% (see Table 4). AUCA and AUCE for the rising portion of the curves resulted in a disparity score of -2.31% (see Table 9). Peak

AUCA and AUCE had a disparity of -0.01% (see Table 9). The falling AUCA and AUCE resulted in a disparity score of -0.31% (see Table 9).

On session four, which occurred five days after session three, the participant received thee alcohol doses as well as placebo caffeine capsule (see Table 5). A peak BAC of 0.065 g/ml was recorded 70 minutes (see Figure 19). The actual BAC curve shows an inverted u-shape with step increases coinciding with alcohol administration and a steady elongated fall (see Figure 19).

The participant met reinforcement criteria for 41% of estimations. AUC scores showed a trend for overestimation with an AUCA at 19.1% and an AUCE of 26.2% resulting in a disparity score of 7.1% (see Table 4). AUCA and AUCE rising scores resulted in a disparity score of 0.78% (see

Table 9). Peak AUCA and AUCE had a disparity score of 0.53% (see Table 9). Falling AUCA and AUCE showed the largest disparity score with a disparity of 6.31% (see Table 9).

DISCUSSION

Based on prior evidence supporting BAC discrimination training it had been hypothesized that participants would acquire this skill to a high degree of accuracy (Bois &

Vogel-Sprott, 1974; Huber, 1975; Huber et al., 1976; Lansky et al., 1978; Lovibond & Caddy,

1970; Ogurzsoff & Vogel-Sprott, 1976; Shortt & Vogel-Sprott, 1978). Evidence that caffeine

67 antagonizes some of the physiological deficits associated with alcohol intoxication, and caffeine’s mixed effect on subjective levels of intoxication, led to the hypothesis that caffeine would cause consistent underestimation in an individual who was trained to accurately discriminate their BAC. The results lent support to the efficacy of BAC discrimination training, but no evidence was found to support caffeine antagonizing subjective levels of intoxication.

In line with the first hypothesis, results support that BAC discrimination can be trained to a high degree of accuracy in social drinkers. Of the five participants that made it to the BAC discrimination training, four were able to acquire the skill with an average 90% of estimations within reinforcement criteria by the end of session two. This is also evidenced by overall disparity scores averaging 0.9% at the end of session two, a reduction from an average disparity of 2.4% from the first session. In addition, disparity scores for the rising, peak, and falling BACs did not exceed 1% for the majority of the participants for session two. These results indicate that reinforced feedback can be an effective means of reducing overall error margins. Accuracy was also maintained when feedback was removed, with an average disparity score of 2.4%, and the majority of participants having met mastery criteria. This increased average disparity score indicates that when reinforced feedback was removed, accuracy did degrade, but estimations were still within a high degree of accuracy. Results also indicate that participants were making estimations based on internal cues, as evidenced by the high degree of accuracy asserted through the placebo counterbalance as well as the dosage shift. These results correspond with prior findings supporting when social drinkers receive reinforced feedback for BAC estimations at regular intervals they can learn to accurately estimate their BAC when this feedback is removed

(Bois & Vogel-Sprott, 1974; Huber, 1975; Huber et al., 1976; Lansky et al., 1978; Lovibond &

Caddy, 1970; Ogurzsoff & Vogel-Sprott, 1976; Shortt & Vogel-Sprott, 1978).

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Participants had several large gaps between several sessions, defined as seven days or more between sessions. Examination of disparity scores for when these gaps occurred reveals that if the gap occurred between training sessions no signs of skill degradation were observed

(see Table 4). This is evidenced by reduced disparity scores in participants with excessively large gaps between training sessions. Participant one had a disparity score of -4% for session one, after

25 days between his first and second sessions his disparity score was 0.5% (see Table 4). This was also seen in Participant eight who had a disparity score of -1.8% for session one, which fell to a -0.7% 29 days later during his second session (see Table 4). Once reinforced feedback was removed disparity scores increased for all participants, though whether or not this skill degradation is due to time or loss of feedback is conflicted. When feedback was removed for participant one, disparity scores increased from 0.5% to 2.9%, though these sessions had a 14 day gap between them. Participant three only had a five day gap before feedback removal and showed an increase in disparity from -1.4% in session two, to 1.7% in session three, suggesting that the removal of feedback may have caused significant overestimations. The first two participants show evidence supporting both time and feedback removal as factors for skill degradation. Participants six and eight both received their caffeine manipulation during the first session without feedback, and both experienced increased disparity scores (see Table 4). This trend for increased disparity continued to session four, with only participant six having a significant gap (10 days) between session three and four.

Prior evidence surrounding the effects of caffeine on subjective levels of intoxication is mixed (Ferreira et al., 2006; Ferreira et al., 2004; Ferreira et al., 2004; Fillmore et al., 2002;

Gulick & Gould, 2009; Marczinski & Fillmore, 2003; Marczinski & Fillmore, 2006; Marczinski,

Harrison, & Fillmore, 2008). Subjective levels of intoxication are often a secondary measure in

69 to the effects of alcohol and caffeine interactions. In this study, it was hypothesized that the stimulatory effects of caffeine would cause individuals who were trained to accurately discriminate their BAC to underestimate their BAC. Based on the data collected from this study, the hypothesis was not supported, with the effects of caffeine varying from participant to participant.

All participants showed a high degree of accuracy for BAC discrimination by the end of session two (see Table 4). This was evidenced by a reduction in overall disparity scores, as well as increases in the percent of reinforced estimations from session one to session two. In addition to overall disparity scores, an examination of the rising, peak, and falling sections of the curve was performed. For session one, participants had the highest degree of accuracy during peak

BAC estimations. Participants one, six, and eight had the largest disparities during the falling portion of curve, while participant three’s largest disparity was during the rising portion.

Participants typically showed a trend for underestimation during the falling portion of the curve, which is consistent with prior BAC discrimination studies (Bois & Vogel-Sprott, 1974). For session two most participants reduced disparity scores to less than 1% for all sections of the curve. The exception to this was Participant six, who showed a slight increase in disparity during his falling BAC.

When examining the non-caffeine challenge days participants showed a trend for increased disparity scores for the overall curve, though hit ratings remained relatively stable (see

Table 4). This suggests that participants were estimating with accuracy, but this accuracy decreased when compared to reinforced feedback sessions. The majority of participants had their highest disparity scores occur during the falling BAC portion of the curve. The exception to this was Participant one, whose highest disparity occurred during the rising portion. Overall

70 participants showed the highest degree of estimation accuracy during peak BAC for non-caffeine challenge days. Participants also showed relatively low disparity scores for the rising portion of the curve. This suggests that participants were able to retain a great deal of accuracy during alcohol administration, but accuracy waned over the course of metabolizing.

Participants all showed increased disparity scores during caffeine sessions when compared to reinforced feedback sessions. However, the overall disparity scores were greatest for those participants that received their caffeine manipulation during the fourth session.

Participants who received the caffeine manipulation on the third session did show an increase in overall disparity as well, but not as great of an increase as those participants who received caffeine on the fourth session. Overall, participants continued to show a trend for having highest accuracy during peak BAC, lowest accuracy during falling BAC, and accuracy during rising

BAC typically falling between the two. The exception to this was Participant eight, who showed an inverse trend, with greatest accuracy occurring during falling BAC and greatest disparity during rising BAC. Some participants did self-report feeling different during the caffeine manipulation, For example, Participant one stated that he felt “more intoxicated” during session four when compared to session three. In addition, several observations of participants showing increased fidgeting and jittery behavior were noted by the experimenter. Evidence from these sessions suggest that greatest accuracy for discrimination of BAC typically occurs during peak

BAC, followed by rising BAC, and that the greatest inaccuracies occur during falling BAC.

Participants showed no definitive trend for underestimation or overestimation during these portions of the curve.

When comparing sessions three and four an interesting trend occurs. Regardless of caffeine manipulation, all participants showed an increase in overall disparity scores from

71 session three to session four. Overall disparity scores followed a trend, where if a participant tended to underestimate during session three, they continued that trend in session four. The exception to this was Participant eight, who showed a reversal of estimation bias from session three to four, shifting from underestimations to overestimations, These shifts were most evident during falling BAC, with all participants showing increased disparity for falling BAC between sessions three and four regardless of caffeine condition. This suggests that caffeine may not necessarily be mechanism behind larger disparity scores; rather, evidence supports the degradation of the BAC discrimination skill over time. This is most evident when comparing participants who received their caffeine manipulation during the third session. Overall disparity scores for both participant six and eight increased between sessions three and four, providing evidence for skill degradation.

In order to determine whether caffeine was causing these shifts, a counterbalance for caffeine administration was also administered. Participants six and eight received their caffeine manipulation on session three, compared to participants one and three who received it on session four. Participants six and eight both showed a high degree of accuracy post session two, though participant six showed a slight trend for underestimations during falling actual BAC (2.3% disparity) (see Figure 13). For participant six, session three showed a small decrease in accuracy from session two which supports no effect for caffeine. Participant eight showed underestimations during the rising portion of his BAC during session 3 (2.3% disparity), but overall showed a high degree of accuracy. Participant six showed a shift towards underestimations over the course of the experiment, with disparity scores increased in a positive direction across sessions culminating in a -4.1% disparity for session four. Participant eight showed an opposite shift, going from underestimations to overestimations most prominently

72 during rising and falling BACs. The participant did show significant underestimation during rising BAC, this may be due to the low doses of alcohol received in each drink which may have rendered the changes in intoxication undetectable, and not because the caffeine manipulation.

