MODIFYING THE CAFFEINE CONSUMPTION QUESTIONNAIRE:

IMPULSIVITY AND EXPECTANCIES AS PREDICTORS OF CAFFEINE CONSUMPTION

Jennifer Ashley Heaton

A Thesis Submitted to the

University of North Carolina Wilmington in Partial Fulfillment

of the Requirements for the Degree of

Master of Arts

Department of Psychology

University of North Carolina Wilmington

2012

Approved by

Advisory Committee

Len Lecci Richard L. Ogle

Nora E. Noel

Chair

Accepted by

Dean, Graduate School

ii

TABLE OF CONTENTS

ABSTRACT ................................................................................................................................... iii

ACKNOWLEDGEMENTS .............................................................................................................v

DEDICATION ............................................................................................................................... vi

LIST OF TABLES ........................................................................................................................ vii

LIST OF FIGURES ..................................................................................................................... viii

INTRODUCTION ...........................................................................................................................1

Measures of Caffeine Consumption .............................................................................................1

Caffeine Expectancies ..................................................................................................................6

Impulsivity .................................................................................................................................14

Caffeine and Impulsivity ........................................................................................................20

A Model of Augmented Caffeine Consumption ........................................................................22

Impulsive Populations ............................................................................................................22

Dopamine and Adenosine.......................................................................................................25

Self-Medication Hypothesis ...................................................................................................26

Summary.................................................................................................................................27

Hypotheses .................................................................................................................................28

GENERAL METHOD ...................................................................................................................29

Overview ....................................................................................................................................29

STUDY 1 .......................................................................................................................................29

Method .......................................................................................................................................30

Participants .............................................................................................................................30 Measures .................................................................................................................................30

Procedure ................................................................................................................................33

Results ........................................................................................................................................34

Discussion ..................................................................................................................................37

STUDY 2 .......................................................................................................................................37

Method .......................................................................................................................................38

Participants .............................................................................................................................38

Measures .................................................................................................................................39

Procedure ................................................................................................................................43

Results ........................................................................................................................................44

Discussion ..................................................................................................................................55

SUMMARY AND CONCLUDING DISCUSSION .....................................................................56

REFERENCES ..............................................................................................................................62

APPENDICES ...............................................................................................................................69

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ABSTRACT

The aim of the current study was to examine the ability of caffeine expectancies and

impulsivity to predict caffeine consumption. The present research modified an existing caffeine

consumption instrument to parallel present day availability of caffeine containing products.

Using the modified instrument, the current research assessed if impulsivity and caffeine

expectancies would predict self-reported weekly caffeine consumption in a college sample. To

this end, two studies were conducted. Study 1 was a pilot study that assessed the usefulness of

the modified version of the self-report instrument. Results from Study 1 indicated that the

measure was useful and that only slight modifications were needed to proceed. Study 2

examined the ability of caffeine expectancies and the personality construct of impulsivity to

predict weekly caffeine consumption. Caffeine expectancies and impulsivity were measured

using relatively new instruments. The association of caffeine expectancies and impulsivity to

weekly caffeine consumption was analyzed through the construction of hierarchical multiple

regression models. Predictor variables included gender, frequency of nicotine consumption

within the last 3 months, four subscales that measured different factors of caffeine expectancies

and five subscales that measured different dimensions of impulsivity. The outcome variable was

weekly caffeine consumption. The current research hypothesized that impulsivity and

expectancy would predict college students’ caffeine consumption. Specifically, it was thought

that those endorsing higher expectancies for withdrawal symptoms would be higher caffeine

consumers. In relation to impulsivity, it was thought that sensation seeking would be the best

predictor of caffeine consumption. The hypotheses were partially supported. Results from

hierarchical multiple regression analyses performed in Study 2 indicated that caffeine

expectancies were good predictors of college students’ self-reported weekly caffeine

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consumption. Additionally, higher expectancies related to withdrawal symptoms were

associated with higher caffeine consumption. However, hypotheses generated in relation to

impulsivity, in general, were not supported. Impulsivity dimensions did not account for a

significant amount of variance in college students’ weekly caffeine consumption. However,

there was evidence of an interaction between gender and an impulsivity dimension measuring a

lack of the tendency to plan ahead and delaying one’s behavior.

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ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to my mentor, Dr. Nora Noel. Without her

invaluable patience, guidance and support none of this would have been possible. Her

contributions to the field of psychology as a professor, researcher and clinical professional are

illustrated through the individuals she has touched with her wisdom. Through her dedication,

she has demonstrated an example of leadership and excellence which have influenced my life in

ways that words cannot express.

I would also like to thank the other members of my committee, Dr. Richard L. Ogle and

Dr. Len Lecci for their advice, support and suggestions. These individuals also provided an

example of excellence within the field of psychology that reinforced my convictions to strive for

continued academic achievement. I would also like to thank the many members of the

B.E.A.C.H. lab for their hours of hard work on this study.

I would like to extend my thanks to my family, especially my mother, Bonny Heaton, and

my grandparents, June and William McDaniel for supporting and encouraging me to pursue this

degree.

vi

DEDICATION

This thesis is dedicated to my mother, Bonny Heaton, and my grandparents, June and

William McDaniel. Their patience, guidance, support and unconditional love have meant more

to me than words could ever express.

vii

LIST OF TABLES

Table Page

1. Weekly Caffeine Consumption Estimates by Gender in Study 1…….…………………….35

2. Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 1….………..36

3. Weekly Caffeine Consumption Estimates by Gender in Study 2……………….………….45

4. Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 2…….……..46

5. Reliability Coefficients for the UPPS-P………….……………………………….………..47

6. Pearson Correlations with Weekly Caffeine Consumption by Gender…………….………49

7. Hierarchical Regression Models with Expectancy and Impulsivity……………….……….52

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LIST OF FIGURES

Figure Page

1. Gender and Premeditation Interaction………………….……….………………………….54

INTRODUCTION

A variety of consumables that are widely available to individuals in western societies,

contain caffeine. Such products include soft drinks, chocolate and many medications. Caffeine,

perhaps due to its availability and common acceptance, is the most consumed psychoactive

substance in the world (Ferré, 2008; Heinz, Kassel & Smith, 2009). Barone and Roberts (1996)

estimated the average daily intake for adults residing in the United States, using various surveys

on food consumption, to be 4 mg/kg body weight. In 2001, Shohet and Landrum estimated that

their sample of 691 college students consumed an average of 1,600 mg of caffeine weekly.

According to Barone and Roberts (1996), researchers have a piqued interest in examining

caffeine as a result of its mere presence in a wide variety of consumable plant species and,

therefore, in food products and beverages. Adding to its scientific appeal, caffeine has a

considerable history of human consumption that dates back to the Paleolithic period (Barone &

Roberts, 1996). Given the prevalence and extensive consumption of caffeine, research aimed at

further understanding its effects and associative factors is beneficial.

Measures of Caffeine Consumption

Previous research has measured caffeine consumption by asking participants to recall

their intake of particular, yet limited, caffeine vehicles through self-report (Landrum, 1992). For

example, Fillmore, Mulvihill, and Vogel-Sprott (1994) measured intake of caffeine by prompting

participants to indicate their consumption of coffee on a daily basis. More than a decade later,

the same method of measurement was used by different researchers. Oei and Hartley (2005)

asked participants to provide their daily use of coffee as a measure of caffeine consumption.

Although, caffeine intake was not central to their work, it, however, played a role in their

caffeine oriented research. That is, Oei and Hartley found it necessary to understand their

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participants in terms of their actual level of caffeine consumption. They wanted to exclude

abstainers and those high in intake. Based on estimates from previous research, Oei and Hartley

portrayed their sample as including participants who were generally low in their caffeine

consumption. However, that description is difficult to ascertain given the only vehicle of

caffeine examined was coffee. Thus, restricted ability to accurately depict caffeine consumption

was a limitation in these studies.

Previous research has used self-report instruments that measure caffeine intake based on

the assumption that it can be represented by one or two vehicles. Further, these methods make

the assumption that the individual’s intake follows a daily pattern. The lack of an instrument that

includes the variety of vehicles that are available to caffeine consumers may account for the

variability seen in college samples in regard to caffeine consumption (Landrum, 1992; Shohet &

Landrum, 2001).

In 1992, Landrum developed a questionnaire to measure caffeine consumption in a more

precise and accurate way. It was the first attempt to provide an instrument for assessing caffeine

consumption that included a variety of caffeine containing products, and thus, complementing

the need existing in caffeine research. Landrum (1992) developed the Caffeine Consumption

Questionnaire (CCQ) which assessed the milligrams of caffeine consumed by participants on a

weekly basis. The instrument was a self-report measure completed by 116 participants.

Landrum wanted to demonstrate that the CCQ could be used to assess the general nature of

caffeine use among college students. The focus included a variety of caffeine containing

products. His original version of the CCQ (Landrum, 1992) contained the following categories:

Coffee, Tea, Cocoa, Chocolate, Soft Drinks and various Over-the-Counter Drugs. Additionally,

the measure divided the day into four categories, which included Morning, Afternoon, Evening

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and Night. This gave participants the opportunity to record caffeine consumption based on the

time of day that they actually ingested the substance.

Landrum (1992) asserted that the questionnaire was sufficient for assessing caffeine

intake and could be used in other research that required a thorough measure of caffeine

consumption. Although his analyses did not find time of day to be a significant factor in relation

to caffeine consumption, it is possible that it served as a valuable memory cue for the

participants. Given that recall is often viewed as a limitation to self-report measures

(Williamson, 2007), it is essential to provide as many memory cues as possible to elicit more

accurate and precise self-report data. The CCQ (Landrum, 1992) was the first of its kind to

assess caffeine consumption via a variety of vehicles. Landrum considered the array of

beverages and foods that contained caffeine and were not only available, but commonly

consumed, and incorporated these products into the much needed self-report measure of caffeine

consumption.

Shohet and Landrum (2001) sought to further establish the usefulness of the CCQ

(Landrum, 1992). In addition to examining caffeine consumption in a college sample, they also

explored morning and evening personality types and whether classifying participants as such

would predict the time of day that they actually consumed caffeine. In sum, the aim of their

research was to provide increased data using an improved method of measuring caffeine intake

while attempting to further examine the relationship between times of consumption to time of

day preference. Their sample included 691 college participants who were administered the CCQ

(Landrum, 1992). Using the data obtained from these participants, the authors were able to

express the average weekly estimate of consumption as 1,600 mg. Only a portion of the sample

received the Morningness-Eveningness Questionnaire developed by Horne and Ostberg (1975)

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that was used in Shohet and Landrum’s study to assess time of day preference. Shohet and

Landrum determined that the time of day preference held by the participants was correlated with

ingestion, such that those with evening preferences consumed more caffeine in the evening. No

significant correlation was found for morning types. The significance of evening preferences

further supports the idea that dividing consumption into time of day may provide further cues for

participants as they consider their regular caffeine intake. Shohet and Landrum argued for the

usefulness of the CCQ (Landrum, 1992) especially in college settings due to the development of

the instrument using such a sample, and suggesting that its continued use would encourage

uniformity within the field.

The utility of the CCQ (Landrum, 1992) is manifest by its appearance in various studies

engaging in research specific to caffeine. Since its development, other investigations have made

use of the questionnaire. For example, Jones and Lejuez (2005) examined various personality

correlates with caffeine dependence. They needed a measure to assess caffeine consumption and

used the CCQ (Landrum, 1992) to determine which participants were caffeine consumers and

which were abstainers. As recent as 2009, Heinz, Kassel and Smith have used a modified

version of the CCQ (Landrum, 1992). Their research was centered on developing a caffeine

expectancy questionnaire, and in order to do so, they required a measure of caffeine

consumption. Although brief and modified, the use of the CCQ (Landrum, 1992) in 2009

demonstrates the continued need of a measure to estimate participants’ caffeine intake in caffeine

oriented research. Landrum (1992) has provided easy access to a relatively accurate and precise

measure of caffeine intake.

Since Landrum (1992) embarked on the endeavor to develop a more precise self-report

measure that would assess weekly caffeine consumption, the availability of caffeine has changed.

5

Nearly two decades later, the current generation witnessed an explosion in products that contain

high concentrations of caffeine, specifically energy drinks (Reissig, Strain & Griffiths, 2009).

These potent vehicles are readily accessible to the everyday person, especially in the United

States (Reissig et al., 2009). The increase in these products has been large enough for members

of the scientific community to begin to question the availability of large amounts of caffeine.

Reissig, Strain and Griffiths (2009) propose that increased caffeine content, as is customary

among energy drinks, has become an issue that society needs to address. They argue that

caffeine intoxication is especially problematic among young male individuals as these beverages

are often targeted to this group. Arguments like these demonstrate not only the increased

prevalence of caffeine and its consumption but that the need for measuring caffeine consumption

exists, especially among young persons. Landrum (1992) began the task of the development of

the CCQ by using college students as his sample. Further, Landrum (1992) and, subsequently,

Shohet and Landrum (2001), demonstrated the importance of measuring caffeine using a

consistent measure. The development of the CCQ (Landrum, 1992) provides a pathway for the

continued assessment of caffeine intake using a self-report measure.

