Revving up and staying up: energy drink use associated with anxiety and sleep quality in a college sample

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From: College Student Journal(Vol. 45, Issue 4)
Publisher: Project Innovation Austin LLC
Document Type: Report
Length: 4,403 words
Lexile Measure: 1390L

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Associations among caffeinated energy drink use, anxiety, and sleep quality were examined in a young adult sample (N = 107). A 7-day retrospective survey methodology was used to assess consumption rates among college student athletes, ROTC cadets, and those in a control group. Regression analyses revealed that energy drink use explained 29% of the variance in anxiety scores and 20% in sleep disturbance scores. Greater frequency of energy drink use was associated with poorer sleep quality, longer sleep latency, shorter sleep duration, and lower habitual sleep efficiency. No group differences in energy drink consumption were revealed. More research into the effects of energy drink use on psychological adjustment variables appears warranted.

Keywords: energy drinks; caffeine; anxiety; sleep quality; college students

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Energy drink use appears to be ubiquitous, particularly among young adults, who may expect these products to improve concentration and performance, combat fatigue, or even to counteract the effects of alcohol intoxication. Data examining the prevalence and pattern of energy drink consumption, as well as the potential risks, have emerged (Malinauskas, Aeby, Overton, Carpenter-Aeby, & Barber-Heidal, 2007; Oteri, Caputi, & Calapai, 2007; Miller, 2008). For example, recent survey data from 602 college students found that frequency of energy drink use was positively related to marijuana use, sexual and other risk-taking, fighting, and failure to wear a seatbelt (Miller, 2008). Few studies have examined relationships among energy drink use and psychological adjustment variables, such as anxiety and sleep quality. A secondary goal was to examine use frequencies in certain subgroups--athletes and ROTC cadets--who regularly participate in physically demanding co-curricula activities.

Caffeine, Anxiety, and Sleep

While energy drinks often contain ingredients such as taurine, glucose, carbohydrates, herbal extracts, and B vitamins, caffeine has been the most studied in relation to anxiety and sleep. A central nervous system (CNS) and cardiac stimulant, caffeine acts as an antagonist to adenosine, a substance that has been shown to promote sleep and to influence autonomic nervous system arousal (Alsene, Deckert, Sand, & de Wit, 2003; Roehrs & Roth, 2008). Moderate doses of 75 to 200 mg improve attention, alertness, and visual vigilance, reduce fatigue and shorten reaction times, and improve subjective mood states (Childs & de Wit, 2008; Herz, 1999; Lieberman, Tharion, Shukitt-Hale, Speckman, & Tulley, 2002; Smith, 2002). Habitual caffeine use, however, may moderate these effects (Hamleers, et al., 2000). Caffeine may also exacerbate pre-existing cardiac conditions and have side effects such as increased heart rate and palpitations, elevated blood pressure, and restlessness.

Consumption of caffeine in higher quantities prompts anxiety-related responses. Veleber and Templer (1984) found that 65% participants who received a 300 mg dose of caffeine showed significant increases in anxiety scores from pre- to post-test controlling for weight and daily caffeine use. Telch, Silverman, and Schmidt (1996) reported that 400 mg or more of caffeine increases autonomic nervous system arousal, respiration rate, oxygen intake, and carbon dioxide elimination, as well as prompts negative somatic sensations, such as shaking and trembling. However, Smith (2002) has questioned the ecological validity of such studies based on evidence that caffeine users do not typically ingest large amounts of caffeine all at once, but rather self-regulate their intake throughout the day to avoid negative side effects and sleep disturbances.

Laboratory data has demonstrated links between caffeine use and sleep. Studies utilizing polysomnography have generally shown that caffeine prior to bedtime may increase sleep onset latency, decrease total sleep time, lower sleep efficiency, and reduce slow-wave sleep (Paterson, Nutt, Ivarsson, Hutson, & Wilson, 2009; Landolt, Werth, Borbely, & Dijk, 1995). Patterson et al. (2009) used caffeine to mimic a state of sleep deprivation in 12 healthy males who were moderate weekly caffeine users (50 to 3000 mg per week). Consumers of higher amounts of daily caffeine were generally less sensitive to its effects on sleep and in particular showed less disruption in sleep onset latency. Interestingly, caffeine administered early in the day may negatively affect sleep later that night.