Evidence from these two participants support no effect for caffeine on the ability to discriminate one’s BAC. Results from all participants indicate that increases in disparity scores, as well as visual shifts in the graphs, are more likely due to skill degradation over time rather than any pharmacological effects of caffeine on alcohol intoxication.

Findings from this study support that at these doses with this measurement, with this specific sample, caffeine did not systematically disrupt subjective levels of intoxication. These findings lend support to a growing body of literature that advocates little behavioral interaction between alcohol and caffeine. Instead evidence seems to support that expectancies, environmental tolerance, and alternative factors play the biggest role in this interaction. There is no uniformity in data for physiological or subjective interactions of alcohol and caffeine.

Fillmore et al. (2002), found that expectancies for the effects of caffeine on alcohol intoxication significantly altered the ability of participants to perform a pursuit rotor task. This study illustrated the importance of expectancy outcomes when examining the interaction between alcohol and caffeine (Fillmore et al., 2002). With a lack of a concrete behavioral interaction found in this or prior studies, the effect seen in alcohol/caffeine intoxication may be purely due to expectancies. Dosages in this study were comparable to those in prior investigations, as well concentrations in commercial alcohol infused energy drinks (Attwood et al., 2011; Marczinski &

Fillmore, 2003; Marczinski & Fillmore, 2006). Several studies have reported using higher dosages of caffeine than this study with little to no effect on subjective intoxication measurements (Liguori & Robinson, 2001; Marczinski & Fillmore, 2006). Future studies could

73 examine higher dosages of caffeine to see if there is a dose dependent effect for this interaction.

In addition, future studies should also consider administering the alcohol dose in one drink instead of several, as this would replicate a more naturalistic form of consumption, and may elicit different results.

A recent paper published by Seigel (2011) posits that it may not be caffeine reducing subjective levels of intoxication that is the culprit for the recent increase in “mass hospitalizations for alcohol intoxication (Siegel, 2011, 359).” Rather, it’s proposed that the highly intoxicating effects of drinks such as Four Loko© are not only due to the high concentration of alcohol in the beverage, but also because drinks like Four Loko© are novel forms of an alcohol delivery system (Siegel, 2011). This novelty comes in several forms. First, drinks such as Four Loko© typically have higher than average alcohol levels in comparison to drinks that are typically served in a 24oz can. Four Loko© is 12% alcohol by volume, which in its 24 oz serving equates to as much alcohol as a six pack of light beer. It is possible that those that consume these premixed drinks are unaware of this higher alcohol content, and assume that

24 oz of a drink such as Four Loko© has an equivalent amount of alcohol as a 24 oz serving of beer. That is, consumers may be assuming that they are drinking 1-2 drinks, when in reality they are receiving an alcohol dose equivalent to six standard drinks. If an individual were to drink two of these beverages in an hour their theoretical BAC could be as high as a 0.24 g/mL. Seigel

(2011) that drinks such as Four Loko© provide other forms of novelty besides alcohol by volume. When a drug is presented in the presence of cues that are not typically associated with the drug, the effects are enhanced. This effect has been observed in numerous drugs, including caffeine (Siegel, 2011). Drinks such as Four Loko© provide novelty in the form of taste, smell and presentation. Drinks such as Four Loko© are sweet, synthetic fruity beverages which is an

74 unusual medium for alcohol (Seigel, 2011). This novelty of a beverage may not induce the situational-specific tolerance that occurs during the consumption of non-novel alcoholic beverages because of the unusual context for intoxication it provides. Since this situational- specific tolerance is not elicited in a novel beverage it’s possible that the drug effect is enhanced.

Seigel (2011) surmises that it is possible that caffeine may not necessarily be the culprit but rather situational specific tolerance and the novelty of the Four Loko © alcohol delivery system may in fact be the cause of these mass intoxications.

Finally, it is possible that other factors influence these alcohol/caffeine interactions. In a study done by Rossheim and Thombs (2011) artificially sweetened beverages were shown to increase BAC when compared to sucrose sweetened beverages. Faster gastric emptying caused by artificial sweeteners may result in an inhibited first pass metabolism (Rossheim & Thombs,

2011). Given that some diet drinks contain higher levels of caffeine than their non-diet counterparts, it’s possible that these increased levels of intoxication may be mediated by these artificial sweeteners and not necessarily just caffeine and should be explored in future studies.

Several limitations exist in this study. The mean days between sessions was also a potential limitation, with sessions taking place an average of 12 (SD=3.3) days apart. These large gaps between sessions were a result of scheduling conflicts with the participants (e.g. missed sessions, availability).This length between sessions could account for the shifts seen in estimation accuracy in participants one and three that were not observed in participants six and eight. Future studies should account for this by requiring no more than seven days to pass between sessions. Breathalyzer readings have an inherent error margin of ±0.01 g/ml which could account for some of the anomalous data points observed. Participants were instructed not to consume alcohol twenty-four hrs, caffeine 8 hrs, or food 4 hrs prior to the session but

75 adherence could not be verified and reliance was placed on self-report. It is possible that participants lied to the experimenters about when they last consumed any of these items.

Participants may have been motivated to deceive the experimenters because violation of any of the above criteria meant an immediate cancellation of the session and the participant being rescheduled for a later date. As the sessions took a considerable amount of time and were scheduled in advance, participants may not have wished to have to come at a later date due to a violation of these criteria. Participants may also have consumed caffeine and had not realized that they had consumed. As the Caffeine Consumption Questionnaire outlines, caffeine is found in several products that individuals may be unaware of (e.g. chocolate, certain over the counter drugs). Participants may have consumed caffeine without realizing, which would be confounding. Future studies could control for this by utilizing a modified version of the CCQ as a screener prior to the start of every session. Participants could fill in an annotated version of the

CCQ designed to measure caffeine consumption for a 24 hour window rather than typical caffeine consumption. This would prompt participants to account for multiple caffeine sources that might normally go unnoticed.

An additional limitation for this study was the level of difficulty the experimenters had in recruiting, and screening participants. Though recruitment was heavily solicited by in class presentations and flyers, only eight participants were scheduled to come in for screening. Of those eight, three were flagged for various disqualification criteria. This high rate of disqualification may be indicative of the type of individual who is interested in taking part in an alcohol administration study. That is, people who are interested in participating in an alcohol study may inherently be heavy drinkers. In addition the study protocol was long and labor intensive, often requiring participants to commit up to 30 hrs over the course of several days to

76 participate. This time commitment may have discourage potential participants from volunteering.

Also, the primary investigator for each session was not blind to the dosing of both alcohol and caffeine, presenting a potential confound

Elimination of female participants also limited the participant pool. Females were disqualified from participation for several reasons. First, ethical protocol states that females must be tested for pregnancy prior to each session that they would receive a drug. This is control for the possibility of a female being unaware of a pregnancy, and protect against potential harm to a fetus. In addition, prior research supports that a woman’s menstrual cycle may affect their ability to acquire the BAC discrimination skill (Hay, Nathan, Heermans, & Frankenstein, 1984). Future studies should examine this gender difference. The passivity of the experimental procedure may have also influenced the outcomes of this study. The lack of movement involved may have reduced participants’ ability to attend to sensations of intoxication at these low doses. This study was also costly to conduct, with participants often acquiring between $30-$40 worth of prizes per each session. It was also costly in the sense of the amount of time required for each participant to complete the study. Future studies may consider only utilizing one training day instead of two in order to reduce the cost of time, and utilizing smaller monetary reinforcers (e.g.

$2 Amazon© gift cards) to try and reduce monetary cost.

The effects of alcohol/caffeine co-administration are varied. Data gathered from this study would suggest that caffeine has no behavioral effect on the subjective levels of intoxication wrought by alcohol, at these doses in this paradigm. Based on evidence gathered from this study as well as the prior body of evidence it is clear that this interaction requires further examination.

77

References

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental

disorders (Revised 4th ed.). Washington, DC: Author.

Arria, A. M., Caldeira, K. M., Kasperski, S. J., Vincent, K. B., Griffiths, R. R., & O'Grady, K. E.

(2011). Energy drink consumption and increased risk for alcohol dependence. Alcohol

Clin Exp Res, 35(2), 365-375. doi: 10.1111/j.1530-0277.2010.01352.x

Attwood, A. S., Rogers, P. J., Ataya, A. F., Adams, S., & Munafo, M. R. (2011). Effects of

caffeine on alcohol-related changes in behavioural control and perceived intoxication in

light caffeine consumers. Psychopharmacology (Berl). doi: 10.1007/s00213-011-2601-0

Bennett, M. E., McCrady, B. S., Frankenstein, W., & Laitman, L. A. (1993). Identifying young

adult substance abusers: The Rutgers Collegiate Substance Abuse Screening Test.

Journal of Studies on Alcohol, 54(5), 522-527.

Berger, L., K. , Fendrich, M., Chen, H.-Y., Arria, A., M., & Cisler, R., A. (2011).

Sociodemographic correlates of energy drink consumption with and without alcohol:

Results of a community survey (Vol. 36, pp. 516-519).

Bois, C., & Vogel-Sprott, M. (1974). Discrimination of low blood alcohol levels and self-

titration skills in social drinkers. Quarterly Journal of Studies on Alcohol, 35(1-A), 86-

97.