Despite the fact that Landrum’s (1992) CCQ was found to be useful in previous studies,

the current research seeks to further develop the questionnaire. The need exists to modify the

questionnaire so that it accurately depicts the vehicles of caffeine consumption that are relevant

to the present day. To meet this end, the current study modified the CCQ (Landrum, 1992) by

adding categories for Ready to Drink Coffees, Energy Drinks and Energy Shots. Additionally,

the categories proposed by Landrum (1992) were expanded by adding new products to better

represent the wide range of availability. In essence, the present research attempted to provide an

extensive list of potential caffeine vehicles and their quantities in milligrams. During this

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process, it became apparent that participants sometimes indicated caffeine containing products

that were not presented on the modified version of the CCQ. Thus, the final modified version

included additional room to write in products not listed, and if the caffeine content was available,

these products were included in the weekly caffeine consumption estimates. The final revised

version of the CCQ was divided into two sections. One section referenced a typical weekday

and the other section referenced a typical weekend day. They were identical in nature except for

asking the participant to answer according to their caffeine consumption as related to day of the

week. Together these measures assessed a typical weeks’ consumption by the participant.

The breakdown of the modified version of the CCQ was based on the assumption that

caffeine intake varies from weekdays to weekend, especially for college students. The basis of

this assumption was that the sample came from a pool of college students who had different

schedules throughout the week as compared to the weekend. It was also thought that the division

of the modified version of the CCQ would allow for memory cues on behalf of the participant

and, thus, provide a picture of a typical weeks’ consumption.

Caffeine Expectancies

Research on drug expectancies has become increasingly important as more insight has

been gained in regard to the influence of expectancies on the drug experience. In a review by

Brown (1993), drug expectancies were generally described as evolving from some type of

experience with a specific drug. Brown explains that this experience can be a result of two

different encounters with the drug in question. The first experience can be an actual interaction

and ingestion of the specific drug. The second can be from additional sources, such as other

individuals or information presented in some sort of media. That is, drug expectancies are what

a person predicts and believes will happen once consumption of a specific substance occurs.

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They can be conceptualized as cognitive representations that can be triggered by external or

internal drug-related stimuli. According to Brown, alcohol has been the topic of interest in the

vast majority of research on drug expectancies. In comparison to alcohol, realistically little has

been studied in regard to the relationship between caffeine and expectancies. However, as with

other drugs, expectancies are assumed to play a role in the experience of the acute effects of

caffeine use as well (Heinz, Kassel & Smith, 2009). Since an individual’s predictions and beliefs

can influence their subsequent experience of a drug, it is important to examine these expectations

and understand of what exactly these expectations consist. The limited research examining

caffeine expectancies illuminates the increasingly apparent need to study this relationship.

Fillmore and Vogel-Sprott (1992) conducted some of the first scientific endeavors

focusing on caffeine expectancies. Their earliest research attempted to manipulate the effects

that participants expected caffeine to produce on a task that involved the use of their motor skills.

The task employed by Fillmore and Vogel-Sprott (1992) was a computerized pursuit rotor task.

Their experiment was divided into two studies that, when combined, used 56 male participants.

Through random assignment, the authors produced four groups of individuals for each study.

Group status was defined by the attempted manipulation of the expected acute effects of caffeine

and their belief as to whether they had ingested the substance. Three of the groups anticipated

administration of caffeine via the vehicle of coffee, however, received only minute amounts of

caffeine in their beverage of decaffeinated coffee. The fourth group, which served as a control,

neither expected nor received caffeine. Thus, three of the four groups acted as placebos. That is,

none of the groups received a significant amount of caffeine in their beverage. Of the three

placebo groups, the attempted manipulation for two of the conditions included statements made

by research assistants as to the expected effects of caffeine on motor performance. Specifically,

8

one group was told that caffeine enhances motor performance; whereas a second group was

instructed to believe the opposite (i.e., diminishes performance). Thus, an attempt at

manipulation of participant’s expectancies was made by telling the volunteers what to anticipate

from caffeine administration. Research assistants made no statements concerning expected

performance to those in the third placebo group.

Fillmore and Vogel-Sprott (1992) found that what they told participants seemed to affect

participants’ performance on the computerized pursuit rotor task. Those who were in the

impairment condition demonstrated diminished functioning when compared to those in the

placebo group that were not subject to the research assistant’s statements. The same was true for

those who were in the enhanced functioning condition. These participants exhibited significantly

better scores on the pursuit rotor task than those in the placebo group who did not experience the

attempted manipulation. Thus, the research by Fillmore and Vogel-Sprott (1992) attempted to

demonstrate that caffeine expectancies could be manipulated and that this manipulation would

affect performance even under placebo conditions. However, these conclusions are difficult to

draw due to the lack of a measure of preconceived expectancies.

In an effort to extend the research on attempted manipulation of drug expectancies,

Fillmore, Mulvihill and Vogel-Sprott (1994) examined alcohol as well as caffeine. As was

previously done (Fillmore and Vogel-Sprott, 1992), Fillmore et al. (1994) attempted to

manipulate drug expectancies by having research assistants inform participants to expect either

improvement or impairment on a computerized pursuit rotor task. Members of their sample,

which included 50 male participants, were randomly assigned to one of five groups, two of

which expected caffeine administration. Within the caffeine conditions, an attempt was made to

manipulate expectancies such that one group was told to anticipate enhanced performance while

9

the other was instructed to expect impairment. Similarly, two of the groups anticipated alcohol

administration. Likewise, they were split by those who were informed to anticipate enhancement

and those informed to impairment. However, none of the four groups actually received any

significant amount of drug. Ultimately, their research used four placebo conditions. The fifth

group was a control that did not expect any drug administration.

The analyses performed by Fillmore et al. (1994) yielded similar results to those found in

Fillmore and Vogel-Sprott (1992). That is, there was an interaction between the drug and

expectancies, such that those who anticipated increased performance demonstrated such when

compared to their respective impairment expecting counterparts. Further, the researchers found

that those in the alcohol condition that expected impairment actually demonstrated increased

performance when compared to those in the caffeine condition that also anticipated impairment.

The authors predicted this relation and proposed that motivation to counteract expected

impairment, especially to alcohol, may be the responsible factor underlying these results. The

authors had no measure to assess preconceived predictions and beliefs in regards to caffeine

consumption. Therefore, their attempted use of expectancy as a manipulation provided much

insight in the research on caffeine during that time.

More than a decade later, other research has continued to attempt to manipulate caffeine

expectancies. A study by Harrell and Juliano (2009) examined how caffeine expectancies can

influence performance tasks as well as subjective reports using a 2 x 2 design. Their sample

included 60 coffee drinkers. As was previously done (Fillmore and Vogel-Sprott, 1992; Fillmore

et al., 1994), Harrell and Juliano attempted to manipulate caffeine expectancies by telling

participants to anticipate either improvements or impairment in functioning. However, their

focus included actual administration as opposed to limiting their scope to only the exploration of

10

the placebo effect. Participants were either given decaffeinated coffee with tonic water or

decaffeinated coffee with 280mg of added caffeine. They were told that they had been

administered caffeine regardless of actual caffeine content. The performance tasks that the

participants completed included a finger tapping task (Heatherly, Hayward, Seers & Rogers,

2005) and the Rapid Visual Information Processing Task (RVIP; Yeomans, Ripley, Davies,

Rusted, & Rogers, 2002). The participants were familiar with both measures due to a previous

practice session used to establish baseline measurements.

Harrell and Juliano (2009) found that administration of caffeine did have an effect on

performance. Relative to placebo, the caffeine condition produced enhancement on both

performance tasks. Further, an interaction between dose and expectancy was found only relative

to performance on the RVIP (Yeomans et al., 2002). Subjects who expected impairment and did

not receive caffeine actually improved performance compared to those who expected

enhancement. This finding was contrary to their predictions, and the authors suggested a

compensatory responding theory previously offered by Fillmore et al. (1994) as an explanation.

That is, since the subjects expected impaired functioning due to caffeine intake, they had

prepared themselves to counteract for that impairment, thus, increasing their performance when

no drug was administered. Despite that the compensatory responding theory seems to explain

the results obtained by Harrell and Juliano, it is interesting to note that Fillmore et al. found this

relation with alcohol and not with caffeine. Harrell and Juliano also found significant results in

regards to expectancy and subjective reports. They found that those who expected caffeine to

impair their functioning and were administered caffeine reported more negative somatic effects

than those who expected enhanced functioning.

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Research that has attempted to manipulate caffeine expectancies has been popular and

has provided interesting results. However, in the absence of measuring preconceived

expectancies, it is difficult to ascertain that they have been, in fact, manipulated. It is possible

that people anticipate different effects based on the vehicle of consumption presented.

Therefore, the need exists to understand what people expect from caffeine itself, before an

attempt can be made to manipulate caffeine expectancies. The literature has been lacking of an

instrument to measure these preconceived beliefs. Despite the absence of such a measure,

Fillmore and Vogel-Sprott (1994) began to explore the idea of measuring caffeine expectancies.

The researchers did not attempt manipulation of drug expectancies; instead, they made an effort

to measure participants’ expectancies for either caffeine or alcohol depending upon group status.

The researchers randomly assigned 40 male participants to one of five groups. The groups

included both caffeine and alcohol administration, in addition to placebos for both substances.

There was also a control group that received no drug. Participants completed a computerized

pursuit rotor task as a measurement of performance. Expectancy measurements were not

obtained through examination of various factors pertaining to the construct. Rather, the variable

of expectancy was expressed as ultimately either enhance or impair. The scoring was based on a

13-point likert scale that allowed for severity of expectation, thus creating a continuum.

Fillmore and Vogel-Sprott (1994) found a positive relationship between the drug

administered and their respective expectancies. That is, the individuals that anticipated more

negative effects demonstrated diminished performance on the computerized pursuit rotor task

while under the influence of either caffeine or alcohol. Additionally, a similar relationship was

found for those participants in the placebo groups. That is, their preconceived beliefs, although

simplified due to the measure, predicted actual performance, so that if the individual expected a

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deficiency in functioning, diminishment was actually seen in their subsequent scores on the

computerized pursuit rotor task. Their results demonstrated the effect that caffeine expectancies

can have on behavior in individuals. Further, it portrayed that there are individual differences in

caffeine expectancies.

Past research has attempted to manipulate caffeine expectancies, but none had attempted

to directly measure them as has been done in the large amount of research conducted with

alcohol (Heinz, Kassel & Smith, 2009). That is, a dimensional exploration of the construct of

caffeine expectancy had not been undertaken. The deficiency of such studies has been due to the

lack of a measurement questionnaire designed to assess caffeine expectancy. However, Heinz,

Kassel, and Smith (2009) have developed a measure that they call the Caffeine Expectancy

Questionnaire. The focus of their research was to develop the Caffeine Expectancy

Questionnaire using an alternative approach to traditional measurement analyses.

Heinz et al. (2009) undertook the making of the Caffeine Expectancy Questionnaire using

what is known as the Rasch model (Rasch, 1960) to determine the important factors contributing

to caffeine expectancy. Their study consisted of five stages, whose succession led to the final

version of the questionnaire. The authors used focus groups in the first phase to determine the

participants’ perceptions of caffeine and their expectancies upon use. The second stage included

using an actual version of the Caffeine Expectancy Questionnaire developed from the responses

given during the focus groups. This form of the Caffeine Expectancy Questionnaire consisted of

47 items and a 10- point likert scale for scoring, all of which were piloted during this stage.

Further, using the Rasch model (Rasch, 1960), Heinz et al. determined which items were

appropriate for use and found the scoring, although representing a continuum, to be insufficient.

The third stage included using a revision of the Caffeine Expectancy Questionnaire based on the

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items that were deemed appropriate according to the Rasch model (Rasch, 1960). This version

contained 40 items. Further, the authors explored scoring of the Caffeine Expectancy

Questionnaire during this stage such that scoring would be ultimately dichotomous in nature.

That is, the authors employed the use of a 4-point scale that assessed the likelihood of a

participant expecting the proposed item with no neutral component. Additionally, Heinz et al.

explored the factors that make up the construct of caffeine expectancy and found, through

exploratory factor analysis, three factors to be present. Once again, the fourth stage saw a

revision of the Caffeine Expectancy Questionnaire and yet another administration. Scoring was

on a 4-point scale that assessed the likelihood of experience. However, the questionnaire

contained only 31 items. Exploratory factor analysis revealed the presence of four factors. The

final version contained 37 items which where categorized into four factors: Positive Effects,

Acute Negative Effects, Mood Effects, and Withdrawal Symptoms. These factors were found to

be present and distinct through the use of exploratory factor analysis. Upon the final revision of

the Caffeine Expectancy Questionnaire, it was decided to have subjects answer the items in an

ultimately dichotomous fashion that ascertained whether or not the subjects agreed with the

statement as opposed to the likelihood of experiencing such factors. Similarly, the options made

up a 4-point likert scale that provided the severity of the amount of agreement or disagreement

and ranged from 1 (Strongly Disagree) to 4 (Strongly Agree). It was decided to assess degree of

agreement as opposed to likelihood of experience due to the fit of the rating scale. That is,

endorsing agreement seemed to fit items on the questionnaire and to better distinguish between

factors.