Finally, clinical evidence also suggests that excess caffeine use is associated with disturbances of anxiety and sleep. The DSM-IV-TR (APA, 2000) criteria for caffeine-induced anxiety disorder centers on the experience of prominent anxiety symptoms during caffeine intoxication or withdrawal that is not the result of a preexisting anxiety disorder. Caffeine-induced sleep disorder is an appropriate classification when insomnia results from use of the drug or when hypersomnia and daytime sleepiness are present in response to caffeine withdrawal. Each diagnosis requires that the severity of the symptoms (e.g., restlessness, nervousness, insomnia) be greater than the levels experienced during caffeine intoxication.

Energy Drinks, Anxiety, and Sleep in College

College students typically report anxiety and sleep problems. Forquer, Camden, Gabriau, and Johnson (2008) recently surveyed 313 students and found that 33% required more than 30 minutes to get to sleep and that 43% woke up more than once per night. Data from the 2008 National College Health Assessment survey, based on responses from 81,121 students, showed that stress and sleep problems were rated as 1 and 3 respectively in a list of top 10 impediments to academic performance (ACHA; 2008). And a recent on-line survey by Lund, et al. (2009) of 1,125 college students at a private university in the Midwest showed that 38% were poor quality sleepers; they reported higher levels of anger, confusion, depression, fatigue, and tension as measured by the Profile of Mood States (POMS).

Sleep disturbances in college have also been associated with health risks. Vail-Smith, Felts, and Becker (2009) examined relationships among sleep quality and health risk behaviors in a large sample of undergraduates using an online survey methodology. Results from the Sleep Quality Index (SQI) showed that 11.8% of the sample reported poor sleep quality, while 76.6% reported occasional sleep problems. Insomnia was reported by 28% of respondents. Poor sleep quality (greater SQI scores) correlated positively with physical aggression and suicidal ideation, as well as with alcohol and tobacco use. There was no relationship between sleep quality and exercise frequency. The authors concluded that instructional programs to improve sleep quality among students should be considered. One limitation in this study is that the majority of participants were first year females; nevertheless, the data suggests that sleep quality is important to behavioral and psychological adjustment in college.

Turning to caffeine use, research has mostly found that it affects anxiety and sleep in college. Energy drinks typically contain 80 to 141 mg of caffeine per 8 ounces, and 12 to 16 ounces per serving is common, and so the caffeine dose in these drinks may be considerable. Earlier studies showed that students who regularly consumed moderate to large amounts of caffeine scored higher on measures of trait anxiety and depression than did abstainers, and that a negative linear relationship existed between daily caffeinated beverage use and hours spent sleeping (Gilliland & Andress, 1981; Hicks, Hicks, Reyes, and Cheers, 1983). More recently, Malinauskas et al. (2007) asked almost 500 students about their beliefs and behaviors linked to energy drink use. Results showed that 51% of students consumed a minimum of 1 drink each month during the semester. The most frequently cited reasons for energy drink use were insufficient sleep (67%) and to counteract fatigue (65%). Consumption was also linked to anxiety-related symptoms, with 19% reporting heart palpitations, 22% noting headaches, and 29% reporting what the authors termed a "jolt and crash" experience, with a significant dose effect related to this finding. However, (Lund, et. al. 2009) examined sleep in a large sample of college students found that caffeine use (coffee, tea, soda, and / or chocolate) did not predict sleep quality scores, although energy drink use was not assessed in this study.