Brache, K., & Stockwell, T. (2011). Drinking patterns and risk behaviors associated with

combined alcohol and energy drink consumption in college drinkers. Addict Behav,

36(12), 1133-1140. doi: 10.1016/j.addbeh.2011.07.003

Burns, M., & Moskowitz, H. (1989). Two experiments on alcohol-caffeine interaction. Alcohol,

Drugs & Driving, 5(4), 303-315.

78

Caddy, G. R., & Lovibond, S. H. (1976). Self-regulation and discriminated aversive conditioning

in the modification of alcoholics' drinking behavior. Behavior Therapy, 7(2), 223-230.

Cleary, K., Levine, D. A., & Hoffman, R. S. (2012). Adolescents and young adults presenting to

the emergency department intoxicated from a caffeinated alcoholic beverage: a case

series. Ann Emerg Med, 59(1), 67-69. doi: 10.1016/j.annemergmed.2011.06.015

Davies, D. L. (1962). Normal drinking in recovered alcohol addicts. (Vol. 23, pp. 94-104).

Duchan, E., Patel, N. D., & Feucht, C. (2010). Energy drinks: a review of use and safety for

athletes. Phys Sportsmed, 38(2), 171-179. doi: 10.3810/psm.2010.06.1796

Evans, S. M., & Griffiths, R. R. (1991). Dose-related caffeine discrimination in normal

volunteers: Individual differences in subjective effects and self-reported cues.

Behavioural Pharmacology, 2(4-5), 345-356.

Ferre, S., & O'Brien, M. C. (2011). Alcohol and Caffeine: The Perfect Storm. Journal of

Caffeine Research, 1(3), 153-162. doi: 10.1089/jcr.2011.0017

Ferreira, S., de Mello, M. T., Pompaia, S., & de Souza-Formigoni, M. L. O. (2006). Effects of

Energy Drink Ingestion on Alcohol Intoxication. Alcoholism: Clinical & Experimental

Research, 30(4), 598-605.

Ferreira, S., de Mello, M. T., Rossi, M. V., & Souza-Formigoni, M. L. O. (2004). Does an

Energy Drink Modify the Effects of Alcohol in a Maximal Effort Test? Alcoholism:

Clinical and Experimental Research, 28(9), 1408-1412.

Ferreira, S., Quadros, I., Trindade, A. A., Takahashi, S., Koyama, R. G., & Souza-Formigoni, M.

(2004). Can energy drinks reduce the depressor effect of ethanol? An experimental study

in mice. Physiology & Behavior, 82(5), 841-847.

79

Fillmore, M. T., Roach, E. L., & Rice, J. T. (2002). Does caffeine counteract alcohol-induced

impairment? The ironic effects of expectancy. Journal of Studies on Alcohol, 63(6), 745-

754.

Fillmore, M. T., & Vogel-Sprott, M. (1995). Behavioral effects of combining alcohol and

caffeine: Contribution of drug-related expectancies. Experimental and Clinical

Psychopharmacology, 3(1), 33-38.

Foy, D. W., Nunn, L. B., & Rychtarik, R. G. (1984). Broad-spectrum behavioral treatment for

chronic alcoholics: Effects of training controlled drinking skills. Journal of Consulting

and Clinical Psychology, 52(2), 218-230.

Griffiths, R. R., Bigelow, G. E., Liebson, I. A., O'Keeffe, M., O'Leary, D., & Russ, N. (1986).

Human coffee drinking: Manipulation of concentration and caffeine dose. Journal of the

Experimental Analysis of Behavior, 45(2), 133-148. doi: 10.1901/jeab.1986.45-133

Griffiths, R. R., Evans, S. M., Heishman, S. J., & Preston, K. L. (1990). Low-dose caffeine

discrimination in humans. The Journal of Pharmacology and Experimental Therapeutics,

252(3), 970-978.

Gulick, D., & Gould, T. J. (2009). Effects of ethanol and caffeine on behavior in C57BL/6 mice

in the plus-maze discriminative avoidance task. Behavioral Neuroscience, 123(6), 1271-

1278.

Hagman, B. T., Clifford, P. R., Noel, N. E., Davis, C. M., & Cramond, A. J. (2007). The utility

of collateral informants in substance use research involving college students (Vol. 32, pp.

2317-2323).

80

Hay, W. M., Nathan, P. E., Heermans, H. W., & Frankenstein, W. (1984). Menstrual cycle,

tolerance and blood alcohol level discrimination ability. Addictive Behaviors, 9(1), 67-77.

doi: 10.1016/0306-4603(84)90008-x

Heaton, J.A. (2012). Modifying the Caffeine Questionaire: Impulsivity and Expectancies as

Predictors of caffeine consumption. A Thesis Submitted to the University of North

Carolina Wilmington in Partial Fulfillment of the Requirmenets of the Degree of Master

of Arts

Heinz, A. J., Kassel, J. D., & Smith, E. V. (2009). Caffeine expectancy: Instrument development

in the Rasch measurement framework. Psychology of Addictive Behaviors, 23(3), 500-

511. doi: 10.1037/a0016654

Howland, J., Rohsenow Damaris, J., & Arnedt, J. T. (2011). The acute effects of caffeinated

versus non-caffeinated alcoholic beverage on driving performance and attention/reaction

time (Vol. 106, pp. 335-341).

Huber, H., & B. (1975). Blood alcohol level discrimination by nonalcoholics: Role of internal

and external cues. 36, ProQuest Information & Learning, US. Retrieved from http://0-

search.ebscohost.com.uncclc.coast.uncwil.edu/login.aspx?direct=true&db=psyh&AN=19

77-24956-001&site=ehost-live

Huber, H., Karlin, R., & Nathan, P. E. (1976). Blood alcohol level discrimination by

nonalcoholics: The role of internal and external cues. Journal of Studies on Alcohol,

37(1), 27-39.

Ishak, W., Ugochukwu, C., Bagot, K., Khalili, D., & Zaky, C. (2012). Energy Drinks:

Psychological Effects and Impact on Well-being and Quality of Life-A Literature

Review. Innov Clin Neurosci, 9(1), 25-34.

81

Julien, R. M., Advokat, C. D., & Comaty, J. E. (2008). A Primer of Drug Action: A

Comprehensive Guide to the Actions, Uses, and Side Effects of Psychoactive Drugs (11th

Edition ed.). New York, NY: Worth Publishers.

Lansky, D., Nathan, P. E., Ersner-Hershfield, S. M., & Lipscomb, T. R. (1978). Blood alcohol

level discrimination: Pre-training monitoring accuracy of alcoholics and nonalcoholics.

Addictive Behaviors, 3(3-4), 209-214.

Lansky, D., Nathan, P. E., & Lawson, D. M. (1978). Blood alcohol level discrimination by

alcoholics: The role of internal and external cues. Journal of Consulting and Clinical

Psychology, 46(5), 953-960.

Liguori, A., & Robinson, J. H. (2001). Caffeine antagonism of alcohol-induced driving

impairment. Drug and Alcohol Dependence, 63(2), 123-129.

Lovibond, S. H., & Caddy, G. (1970). Discriminated aversive control in the moderation of

alcoholics' drinking behavior. Behavior Therapy, 1(4), 437-444.

Mackay, M., Tiplady, B., & Scholey, A. B. (2002). Interactions between alcohol and caffeine in

relation to psychomotor speed and accuracy. Human Psychopharmacology: Clinical and

Experimental, 17(3), 151-156.

Marczinski, C., & Fillmore, M. T. (2003). Dissociative antagonistic effects of caffeine on

alcohol-induced impairment of behavioral control. Experimental and Clinical

Psychopharmacology, 11(3), 228-236.

Marczinski, C., & Fillmore, M. T. (2006). Clubgoers and their trendy cocktails: Implications of

mixing caffeine into alcohol on information processing and subjective reports of

intoxication. [Article]. Experimental and Clinical Psychopharmacology, 14(4), 450-458.

doi: 10.1037/1064-1297.14.4.450

82

Marczinski, C., Fillmore, M. T., Bardgett, M. E., & Howard, M. A. (2011). Effects of energy

drinks mixed with alcohol on behavioral control: risks for college students consuming

trendy cocktails. Alcohol Clin Exp Res, 35(7), 1282-1292. doi: 10.1111/j.1530-

0277.2011.01464.x

Marczinski, C., Harrison, E. L. R., & Fillmore, M. T. (2008). Effects of alcohol on simulated

driving and perceived driving impairment in binge drinkers. Alcoholism: Clinical and

Experimental Research, 32(7), 1329-1337. doi: 10.1111/j.1530-0277.2008.00701.x

Martin, C., Rose, R., & Obremski, K. (1991). Estimation of blood alcohol concentrations in

young male drinkers. Alcoholism: Clinical and Experimental Research, 15(3), 494-499.

doi: 10.1111/j.1530-0277.1991.tb00549.x

Martin, F., & Garfield, J. (2006). Combined effects of alcohol and caffeine on the late

components of the event-related potential and on reaction time. Biological Psychology,

71(1), 63-73.

Miller, P. M., Becker, J. V., Foy, D. W., & Wooten, L. S. (1976). Instructional control of the

components of alcoholic drinking behavior Behavior Therapy (Vol. 7, pp. 472-480).

Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of

discounting. Journal of the Experimental Analysis of Behavior, 76(2), 235-243. doi:

10.1901/jeab.2001.76-235

O'Brien, M. C., McCoy, T. P., Rhodes, S. D., Wagoner, A., & Wolfson, M. (2008). Caffeinated

Cocktails: Energy Drink Consumption, High-risk Drinking, and Alcohol-related

Consequences among College Students Academic Emergency Medicine (Vol. 15, pp.