In terms of content validity, it appeared that the Caffeine Expectancy Questionnaire

(Heinz et al., 2009) did assess the construct of caffeine expectancies. Adding evidence to the

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content validity of the measure, Heinz et al. (2009) used focus groups in the first stage of

development. Thus, they employed others’ perceptions of caffeine expectancies and then used

these perceptions to construct the Caffeine Expectancy Questionnaire. Further, each item in the

final version of the Caffeine Expectancy Questionnaire (Heinz et al., 2009) had a correlation

coefficient greater than .35, with most items exceeding .40. In terms of external validity, the

authors explored the relationship between the factors and reported withdrawal symptoms and

dependence on caffeine. They found that those who were more dependent on caffeine scored

higher on each factor than those who were not dependent. Thus, the authors concluded that

external validity was relatively high, in that the measure correlated with another instrument that

participants should score high on, especially if they were dependent upon caffeine. The authors

performed alpha reliability coefficients on the four factors and found them all to be higher than

.80. Based on these analyses, the Caffeine Expectancy Questionnaire (Heinz et al., 2009) seems

to be reliable and valid measure for assessing caffeine expectancy. Nonetheless, further

research is needed to continue to validate the measure. There were limitations within the study,

including lower rates of participation for those persons endorsing higher consumption of

caffeine. That is, Heinz et al. found that most of the participants were relatively inexperienced in

using caffeine and thus, would be likely to endorse caffeine expectancy differently.

Impulsivity

While drug specific expectancies seem to be a reasonable concept to consider when

researching any drug, another area that is often explored is personality. In alcohol research, one

personality construct that has been found to be related to college alcohol use is impulsivity or

impulsive-like traits (Magid & Colder, 2007; Schwartz, Burkhart, & Green, 1978). Specifically,

alcohol research has often cited a positive relationship between alcohol use and sensation

15

seeking (Schwartz et al., 1978). Regarding the relationship between caffeine consumption and

personality traits, the field is lacking in availability of research. There have been a limited

number of studies that have examined caffeine consumption, caffeine dependence and other

caffeine-related variables and their relationship to impulsivity or impulsive-like traits. The

evidence generated from this body of research contains mixed results. The lack of evidence for a

robust association may be due to the variety of instruments that are used to assess impulsivity or

impulsive-like traits.

The term impulsivity is often used colloquially with the implication that its meaning is

not only understood but clear and un-muddled. The same conundrum appears to plague the

academic literature. However, researchers have begun to highlight that the use of this term gives

rise to a multitude of definitions, and there are many methods for its assessment (Evenden, 1999;

Lynam & Miller, 2004). In fact, it appears that the sole consensus in relation to the construct is

that it is multi-dimensional (Evenden, 1999), and this, in and of itself, seems to stem from the

variety of measures that are thought to assess impulsivity or tap into related factors. Evenden

(1999) states that “. . . there is not one unitary ‘impulsivity’ or only one type of impulsive

behaviour. Instead, there are several related phenomena which are usually classified together as

impulsivity. . .” (p. 348). Despite this commonality, the differences in operationally defining the

construct become evident when the varieties of instruments that are used to assess impulsivity

are considered. This observation, alone, demonstrates the lack of consensus within the

personality literature as to the nature of this seemingly elusive construct (Evenden, 1999; Lynam

& Miller, 2004).

Not only are there a variety of instruments, but each instrument seems to contain different

subscales that are thought to tap into different dimensions of impulsivity, which elucidates its

16

multi-dimensional nature. For example, the variation in impulsivity can be seen in that in some

instances impulsivity is considered a first order construct (e.g., the UPPS-P; Lynam, Smith,

Whiteside, & Cyders, 2006) while in other instances it is a second or third order construct (e.g.,

the Temperament and Character Inventory; TCI-125; Cloninger, Przybeck, Svrakic & Wetzel,

1994). It appears that impulsivity is elusive, and that even when it is observed solely as a

personality construct, it is difficult to ascertain an operational definition, much less its factors, an

idea elaborated on by Smith, et al. (2007). Smith, et al. went so far as to say that the term

impulsivity should not be used. However, based on the research, the construct of impulsivity

appears to exist. Researchers should be weary of the ways in which they report their findings

regarding impulsivity. Being specific as to the personality dimension or behavior they are

attempting to define should help to alleviate any confusion.

An instrument that is often cited in the personality literature as measuring impulsivity is

the Eysenck Impulsiveness Questionnaire (I7; Eysenck, Pearson, Easting, & Allsopp, 1985).

The I7 is based on Eysenck & Eysenck’s (1975) three-factor conceptualization of personality.

The three proposed dimensions include Extraversion-Introversion, Neuroticism-Stability and

Psychoticism-Conformity. The I7 (Eysenck et al., 1985) is composed of three factors:

Impulsiveness, Venturesomeness and Empathy. However, only two of the three factors are

thought to tap into the construct of impulsivity, namely, Impulsiveness and Venturesomeness.

Items assessing empathy were added to act as a buffer due to the concern that a greater degree of

diversity would be needed because items assessing Impulsiveness and Venturesomeness may

have been too seemingly related. These two factors may appear to be alike, but S.B.G. Eysenck

(1993) asserts that they are in fact different:

Our concept of Imp[ulsiveness] and Vent[uresomeness] can best be described by

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analogy to a driver who steers his car around a blind bend on the wrong side of the road.

The driver who scores high on Imp never considers the danger he might be exposing

himself to and is genuinely surprised when an accident occurs. The driver who scores

high on Vent, on the other hand, considers the position carefully and decides consciously

to take the risk, hoping no doubt for the ‘thrill’ of the sensation-seeking arousal caused by

what he hopes will be merely a ‘near miss (p. 144).

According to S.B.G. Eysenck’s (1993) description, the Venturesomeness factor appears to

contain elements of risk-taking whereas the Impulsiveness factor involves acting without

considering the consequences. According to S. B. G. Eysenck (1993), the Venturesomeness

factor maps onto the extraversion dimension of Eysenck & Eysenck’s (1975) conceptualization

of personality, while Impulsiveness maps onto the Neuroticism dimension.

Another, recently constructed, personality measure of impulsivity is the UPPS Behavior

Scale (Whiteside & Lynam, 2001). With the aim of developing an inclusive instrument,

Whiteside and Lynam (2001) combed the available personality literature and chose impulsivity-

related self-report measures that were well known and often cited. The researchers performed

exploratory factor analyses to produce a new measure of impulsivity. Whiteside and Lynam

argued that the UPPS highlighted the multi-dimensional nature of impulsivity by allowing for

multiple “pathways to impulsive behavior” (see also Lynam & Miller, 2004).

Whiteside and Lynam (2001) grounded their analysis of impulsivity in the Five Factor

Model of personality (McCrae & Costa, 1990). The researchers used four impulsivity-related

subscales from the NEO-PI-R (McCrae & Costa, 1990), a measure that is used to assess the Five

Factor Model of personality, to begin to construct a multi-factorial conceptualization of the ways

in which an individual can come to behave impulsively (Lynam & Miller, 2004; Whiteside &

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Lynam, 2001). The five factors of personality as proposed by the Five Factor Model include

Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness (McCrae & Costa,

1990). Whiteside and Lynam argued that the four traits that map onto three of the five

overarching personality factors provide the aforementioned pathways to impulsive behavior

(Lynam & Miller, 2004; Whiteside & Lynam, 2001). The four pathways include Impulsiveness,

Self-Discipline, Excitement Seeking and Deliberation (Lynam & Miller, 2004; Whiteside &

Lynam, 2001).

Whiteside and Lynam (2001) administered a variety of self-report measures that

considered impulsivity to be a first-order construct or that contained subscales thought to tap into

impulsive-like traits to 437 college students. Items from eight instruments were administered

and included the EASI-III (Buss & Plomin, 1975), BIS-11 (Patton, Stanford & Barratt, 1995),

the I7 (Eysenck, Pearson, Easting & Allsopp, 1985), the PRF (Jackson, 1984), the MPQ

(Tellegen, 1982), the TCI (Cloninger, Przybeck & Svrakic, 1991), the SSS (Zuckerman, 1994)

and the impulsivity scales proposed by Dickman (1990). It should be noted that when

appropriate, only the scales that were thought to tap into a dimension of impulsivity were used.

Thus, in many cases, portions of a measure were not incorporated in Whiteside and Lynam’s

work.

Whiteside and Lynam (2001) performed an exploratory factor analysis that generated a

four factor model of impulsivity. This four factor model accounted for 66% of the variance (see

also Lynam & Miller, 2004). The four factors mapped onto the four traits from the NEO-PI-R

(McCrae & Costa, 1990) and were renamed Urgency, Premeditation, Perseverance and Sensation

Seeking. Urgency, derived from the Impulsiveness trait is associated with rash actions while

experiencing a negative mood. It should be noted that in relation to Premeditation and

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Perseverance, these scales imply a lack of these traits due to the overarching construct they are

thought to be measuring, and they are derived from the Deliberation and Self-Discipline traits,

respectively. Sensation Seeking is the tendency to seek out new or novel situations and

experiences and is derived from the Excitement Seeking trait. The resulting measure was the

UPPS Behavior Scale that uses 45 items to assess the variety of ways in which an individual can

come to behave impulsively (Whiteside & Lynam, 2001; see also Lynam & Miller, 2004).

After the development of the UPPS (Whiteside & Lynam, 2001), Cyders et al. (2007)

explored the possibility of a fifth factor that could lead to impulsive behavior, Positive Urgency.

The proposed factor did not originate from any of the NEO-PI-R (McCrae & Costa, 1990)

subscales. The notion of the fifth factor came from accumulating research that positive mood

may increase the likelihood to engage in risky behaviors for some individuals. On the surface

level, the proposed factor of Positive Urgency appears to be strikingly similar to that of, what is

now termed, Negative Urgency which is the tendency to engage in risky behavior as a result of a

negative mood or state. In order to determine if a Positive Urgency factor would indeed be

useful to the conceptualization of impulsivity, Cyders et al. first constructed a Positive Urgency

subscale, originally termed the Positive Urgency Measure (PUM), to assess the proposed trait,

which resulted in a 14 item subscale. Cyders et al. then tested it alongside the UPPS dimensions

(Whiteside & Lynam, 2001) using exploratory factor analysis to determine whether the

hypothesized five factors would emerge. Exploratory factor analysis confirmed the presence of

five separate factors (Cyders et al., 2007). Additionally, using hierarchical regression analyses,

Cyders et al. found that the Positive Urgency factor explained significantly more variance in a

college sample’s risky behavior than the four factors from the UPPS (Whiteside & Lynam, 2001)

alone. Their research supports the inclusion of this scale as a measure of impulsivity. The

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culmination of the UPPS (Whiteside & Lynam, 2001) and the Positive Urgency subscale created

what is now known as the UPPS-P (Lynam, Smith, Whiteside & Cyders, 2006).

Caffeine and Impulsivity

A study by Jones and Lejuez (2005) compared impulsivity, sensation seeking and risk

taking with caffeine use and dependence. Their sample included 60 college students who were

prescreened to exclude abstainers. Based on their level of consumption and dependence, each

participant was placed into one of two groups. One division included those who were considered

high consumers. These students met criteria for dependence, modified for caffeine. The other

participants were considered low consumers. These persons did not meet criteria for

dependence. Each group consisted of 30 students, 15 of each gender. The screening process was

used in participant selection to obtain the group makeup. Self-report measures were used to

assess impulsivity and sensation seeking. These measures included a shortened version of the

Sensation Seeking Scale (Zuckerman, Eysenck, & Eysenck, 1978) and the Eysenck

Impulsiveness Questionnaire (I7; Eysenck, Pearson, Easting, & Allsopp, 1985). Risk taking was

measured behaviorally using the BART (Lejuez et al., 2002).

According to Jones and Lejuez (2005), the two groups did not significantly differ in

terms of their risk taking. Nonetheless, their initial investigation revealed that impulsivity and

sensation seeking were related to caffeine consumption and dependence. That is, those scoring

higher on both dependence and consumption also scored higher on the scales of impulsivity and

sensation seeking. The experimenters then entered the two variables into a logistic regression.

This analysis revealed sensation seeking to be the only significant factor. Although, Jones and

Lejuez reported that there was evidence of multicolinearity in that sensation seeking and

impulsivity were highly correlated. When multicolinearity is present in a regression model, the

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resulting coefficients are not readily interpretable. The unclear association between caffeine

consumption and impulsivity as exemplified by the results reported by Jones and Lejuez

highlight the importance of examining these variables using factors that are only moderately

correlated.

Other research has sought to explore personality in relation to caffeine use while being

keenly aware of smoking’s possible interacting effects. In a study by Gurpegui et al. (2007), one

objective was to investigate the relationship between caffeine and personality. The authors

statistically controlled for smoking. According to Gurpegui et al., previous research has shown

smoking to be associated to various personality factors as well as caffeine intake. The authors

argue that due to these findings it is necessary to control for smoking behavior in order to

examine the effects of personality on caffeine use. The experimenters recruited 498 individuals

as participants in their study. Their sample included individuals from the city of Granada, Spain.

The average age of their participants was 45.1 years. Thus, the results obtained from their

research may be difficult to generalize to other populations (i.e., college students). Gurpegui et

al. used the Temperament and Character Inventory (TCI-125; Cloninger, et al., 1994) as a

measure of personality. They assessed, via self-report, demographic information, consumption

of caffeine and smoking behavior.

Gurpegui et al. (2007) did not find a significant relation between personality and general

caffeine intake. However, they did conclude that high consumption of caffeine was associated

with novelty seeking. The subscales of novelty seeking were further analyzed to determine that

only impulsivity correlated with high caffeine consumption. Their research has demonstrated

that caffeine use may be related to personality, especially for those who are high consumers.