Energy drink use among college athletes and Reserve Officers Training Corp (ROTC) cadets is of interest because of the physically demanding nature of these co-curricula activities. Since energy drinks are marketed as physical performance enhancers, might membership in these groups be associated with energy drink use? The suggestion that group membership itself influences health-related behavior fits well within a social normative framework (Perkins & Berkowitz, 1986). Athletes likely receive ambiguous information regarding caffeinated drinks. For example, caffeine has been shown to increase exercise metabolism and to lower perceptions of fatigue in athletes, but its potential side effects include insomnia, headache, muscle tremor, and dehydration associated with the diuretic action of the drug. The National Collegiate Athletic Association policy on caffeine use reflects this ambiguity: while caffeine is listed as banned in quantities that would enhance performance, use of the drug is allowed in modest amounts not to exceed 15 g/mL in urine concentration (Maughan, Depiesse, & Geyer, 2007).

Alternatively, ROTC cadets may be more likely than athletes to receive unambiguously positive messages about energy drink use. Caldwell and Caldwell (2005) noted that the U.S. military has authorized the use of stimulants---caffeine, modafinil, and dextroamphetamine--to maintain the alertness and performance of personnel during periods of sleep deprivation. The use of caffeine as a stimulant is preferable according to the authors because it is available without prescription in tablet form, chewing gum (specially made for military use), candy, and beverages. Given these practices, ROTC cadets may view positively the use of certain types of stimulants and caffeinated products.

The hypotheses for the current study involved relationships among energy drink use, anxiety, and sleep quality. It was first predicted that the number of energy drinks consumed would be positively related to anxiety scores. Similarly, it was predicted that the number of energy drinks consumed would be positively related to sleep disturbances. Finally, we expected that ROTC cadets would use energy drinks more frequently than either athletes or controls.

Method

Participants

Participants were recruited during the first 2 weeks of the fall semester. Athletes (21 males, 23 females) and ROTC cadets (18 males) were recruited at their respective general meetings. Students in the control group (21 males, 24 females) were recruited in general psychology classrooms. From among those recruited, 107 students agreed to participate (Mage = 20.78, SD = 1.76; response rate = 73.4%). Students received no compensation for participation. Juniors (49.6%) and seniors (23.7%) comprised a majority of the sample, followed by sophomores (13.1%) and freshmen (4.7). Percentages of athletes from each sport were as follows: soccer (34.1%), tennis (18.2%), swimming (13.6%), baseball (13.6%), golf (9.1%), volleyball (9.1%), and crew (2.3%). The research was approved by the IRB, and all procedures involving human participants adhered to standard ethical practices (APA, 1992).

Materials and Procedure

Beck Anxiety Inventory. The BAI is a 21-item self-report measure that lists common anxiety symptoms. Items include "unable to relax" and "difficulty in breathing". Participants rate the degree to which symptoms have been experienced during the past month on a scale ranging from 0 ("not at all") to 3 ("severely"). The BAI has very good internal consistency and testretest reliability (Beck, Epstein, Brown, & Steer; 1988; Leyfer, Ruberg, & Woodruff-Borden, 2006).

Pittsburg Sleep Quality Index. The PSQI is a 19-item self-report measure of sleep behaviors and sleep quality during the past month. Items include "How long has it taken you to fall asleep each night?" and "How often have you had trouble staying awake while driving, eating meals or engaging in social activity?" The measure yields a total score that ranges from 0 to 21, with higher numbers indicating more sleep disturbance; it includes 7 sleep component scores. Reliability of the PSQI has been found to be good, with Cronbach's = .83 and a test-retest reliability coefficient of .84 (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989).

Caffeinated Beverage Use. Frequency of use for caffeinated beverages -coffee, tea, soft drinks, and energy drinks--was collected retrospectively for a 7-day period using a paper and pencil record form.

Procedure

Most participants completed the survey materials in classroom settings. Athletes completed the measures in an athletic training room. Surveys were pre-assembled into packets stapled in counterbalanced order. The experimenter verbally reviewed the informed consent procedures, and students gave consent prior to participating. The experimenter then distributed the surveys along with plain envelopes to participants. Students sealed their own surveys in an envelope after completing them. The experimenter collected all the envelopes and reviewed the debriefing statement to conclude the session.