453-460).

83

Ogurzsoff, S., & Vogel-Sprott, M. (1976). Low blood alcohol discrimination and self-titration

skills of social drinkers with widely varied drinking habits. Canadian Journal of

Behavioural Science/Revue canadienne des sciences du comportement, 8(3), 232-242.

Rath, M. (2012). Energy drinks: what is all the hype? The dangers of energy drink consumption.

J Am Acad Nurse Pract, 24(2), 70-76. doi: 10.1111/j.1745-7599.2011.00689.x

Reissig, C. J., Strain, E. C., & Griffiths, R. R. (2008). Caffeinated energy drinks - A growing

problem. Drug & Alcohol Dependence, 99(1-3), 1-10.

Rossheim, M. E., & Thombs, D. L. (2011). Artifical Sweeteners, Caffeine, and Alcohol

Intoxication in Bar Patrons. Alcoholism: Clinical & Experimental Research, 35(10),

1891-1896. doi: 10.111/j.1530-0277.2011.01534.x

Rush, C. R., Higgins, S. T., Hughes, J. R., & Bickel, W. K. (1993). Acute behavioral and cardiac

effects of alcohol and caffeine, alone and in combination, in humans. Behavioural

Pharmacology, 4(6), 562-572.

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.

doi: 10.1542/peds.2009-3592

Shapiro, A. P., & Nathan, P. E. (1986). Human tolerance to alcohol: The role of Pavlovian

conditioning processes. Psychopharmacology, 88(1), 90-95. doi: 10.1007/bf00310519

Shapiro, A. P., Nathan, P. E., Hay, W. M., & Lipscomb, T. R. (1980). Influence of dosage level

on blood alcohol level discrimination by alcoholics. Journal of Consulting and Clinical

Psychology, 48(5), 655-656. doi: 10.1037/0022-006x.48.5.655

84

Shohet, K. L., & Landrum, R. E. (2001). Caffeine Consumption Questionnaire: A standardized

measure for caffeine consumption in undergraduate students. Psychological Reports,

89(3), 521-526. doi: 10.2466/pr0.89.7.521-526

Shortt, R. G., & Vogel-Sprott, M. D. (1978). Social drinkers' self-regulation of alcohol intake.

Journal of Studies on Alcohol, 39(7), 1290-1293.

Siegel, S. (2011). The Four-Loko Effect. [Article]. Perspectives on Psychological Science (Sage

Publications Inc.), 6(4), 357-362. doi: 10.1177/1745691611409243

Silverman, K., & Griffiths, R. R. (1992). Low-dose caffeine discrimination and self-reported

mood effects in normal volunteers (Vol. 57, pp. 91-107).

Silverstein, S. J., Nathan, P. E., & Taylor, H. A. (1974). Blood alcohol level estimation and

controlled drinking by chronic alcoholics. Behavior Therapy, 5(1), 1-15.

Thombs, D. L., O'Mara, R. J., Tsukamoto, M., Rossheim, M. E., Weiler, R. M., Merves, M. L.,

& Goldberger, B. A. (2010). Event-level analyses of energy drink consumption and

alcohol intoxication in bar patrons. Addictive Behaviors, 35(4), 325-330.

Vogler, R. E., Compton, J. V., & Weissbach, T. A. (1975). Integrated behavior change

techniques for alcoholics. Journal of Consulting and Clinical Psychology, 43(2), 233-

243.

White, H. R., & Labouvie, E. W. (1989). Towards the assessment of adolescent problem

drinking. Journal of Studies on Alcohol, 50(1), 30-37.

85

Blood Alcohol Concentration Behavioral Effects 0.01% - 0.04% Minimal Behavioral Change Four times more likely to be in an accident (automobile or otherwise) Common Clinical Symptoms: perceptual speed 0.05% - 0.1% impairment, slowed information processing, declines in judgment, attention, control, and decreased inhibitions Gross intoxication 0.12% - 0.2% 25x increase in the probability of an accident Cardiovascular and respiratory systems 0.24% - 0.52% become so depressed that the imbiber will experience stupor, coma, and death

Table 1. Correlations of BACs with Degrees of Intoxication

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Drink # Body Weight in Pounds 100 120 140 160 180 200 220 240 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 0.04 0.03 0.03 0.02 0.02 0.02 0.02 0.02 2 0.08 0.06 0.05 0.05 0.04 0.04 0.03 0.03 3 0.11 0.09 0.08 0.07 0.06 0.06 0.05 0.05 4 0.15 0.12 0.11 0.09 0.08 0.08 0.07 0.06 5 0.19 0.16 0.13 0.12 0.11 0.09 0.09 0.08 6 0.23 0.19 0.16 0.14 0.13 0.11 0.10 0.09 7 0.26 0.22 0.19 0.16 0.15 0.13 0.12 0.11 8 0.30 0.25 0.21 0.19 0.17 0.15 0.14 0.13 9 0.34 0.28 0.24 0.21 0.19 0.17 0.15 0.14 10 0.38 0.31 0.27 0.23 0.21 0.19 0.17 0.16

Table 2. Approximate Blood Alcohol Percentage in Males Based on Body Weight

87

Drink # Body Weight in Pounds

90 100 120 140 160 180 200 220

0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 0.05 0.05 0.04 0.03 0.03 0.03 0.02 0.02

2 0.10 0.09 0.08 0.07 0.06 0.05 0.05 0.04

3 0.15 0.14 0.11 0.11 0.09 0.08 0.07 0.06

4 0.20 0.18 0.15 0.13 0.11 0.10 0.09 0.08

5 0.25 0.23 0.19 0.16 0.14 0.13 0.11 0.10

6 0.30 0.27 0.23 0.19 0.17 0.15 0.14 0.12

7 0.35 0.32 0.27 0.23 0.20 0.18 0.16 0.14

8 0.40 0.36 0.30 0.26 0.23 0.20 0.18 0.17

9 0.45 0.41 0.34 0.29 0.26 0.23 0.20 0.19

10 0.51 0.45 0.38 0.32 0.28 0.25 0.23 0.21

Table 3. Approximate Blood Alcohol Percentage in Females Based on Body Weight

88

Session 1 Session 2 Session 3 Session 4

A E D A E D A E D A E D

S1 0.243 0.203 -0.04 0.212 0.217 0.005 0.144 0.137 0.029 0.208 0.252 0.045

S3 0.079 0.099 0.021 0.148 0.134 -0.014 0.147 0.164 0.017 0.108 0.173 0.064

S6 0.257 0.240 -0.017 0.256 0.235 -0.021 0.207 0.179 -0.028 0.230 0.188 -0.041

S8 0.238 0.220 -0.018 0.234 0.226 -0.007 0.203 0.183 -0.020 0.191 0.262 0.071

Table 4. Area Under the Curve (AUC) and Disparity Scores for all subjects (%)

89

Session 1 Session 2 Session 3 Session 4 Caff 0 .0 0.0 0.0 3.0 0.33 0.0 0.22 0.22 S1 EtOH 0.33 0.33 0.22 0.22 0.0 0.33 0.22 0.22 Caff 0.0 0.0 0.0 3.0 0.0 0.33 0.22 0.22 S3 EtOH 0.33 0.33 0.22 0.22 0.33 0.0 0.22 0.22 Caff 0.0 0.0 3.0 0.0 0.33 0.0 0.22 0.22 S6 EtOH 0.33 0.33 0.22 0.22 0.0 0.33 0.22 0.22 Caff 0.0 0.0 3.0 0.0 0.0 0.33 0.22 0.22 S8 EtOH 0.33 0.33 0.22 0.22 0.33 0.0 0.22 0.22

Table 5. Ethanol (g/kg) & Caffeine (mg/kg) doses for subjects across sessions

90

Session 1 Session 2 Session 3 Session 4

A E D A E D A E D A E D

Rise 0.063 0.046 -0.017 0.069 0.077 0.008 0.054 0.093 0.039 0.056 0.066 0.010

Peak 0.056 0.058 0.002 0.045 0.046 0.001 0.024 0.026 0.002 0.040 0.059 0.020

Fall 0.180 0.157 -0.023 0.144 0.141 -0.003 0.091 0.081 -0.010 0.152 0.186 0.034

Table 6. Area under the Curve (AUC) & Disparity Scores (%) for rising, peak, and falling BAC:

Participant 1

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Session 1 Session 2 Session 3 Session 4

A E D A E D A E D A E D

Rise 0.028 0.055 0.027 0.032 0.025 -0.007 0.041 0.045 0.004 0.048 0.073 0.024

Peak 0.026 0.034 0.008 0.032 0.026 -0.006 0.026 0.038 0.012 0.018 0.037 0.019

Fall 0.050 0.044 -0.006 0.116 0.109 -0.007 0.106 0.118 0.013 0.059 0.099 0.039

Table 7. Area under the Curve (AUC) & Disparity Scores (%) for rising, peak, and falling BAC: Participant 3

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Session 1 Session 2 Session 3 Session 4

A E D A E D A E D A E D

Rise 0.056 0.057 -0.006 0.038 0.04 0.002 0.065 0.058 -0.007 0.061 0.055 -0.006

Peak 0.048 0.043 -0.005 0.036 0.031 -0.005 0.036 0.036 0.0 0.034 0.035 0.001

Fall 0.2 0.183 -0.017 0.218 0.195 -0.023 0.135 0.114 -0.022 0.168 0.133 -0.035

Table 8. Area under the Curve (AUC) & Disparity Scores (%) for rising, peak, and falling BAC: Participant 6

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Session 1 Session 2 Session 3 Session 4

A E D A E D A E D A E D

Rise 0.044 0.041 -0.003 0.052 0.049 -0.003 0.074 0.051 -0.023 0.066 0.074 0.008

Peak 0.039 0.036 -0.004 0.047 0.05 0.003 0.028 0.027 -0.001 0.04 0.045 0.005

Fall 0.194 0.179 -0.015 0.182 0.177 -0.005 0.13 0.132 0.003 0.125 0.188 0.063

Table 9. Area under the Curve (AUC) & Disparity Scores (%) for rising, peak, and falling BAC: Participant 8

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%BAC

0.12 Estimate

0.11 W/Caffeine

0.10

0.09

0.08

0.07

0.06

0.05 %BAC (g/ml) %BAC 0.04

0.03

0.02

0.01

0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 1: Represents a hypothetical subjects BAC discrimination graph.