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A Model of Augmented Caffeine Consumption

The current research proposes a model of increased caffeine consumption that is

hypothesized to be a result of high trait impulsivity. This model is based on literature concerning

impulsive populations and dopaminergic differences in these populations. The proposed model

hypothesizes that those higher on impulsivity dimensions will be augmented caffeine consumers.

The basic premise is that there may be a dysfunction in dopaminergic functioning in those

individuals who score high on measures assessing trait impulsivity and through consumption of

large amounts of caffeine, in an attempt to self-medicate, some of the impulsive symptomology

would be alleviated through indirect increases in dopamine by inhibitory adenosine receptor

binding.

Impulsive Populations

In his review, Steinberg (2008) discusses the general consensus within the literature that

adolescents and young adults seem to be more likely to engage in risky behaviors that may result

in harm either to the self or others. Steinberg (2008) proposes that the differential development

of two interacting systems in the brain may explain why risky behavior is more prevalent during

adolescence (see also Steinberg, 2010). In Steinberg’s (2010) dual systems model, the central

tenet is that there are two systems within the adolescent brain that are developing at different

rates and along different timelines. One such system that Steinberg (2008) terms the “socio-

emotional” system is linked to reward-seeking and consists of structures such as the amygdala,

ventral striatum, and medial prefrontal cortex. During adolescence, dopamine activity changes

drastically, and it is these changes that are thought to influence the increases in reward-seeking

behavior observed during adolescence (Steinberg, 2008; Steinberg, 2010). Another system that

Steinberg (2008) terms the “cognitive control” system is also implicated in adolescence risk

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taking (see also Steinberg, 2010). This system is associated primarily with the prefrontal cortex

and is responsible for impulse control. During adolescence, this system is relatively immature

relative to the socio-emotional system. One of Steinberg’s (2010) hypotheses is that the

development of the socio-emotional system follows a curvilinear time course, decreasing after

adolescence. However, Steinberg (2010) asserts that the cognitive control system follows a

linear pattern, with self-control and regulation increasing with age. Steinberg (2010)

hypothesizes that it is the interaction of these two systems and their degree of maturity during

adolescence that results in the prevalence of adolescent risky behavior and also explains the

subsequent decreases in risky behavior that are observed in adults.

Another model that attempts to explain adolescent risk-taking behavior was proposed by

Ernst, Pine & Hardin (2006) and is termed the triadic model. According Ernst et al. (2006), “the

passage through adolescence is characterized by typical patterns of motivated behavior, namely

risk-taking, sensation/novelty/reward seeking, and impulsivity” (p. 300). Differing from

Steinberg’s (2008; see also Steinberg, 2010) conceptualization, Ernst et al. propose that it is the

interaction of three neural systems that result in these types of behaviors observed during this

developmental time period. One system includes what the authors term the “approach (reward-

driven)” system whose structure of primary concern is the ventral striatum, specifically the

nucleus accumbens. The neurotransmitter dopamine is thought to be of particular importance in

relation to goal-directed behavior in this system. Another system is the “avoidance (harm-

avoidant)” system whose structure of primary importance is the amygdala. The third system

that the authors term the “regulatory” system includes circuits of the prefrontal cortex and is

thought to mediate the relative influence of the aforementioned approach and avoidance systems.

Ernst et al. “. . . propose that adolescence is the period during which the activity of the reward

24

system prevails over that of the avoidant system while the still immature regulatory system fails

to adaptively balance these two behavioral controllers” (2006, p. 303).

The underlying theme of these two models in relation to adolescent risk taking is that

there is an imbalance between systems that involve, especially reward and impulse control.

These models propose that it is the developmental time course and relative maturity of these

systems that results in an imbalance which contributes to the increased likelihood of adolescent

risky behavior. It is the combination of a relatively weak or underdeveloped regulatory system,

which is responsible for inhibiting responses, and an overactive reward-driven or goal oriented

dopaminergic system. Despite the disparity in relation to the number of systems involved, the

prevailing view is that dopaminergic and regulatory control systems perpetuate risky and

impulsive behaviors when imbalanced or underdeveloped.

Another population that is often considered to be impulsive is those individuals

diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). According to the Diagnostic

and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association,

2000), a diagnosis of ADHD is characterized by developmentally excessive inattention,

hyperactivity or impulsivity that persists for 6 months. There are a total of 18 criteria that are

divided between two key domains, namely, inattention and hyperactivity-impulsivity. A

diagnosis, of which there are three subtypes, is warranted only if at least six symptoms are

present in at least one of the domains (American Psychiatric Association, 2000). Although

ADHD is typically equated with pediatrics and adolescents, there is increasing evidence to

suggest that the disorder continues into adulthood. As such, increasing numbers of adults are

receiving treatment for the disorder (Advokat, 2009). Psychopharmacological treatment for

individuals diagnosed with ADHD is often stimulants, such as methylphenidate and

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amphetamine (Advokat, 2009; Tang, Wanchoo, Swann & Dafny, 2009). These psychostimulants

increase levels of dopamine in the brain (Tang et al., 2009). Despite the plethora of research on

the topic, a consensus as to why psychostimulants alleviate symptomology is unclear. However,

given that stimulants improve functioning in these individuals and that these drugs increase

dopaminergic transmission, it is reasonable to infer that dopaminergic systems within the brain

may be dysfunctional in this population as well.

Dopamine and Adenosine

According to Lodge, Buffalari and Grace (2009), there are three proposed dopamine

systems or pathways within the brain. Two of which are thought to contribute substantially to

the addiction process and include the mesolimbic and the mesocortical dopamine systems

(Goldstein, Alia-Klein, & Volkow, 2009; Heatherton & Wagner, 2011). The mesolimbic system

has its origins in the ventral tegmental area and projects to various structures that are thought to

regulate affect including the ventral striatum, nucleus accumbens, hippocampus and the

amygdala. This system is thought to play a major role in drug use. The mesocortical system also

has its origins in the ventral tegmental area but its projections terminate in the frontal cortex.

Specifically, this system includes prefrontal cortex, orbitofrontal cortex and the anterior

cingulate. This system is thought to be implicated in higher order functioning, such as, executive

control.

Caffeine increases the availability of the neurotransmitter dopamine in those areas of the

brain where dopamine is abundant (Brunyé, Mahoney, Lieberman & Taylor, 2010). The

mechanism by which caffeine is responsible for increases in dopamine activity and other

neurotransmitter activity such as norepinephrine and glutamate is the adenosine receptors,

specifically A1 and A2A. Caffeine acts as an antagonist at these receptor cites, which ordinarily

26

(i.e., in the absence of caffeine binding) act as inhibitory agents to the production of dopamine,

norepinephrine and glutamate. It is speculated that when caffeine binds to the A1 and A2A

receptors, it inhibits these receptors and prevents adenosine from binding. This inhibitory

process leads to an increase in dopamine in dopamine concentrated areas of the brain. That is,

adenosine, when bound to A1 and A2A receptors inhibits the transmission of dopamine. By this

action, caffeine increases dopamine availability through antagonistic actions (Brunyé et al.,

2010). It is this process of binding to A1 and A2A receptors that is thought to influence the

increases in cognitive stimulation and alertness produced when caffeine is ingested (Brunyé et

al., 2010).

Self-Medication Hypothesis

One predominant model of substance dependence is the Self-Medication Hypothesis

(Khantzian, 1985). In 1985, Edward J. Khantzian outlined a model of substance dependence

whose central tenet was that predisposed individuals seek out specific drugs in order to “self-

medicate” or temporarily relieve negative affective states and/or psychopathology. Specifically,

Khantzian’s (1985) model, which he has coined the Self-Medication Hypothesis, assumes that

individuals who become dependent on specific drugs do so as a result of the pharmacological

actions of the drugs and their interplay with affective and psychopathological states. The

principal assumption of the model is that dependence on a specific substance does not occur

randomly. Rather, predisposed individuals may try a variety of substances before their “drug-of-

choice” (Wieder & Kaplan, 1969) is defined. An assumption of the Self-Medication Hypothesis

(Khantzian, 1985) is that the predisposed individual develops a preference for a specific drug due

to its pharmacological actions that are believed to relieve the individual’s disturbing and

27

uncomfortable affective and psychopathological states which perpetuate continued use and

eventual dependence.

Khantzian (2003) explains that individuals who become addicted to a particular drug do

not set out with the intention of becoming addicted. Instead, these individuals are more likely to

experiment with a variety of drugs, some of which may produce more aversive as opposed to

euphoric states as a result of the pharmacological action of the drug and its interaction with their

affective state. That is, depending on the individual’s affective state or psychopathology in

which they are attempting to medicate, certain drugs may potentiate the problem as opposed to

relieve it. Under the Self-Medication Hypothesis (Khantzian, 1985), those drugs which alleviate

psychiatric symptomology or aversive affective states would be more likely to be used with

increasing frequency and therefore, dependence on that particular drug may develop.

Despite the emphasis on psychodynamic theory, Khantzian’s (1985) principal tenet is that

an individual’s drug of choice is chosen for its pharmacological action which effectively relieves

dysphoric, uncomfortable affective states and psychopathology. According to this theory, it is

not the case that individuals seek altered realities, but that they are medicating themselves to

experience an essentially “normal” state that they feel they would not be able to obtain

otherwise.

Summary

The current research proposes a model of augmented caffeine consumption. The central

premise is that impulsive populations may exhibit a dopaminergic deficit, which, in turn, leads to

impulsive behavior. Grounded in the Self-Medication Hypothesis (Khantzian, 1985), the current

research speculates that the dysfunction in dopamine systems in the brain leads to dysphoria

which the individual attempts to relieve. Given that psychostimulants are often prescribed to

28

treat ADHD, impulsive populations may consume increased amounts of caffeine, especially

given its abundant availability in comparison to other psychostimulants.

Hypotheses

Until the endeavor undertaken by Heinz et al. (2009) no previous research had examined

caffeine expectancies using a measure that is similar to those found in the alcohol literature.

Furthermore, any research that had sought to measure caffeine expectancy did so using a

simplified self-report of the construct. That is, they assumed it to be one-dimensional. The

Caffeine Expectancy Questionnaire (Heinz et al., 2009) is a dimensional approach to measuring

caffeine expectancies. The division of caffeine expectancy into four factors is a new concept in

the field of caffeine research. It would be beneficial to use the Caffeine Expectancy

Questionnaire to examine expectancies held by participants and to further establish the

usefulness of the measure. The present research examined caffeine expectancies held by subjects

using the Caffeine Expectancy Questionnaire as developed by Heinz et al. (2009). Thus, the

current study provided additional data relative to the usefulness of the Caffeine Expectancy

Questionnaire and examined the predictive ability of caffeine expectancies in relation to caffeine

consumption. The present research hypothesized that out of the four factors of caffeine

expectancy, the Withdrawal Symptoms subscale would be the best predictor of caffeine intake.

That is, those indicating more withdrawal symptoms were thought to be more likely to be high

consumers of caffeine. This relation was expected due to the probability that those who are

addicted to caffeine will experience withdrawal symptoms on a daily basis, and therefore, will

consume more caffeine to alleviate these symptoms.

Given that there are mixed results concerning the relationship between impulsivity and

caffeine consumption, one aim of the present study was to further examine the relationship

29

between self-reported caffeine consumption and the construct of impulsivity using the UPPS-P

(Lynam et al., 2006). It was thought that measuring impulsivity using the UPPS-P (Lynam et

al., 2006) would provide a dimensional approach to the assessment of the construct. Further, the

measure has been applied in other research examining nicotine (Spillane, Smith & Kahler, 2010),

which has also been found to be related to caffeine use (Gurpegui et al., 2007). No other

research has used the UPPS-P (Lynam et al, 2006) to examine the association between caffeine

use and impulsivity. The current study hypothesized that impulsivity would be related to

caffeine consumption. Specifically, it was predicted that sensation seeking would be a good

predictor of caffeine consumption.

GENERAL METHOD

Overview

The current study was divided into two phases. Study 1 piloted a caffeine consumption

instrument. Study 2 was longitudinal and asked volunteers to return in exactly one week to

complete all measures excluding those that would provide redundant information (i.e.,

demographics and contact information under the guise of code names). The current study

measured caffeine consumption using the instrument that was piloted in Study 1 to determine the

predicative ability of caffeine expectancies and the personality construct of impulsivity.

STUDY 1

The main objective of Study 1 was to establish the usefulness of a modified version of the

Caffeine Consumption Questionnaire (Landrum, 1992), henceforth referred to as the Pilot CCQ.

In order to understand its usefulness and how to proceed for Study 2, the amount of time required

to complete the measure, its level of difficulty and its ability to represent the most common

vehicles available to persons likely to encompass the sample (i.e., college students) needed to be

30

understood. With these goals in mind, the Pilot CCQ and a series of questionnaires were

administered to students attending a southeastern university during the Spring semester of 2010.

Method

Participants

The original sample size included 100 students; however, data from three participants

were excluded due to preselected age restrictions. Analyses were conducted on data obtained

from 97 students between the ages of 18 and 30 years. Those that indicated that they were 21 or

younger made up 92.8% of the sample (M=19.35; SD=1.633). The majority of the sample,

63.9%, indicated that they were of freshmen status, 15.5% were college sophomores, 15.5%

were juniors, and the remaining 5.2% were seniors. Most of the sample was females,

representing 72.2% (n = 69). Males made up the other 27.8% of participants (n = 25).