Results

Sample statistics for the variables of interest were computed prior to the main analyses. Sixty-one participants (57% of the sample) consumed at least 1 caffeinated energy drink in the past week. A total of 369 energy drinks were reportedly consumed during this period; the number used ranged from 0 to 17, with M= 3.45, SD = 4.35, and Mdn. = 2.0. The number of caffeinated coffee, tea, or soft drinks consumed ranged from 0 to 7 with M = 1.00, SD = 2.00, and Mdn. = 0.5. Scores on the Beck Anxiety Inventory (BAI) ranged from 0 to 40 with M = 13.94, SD = 9.53, and Mdn. = 13.0. Pittsburg Sleep Quality Index (PSQI) global index scores ranged from 0 to 17 with M = 6.97, SD = 3.55, and Mdn. = 6.0.

Data distributions were further screened prior to the regression analyses. Plots revealed the presence of outliers in the distributions for the number of energy drinks and also for the number of coffee, tea, or soft drinks. Each variable was transformed using a Winsorizing procedure whereby extreme outliers were replaced with either the 5th or 95th percentile scores. Bivariate correlations revealed that the frequency of energy drink use was positively correlated with BAI scores, r = .54, p < .001, with PSQI global scores, r = .44, p < .001, and with the number of coffee, tea, and soft drinks consumed, r = .36, p < .001. PSQI global scores were unrelated to coffee, tea, or soft drink use.

The number of energy drinks, coffee, tea, soft drinks, and PSQI global scores were entered simultaneously into a regression analysis to predict BAI scores. Statistics for tolerance and the variance inflation factor (VIF) were examined to rule out multicollinearity among predictors as outlined in Keith (2005). Frequency of energy drink use significantly predicted BM scores, accounting for 29% of score variance. The same held for PSQI global scores, which explained 8% of the variance in BAI scores. In a second analysis, PSQI global scores served as the dependent variable. Frequency of energy drink use significantly accounted for 20% of score variance in PSQI global scores, while BAI scores accounted for 9%. Coffee, tea, or soft drink use did not predict PSQI scores. Results are presented in Table 1.

Next examined were bivariate correlations among energy drinks consumed and the 7 individual PSQI components scores. Frequency of energy drink use was positively correlated with disturbances in subjective sleep quality, sleep latency, sleep duration, and habitual sleep efficiency. Effects sizes ranged from small to medium for these tests. Energy drink use was unrelated to sleep disturbances, use of sleeping medication, or to daytime dysfunction. These data are presented in Table 2.

The extent to which PSQI components predicted energy drink use was tested using linear regression. Frequency of energy drink was the dependent variable here, while the 7 components were entered simultaneously as predictors. Multicollinearity was again examined and ruled out. Three components predicted significant variance in energy drink scores: subjective sleep quality explained 14%, habitual sleep efficiently explained 8%, and sleep duration accounted for about 4%. The remaining PSQI component scores were unrelated to frequency of energy drink use. Results are presented in Table 3.

Group differences among athletes, ROTC cadets, and controls were examined. Since there were no female ROTC cadets who participated, only the data for males will be reported here. The average number of energy drinks was computed for athletes (n = 21;M= 4.14; SD = 4.37), ROTC cadets (n = 18; M= 5.78; SD = 6.08) and controls (n = 21; M = 3.43; SD = 3.61). Consumption data for males was submitted to a Kruskal-Wallis test. There were no differences in the frequency of energy drink use in the past week among athletes (M Rank = 30.95), ROTC cadets (M Rank = 31.81), and controls (M Rank = 28.93), 2(2, n = 60) = 0.30, p > .05, ns.