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n=8

Completed Study Disqualified (n=4) (n=4)

S2 S4 S1 - AC2 S3 - PC2

S5 S7 S6 - AC1 S8 - PC1

Figure 2: Subject recruitment and group assignment

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1.0 Scaled (Actual) 0.9 Scaled (Estimate) 0.8

0.7 0.6 0.5 0.4 %BAC(scaled) 0.3 0.2 0.1 0.0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Minutes (Scaled) Figure 3: Example of scaled scores and trapezoids for area under the curve analysis

97

0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 4: Actual and Estimate Blood Alcohol Concentration (BAC) curves for participant one session one. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05

%BAC (g/ml)

0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes)

Figure 5: Actual and Estimate BAC curves for participant one session two. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes)

Figure 6: Actual and Estimate BAC curves for participant one session three. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 7: Actual and Estimate BAC curves for participant one session four. Error bars indicate the range of responses deemed a hit. Participant’s caffeine manipulation day.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 8: Actual and Estimate BAC curves for participant three session one. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 BAC (g/ml) BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 9: Actual and Estimate BAC curves for participant three session two. Error bars indicate the range of responses deemed a hit.

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0.12 Actual

0.11 Estimate Drink 0.10

0.09

0.08

0.07

0.06

0.05 %BAC (g/ml) %BAC 0.04

0.03

0.02

0.01

0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Minutes

Figure 10: Actual and Estimate BAC curves for participant three session three. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 11: Actual and Estimate BAC curves for participant three session four. Error bars indicate the range of responses deemed a hit. Participant’s caffeine manipulation day.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 12: Actual and Estimate BAC curves for participant six session one. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 13: Actual and Estimate BAC curves for participant six session two. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes)

Figure 14: Actual and Estimate BAC curves for participant six session three. Error bars indicate the range of responses deemed a hit. Participant’s caffeine manipulation day.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC(g/ml) 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes)

Figure 15: Actual and Estimate BAC curves for participant six session four. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 16: Actual and Estimate BAC curves for participant eight session one. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes)

Figure 17: Actual and Estimate BAC curves for participant eight session two. Error bars indicate the range of responses deemed a hit.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 18: Actual and Estimate BAC curves for participant eight session three. Error bars indicate the range of responses deemed a hit. Participant’s caffeine manipulation day.

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0.12 Actual 0.11 Estimate 0.10 Drink 0.09

0.08

0.07 0.06

0.05 %BAC (g/ml) %BAC 0.04 0.03 0.02 0.01 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 Time (Minutes) Figure 19: Actual and Estimate BAC curves for participant eight session four. Error bars indicate the range of responses deemed a hit.

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APPENDIX

Appendix A. Informed Consent

The Effects of Caffeine on Blood Alcohol Discrimination

What Is The Research About?

You are being invited to take part in a research study about caffeine and alcohol interactions. If you take part in this study, you will be one of about 20 people to do so.

Who Is Doing The Study?

The person in charge of this study is Wendy Donlin Washington (PI) of the University of North Carolina Wilmington. UNCW student, Bryan Messina, will be gathering and analyzing the information for the study. There may be other people on the research team assisting at different times during the study, including student research assistants.

Do Any Of The Researchers Stand To Gain Financially Or Personally From This Research?

This research is being funded by UNCW’s Department of Psychology. None of the researchers participating in this study stand to gain financially or personally.

What Is The Purpose Of This Study?

By doing this study we hope to learn how to train individuals to be able to accurately report their own blood alcohol levels. We are also interested in how alcohol affects performance on simple tasks and mood. Additionally, we will sometimes be administering caffeine, to see if there is an interaction between alcohol and caffeine on performance.

Where Is The Study Going To Take Place And How Long Will It Last?

The research procedures will be conducted at UNCW. You will need to come to the Natural Science building 3-4 times during the study. Each visit will take about 3 hours. The total amount of time you will be asked to volunteer for this study is 3-12 hours over the next month.

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What Will I Be Asked To Do?

You will first answer some questions about your alcohol and caffeine use, and your general health. Your weight will be taken. You will blow into an alcohol breathalyzer. If it is determined that you are qualified to participate you will move on to the following steps. If you are not qualified to continue participation, we will inform you at this point, allow you to draw from a prize bowl, and leave.

If you qualify, you will then be asked to ingest a capsule, which may include caffeine or an inert substance. You’ll then be given a mixed drink containing that may or may not contain alcohol, mixed with juice (either cranberry juice, or orange juice, whichever you prefer.) The amount of alcohol in a drink may vary, but we will make sure that you never drink enough to go higher than a 0.12% Blood Alcohol Level in a session.

Once you drink the mixed drink, you will listen to a relaxation tape. We will ask then ask you to estimate your intoxication level on a Blood Alcohol Level scale (BAC). You’ll blow into the breathalyzer again, and be given immediate feedback on your actual BAC. If you guessed within ±0.01% of your actual BAC, you will earn an additional prize draw.

We will then ask you to drink a second mixed drink. You’ll again listen to the relaxation tape. You’ll blow into the breathalyzer again, and be given immediate feedback on your actual BAC. If you guessed within ±0.01% of your actual BAC, you will earn an additional prize draw.

We will then ask you to drink a third mixed drink. You’ll again listen to the relaxation tape. You’ll blow into the breathalyzer again, and be given immediate feedback on your actual BAC. If you guessed within ±0.1% of your actual BAC, you will earn an additional prize draw.

On a second session, we will ask you to give a breath sample and weigh you. You will again ingest a capsule containing caffeine or an inert substance. Again you will drink 3 drinks, spaced 20 minutes apart, with breathalyzer samples taken every 10 minutes. You will again guess your BAC, and be given prize draws if you are within 0.01% of your actual BAC. At the end of this session, you may be asked to return for a third session.

During the third session, you will take the capsule and drink exactly as you did in the first two sessions. However you will not receive feedback or prize draws immediately after each breathalyzer reading. You will receive a prize draw for every hour you are required to stay at the end of the session. You may be asked back for a fourth session , in which you’d be asked to do the exact same things that you did in the third session, also receiving prize draws for each hour you had to stay in the lab.

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Are There Reasons Why I Should Not Take Part In This Study?

If you not of legal drinking age (<21 years old)

If you have any substance abuse history

If you are on any medications that restrict alcohol intake

If you have any major psychiatric disorder, including major depression, anxiety or panic disorders

What Are The Possible Risks And Discomforts?

During the course of the first study, you will be asked to consume beverages that contain a quantity of alcohol based upon your weight. Alcohol is a drug, a toxin and a reinforcing agent which may cause changes in behavior. Everyone who drinks alcohol is at some risk. Risk may include acute physical and behavioral impairment, acute nausea, headaches, dizziness, and hangover

If you have any current physical condition or are taking any incompatible medications that would make it a problem for you to drink alcohol now, please let us know now. We can reschedule you at a time when you are safe to drink (for example, after finishing a prescription that advises against drinking.)

Additionally, although the amount of alcohol is at most no more than the equivalent of four to five standard drinks, you may become intoxicated as a result. Therefore, you must agree to stay in the laboratory, under the supervision of the Experimenters, until your Blood Alcohol Concentration returns to zero, as measured by our Breathalyzer. In extreme cases, this may take up to six hours from now (depending on how fast you process the alcohol). However, you can earn prizes up to every 10 minutes that you’re required to stay in the laboratory. Additionally, we will allow you to watch a movie, listen to music, or read a book while you are sobering up.

If your time in the lab exceeds 3 hours, we will provide you with a meal. Please understand that if you cannot consent to this, we will not be able to allow you to be in the experiment. If during the course of the experiment, you change your mind and decide you do not want to participate, we will stop the experimental procedure, but for safety reasons, YOU WILL STILL BE REQUIRED TO STAY IN THE LABORATORY UNTIL YOUR BLOOD ALCOHOL CONCENTRATION IS ZERO, as measured by our Breathalyzer. If you try to leave before then, we will be required to contact UNCW Campus Police to alert them of your whereabouts. Having read this, if you do not wish to participate in the study, please let us know now.

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Additionally , on some occasions you will be asked to ingests a capsule which may contain caffeine or an inert substance. Caffeine is a stimulant, and may cause jitters, anxiety and affect sleep. It can also cause elevated blood pressure and pulse. However, the amount of caffeine you’ll be receiving will be based upon your weight, and will never exceed the amount of caffeine in a grande starbucks coffee.