Participants describing themselves as White or Caucasian made up 81.4% of the sample. Those

indicating that they were of African American decent included 4.1% while 3.1% specified that

they were of Asian/Pacific Islander decent. Most students who volunteered were enrolled in an

Introductory Psychology course. Participation was completely voluntary. Each person received

one research credit as compensation.

Measures

In order to better describe the sample, an instrument that would measure participants’

characteristics was needed. To meet this need, a demographics questionnaire that asked

participants to provide their gender, age, height, weight, ethnicity, educational level and marital

status was developed. Additionally, the current research asked participants to describe their

physical health as either poor, fair or excellent. Participants’ frequency of use of other

substances was also examined which included the use of non-prescribed drugs (16 items) and

31

prescribed substances (10 items) over the past three months using a 7-point likert scale that

ranged from 0 (Never) to 6 (Almost Every day). The format used for assessing other substance

use was derived from the Quantity Frequency Index that has been commonly used in alcohol-

related research (Cahalan, Cisin & Crossley, 1969).

To measure caffeine consumption, the current study developed the Pilot CCQ. The

modified version of Landrum’s (1992) measure consisted of an extensive list of caffeine

containing products. All of the original categories were incorporated, including Coffee, Tea,

Coca, Chocolate Milk, Soft Drinks, Over-the-Counter Drugs and Food. To exemplify the most

commonly available size of a product in question, serving sizes were increased for every

classification except Over-the-Counter Drugs. To better represent the diversity of caffeinated

goods, items were either added or changed to five of the divisions including Coffee, Tea, Soft

Drinks, Over-the-Counter Drugs and Chocolate. Three categories were added: Ready to Drink

Coffee, Energy Drinks and Energy Shots. The Pilot CCQ assessed participants’ consumption of

a total of 89 items.

Mimicking Landrum’s (1992) work, the Pilot CCQ allowed participants to indicate what

time of day they actually consumed various caffeinated products. The hours of the day were

grouped into four sections, including Morning, Afternoon, Evening and Night. In order to

provide additional memory cues, the Pilot CCQ was separated into two parts. This division

differed from the format of Landrum’s version. The first section, Monday-Friday, asked

participants to indicate how many servings of each caffeinated product that they consumed

during the typical weekday. The second portion, Saturday-Sunday, examined the consumption

of caffeine on the typical weekend day. Each division contained identical categories and items to

assess caffeine intake by participants. The difference between the two sections was the part of

32

the week that the participants were asked to consider for their answers. The Pilot CCQ provided

two average estimates of consumption. The weekday estimate was multiplied by 5, and the

weekend day estimate was multiplied by 2. These products, added together, created an average

weekly consumption estimate, in milligrams, for any given participant.

Throughout the measure, participants were provided with room to write suggestions,

comments and items they would add. The end of a section was denoted with instructions and

encouragement to provide feedback regarding the research. Following the end of the Monday-

Friday section of the Pilot CCQ, another segment of the measure addressed various caffeine

related topics. Participants were asked to indicate any times during which they abstained from

using the substance. Further, they described themselves in terms of their level of consumption

and whether they should lower, increase or do nothing about it. This section also prompted

participants to indicate any substances that they used in combination with caffeine. Withdrawal

symptoms and caffeine intoxication were also assessed.

Caffeine expectancies were measured with a relatively new self-report instrument.

Using exploratory factor analysis, Heinz, Kassel and Smith (2009) developed the first

multidimensional Caffeine Expectancy Questionnaire. The use of the Caffeine Expectancy

Questionnaire allowed for analysis of four factors and their relation to caffeine consumption.

These factors included Positive Effects, Acute Negative Effects, Withdrawal Symptoms and

Mood Effects. The Positive Effects and Withdrawal Symptoms scales were measured using 11

items each. The Acute Negative Effects scale contained eight items. Mood Effects were

measured with a total of seven items. Items were rated using a 4-point likert scale ranging from

1 (Strongly Disagree) to 4 (Strongly Agree). The original version of the Caffeine Expectancy

Questionnaire was administered to all participants; however, three items were added. Two of the

33

additional items assessed the expected use of caffeine during a woman’s premenstrual cycle and

throughout the entire menstrual period. The third additional item assessed the expectation held

by participants for caffeine to sober them up.

Procedure

In Study 1, the demographics questionnaire, the Pilot CCQ and the Caffeine Expectancy

Questionnaire (Heinz et al., 2009) were administered. The measures were completed in groups

of no larger than five participants. The room contained several seating options, including, two

sofas, two student desks and one recliner. There was a one-way mirror that was used to

randomly monitor the two research assistants as they administered the study. Study 1 was

completely anonymous. At the beginning of each session, an experimenter read the rights of

volunteers participating in research aloud to the group before handing out the demographics

questionnaire, which contained the same statement of participants’ rights. Upon completion of

the demographics portion, the students were asked to place their form in a manila folder that was

provided for each individual. After everyone in the group had finished, the research assistant

read the instructions aloud for the next questionnaire. Administration of the Pilot CCQ and the

Caffeine Expectancy Questionnaire continued in this manner. After all measures were

completed, the participants were then asked to answer a series of questions that were presented

orally by a research assistant. These questions addressed the difficulty of the Pilot CCQ, items

that could be added or changed, and any other suggestions. The participants were asked to

contemplate each question and write down their responses. Afterwards, the research assistants

encouraged the group to discuss their answers orally while one of the experimenters recorded

their responses on a separate form.

34

Results

Descriptive statistics and further analyses were performed using PASW statistics

software version 18.0. Males were between the ages of 18 and 23 (M=19.22; SD=1.34). Their

self-reported weekly consumption of caffeine averaged at 4,101 mg. Female participants were

between the ages of 18 and 30 (M= 19.40; SD= 1.74). Females consumed, on average, 3,398 mg

of caffeine weekly. Table 1 contains means and ranges of relevant weekly caffeine consumption

estimates by gender.

Independent samples t-tests revealed that there were no significant differences between

males and females in terms of total weekly caffeine consumption, t (92) = 0.87, p = .387

Likewise, independent means t-tests revealed that there were no significant differences between

males and females in terms of weekly morning caffeine consumption, t (92) = -0.43, p = .669.

Independent means t-tests between males and females also revealed that there were no

significant differences between males and females in terms of weekly evening caffeine

consumption, t (92) = 1.68, p = .097.

Internal consistency for the subscales of the Caffeine Expectancy Questionnaire (Heinz et

al., 2009) was assessed. The reliability coefficient, Cronbach’s alpha, exceeded .80 for all four

subscales, suggesting that the items that make up each subscale appear to assess the same

construct. These results reflect the reliability coefficients reported by Heinz et al. (2009).

Means, standard deviations and reliability coefficients for the Caffeine Expectancy

Questionnaire are reported in Table 2.

35

Table 1

Weekly Caffeine Consumption Estimates by Gender in Study 1

Portion of the Week Range M SD

Malesa

Total Weekly Consumption 126 – 18,282 4,101 3,938

Weekly Morning Consumption 30 – 15,158 2,180 2,962

Weekly Evening Consumption 0 – 7,228 1,920 1,957

Femalesb

Total Weekly Consumption 0 – 14,923 3,398 3,283

Weekly Morning Consumption 0 – 36,620 2,614 4,723

Weekly Evening Consumption 0 – 9,728 1,251 1,614

Note. Minimum, Maximum and Mean values are round to the nearest milligram. a n = 25. b n = 69.

36

Table 2

Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 1

Subscale Items M SD α

Acute Negative Effects 8 15.48 4.56 .84

Positive Effects 11 25.81 5.84 .84

Withdrawal Symptoms 11 18.53 6.55 .91

Mood Effects 7 12.80 3.68 .81

Note. 1 = Strongly Disagree 2 = Disagree 3 = Agree 4 = Strongly Agree

37

Discussion

The pilot study provided qualitative information as to the appropriateness of each of the

items on the questionnaire. Participants had the opportunity to discuss changes, difficulties and

additions to the Pilot CCQ. Most individuals described the Pilot CCQ as being low in level of

difficulty. Furthermore, these discussions highlighted the appropriateness of the items contained

on the questionnaire. Most participants indicated that the items presented represented the

vehicles available for consumption. Although most individuals indicated that the questionnaire

was complete, there were instances in which other possible vehicles of consumption were

suggested. Some of the products contained caffeine and some products did not. Thus, one

change that was made to the questionnaire included the option, for each category, to write in a

product and the corresponding servings consumed. This option was added to each category and

contained the same elements as every item. Thus, additional items could be quantified if the

caffeine content was available to the public. Additionally, Study 1 also highlighted the need to

emphasize, visually, correct and incorrect methods of completing the questionnaire.

Specifically, several participants attempted to fill in zeros for all products that they did not

consume. Given that the questionnaire contains 89 items, this was a daunting task. Thus,

another change that was needed included visual examples of the completion of the questionnaire

using Microsoft PowerPoint 2007.

STUDY 2

Similar to Study 1, an objective of Study 2 was to continue to establish the usefulness of

a modified version of the CCQ (Landrum, 1992). The version used in Study 2 was referred to as

the CCQ 2010. Study 2 was longitudinal, and participants were asked to complete measures on

two occasions separated by exactly one week. Most volunteers were students enrolled in an

38

Introductory Psychology course at a southeastern university. They were asked to come to a

classroom or a lab for both portions during the Fall 2010, the Spring 2011 and the Fall 2011

semesters.

Method

Participants

The original sample size included 323 students. However, data from a total of 11

participants were omitted: three exceeded preselected age restrictions, four had extreme values

on the dependent measure that exceeded four standard deviations above the mean, and four

incorrectly completed the dependent measure and their weekly caffeine consumption estimate

could not be quantified. Analyses were conducted on data obtained from 312 participants

(96.6% of the original sample) that were between the ages of 18 and 29 years (M= 18.92; SD =

1.73). Those that indicated that they were 21 or younger made up 93.9% of the sample. Most of

the sample was males, representing 51.6% (i.e., 161 participants). Females made up the other

48.4% of the sample (i.e., 151 participants). The majority of participants, 70.2%, indicated that

they were of freshmen status, 18.6% were college sophomores, 6.4% were juniors, and 3.8%

were seniors. Participants who described themselves as White or Caucasian made up 85% of the

sample. Those indicating that they were of African American decent included 5.2% while 4.9%

specified that they were of Hispanic descent. The majority of the sample, 92.6%, returned

exactly one week after the initial questionnaire administration (i.e., Part I) for another

administration of questionnaires (i.e., Part II). Participation was entirely voluntary. As

compensation, each person received two research credits which fulfilled their need for the

semester.

39

Study 2 required protective measures to ensure that confidentiality was maintained. The

students that volunteered for Study 2 were asked to return to provide longitudinal information.

As a result, it was necessary to match the measures that were completed on the first occasion

with those completed on the second occasion for each student. To ensure confidentiality,

volunteers were asked to make up a code name that was to be used in place of their real

identification. Participants were informed that their code could not be offensive or obscene in

nature. The only form on which participants wrote their real name was the informed consent.

Each participant signed two copies of the informed consent, one of which a research assistant

kept in a separate manila envelope that was intended for the entire group, and the other the

participant kept for their own records. The participants’ code names and real names were never

matched. The students were asked to provide a telephone number that they could be reached in

the week to come. A master list that contained the code names and respective telephone

numbers was kept in a locked cabinet and was only available to the primary investigators. The

participants were forewarned to expect a call from one of the primary investigators who asked

for them by their code name to remind them of their testing appointment. The room where the

locked cabinet was located was also locked and only those with explicit permission were allowed

to enter.

Measures

Study 2 used the same demographics questionnaire that was developed for Study 1. The

instrument assessed the following participant characteristics: gender, age, height, weight,

ethnicity, education level and marital status. As in Study 1, the demographics portion asked

participants to indicate their level of physical health as either poor, fair or excellent. One

difference was the ability to indicate veteran status. Each volunteer was also asked to specify

40

their form of nicotine intake, if applicable. Another difference was that the demographics

portion no longer included frequency of use of other substances due to the full administration of

the Quantity Frequency Index (Cahalan et al., 1969) which contained this information.

The current study created a questionnaire that addressed various caffeine related topics.

Specifically, participants were asked to indicate how many milligrams of caffeine and cups of

coffee they consumed weekly. Participants were also asked to indicate any times during which

they abstained from using caffeine. Further, they described their level of consumption as to

whether they felt that it should be lowered, increased or if it was about right. This section also

prompted participants to indicate any substances that they used in combination with caffeine

specifically. Withdrawal symptoms and caffeine intoxication were also assessed using a 5-point

likert scale, ranging from 0 (Never) to 4 (Very Often). This questionnaire also included a section

specifically for females that assessed the current week in their menstrual cycle.

Caffeine consumption was measured using a modified version of the Pilot CCQ. This

version was referred to as the CCQ 2010. The measure included all 89 items presented in Study

1 and two additional items, the inclusion of which was determined by the statements and

suggestions made by participants in Study 1. The option to write the name of a possible caffeine

containing product, not listed, was added to each category to allow participants to indicate any

possible caffeine containing vehicles they may have consumed. The caffeine content of any

additional products that a particular individual indicated they consumed was added to the

participants’ consumption estimate if it was available to the general public.