Discussion

This exploratory study examined associations among caffeinated energy drink use, anxiety, and sleep disturbances in a young adult sample. As predicted, findings indicated that as frequency of energy drink use increased, experiences of anxiety and sleep disturbances also increased significantly. Regression analyses demonstrated that energy drink use independently accounted for 29% of the variance in anxiety scores as measured by the BAI and 20% of the variance in sleep disturbance as measured by the PSQI. Furthermore, subjective ratings of sleep quality accounted for 14% of the variance in use of energy drinks; sleep efficiency and duration accounted for 8% and 4% of the variance, respectively.

One explanation for these findings is that the increased dietary caffeine from energy drink use is related to anxiety and sleep problems in a dose-response fashion. Alternatively, these associations might have been due to the additive effects of caffeine and other active ingredients contained in energy drinks. For example, Scholey and Kennedy (2004) compared the effects of energy mixtures on general cognitive performance using drinks that contained either caffeine alone, glucose alone, or a whole set of ingredients plus herbal ginseng and ginkgo. The whole drink improved both attention and memory scores (delayed word recall and recognition) compared to the caffeine-only mixture. The authors suggested that these findings might have been the result of synergism among caffeine and the drink's other ingredients.

Findings here may also raise further questions regarding psychological adjustment. For example, is there a pattern of energy drink use whereby disturbances of anxiety and sleep would be severe enough to warrant a clinical diagnosis? Might some also consume these products as a coping response to negative mood states? Anxiety and sleep problems have, in fact, been linked to depressed mood states. Chorney, Detweiler, Morris, & Kuhn (2008) drew upon longitudinal data to conclude that sleep disturbances in childhood are associated with symptoms of both anxiety and depressed mood in adulthood. Roane and Taylor (2008) used a cross-sectional, longitudinal design to examine 4,500 adolescents who were assessed for sleep and adjustment problems at age 15 and then followed for 6-7 years afterwards. Findings showed that insomnia during adolescence was a risk factor for depression in early adulthood.

And sleep disturbances have been linked to depression in college. In a recent study, Brooks, Girgenti, and Mills (2009) associated sleep disturbances with symptoms of depressed mood in a sample of undergraduates. Participants completed measures of depression and sleep quality and then generated daily sleep diaries for the following two weeks. Results showed that shorter sleep duration at the initial assessment and over the next two weeks were associated with greater mood disturbance as measured by the Beck Depression Inventory (BDI). At the initial assessment, shorter sleep duration negatively correlated with several symptoms of depression, including sadness, agitation, loss of interest and pleasure, irritability, concentration problems, tiredness, and fatigue (p. 470). Finally, and unexpectedly, sleep duration was unrelated to depressed mood symptoms at the follow-up assessment. The authors noted that sleep problems were particularly important to the experience of depressed mood among females. While these mixed results could not establish sleep disturbance as an etiological risk factor in depression, they did demonstrate links among sleep disturbance and symptoms of depressed mood. Further efforts are needed to investigate the possible etiological role of energy drink consumption in the development of adjustment problems, including depressed mood.

Regarding ROTC cadets in this study, it was predicted that they would use energy drinks more frequently than students in either of the other two groups. The data did not support this hypothesis. While the frequency of energy drink use tended to be higher for ROTC cadets than for athletes or controls, this difference was not statistically meaningful. The reasons for this are unclear. Possible explanations include that the group membership variable was conceptualized too broadly here or that its effect was minor and went undetected in the current sample.

There are limitations to the current work. First, survey data relying on retrospective memory recall may be less reliable than other types of methods. Future studies in this area might employ real-time data collection methods, such as ecological momentary assessment using a portable electronic device. Second, while there appeared to be a dose-response relationship between caffeinated energy drink use and anxiety and sleep concerns, the exact amount of caffeine ingested by participants was undetermined. Finally, participants were not pre-screened for the presence of anxiety disorders, which may increase one's sensitivity to caffeine (Totten & France, 1995).

In conclusion, these data contribute to the growing body of knowledge regarding the use of caffeinated energy drinks in college. Frequency of energy drink use independently accounted for two problems commonly reported by students--anxiety and sleep disturbance, which have been linked to depressed mood states. The popularity of these products suggests that more research in the area of energy drink use and psychological adjustment is warranted.