Although we have made every effort to minimize this, you may find some of the questions we ask (or some procedures we ask you to do) to be upsetting or stressful. If so, we can tell you about some people who may be able to help you with these feelings.

In addition to the risks listed above, you may experience a risk or side-effect that we cannot predict. During the course of this research, if we find out any new reason why you may no longer wish to participate, we will provide you with that information.

Will I Benefit From Taking Part In This Study?

You will not get any personal benefit from taking part in this study.

Do I Have To Take Part In This Study?

If you decide to take part in the study, it should be because you really want to volunteer. There will be no penalty and you will not lose any benefits or rights you would normally have if you choose not to volunteer. No one on the research team will behave any differently toward you if you choose not to participate in the study. You can stop at any time during the study and still keep the benefits and rights you had before volunteering.

What Will It Cost Me To Participate?

There are no costs associated with taking part in this study.

Will I Receive Any Payment Or Reward For Taking Part In This Study?

You will earn prize draws for participating in this study. For each hour spent in the laboratory, you will be able to draw at least one time out of a prize bowl. There is a 50% chance of winning a prize on any

117 draw. Some prizes are small (<$5) and some are large (~$50). You will be able to pick out a prize immediately when you draw a winning ticket, and you cannot have your chosen prize revoked if you decide to leave the study. Whatever you win is yours to keep.

Who Will See The Information I Give?

Your information will be combined with information from other people taking part in the study. When we write up the study to share it with other researchers, we will write about the combined information. You will not be identified in any published or presented materials.

We will make every effort to prevent anyone who is not on the research team from knowing that you gave us information or what that information is. We will keep your name separate from your subject number. That way, nobody will be able to “match” the answers on the questionnaires you fill out to you. Your name will be on this informed consent, and on a master list linking name and subject number. That information will be locked in a cabinet in a locked room, and will only be available to the principle investigator and graduate research assistant. We take your privacy seriously, and will take all steps to protect your information.

However, there are some circumstances in which we may have to show your information to other people. We may be required to show information that identifies you to people who need to be sure that we have done the research correctly, such as the UNCW Institutional Review Board.

Can My Taking Part In The Study End Early?

If you decide to take part in the study you still have the right to decide at any time that you no longer want to continue. There will be no penalty and no loss of benefits or rights if you stop participating in the study. No one on the research team will behave any differently to you if you decide to stop participating in the study.

We will notify you if you should no longer participate in this study.

If you decide to quit participating during a session, after ingesting alcohol, we will cease collecting experimental data from you. However, you will be required to stay in the laboratory until your BAC reaches 0.0%. This safeguard is for your protection. If you try to leave before your BAC reaches 0.0%, we will be forced to call campus police and report that you are leaving the premises intoxicated. If you do not agree to abide by this requirement, you cannot participate in this study.

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What Happens If I Get Hurt Or Sick During The Study?

If you believe you are injured because of something that is done during the study, you should call Wendy Donlin Washington (PI) at (910) 962-2453 immediately. We will make sure you receive any needed care or treatment. However, it is important for you to understand that the University of North Carolina Wilmington will not pay for the cost of any care or treatment that might be necessary because you get hurt or sick while taking part in this study. That cost will be your responsibility

What If I Have Questions?

Before you decide whether or not to participate in the study, please ask any questions that come to mind now. Later, if you have questions about the study, you can contact the investigator, Wendy Donlin Washington (PI) at (910) 962-2453. If you have any questions about your rights as a research participant, contact Dr. Candace Gauthier, Chair of the UNCW Institutional Review Board, at 910-962-3558.

What Else Do I Need To Know?

I am required by federal law to provide you with a copy of this informed consent form.

Research Participant Statement and Signature

I understand that my participation in this research study is entirely voluntary. I may refuse to participate without penalty or loss of benefits. I may also stop participating at any time without penalty or loss of benefits. I have received a copy of this consent form to take home with me.

______

Signature of person consenting to take part Date in the study

______

Printed name of person consenting to take part in the study

______

Name of person providing information to Date the participant

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Appendix B. Quantity and Frequency Index (Hagman, et al., 2007)

MODIFIED QFI

I. Frequency of alcohol use in last three months:

a. If you have never had an alcoholic beverage (beer, wine or liquor) in your life, check here and go to Ic.

b. If you have not had any alcoholic beverage in the LAST THREE MONTHS, check here and go on to Ic.

c. If you checked Ia or Ib, please check the reasons for deciding not to drink (check all that apply)

1. Not old enough (it's illegal)

2. Religious or moral disapproval of alcohol use

3. Health Reasons (e.g. illness, pregnancy)

4. Concern that you might have (or develop) an alcohol problem

5. Other (specify)

d. If you did not check I a, b, or c, please answer the following questions:

During the LAST THREE MONTHS (about 90 days) about how many days would you estimate that you drank at least one alcoholic beverage? (Think about weekends, parties, stressful events, celebrations with friends, meals, and so on). Remember to estimate between 1 and 90 days:

Days

e. During the LAST THREE MONTHS (about 90 days), have you experienced a major change on your drinking habits?

1. No, my drinking stayed the same as usual

2. Yes, I quit drinking altogether

3. Yes, I started drinking for the first time

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4. Yes, I started drinking much more than I usually do

5. Yes, I started drinking much less than I usually do

II. Varieties of alcohol used in the last three months

a. Think carefully about all the times in the LAST THREE MONTHS that you drank any HARD LIQUOR (including, for example, scotch, gin, bourbon, creme de menthe, khalua, schnapps, mixed drinks or similar beverages with high alcohol content.

1. In the last THREE MONTHS, how often did you drink HARD LIQUOR?

almost everyday 5-6 days/wk 3-4 days/wk 1-2 days/wk

1-3 days/month less than once per month Never (go to II b)

2. In the last THREE MONTHS, on the average, how much HARD LIQUOR did you drink PER DAY on the days you drank?

4 or more pints 1-3 pints 8-10 shots/drinks

5-7 shots/drinks 3-4 shots/drinks 1-2 shots/drinks

b. Think carefully about all the times in the LAST THREE MONTHS that you drank any WINE (including, for example, table wine, dinner wine, dessert wine, port, or sherry).

1. In the last THREE MONTHS, how often did you drink WINE?

almost everyday 5-6 days/wk 3-4 days/wk 1-2 days/wk

1-3 days/month less than once per month Never (go to II c)

2. In the last THREE MONTHS, on the average, how much WINE did you drink PER DAY on the days you drank?

5 fifths or more 3-4 fifths 2 fifths 1 fifth

16 oz (3-4 wine glasses or 2 water glasses) 8 oz (1-2 wine glasses)

121 c. Think carefully about all the times in the LAST THREE MONTHS that you drank any BEER or similar low alcohol beverages (including, for example, beer, ale, wine coolers, Zima, light or ice beer).

1. In the last THREE MONTHS, how often did you drink BEER?

almost everyday 5-6 days/wk 3-4 days/wk 1-2 days/wk

1-3 days/month less than once per month

2. In the last THREE MONTHS, on the average, how much BEER did you drink PER DAY on the days you drank?

16 or more 12 oz cans or bottles (or 6 or more quarts)

13 - 15 12 oz cans or bottles (5 - 6 quarts)

11 - 12 12 oz cans or bottles (4 - 5 quarts)

8 - 10 12 oz cans or bottles (3 - 4 quarts)

3 - 7 12 oz cans or bottles (1 - 2 quarts)

1 - 2 12 oz cans or bottles

d. Think carefully about all the times in the LAST THREE MONTHS that you drank any CAFFEINATED ALCOHOLIC BEVERAGES (e.g. Four Loko, , Red Bull and Vodka, Jack and Coke)

1. In the last THREE MONTHS, how often did you drink CAFFEINATED ALCOHOLIC BEVERAGES?

almost everyday 5-6 days/wk 3-4 days/wk 1-2 days/wk

1-3 days/month less than once per month Never (go to III)

2. In the last THREE MONTHS, on the average, how much CAFFEINATED ALCOHOL did you drink PER DAY on the days you drank?

16 or more 12 oz cans or bottles (or 6 or more quarts)

13 - 15 12 oz cans or bottles (5 - 6 quarts)

11 - 12 12 oz cans or bottles (4 - 5 quarts)

8 - 10 12 oz cans or bottles (3 - 4 quarts)

3 - 7 12 oz cans or bottles (1 - 2 quarts)

1 - 2 12 oz cans or bottles

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III. Quantity of alcohol used in the last three months

a. People often drink more than one type of alcoholic beverage on a given day. In addition, their drinking often varies depending on whether it is a weekday or weekend. Therefore, we want you to think of a TYPICAL WEEKDAY on which you drank, and estimate the amounts of each of these three beverages you had to drink.

(Example: "On Thursdays, when I would get together with friends, I would drink about three 12 oz beers and two mixed drinks")

1. Estimated average drinking on a TYPICAL WEEKDAY in the LAST THREE MONTHS:

Now we want you to think of a typical WEEKEND DAY (Friday, Saturday or Sunday) on which you typically drank, and estimate your average drinking on that day.

2. Estimated average drinking on a TYPICAL WEEKEND DAY in the LAST THREE MONTHS:

3. Finally, of all the days in the last three months, what is the LARGEST AMOUNT of alcohol you have had in one 24 hour period?

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OTHER SUBSTANCE USE

How often have you used any of these psychoactive substances in the LAST THREE MONTHS?