The CCQ 2010 followed the same format as the pilot version in that it was divided into

two parts that assessed Monday-Friday and Saturday-Sunday consumption. However, the

separation between the two sections did not include additional questions regarding caffeine use

41

(i.e., withdrawal and intoxication symptoms). These issues were addressed using a separate

questionnaire. As in Study 1, participants were asked to complete the CCQ 2010 based on a

typical week’s consumption. Instructions for completing the instrument were written on the

form as well as presented visually using Microsoft PowerPoint 2007. Correct and incorrect

examples of completed portions of the questionnaire were provided. The visual aid also

demonstrated pictured examples of serving sizes for various products. The CCQ 2010 was used

to produce a typical week’s average estimate of caffeine consumption in milligrams.

The second administration asked participants to complete a similar version of the CCQ

2010. The entire format of the CCQ 2010 presented on the second administration was the same

except caffeine consumption was referenced to the previous week. The second administration

did not include a visual aid.

As in Study 1, the Caffeine Expectancy Questionnaire developed by Heinz et al. (2009)

was used to determine the relationship between expectancies and caffeine consumption. The

instrument allowed for analysis of four factors, which included Positive Effects, Acute Negative

Effects, Withdrawal Symptoms and Mood Effects. The Positive Effects and Withdrawal

Symptoms scales were measured using 11 items each. The Acute Negative Effects scale

contained eight items. Mood Effects were measured with a total of seven items. Items were

rated using a 4-point likert scale ranging from 1 (Strongly Disagree) to 4 (Strongly Agree). The

same modification that was made in Study 1 was used in Study 2. That is, three items were

added that addressed using caffeine during the menstrual cycle and the use of caffeine to

counteract the effects of alcohol.

The personality construct of impulsivity was measured by using the UPPS-P (Lynam et

al., 2006). This instrument allows for a dimensional approach to defining impulsivity. It is a

42

self-report measure that assesses the construct based on five subscales: Positive Urgency,

Negative Urgency, Sensation Seeking, Perseverance and Premeditation. The instrument contains

a total of 59 items which are rated using a 4-point likert scale, ranging from 1 (Extremely

Uncharacteristic of me) to 4 (Extremely Characteristic of me). Negative Urgency and Sensation

Seeking are comprised of 12 items each. The subscale, Negative Urgency, is associated with

rash actions while experiencing a negative mood. Positive Urgency relates to the tendency to

engage in reckless behavior in response to a positive mood and is assessed using 14 items.

Perseverance contains 10 items. Premeditation is measured using 11 items. All five subscales

were employed in the current research to further examine the relationship between caffeine

consumption, caffeine expectancies and impulsivity.

The Eysenck Impulsiveness Questionnaire (I7; Eysenck et al., 1985) was used as another

measure of impulsivity due to the relatively recent development of the UPPS-P (Lynam et al.,

2006). The I7 (Eysenck et al., 1985) consists of 54 dichotomous yes/ no items that make up three

subscales: Venturesomeness, Impulsiveness and Empathy. While Venturesomeness and

Impulsiveness are thought to tap into the construct of impulsivity, Empathy was included to add

variety to the questions asked (Eysenck, 1993).

The Quantity Frequency Index (Cahalan et al., 1969) was used to assess quantity and

frequency of alcohol use during the last 90 days. Frequency and amount were assessed for three

vehicles of alcohol consumption (i.e., hard liquor, wine and beer). The participants’ frequency

of other substances was also assessed. Other substances that were examined included the use of

non-prescribed drugs (i.e., 16 items) and prescribed substances (i.e., 10 items) over the past three

months using a 7-point likert scale that ranged from 0 (Never) to 6 (Almost Every day).

43

Procedure

In Study 2 several self-report questionnaires were administered which included an

informed consent form, the demographics questionnaire, the general caffeine use questionnaire,

the Caffeine Expectancy Questionnaire (Heinz et al., 2009), the CCQ 2010, the I7 (Eysenck et

al., 1985), the UPPS-P (Lynam et al., 2006), and the Quantity Frequency Index (Cahalan et al.,

1969). Participants were tested in groups no larger than 20 in a classroom or in groups no larger

than seven in a laboratory setting. Participants in Study 2 were asked to physically attend two

sessions that were separated by exactly one week. Study 2 was confidential and required that the

students receive and sign an informed consent form which explained their rights as a research

participant and the confidential nature of the study. To aid in further understanding and accuracy

when completing each measure, the presentation of the instructions for all measures during Part I

was both oral and visual. Microsoft PowerPoint 2007 was used as a visual aid. Upon

completion of each questionnaire, the students were asked to place their form in a manila folder

that was provided for each individual. After everyone in the group had finished, the research

assistant read the instructions aloud for the next questionnaire. Administration of all measures

continued in this manner. Part II required participants to complete several measures for a second

time, including a general caffeine use questionnaire, the Caffeine Expectancy Questionnaire

(Heinz et al., 2009), the CCQ 2010, the I7 (Eysenck et al., 1985), the UPPS-P (Lynam et al.,

2006), and the Quantity Frequency Index (Cahalan et al., 1969). Directions for the general

caffeine use questionnaire and the CCQ 2010 explicitly referenced the previous week. All other

directions were the same, with the exclusion of the visual aid. Questionnaires given in Part II

were administered as a packet that participants were to give to the research assistant when

complete.

44

Results

Descriptive statistics and further analyses were performed using PASW statistics

software version 18.0. The current study assessed mean age and caffeine consumption by

gender. Males were between the ages of 18 and 29 (M=19.04; SD=1.94). Their self-reported

weekly consumption of caffeine averaged 3,111 mg. Female participants were between the ages

of 18 and 27 (M= 18.79; SD= 1.47). Their self-reported weekly consumption of caffeine

averaged 2,990 mg. Table 3 contains means and ranges of relevant weekly caffeine consumption

estimates by gender.

Independent samples t-tests revealed that there were no significant differences between

males and females in terms of total weekly caffeine consumption, t (310) = 0.36, p = .718.

Likewise, independent means t-tests revealed that there were no significant differences between

males and females in terms of weekly morning or evening caffeine consumption, t (310) = -0.24,

p = .810 and t (310) = 1.09, p = .278, respectively.

Internal consistency for the subscales of the Caffeine Expectancy Questionnaire (Heinz et

al., 2009) and the UPPS-P (Lynam et al., 2006) were assessed using the reliability coefficient

Cronbach’s alpha. Reliability coefficients for all four subscales of the Caffeine Expectancy

Questionnaire and all five subscales of the UPPS-P met or exceeded .80, suggesting that the

items that make up each subscale appear to assess the same construct. Means, standard

deviations and reliability coefficients for the Caffeine Expectancy Questionnaire (Heinz et al.,

2009) and UPPS-P (Lynam et al., 2006) are reported in Tables 4 and 5, respectively.

45

Table 3

Weekly Caffeine Consumption Estimates by Gender in Study 2

Portion of the Week Range M SD

Malesa

Total Weekly Consumption 0 - 15,231 3,111 3,181

Weekly Morning Consumption 0 – 11,521 1,856 2,091

Weekly Evening Consumption 0 – 7,989 1,254 1,462

Femalesb

Total Weekly Consumption 7 – 14,670 2,990 2,702

Weekly Morning Consumption 0 – 8,731 1,910 1,794

Weekly Evening Consumption 0 – 11,112 1,080 1,367

Note. Minimum, Maximum and Mean values are round to the nearest milligram. a n = 161. b n = 151.

46

Table 4

Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 2

Subscale Items M SD α

Acute Negative Effects 8 15.50 4.39 .86

Positive Effects 11 28.04 5.70 .85

Withdrawal Symptoms 11 19.43 6.44 .90

Mood Effects 7 14.04 3.71 .80

Note. 1 = Strongly Disagree 2 = Disagree 3 = Agree 4 = Strongly Agree

47

Table 5

Reliability Coefficients for the UPPS-P

Subscale Items M SD α

Premeditation 11 30.80 5.34 .89

Negative Urgency 12 27.74 6.14 .87

Positive Urgency 14 28.50 7.52 .93

Sensation Seeking 12 35.72 6.75 .88

Perseverance 10 29.82 4.47 .83

Note. 1 = Extremely Uncharacteristic of me 2 = Uncharacteristic of me 3 = Characteristic of me

4 = Extremely Characteristic of me

48

Pearson product-moment correlation coefficients were calculated between potential

predictor variables and the outcome variable and are presented in Table 6. Significant Pearson’s

correlations revealed a potential substance use predictor beyond the subscales of the UPPS-P

(Lynam et al., 2006) and the Caffeine Expectancy Questionnaire (Heinz et al., 2009). For males,

frequency of nicotine consumption (r = .16, p = .05) in the past three months was significantly

positively related to weekly caffeine consumption. No other demographic variables or substance

use variables were significantly correlated with weekly caffeine consumption for either males or

females. Frequency of nicotine consumption was included in further analyses. All expectancy

and impulsivity factors were included in further analyses due to the relative interest in the current

study despite the absence of significant relationships for some of these variables.

49

Table 6

Pearson Correlations with Weekly Caffeine Consumption by Gender

Predictor Males

(n=161)

Females

(n=151)

Age .06 0.10

Drinking Status -.10 .09

Nicotine Use .16* .07

Mood Effects .28*** .22**

Withdrawal Symptoms .22** .23**

Positive Effects .22** .13

Acute Negative Effects -.08 -.09

Premeditation -.17* -.01

Negative Urgency .09 -.03

Positive Urgency .07 -.00

Sensation Seeking .17~ .02

Perseverance -.06 .07

Note. Statistically significant and marginally significant results (α=0.10) are presented in bold text. ~p≤ .1; *p≤ .05; **p≤ .01; ***p≤ .001

50

In order to perform the hierarchical multiple regression analyses, the current research

used R version 2.13.1 to determine if a transformation was needed on the outcome variable

weekly caffeine consumption. A first-order multiple regression model was constructed by

entering 11 predictor variables simultaneously into the program. The 11 predictor variables

included gender, frequency of nicotine consumption in the past three months, the five subscales

of the UPPS-P (Lynam et al., 2006) and the four subscales of the Caffeine Expectancy

Questionnaire (Heinz et al., 2009). Weekly caffeine consumption was used as the outcome

variable. The construction of a first-order multiple regression model was necessary to determine

if a transformation on the response variable was needed. Due to the presence of the value zero in

the response variable, a constant of 1 was added to the response in order to run a Box-Cox

transformation. The Box-Cox transformation is an automatic procedure that determines if a

power transformation on the response is appropriate. The shift in the response was used in all

further analyses. The resulting plot from the Box-Cox suggested that a square root

transformation was needed. A square root transformation on the outcome variable weekly

caffeine consumption was used in all further analyses. No other informal or formal diagnostic

procedures were run using the first-order multiple-regression model that was constructed for the

purpose of examining the possibility of a transformation on the response variable.

The prediction of caffeine consumption by expectancies and impulsivity was examined

by performing a hierarchical multiple-regression analysis. The hierarchical multiple regression

analysis consisted of the 11predictor variables and the transformed outcome variable. The

hierarchical multiple-regression analysis was performed using the program PASW version 18.0.

For the analysis, the predictor variables were entered into the program in three blocks. At each

step, variables added to the model were entered simultaneously. The first step controlled for the

51

variables gender and nicotine frequency. The second step added the expectancy subscales from

the Caffeine Expectancy Questionnaire (Heinz et al., 2009). The final step added the impulsivity

subscales from the UPPS-P (Lynam et al., 2006).

Table 7 provides the results of the hierarchical regression analysis. The model produced

in the first step was significant and accounted for a significant amount of variance in weekly

caffeine consumption, R2 = .03, F (2, 282) = 3.67, p = .027. Specifically, those that consumed

nicotine more frequently were also higher caffeine consumers (β = .16, p= .008). The second

step in which caffeine expectancies were entered into the model accounted for a significant

amount of variance in weekly caffeine consumption above and beyond those variables entered

into the first step, ∆R2 = .15, F (4, 278)= 12.35, p < .001. Mood Effects (β = .23, p = .002) and

Withdrawal Symptoms (β = .16, p = .026) were positively related to weekly caffeine

consumption. Acute Negative Effects (β = -.18, p= .004) were negatively associated with

weekly caffeine consumption. Even with the inclusion of caffeine expectancies, frequency of

nicotine use continued to be significantly associated with weekly caffeine consumption (β = .15,

p = .01). The third step in which impulsivity variables were entered into the model did not

account for a significant amount of variance in weekly caffeine consumption above and beyond

those variables entered in the second step, ∆R2 = .01, F (5, 273) = 0.65, p = .666. Not one

impulsivity variable was significantly associated with weekly caffeine consumption. However,

frequency of nicotine consumption, Mood Effects, Withdrawal Symptoms and Acute Negative

Effects continued to be associated with greater weekly caffeine consumption.