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MICHAEL J. STASIO

KIM CURRY

ALEXANDRA L. WAGENER

DESTINEE M. GLASSMAN

The University of Tampa

Table 1
Simultaneous Linear Regression Models Predicting Beck Anxiety
Inventory (BAI) Scores and Pittsburg Sleek Quality Index (PSQI)
Global Scores

DEPENDENT: BAI Scores          b ([SE.sub.b])   [beta]

Energy Drinks                   1.35 (0.20)      0.54
Coffee, Tea, Soft Drinks        1.09 (0.57)      0.17
PSQI Global Scores              0.84 (0.23)      0.31

DEPENDENT: PSL Global Scores   b ([SE.sub.b])   [beta]

Energy Drinks                   0.41 (0.08)      0.44
Coffee, Tea, Soft Drinks        0.06 (0.23)      0.02
BAI Scores                      0.14 (0.04)      0.37

DEPENDENT: BAI Scores          [DELTA][R.sup.2]   [F.sub.change]

Energy Drinks                        0.29           43.66 ***
Coffee, Tea, Soft Drinks             0.02              3.66
PSQI Global Scores                   0.08           13.38 ***

DEPENDENT: PSL Global Scores   [DELTA][R.sup.2]   [F.sub.change]

Energy Drinks                        0.20           25.64 ***
Coffee, Tea, Soft Drinks             0.06              0.80
BAI Scores                           0.09           13.38 ***

*** p <.001

Table 2
Bivariate Correlation Coefficients Among Number of Energy Drinks
Consumed and Individual Pittsburg Sleek, Quality Index (a) Component
Scores

Variable                       1       2         3         4

1. Energy Drinks               --   .38 ***   .32 **    .34 ***
2. Subjective Sleep Quality           --      .51 ***   .43 ***
3. Sleep Latency                                --      .18 *
4. Sleep Duration                                         --
5. Habitual Sleep Efficiency
6. Sleep Disturbances
7. Sleep Medication Use
8. Daytime Dysfunction

Variable                          5         6        7         8

1. Energy Drinks               .45 ***   .17       .09      .09
2. Subjective Sleep Quality    .35 ***   .46 ***   .19 *    .22 *
3. Sleep Latency               .30 **    .37 ***   .24 **   .27 **
4. Sleep Duration              .43 ***   .25 **    .07      .17
5. Habitual Sleep Efficiency     --      .34 ***   .00      .26 **
6. Sleep Disturbances                      --      .23 **   .35 ***
7. Sleep Medication Use                              --     .14
8. Daytime Dysfunction                                        --

(a) Higher scores indicate more disturbance

* p <.05; ** p <.01; *** p <.001

Table 3
Simultaneous Linear Regression Predicting Energy Drink Use From the
7 Component Scores of the Pittsburgh Sleep Quality Index

DEPENDENT: Energy Drinks    b ([SE.sub.b])   [beta]   [DELTA][R.sup.2]

Subjective Sleep Quality      1.75 (0.48)      0.38         0.14
Sleep Latency                 0.69 (0.42)      0.17         0.02
Sleep Duration                0.83 (0.37)      0.22         0.04
Habitual Sleep Efficiency     1.56 (0.48)      0.32         0.08
Sleep Disturbances           -0.80 (0.67)     -0.12         0.01
Sleeping Medication           0.25 (0.35)      0.06         0.01
Daytime Dysfunction          -0.33 (0.45)     -0.07         0.01

DEPENDENT: Energy Drinks    [F.sub.Change]

Subjective Sleep Quality    17.71 ***
Sleep Latency                2.75
Sleep Duration               5.07 *
Habitual Sleep Efficiency   10.70 **
Sleep Disturbances           1.42
Sleeping Medication          0.51
Daytime Dysfunction          0.55

* p<.05; ** p<.01; *** p<.001

Source Citation

Source Citation   

Gale Document Number: GALE|A278276697