Code frequency of use according to the following:

0 = Never

1 = 1 or 2 times in the last three months

2 = once per month

3 = once every two weeks

4 = once per week

5 = 2 - 4 times per week

6 = almost everyday

Substance Frequency of Use

Alcohol

Caffeine

Nicotine

Marijuana

Hashish

Crack

Cocaine

Amphetamines (not prescribed)

Barbiturates (not prescribed)

Benzodiazapines (not prescribed)

Other Tranquilizers ( " " )

Heroin

Other opiates (not prescribed)

Hallucinogens

Inhalants

Birth Control 124

Any drugs by injection ever

Current Prescribed medications:

Amphetamines

Barbiturates

Benzodiazapines _

Other Tranquilizers

Opiates (e.g. Methadone, Darvon)

Antidepressants (e.g. Prozac)

Antipsychotics (e.g. Haldol)

Antimanic (e.g. Lithium)

Other psychoactive medication

Do you feel you currently have a drinking or drug problem? N Y

(What substances and when did the problems first begin?)

Have you ever in the past had a problem with or been dependent on any of these substances? N Y (what? and when did it first become a problem? When did it stop being a problem?)

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Have you ever "needed" a drink, or a "hit" or a dose of a drug first thing in the morning? N Y

Have you ever had a blackout (a period of time when you continued to behave normally, but didn't remember at all the next day) from alcohol or other drugs? N Y (what substances?)

Have you ever had bad "shakes" or high fevers, seizures, hallucinations, heavy sweating or other such withdrawal symptoms when you have gone without drinking or substance use for awhile? N Y

Have you ever attended a self-help group (like Alcoholics Anonymous, or Women for Sobriety, or Narcotics Anonymous) for yourself? N Y

Have you ever had treatment for an alcohol or drug problem? N Y

Do, or did, any of your family members have an alcohol or drug problem? N Y

If yes, closest relative and what kind of problem (alcohol, drugs or both?)

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Appendix C. Rutgers Collegiate Substance Abuse Screening Test (RCSAST) (Bennett, et al., 1993)

Rutgers Collegiate Substance Abuse Screening Test (RCSAST) Please circle Y (Yes) or N (No) in response to each of the following items.

1. Have you gotten into financial trouble as a result of drinking or other drug use? Y N 2. Is alcohol or other drug use making your college life unhappy? Y N 3. Do you use alcohol or other drugs because you are shy with other people? Y N 4. Has drinking alcohol or using other drugs ever caused conflicts with close friends of the opposite sex? Y N 5. Has drinking alcohol or using other drugs ever caused conflicts with close friends of the same sex? Y N 6. Has drinking alcohol or using other drugs ever damaged other friendships? Y N 7. Has drinking alcohol or using other drugs ever been behind your losing a job (or the direct reason for it)? Y N 8. Do you lose time from school due to drinking and / or other drug use? Y N 9. Has drinking alcohol or using other drugs ever interfered with your preparations for exams? Y N 10. Has you efficiency decreased since drinking and / or using other drugs? Y N 11. Do you drink alcohol or use other drugs to escape from worries or troubles? Y N 12. Is your drinking and / or using other drugs jeopardizing your academic performance? Y N 13. Do you drink or use other drugs to build up your self-confidence? Y N 14. Has your ambition decreased since drinking and / or drug using? Y N 15. Does drinking or using other drugs cause you to have difficulty sleeping? Y N 16. Have you ever felt remorse after drinking and / or using other drugs? Y N 17. Do you drink or use drugs alone? Y N 18. Do you crave a drink or other drug at a definite time daily? Y N 19. Do you want a drink or other drug the next morning? Y N 20. Have you ever had a complete or partial loss of memory as a result of drinking or using other drugs? Y N 21. Is drinking or using other drugs affecting your reputation? Y N 22. Does your drinking and / or using other drugs make you careless of your family's welfare? Y N 23. Do you seek out drinking / drugging companions and drinking / drugging environments? Y N 24. Has your physician ever treated you for drinking and / or other drug use? Y N 25. Have you ever been to a hospital or institution on account of drinking or other drug use? Y N

Number of Yes responses _____

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Appendix D. Caffeine Consumption Questionnaire (Heaton, 2012)

Caffeine Consumption Questionnaire CCQ 2010

The following questionnaire concerns your caffeine use during a typical week. This form is divided into two sections. In the first section, you are asked to numerically indicate the average serving of the items you consume during the typical weekday. Provide your answer as if it were any of the days Monday through Friday, thus providing an average daily indicator of your consumption. Likewise, the second section asks that you numerically indicate the average serving of the items you consume during the typical weekend day. Provide your answer as if it were either Saturday or Sunday, thus, providing an average daily indicator of your consumption.

In the spaces provided below, please write the number of servings that you consume for each caffeinated product. As you complete each section, notice the serving size for each category and respond accordingly. Depending on your consumption, you may answer in fractions. For example, if you consume only half of the serving provided, your answer should be one half. Placing zeros in the spaces provided is unnecessary. However, do not skip over any sections. If you do not consume any of the products for a given category, please check the box provided for the corresponding section.

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The following questions concern your caffeine use on a typical weekday (Monday-Friday).

COFFEE (8 fl. oz.) I do not consume coffee on a typical weekday.

Morning Afternoon Evening Night Number of servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Regular Brewed ______Regular Instant ______Decaf. Brewed ______Decaf. Instant ______Espresso-restaurant style ______Other______

READY TO DRINK COFFEE I do not consume ready to drink coffee on a typical weekday. (8 fl.oz) Morning Afternoon Evening Night Number of servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Starbucks Doubleshot Coffee ______Starbucks Doubleshot Light ______Starbucks Doubleshot Energy ______Starbucks Frappuccino Caramel ______Starbucks Frappuccino Mocha ______Starbucks Frappuccino Coffee ______Other______

TEA (8 fl. oz.) I do not consume tea on a typical weekday.

Morning Afternoon Evening Night Number of servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Tea-Brewed ______Decaf. Tea-Brewed ______Tea-Instant ______Nestle iced tea lemon ______Nestle Green Tea Citrus ______Nestle Iced Tea Raspberry ______Lipton Brisk iced tea w/lemon ______Arizona iced tea w/lemon ______Other______

HOT COCOA (6 fl. oz.) I do not consume cocoa on a typical weekday.

Number of servings: ______

CHOCOLATE MILK (8 fl. oz.) I do not consume chocolate milk on a typical weekday.

Number of servings: ______

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SOFT DRINKS (20 oz.) I do not consume soft drinks on a typical weekday.

Morning Afternoon Evening Night Number of servings 6am-12nn 12nn-6pm 6pm-2am 2am-6am Coca-Cola (All Flavors) ______Coca-Cola Zero (All Flavors) ______Diet Coke (All Flavors) ______Barq’s Root Beer ______Barq’s Floatz ______Tab ______Pibb Xtra ______Pibb Zero ______Mello Yello (All Flavors) ______Dr. Pepper (All Flavors) ______Diet Dr. Pepper (All Flavors) ______Mountain Dew (All Flavors) ______Diet Mnt. Dew (All Flavors) ______Pepsi Cola ______Pepsi One ______Diet Pepsi ______Pepsi Max ______Sunkist Orange ______Diet Sunkist Orange ______A&W Crème Soda ______Diet A&W Crème Soda ______Cheerwine ______Diet Cheerwine ______*Red Bull Simply Cola (8.4 oz) ______Other______

ENERGY DRINKS (8 oz.) I do not consume energy drinks on a typical weekday.

Morning Afternoon Evening Night Number of servings 6am-12nn 12nn-6pm 6pm-2am 2am-6am Red Bull ______Monster ______Rockstar ______Full throttle ______No Fear ______Amp Energy ______Other Amp Varieties ______Other Amp Varieties ______Nos ______Venom Varieties ______Vault Varieties ______180 ______Blow Energy drink mix ______

Other______

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OVER-THE-COUNTER DRUGS I do not consume over the counter drugs on a typical weekday. (1 tablet) Morning Afternoon Evening Night Number of servings ` 6am-12nn 12nn-6pm 6pm-2am 2am-6am NoDoz ______Excedrin Extra Strength ______Excedrin Menstrual Complete ______Dexatrim ______Midol: Menstrual Complete ______Anacin ______Goody’s Headache Powder ______Other______

ENERGY SHOTS (2 oz.) I do not consume energy shots on a typical weekday.

Morning Afternoon Evening Night Number of Servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Red Bull ______NOS Powershot ______Stok Coffee Shot ______5 Hour Energy ______Other______

FOOD I do not consume food products that are chocolate flavored on a typical weekday. (Flavored with chocolate) Morning Afternoon Evening Night Number of servings 6am-12nn 12nn-6pm 6pm-2am 2am-6am Chocolate Pudding (4oz) ______Chocolate Cereal (1 cup) ______Candies, Milk Choc. (1.55oz) ______Choc. Chip Cookies (55g) ______Choc. Ice Cream(0.5 cup) ______Choc. Chip Waffles (2 waffles) ______Choc. Glazed Éclair (1 éclair) ______Choc. Fudge (1 piece) ______Choc. Hazelnut Spread (2TBSP) ______Other______

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The following questions concern your caffeine use on a typical weekend day (Saturday- Sunday).

COFFEE (8 fl. oz.) I do not consume coffee on a typical weekend day.