52

Table 7

Hierarchical Regression Models with Expectancy and Impulsivity Factors as Predictors of Caffeine

Consumption Step in regression model ∆R2 F∆R2 df Step 1 Step 2 Step 3

β p-Value for β β p-Value for β β p-Value for β

Step 1 .03 3.67* 282

Gender .06 .314 -.02 .694 -.04 .565

Nicotine Use .16** .008 .15** .010 .14* .019

Step 2 .15 12.35*** 278

Mood Effects .23** .002 .25*** .001

Withdrawal Symptoms .16* .026 .17* .020

Positive Effects .05 .523 .02 .842

Acute Negative Effects -.18** .004 -.15* .018

Step 3

Premeditation .01 0.65 273 -.09 .179

Negative Urgency .04 .633

Positive Urgency -.08 .327

Sensation Seeking .01 .946

Perseverance .06 .317

Note. Regression analyses were performed using the square root transformed outcome variable, weekly caffeine consumption. Statistically significant results (α=0.05) are presented in bold text. *p≤ .05; **p≤ .01; ***p≤ .001

53

Given that for males significant and marginally significant correlation coefficients were

found between two impulsivity dimensions (i.e., Premeditation and Sensation Seeking) and

weekly caffeine consumption, the current research conducted data driven exploratory

hierarchical multiple regression analyses to determine the presence of possible interactions.

Gender was effect coded (male = -1, female =1). Scores on the continuous variables

Premeditation and Sensation Seeking were centered. Interaction terms were computed as

products of the centered continuous variables and the dichotomous variable, gender, which was

effect coded. A hierarchical multiple regression analysis was conducted to explore each

interaction term. Each regression analysis consisted of four blocks. The first step included

variables that were previously found to be significant in the full model and included frequency of

nicotine consumption, Withdrawal Symptoms, Acute Negative Effects, and Mood Effects. The

second step in the model entered gender, effect coded. The third step included the centered

impulsivity variable. The fourth step in the model entered the interaction term. A marginally

significant interaction emerged concerning gender and Premeditation, β = .11, t = 1.92, p = .055

and uniquely accounted for an additional 1.1% of the variance in weekly caffeine consumption.

To better understand this interaction, separate regression analyses were performed for males and

females. The results of these analyses indicate that only male scores on the Premeditation

subscale were uniquely related to weekly caffeine consumption, β = -.14, t = -1.94, p = .055,

such that as scores on the subscale Premeditation decrease for males, weekly caffeine

consumption increases. For females, the inverse was seen, that is, as premeditation scores

increased, weekly caffeine consumption increased. However, this positive association for

females was not statistically significant, β = .06, t = .68, p = .498. The gender by Sensation

Seeking interaction was not significant, β = -.06, t = -1.18, p = .239.

54

23

24

25

26

27

28

Week

ly C

aff

ein

e C

on

sum

pti

on

Males

Females

1 -1 Standardized Premeditation

Scores

Figure 1. Gender and Premeditation Interaction

Figure 1. Regression analyses were performed using the square root transformed outcome

variable, weekly caffeine consumption.

55

Participants’ typical weekly caffeine consumption was assessed using the CCQ 2010 in

Part I. One week later, Part II, participants were asked to complete another version of the CCQ

2010 that instructed them to reflect on the previous week, which produced an estimate of

participants’ last week caffeine consumption. To determine the degree to which participants’

self-reported typical weekly caffeine consumption was related to their self-reported last week

caffeine consumption, a Pearson product-moment correlation coefficient was calculated. The

correlation coefficient between the variables typical weekly caffeine consumption and last week

caffeine consumption was relatively high (r = .65, p < .001).

Discussion

The current study predicted that caffeine expectancies measured by the Caffeine

Consumption Questionnaire (Heinz et al., 2009) and impulsivity measured by the UPPS-P

(Lynam et al., 2006) would predict weekly caffeine consumption. Specifically, it was

hypothesized that the Withdrawal Symptoms subscale of Caffeine Expectancy Questionnaire

would relate to higher caffeine consumption. This hypothesis was supported by the hierarchical

regression analysis in the expected direction. Furthermore, based on previous research, it was

expected that the Sensation Seeking subscale would be the best predictor of weekly caffeine

consumption of the impulsivity factors, and this association was expected to be positive. This

hypothesis was not supported by the hierarchical regression analysis. Not only was sensation

seeking not a significant predictor, the impulsivity subscales did not explain significantly more

variance than the expectancy subscales and frequency of nicotine consumption. However, a data

driven exploratory hierarchical multiple regression analysis, did reveal the presence of a

marginally significant gender and Premeditation interaction.

56

SUMMARY AND CONCLUDING DISCUSSION

The current research examined multiple variables that were hypothesized to be associated

with caffeine consumption in a college sample. Currently, caffeine intake is not thoroughly

understood, especially as it relates to personality traits. Although, Landrum (1992) developed

the original CCQ with the intent that it could be used in a variety of settings which would

provide consistency across research, its modification was deemed necessary to continue the

process of an accurate portrayal of self-reported caffeine consumption. The current research

modified the CCQ (Landrum, 1992), termed the CCQ 2010, to provide a self-report instrument

to assess caffeine consumption using modern day vehicles. The implications of the use of the

CCQ 2010 are many, including the continued use of a measure to provide consistency across

research settings, thus, allowing for better comparisons in relation to caffeine consumption

especially in college samples. Furthermore, the CCQ 2010 may have practical and clinical

applications. The CCQ 2010 could be used in clinical settings to provide feedback for

individuals who may benefit from restricting their caffeine intake. For example, a literature

review conducted by Vilarim, Rocha Araujo and Nardi (2011) suggested that consumption of

caffeine by populations endorsing anxiety symptoms may exacerbate these symptoms.

Therefore, individuals diagnosed with anxiety disorders may benefit from a heightened

awareness of their weekly caffeine intake and vehicles that contain caffeine. The current study

modified a measure of caffeine consumption that not only represents the present availability of

caffeine vehicles, but also one that could be used in real-world instances where an estimate of

weekly caffeine consumption could be used to provide feedback to clients that would benefit

from a heightened awareness of their consumption of this psychostimulant.

57

In order to examine and understand caffeine consumption, it was necessary to study

caffeine expectancies. The current research used a relatively new instrument for the assessment

of caffeine expectancies, the Caffeine Expectancy Questionnaire (Heinz et al., 2009), thus,

providing a clearer picture of the caffeine consumption and expectancy phenomenon. The

present study extends the literature on caffeine expectancies by demonstrating, through

hierarchical multiple regression analysis, their ability to predict self-reported weekly caffeine

intake in a college sample. The first hypothesis of the current study was that caffeine

expectancies as measured by the Caffeine Expectancy Questionnaire (Heinz et al., 2009) would

predict caffeine consumption. The results from the hierarchical multiple regression analysis

performed in Study 2 support this hypothesis. On the second step of the hierarchical multiple

regression analysis the four caffeine expectancy subscales were entered simultaneously and

accounted for a significant amount of variance above and beyond frequency of nicotine

consumption and gender. Specifically, it was predicted that those scoring higher on the

Withdrawal Symptoms subscale would also be higher caffeine consumers. This hypothesis was

also supported. The Withdrawal Symptoms subscale was uniquely associated with higher

caffeine consumption. Predictions regarding the relative contributions of the other expectancy

factors were not generated and this information adds to the literature on caffeine expectancies.

The Mood Effects subscale was positively related to weekly caffeine consumption, whereas the

Acute Negative Effects subscale was negatively associated to weekly caffeine consumption. The

positive association between the Mood Effects subscale and weekly caffeine consumption

suggests that endorsing greater expectancies for positive mood when consuming caffeine

predicts greater weekly caffeine consumption. On the other hand, the negative association

between the Acute Negative Effects subscale and weekly caffeine consumption suggests that

58

endorsing greater expectancies for unpleasant effects such as jitteriness predicts lower weekly

caffeine consumption. The Positive Effects subscale was not uniquely related to weekly caffeine

consumption. The results regarding the relationship between the expectancy factors and weekly

caffeine consumption provide new information as to how these concepts are related, especially

given that the Caffeine Expectancy Questionnaire (Heinz et al., 2009) is a relatively new

measure. Future research should aim to replicate these findings with college samples in other

geographic locations.

The second hypothesis of the current study was that impulsivity dimensions would

predict caffeine consumption in a college sample. The present study is the first of its kind to use

the UPPS-P (Lynam et al., 2006) to examine the relationship of impulsivity, assuming five

dimensions, to weekly caffeine consumption. Although, caffeine intake, among other variables,

has been studied in relation to personality, none has attempted to use this five factor model of

impulsivity. The third step of the hierarchical multiple regression analysis conducted in Study 2

indicated that the impulsivity factors did not account for a significant amount of variance above

and beyond frequency of nicotine consumption and caffeine expectancies. The second

hypothesis was not supported. Given that the impulsivity dimensions did not account for a

significant amount of additional variance, there was no evidence for the specific prediction that

sensation seeking would be positively associated to caffeine consumption. However, based on

the significant and marginally significant Pearson product moment correlations that were

conducted by gender, data driven exploratory analyses were performed to determine if there was

evidence of gender interactions with two of the impulsivity subscales, namely, Premeditation and

Sensation Seeking. Although the hierarchical multiple regression analysis exploring a possible

gender by Sensation Seeking interaction was not significant, the gender by Premeditation

59

interaction was marginally significant. Specifically for males, lower scores on the Premeditation

subscale were predictive of higher weekly caffeine consumption and this relationship was

marginally significant. The Premeditation subscale measures the tendency for an individual to

delay immediate behavior in service of forethought and planning. Thus, lower scores on this

subscale are in line with what would be characterized as impulsive behavior. Therefore, the

marginally significant interaction suggests that for males who engaged in less forethought and

planning, higher weekly caffeine consumption was more likely. For females, there was a

positive association between Premeditation and weekly caffeine consumption, but it was not

significant. Given that these analyses were data driven, it should be noted that the interaction

findings presented here are capitalizing on chance. Future research should aim to replicate these

findings and examine why male caffeine consumption might be negatively associated with

Premeditation.

Given that over half of the sample self-reported weekly caffeine consumption estimates

below the mean (64.4%) and that a square root transformation was needed to assume normality

of the response, it may be that low to moderate caffeine consumption is not predicted by

impulsivity dimensions. The current study proposed a model of augmented caffeine

consumption by synthesizing available research regarding trends in impulsive populations,

treatment for ADHD and the dopaminergic effects of caffeine intake. The augmented caffeine

consumption model would be more likely to predict a relationship between caffeine consumption

and impulsivity for those at the higher end of the caffeine consumption and impulsivity

distributions, and there is evidence to suggest that these individuals were underrepresented.

Future research may aim to procure the participation of not only higher caffeine consumers but

also populations where an impulsivity relation is known to exist, such as those diagnosed with

60

ADHD. Such research would further clarify the relationship between impulsivity and caffeine

consumption.

There were limitations to the current study. The CCQ 2010 was developed with the

intention to provide as many memory cues as possible in an attempt to obtain accurate self-report

data on caffeine consumption. To this end, the instrument was divided such that participants

were asked to provide a typical weekday estimate and a typical weekend day estimate.

Additionally, consistent with Landrum’s (1992) original iteration, time of day was broken down

into four categories. Furthermore, to aid in accurate completion of the questionnaire, Microsoft

PowerPoint 2007 was used as a visual aid and demonstrated correct and incorrect examples of

completion. Although the current research is confident that the CCQ 2010 administration was

clear, it is unknown whether participants completely understood the instructions. Therefore,

subsequent research with this instrument should include a screening questionnaire after the

presentation of the instructions for the instrument. Such a screening measure could include

written scenarios of caffeine consumers and their consumption during a typical week. A series

of multiple choice questions based on the written scenarios would clearly demonstrate the

participants’ understanding of the instructions. Such a screening measure would ensure the

collected data was in fact measuring what was intended.

Additionally, the generalizability of these results are limited given that the sample

recruited in the present study included participants from one southeastern university in the

United States, and it is difficult to say how these results would generalize to populations in other

geographic areas. Replication of these results to other college populations is needed.

The current study also included longitudinal information regarding the stability of the

CCQ 2010. It is still unknown whether the act of completing the CCQ 2010 in fact changes

61

behavior. Although a subsequent administration of the measure was given, the directions were

not identical to the first administration and do not speak to the measure’s test-retest reliability.

Although, the Pearson product moment correlation does suggest stability, given that the

correlation was relatively high despite the difference in the administration of the questionnaires.

The current research modified a caffeine consumption instrument with the aim of

producing a measure that would reflect modern day availability. Based on the available

literature, the present study generated hypotheses concerning potential predictors of caffeine

consumption. Specifically, it was suggested that caffeine expectancies and impulsivity

dimensions would predict weekly caffeine consumption in a college sample. These hypotheses

were partially supported. Caffeine expectancies were found to account for a significant amount

of variance in weekly caffeine consumption. However, impulsivity dimensions were not

predictive of weekly caffeine consumption. A data driven exploratory analysis revealed a

marginally significant interaction between gender and Premeditation. Specifically, for males, as

Premeditation scores decreased, weekly caffeine consumption increased. Although the results

from the current research suggest that caffeine expectancies are good predictors of weekly

caffeine consumption and that impulsivity dimensions do not account for a significant amount of

variance above and beyond expectancies, it is possible that higher consumers of caffeine were

underrepresented in the current sample. Future research should aim to procure the participation

of higher caffeine consumers and aim to replicate the gender and Premeditation interaction, to

further elucidate the association between impulsivity and caffeine consumption.

62

REFERENCES

Advokat, C. (2009). What exactly are the benefits of stimulants for ADHD? Journal of

Attention Disorders, 12, 495-498. doi:10.1177/1087054708329781

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

disorders (4th

ed., Text Revision). American Psychiatric PressWashington, DC.

Barone, J. J., & Roberts, H. R. (1996). Caffeine consumption. Food and Chemical Toxicology,

34, 119-129.

Brown, S. A. (1993). Drug effect expectancies and addictive behavior change. Experimental

and Clinical Psychopharmacology, 1, 55-67. doi:10.1037/1064-1297.1.1-4.55

Brunyé, T. T., Mahoney, C. R., Lieberman, H. R., & Taylor, H. A. (2010). Caffeine modulates

attention network function. Brain and Cognition, 72, 181-188. doi:10.1016 /j.bandc.200

9.07.013

Buss, A. H. & Plomin, R. (1975). A temperament theory of personality development. New

York: John Wiley & Sons.

Cahalan, D., Cisin I. H., & Crossley, H. M. (1969). American drinking practices: a national

study of drinking behavior and attitudes. Publications Division, Rutgers Center of

Alcohol Studies; distributed by College & University Press, New Haven, Conn., New

Brunswick, N.J.

Cloninger, C. R., Przybeck, T. R. & Svrakic, D. M. (1991). The Tridimensional Personality

Questionnaire: US normative data. Psychological Reports, 69, 1047-1057.

Cloninger, C. R., Przybeck, T. R., Svrakic, D. M., & Wetzel, R. M. (1994). The temperament

and character inventory (TCI): A guide to its development and use. St. Louis (MO):

Center for Psychobiology of Personality, Washington University.

63

Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007).

Integration of impulsivity and positive mood to predict risky behavior: Development and

validation of a measure of positive urgency. Psychological Assessment, 19, 107-118.

doi:10.1037/1040-3590.19.1.107

Dickman, S. J. (1990). Functional and dysfunctional impulsivity: Personality and cognitive

correlates. Journal of Personality and Social Psychology, 58, 95-102.

Ernst, M., Pine, D. S., & Hardin, M. (2006). Triadic model of the neurobiology of motivated

behavior in adolescence. Psychological Medicine, 36, 299-312. doi:10.1017/S0033291

705005891

Evenden, J. L. (1999). Varieties of impulsivity. Psychopharmacology, 146, 348-361.

Eysenck, H. J. & Eysenck, S. B. G. (1975). Manual of the Eysenck Personality Questionnaire.

London: Hodder & Stoughton.

Eysenck, S. B. G. (1993). The I7: Development of a measure of impulsivity and its relationship

to the superfactors of personality. In: W. G. McCown, J. L. Johnson & M. B. Shure

(Eds.), The impulsive client: Theory, research and treatment (pp. 141-149). American

Psychological Association: Washington, D.C.

Eysenck, S. B., Pearson, P. R., Easting, G., & Allsopp, J. F. (1985). Age norms for

impulsiveness, venturesomeness, and empathy in adults. Personality and Individual

Differences, 6, 613-619.

Ferré, S. (2008). An update on the mechanisms of the psychostimulant effects of caffeine.

Journal of Neurochemistry, 105, 1067-1079. doi:10.1111/j.1471-4159.2007.05196.x

64

Fillmore, M. T., Mulvihill, L. E., & Vogel-Sprott, M. (1994). The expected drug and its

expected effect interact to determine placebo responses to alcohol and caffeine.

Psychopharmacology, 115, 383-388.

Fillmore, M., & Vogel-Sprott, M. (1992). Expected effect of caffeine on motor performance

predicts the type of response to placebo. Psychopharmacology, 106, 209-214.

Fillmore, M. T., & Vogel-Sprott, M. (1994). Psychomotor performance under alcohol and

under caffeine: Expectancy and pharmacological effects. Experimental and Clinical

Psychopharmacology, 2, 319-327.

Goldstein, R. Z., Alia-Klein, N. & Volkow, N. D. (2009). Drug addiction: Neuroimaging.

In L. R. Squire (Ed.), The Encyclopedia of Neuroscience (pp. 699-711).

Gurpegui, M., Jurado, D., Luna, J. D., Fernández-Molina, C., Moreno-Abril, O., & Gálvez, R.

(2007). Personality traits associated with caffeine intake and smoking. Progress in

Neuro-Psychopharmacology & Biological Psychiatry, 31, 997-1005.

Harrell, P.l T., & Juliano, L. M. (2009). Caffeine expectancies influence the subjective and

behavioral effects of caffeine. Psychopharmacology, 207, 335-342. doi:10.1007/s0

0213-009-1658-5

Heatherly, S. V., Hayward, R. C., Seers, H. E., & Rogers, P. J. (2005). Cognitive and

psychomotor performance, mood, and pressor effects of caffeine after 4, 6, and 8 h

caffeine abstinence. Psychopharmacology, 178, 461-470.

Heatherton, T. F. & Wagner, D. D. (2011). Cognitive neuroscience of self-regulation failure.

Trends in Cognitive Sciences, 15, 132-139. doi:10.1016/j.tics.2010.12.005

65

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

in the rasch measurement framework. Psychology of Addictive Behaviors, 23, 500

-511. doi:10.1037/a0016654

Horne, J. A., & Ostberg, O. (1975). Time of day effects on extroversion and salivation.

Biological Psychology, 3, 301-307.

Jackson, D. N. (1984). Personality research form manual. Goshen, NY: Research

Psychologists Press.

Jones, H. A., & Lejuez, C. W. (2005). Personality correlates of caffeine dependence: The role of

sensation seeking, impulsivity, and risk taking. Experimental and Clinical

Pscyhopharmacology, 13, 259-266. doi:10.1037/1064-1297.13.3.259

Khantzian, E. J. (1985). The self-medication hypothesis of addictive disorders: Focus on heroin

and cocaine dependence. American Journal of Psychiatry, 142, 1259-1264.

Khantzian, E. J. (2003). Understanding addictive vulnerability: An evolving psychodynamic

perspective. Neuropsychoanalysis, 5, 5-21.

Landrum, R. E. (1992). College students’ use of caffeine and its relationship to personality.

College Student Journal, 26, 151-155.

Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., et al.

(2002). Evaluation of a behavioral measure of risk-taking: The balloon analogue risk task

(BART). Journal of Experimental Psychology: Applied, 8, 75-84.

Lodge, D.J., Buffalari, D.M. & Grace, A.A. (2009) Dopamine: CNS pathways and

neurophysiology. In L. R. Squire (Ed.) Encyclopedia of Neuroscience (pp. 549-555).

66

Lynam, D.R., & Miller, J. D. (2004). Personality pathways to impulsive behavior and their

relations to deviance: Results from three samples. Journal of Quantitative Criminology,

20, 319-341.

Lynam, D. R., Smith, G. T., Whiteside, S. P., & Cyders, M. A. (2006). The UPPS-P: Assessing

five personality pathways to impulsive behavior (Technical Report). West Lafayette:

Purdue University.

Magid, V. & Colder, C. R. (2007). The UPPS Impulsive Behavior Scale: Factor structure

associations with college drinking. Personality and Individual Differences, 43, 1927

-1937.

McCrae, R. R. & Costa, P. T. Jr (1990). Personality in adulthood. New York: Guilford.

Oei, A., & Hartley, L. R. (2005). The effects of caffeine and expectancy on attention and

memory. Human Psychopharmacology, 20, 193-202. doi:10.1002/hup.681

Patton, J. H., Stanford, M.S. & Barratt, E. S. (1995). Factor structure of the Barratt

Impulsiveness Scale. Journal of Clinical Psychology, 51, 768-774.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen:

Danish Institute for Educational Research. (Expanded edition, 1980. Chicago:

University of Chicago Press)

Reissig, C. J., Strain, E. C., & Griffiths, R. R. (2009). Caffeinated energy drinks-A growing

problem. Drug and Alcohol Dependence, 99, 1-10. doi:10.1016/j. drugalcdep.2008

.08.001

Schwartz, R. M, Burkhart, B. R. & Green, S. B. (1978). Turning on or turning off: Sensation

seeking or tension reduction as motivational determinants of alcohol use. Journal of

Consulting and Clinical Psychology, 46, 1144-1145.

67

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

measure for caffeine consumption in undergraduate students. Psychological Reports, 89,

521-526.

Smith, G. T., Fishcher, S., Cyders, M. A., Annus, A. M., Spillane, N. S., & McCarthy, D. M.

(2007). On the validity and utility of discriminating among impulsivity-like traits.

Assessment, 14, 155-170. doi:10.1177/1073191106295527

Spillane, N. S., Smith, G. T., & Kahler, C. W. (2010). Impulsivity-like traits and smoking

behavior in college students. Addictive Behaviors, 35, 700-705. doi:10.1016/ j.add

beh.2010.03.008

Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking.

Developmental Review, 28, 78-106. doi:10.1016/j.dr.2007.08.002

Steinberg, L. (2010). A dual systems model of adolescent risk-taking. Developmental

Psychobiology, 52, 216-224. doi:10.1002/dev.20445

Tang, A., Wanchoo, S. J., Swann, A. C., & Dafny, N. (2009). Psychostimulant treatment for

ADHD is modulated by prefrontal cortex manipulation. Brain Research Bulletin, 80,

353-358. doi:10.1016/j.brainresbull.2009.08.022

Tellegen, A. (1982). Multidimensional Personality Questionnaire manual. Minneapolis, MD:

University of Minnesota Press.

Vilarim, M. M., Rocha Araujo, D. M. & Nardi, A. E. (2011). Caffeine challenge test and panic

disorder: A systematic literature review. Expert Reviews Neurotherapeutics, 11, 1185

-1195. doi:10.1586/ERN.11.83

68

Whiteside, S. P. & Lynam, D. R. (2001). The five factor model and impulsivity: Using a

structural model of personality to understand impulsivity. Personality and Individual

Differences, 30, 669-689.

Wieder, H., & Kaplan, E. H. (1969). Drug use in adolescents: Psychodynamic meaning and

pharmacogenic effect. Psychoanalytic study of the child, 24, 399-431.

Williamson, A. (2007). Using self-report measures in neurobehavioural toxicology: Can they be

trusted? NeuroToxicology, 28, 227-234. doi:10.1016/j.neuro.2006.03.009

Yeomans, M., Ripley, T., Davies, L., Rusted, J., & Rogers, P. (2002). Effects of caffeine on

Performance and mood depend on the level of caffeine abstinence.

Psychopharmacology, 164, 241-249.

Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New

York: Cambridge University Press.

Zuckerman, M., Eysenck, S. B., & Eysenck, H. J. (1978). Sensation seeking in England and

America: Cross-cultural, age, and sex comparisons. Journal of Consulting and Clinical

Psychology, 46, 139-149. doi:10.1037/0022-006X.46.1.139

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APPENDIX

Appendix A. Demographics Questionnaire for Study 1

A CLOSE LOOK AT CAFFEINE We are interested in studying caffeine. In order to do so, we need you to honestly and thoroughly answer the questions presented in the present study. Your participation is entirely voluntary. At any time you may stop your involvement or decline to answer any question without being treated differently by the researcher. Any document received from the researcher is ANONYMOUS. Please do not write your name on any of the forms presented to you today. If you have any questions about this study or want to learn more about the results, please contact Jennifer Heaton JAH7711@UNCW.EDU or Dr. Nora Noel NNOEL@UNCW.EDU in the psychology department. In addition, if you have questions about your rights as a participant, you may contact the Chair of the UNCW Institutional Review Board, Dr. Candace Gauthier (Gauthierc@uncw.edu). Thank you for your participation. First, please tell us some things about yourself Today’s date ____/____/____ Age__________ Height________ Weight_________ Sex___________ -Current Marital/Dating Status (please circle one) Married/cohabitating Divorced/Separated Steady Dating Someone Single Other -Current Educational Status (please circle one) Less than High School High School Graduate College Freshman College Sophomore College Junior College Senior Other_________________________________ -Are you a Full Time or Part Time Student? -Your Ethnic Background: __________________________________________________

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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 Prescribed Medications ______Alcohol ______Birth Control ______Caffeine ______Amphetamines ______Nicotine ______Barbiturates ______Marijuana ______Benzodiazapines ______Hashish ______Other Tranquilizers ______Crack ______Opiates (e.g. Methadone, Darvon) ______Cocaine ______Anti-depressants ______Amphetamines (not prescribed) ______Anti-psychotics ______Barbiturates (not prescribed) ______Anti-manic (e.g. Lithium) ______Benzodiazapines (not prescribed) ______Other psychoactive medication ______Other Tranquilizers (not prescribed) ______Heroin ______Other Opiates (not prescribed) ______Hallucinogens ______Inhalants ______Any drugs by injection ever

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Appendix B. Pilot Caffeine Consumption Questionnaire Typical Weekday for Study 1

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Appendix C. Pilot Caffeine Consumption Questionnaire Typical Weekend Day for Study 1

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Appendix D. Informed Consent for Study 2

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Appendix E. Code Name Form: Experimenter’s Copy for Study 2

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Appendix F. Code Name Form- Participant’s Copy for Study 2

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Appendix G. Code Name and Telephone Number Form for Study 2

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Appendix H. Demographics Questionnaire for Study 2

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Appendix I. General Caffeine Questions for Study 2

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Appendix J. Previous Week General Caffeine Questions for Study 2

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Appendix K. Typical Week Caffeine Consumption Questionnaire (2010) for Study 2

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Appendix L. Previous Week Caffeine Consumption Questionnaire (2010) for Study 2

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Appendix M. Modified Quantity Frequency Index for Study 2

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