Morning Afternoon Evening Night Number of servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Regular Brewed ______Regular Instant ______Decaf. Brewed ______Decaf. Instant ______Espresso-restaurant style ______Other______

READY TO DRINK COFFEE I do not consume ready to drink coffee on a typical weekend day. (8 fl.oz) Morning Afternoon Evening Night Number of servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Starbucks Doubleshot Coffee ______Starbucks Doubleshot Light ______Starbucks Doubleshot Energy ______Starbucks Frappuccino Caramel ______Starbucks Frappuccino Mocha ______Starbucks Frappuccino Coffee ______Other______

TEA (8 fl. oz.) I do not consume tea on a typical weekend day.

Morning Afternoon Evening Night Number of servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Tea-Brewed ______Decaf. Tea-Brewed ______Tea-Instant ______Nestle iced tea lemon ______Nestle Green Tea Citrus ______Nestle Iced Tea Raspberry ______Lipton Brisk iced tea w/lemon ______Arizona iced tea w/lemon ______Other______

COCOA (6 fl. oz.) I do not consume cocoa on a typical weekend day.

Number of servings: ______

CHOCOLATE MILK (8 fl. oz.) I do not consume chocolate milk on a typical weekend day.

Number of servings: ______

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SOFT DRINKS (20 oz.) I do not consume soft drinks on a typical weekday.

Morning Afternoon Evening Night Number of servings 6am-12nn 12nn-6pm 6pm-2am 2am-6am Coca-Cola (All Flavors) ______Coca-Cola Zero (All Flavors) ______Diet Coke (All Flavors) ______Barq’s Root Beer ______Barq’s Floatz ______Tab ______Pibb Xtra ______Pibb Zero ______Mello Yello (All Flavors) ______Dr. Pepper (All Flavors) ______Diet Dr. Pepper (All Flavors) ______Mountain Dew (All Flavors) ______Diet Mnt. Dew (All Flavors) ______Pepsi Cola ______Pepsi One ______Diet Pepsi ______Pepsi Max ______Sunkist Orange ______Diet Sunkist Orange ______A&W Crème Soda ______Diet A&W Crème Soda ______Cheerwine ______Diet Cheerwine ______*Red Bull Simply Cola (8.4 oz) ______Other______

ENERGY DRINKS (8 oz.) I do not consume energy drinks on a typical weekend day.

Morning Afternoon Evening Night Number of servings 6am-12nn 12nn-6pm 6pm-2am 2am-6am Red Bull ______Monster ______Rockstar ______Full throttle ______No Fear ______Amp Energy ______Other Amp Varieties ______Nos ______Venom Varieties ______Vault Varieties ______

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180 ______Blow Energy drink mix ______Other______

OVER-THE-COUNTER DRUGS I do not consume over the counter drugs on a typical weekend day. (1 tablet) Morning Afternoon Evening Night Number of servings ` 6am-12nn 12nn-6pm 6pm-2am 2am-6am NoDoz ______Excedrin Extra Strength ______Excedrin Menstrual Complete ______Dexatrim ______Midol: Menstrual Complete ______Anacin ______Goody’s Headache Powder ______Other______

ENERGY SHOTS (2 oz.) I do not consume energy shots on a typical weekend day.

Morning Afternoon Evening Night Number of Servings: 6am-12nn 12nn-6pm 6pm-2am 2am-6am Red Bull ______NOS Powershot ______Stok Coffee Shot ______Other______

FOOD I do not consume food products that are chocolate flavored on a typical weekend day. (Flavored with chocolate) Morning Afternoon Evening Night Number of servings 6am-12nn 12nn-6pm 6pm-2am 2am-6am Chocolate Pudding (4oz) ______Chocolate Cereal (1 cup) ______Candies, Milk Choc. (1.55oz) ______Choc. Chip Cookies (55g) ______Choc. Ice Cream(0.5 cup) ______Choc. Chip Waffles (2 waffles) ______Choc. Glazed Éclair (1 éclair) ______Choc. Fudge (1 piece) ______Choc. Hazelnut Spread (2TBSP) ______Other______

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Appendix E. Caffeine Expectancy Questionnaire (Heinz, et al., 2009)

CAFFEINE EXPECTANCY QUESTIONAIRE (2009) Heinz, Kassel, and Smith ______The following pages contain statements about the effects of caffeine. Read each statement carefully and respond according to your own personal thoughts, feelings and beliefs about caffeine now.

We are interested in what you think about caffeine, regardless of what other people might think. If you feel that a statement is true or mostly true, then place the corresponding number for "Agree" (3) or “Strongly Agree” (4) in the space provided. If the statement is false or mostly false, then place the corresponding number for "Disagree" (2) or “Strongly Disagree” (1). When the statements refer to drinking caffeine, you may think in terms of drinking or consuming any caffeinated product, such as coffee, soda, tea, energy drinks, food or medications . Whether or not you have had these actual experiences while consuming caffeine yourself, you are to answer in terms of your beliefs about caffeine. It is important that you answer every question. PLEASE BE HONEST. REMEMBER, YOUR ANSWERS ARE COMPLETELY CONFIDENTIAL.

RESPOND TO THESE ITEMS ACCORDING TO WHAT YOU PERSONALLY BELIEVE TO BE TRUE ABOUT CAFFEINE. ______1 2 3 4 Strongly Disagree Disagree Agree Strongly Agree

______1. Caffeine makes me tense. ______30. Caffeine helps me relax. ______2. I feel more energized while drinking caffeine ______31. I get headaches, if I don’t drink regularly. ______3. I have trouble concentrating if I drink caffeine ______32. Drinking caffeine is satisfying. ______4. Drinking caffeine improves my mood. ______33. Drinking caffeine is good for dealing with boredom. ______5. I feel hyper or jacked if I drink caffeine. ______34. I like to drink caffeine in social situations. ______6. Caffeine helps calm me down. ______35. Drinking caffeine makes me anxious. ______7. Caffeine helps center me. ______36. I get drowsy if I don’t caffeine drink regularly. ______8. Caffeine makes me restless. ______37. Caffeine causes me to shake or be jittery. ______9. I have trouble focusing if I don’t drink caffeine regularly *______38. Caffeine can sober me up when I have ______10. I have less motivation if I don’t drink caffeine regularly been drinking alcohol. ______11. Caffeine helps sharpen my memory. *______39. (Women only) I consume more caffeine when ______12. Caffeine helps me feel more carefree. I am premenstrual.

______13. I get more excited when I drink caffeine. *______40. (Women only) I consume more caffeine ______14. I feel more content when I drink caffeinated beverages. throughout my entire period. ______15. I pay attention more efficiently when I consume caffeine. ______16. I feel nauseous if I don’t drink caffeine regularly. ______17. I feel fatigued if I don’t drink caffeine regularly. ______18. I feel less sleepy when I drink caffeine. ______19. Drinking caffeine makes me more outgoing.

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______20. Caffeine makes my heart race. ______21. I have muscle pain or stiffness if I don’t drink caffeine regularly. ______22. Caffeine makes my thoughts race. ______23. The more I drink caffeine, the more addicted I become. ______24. Caffeine makes me feel more alert. ______25. I feel flushed when I drink caffeine. ______26. I get more talkative or chatty when I drink caffeine. ______27. The longer I drink the harder it is to quit using caffeine. ______28. I think more clearly when I drink caffeine. ______29. I get irritable if I don’t drink caffeine regularly.

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Appendix F. Rutgers Alcohol Problem Index (RAPI) (White & Labouvie, 1989) RUTGERS ALCOHOL PROBLEM INDEX RAPI (23-item version)

Different things happen to people while they are drinking ALCOHOL or because of their ALCOHOL drinking. Several of these things are listed below. Indicate how many times each of these things happened to you WITHIN THE LAST YEAR.

Use the following code: 0 = None 1 = 1-2 times 2 = 3-5 times 3 = More than 5 times

HOW MANY TIMES HAS THIS HAPPENED TO YOU WHILE YOU WERE DRINKING OR BECAUSE OF YOUR DRINKING DURING THE LAST YEAR?

0 1 2 3 Not able to do your homework or study for a test 0 1 2 3 Got into fights with other people (friends, relatives, strangers) 0 1 2 3 Missed out on other things because you spent too much money on alcohol

0 1 2 3 Went to work or school high or drunk 0 1 2 3 Caused shame or embarrassment to someone 0 1 2 3 Neglected your responsibilities

0 1 2 3 Relatives avoided you 0 1 2 3 Felt that you needed more alcohol than you used to in order to get the same effect

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0 1 2 3 Tried to control your drinking (tried to drink only at certain times of the day or in certain places, that is, tried to change your pattern of drinking) 0 1 2 3 Had withdrawal symptoms, that is, felt sick because you stopped or cut down on drinking 0 1 2 3 Noticed a change in your personality 0 1 2 3 Felt that you had a problem with alcohol 0 1 2 3 Missed a day (or part of a day) of school or work 0 1 2 3 Wanted to stop drinking but couldn't 0 1 2 3 Suddenly found yourself in a place that you could not remember getting to 0 1 2 3 Passed out or fainted suddenly 0 1 2 3 Had a fight, argument or bad feeling with a friend 0 1 2 3 Had a fight, argument or bad feeling with a family member 0 1 2 3 Kept drinking when you promised yourself not to 0 1 2 3 Felt you were going crazy 0 1 2 3 Had a bad time 0 1 2 3 Felt physically or psychologically dependent on alcohol 0 1 2 3 Was told by a friend, neighbor or relative to stop or cut down drinking

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