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Online drinking: an exploratory study of alcohol use and intoxication during internet activity.
Abstract:
Despite the commonplace use of the internet for socializing and recreating, little is known about alcohol use during online activity. This study investigates the prevalence of online drinking (drinking/ intoxication during internet use) in an American college student sample, differences in internet use associated with online drinking, consequences of online drinking, and the relationship between alcohol problems and internet addiction. Because social anxiety has been found to increase risk for both internet addiction and alcohol problems, this study also examines the relationship between social anxiety and online drinking. Results demonstrate that online drinking is commonplace, and tends to occur in conjunction with entertainment/social networking-based internet activities. For females, a significant positive correlation was found between scores on problem drinking and internet addiction screening measures. Online drinkers also had significantly higher internet addiction scores. Given the apparent lack of a role for social anxiety in explaining online drinking, other directions are proposed to advance this new area of research within the alcohol/addictions field.

Article Type:
Report
Subject:
Drinking of alcoholic beverages (Risk factors)
Drinking of alcoholic beverages (Research)
Internet (Usage)
Author:
Wolfe, Wendy L.
Pub Date:
03/01/2012
Publication:
Name: North American Journal of Psychology Publisher: North American Journal of Psychology Audience: Academic Format: Magazine/Journal Subject: Education; Psychology and mental health Copyright: COPYRIGHT 2012 North American Journal of Psychology ISSN: 1527-7143
Issue:
Date: March, 2012 Source Volume: 14 Source Issue: 1
Topic:
Event Code: 310 Science & research Computer Subject: Internet
Geographic:
Geographic Scope: United States Geographic Code: 1USA United States
Accession Number:
281111807
Full Text:
Skyping, gaming, friending, tweeting--our society has embraced the internet as an alternate, and sometimes preferable, social universe. This is particularly true for young people. According to the Pew Research Center's Internet and American Life Project (2010), 95% of 18 to 29 year-olds use the internet, more than any other age group. Among internet users, teens and young adults are more likely than older adults to use the internet for entertainment purposes, such as gaming, watching videos, and downloading music; and for social purposes, such as using social networking sites, blogs, and instant messaging (Pew Research Center, 2009). While internet use has many beneficial aspects, it can also be misused and overused, with internet scams and cyber bullying as examples of the former and internet addiction an example of the latter. Indeed, internet addiction is under review for inclusion in the DSM-V and has been conceptualized as an impulse control disorder (similar to compulsive gambling) involving symptoms such as preoccupation with going online, spending increasing amounts of time online, having difficulty cutting back on internet use, and continuing online activity in the face of negative consequences (Young, 1996).

Despite the conceptualization of excessive internet use as an addiction, and the increasingly commonplace use of the internet as a vehicle for socializing and recreating, the relationship between internet activity (and addiction) and substance use has garnered little research attention. In one of the few studies in this area, Ko et al. (2008) examined the co-occurrence of problematic alcohol use and internet addiction among high school students in Taiwan and found a significant positive correlation between the two. Further, they found that those with both problematic behaviors were more often males with co-occurring problems in the areas of family conflict, family alcohol use, and deviant behaviors among friends (Ko et al., 2008). Yen, Ko, Yen, Chen, and Chen (2009) also investigated the relationship between problematic alcohol use and internet addiction, but among college students in Taiwan. They also found a significant positive correlation between the two problem areas, and additionally found both to be positively correlated with depression. In the only published investigation of internet and alcohol use using an American sample, Epstein (2011) sought to examine the association between occurrence of drinking (lifetime and past month) and computer use (both time spent on the computer, and self-reported frequency of engaging in various online activities) in a sample of 13-17 year-olds. Participants who reported using alcohol in the past month reported significantly more time spent on the internet engaging in nonacademic tasks than participants who denied using alcohol in the past month. Moreover, participants reporting alcohol use at some point in their lifetime reported significantly greater frequency of internet use for social networking and for downloading and listening to music (past month use was also associated with greater frequency of the latter) than participants who denied ever having used alcohol (Epstein, 2011).

These studies suggest a relationship between drinking and internet use, and (in the case of the studies conducted in Taiwan) an association between problematic alcohol use and internet use. However, it is unclear if the correlation between problematic alcohol use and internet addiction would generalize to an American population. While the Epstein study opens the door to investigating this connection in an American population, the investigation focused on occurrence, duration, or frequency of use and did not include measures of problematic use (of either drinking or internet use). In addition, there remains no empirical information about whether alcohol use is combined with online activity and, if this is the case, what typifies online drinking. An article on the Japan Trends website focuses on the rising popularity of online drinking among young people through "net nomikai" in which individuals socialize through webcams while drinking (Andrews, 2010). However, the author notes that there is no research or data to support this "boom." In addition to gleaning insight into similarities and differences between online drinking and more traditional alcohol use behavior (e.g., drinking during in-person social activities), the relevance of online drinking is clear given the immediacy of online activity and the disinhibiting effect of alcohol. For example, it is quite possible that problematic online behaviors such as cyber bullying or sending/posting sexually explicit photos are more likely when alcohol and internet use are combined. Finally, given that social anxiety has been identified as a risk factor for both internet addiction (e.g., Caplan, 2010; Chak & Leung, 2004; Kraut et al., 1998) and problematic alcohol use (e.g., Crum & Pratt, 2001; Kushner, Sher, & Beitman, 1990; Morris, Stewart, & Ham, 2005; Thomas, Randall, & Carrigan, 2003), the question begs as to whether individuals who combine internet activity (particularly activity that is social in nature) with alcohol use are higher in social anxiety than those who do not.

The current study sought to gather data to begin to better understand the relationship between alcohol use and internet activity. The following questions were addressed:

What percent of college student drinkers consume alcohol during online activities, or use the internet while under the influence of alcohol (hereafter combined and referred to as online drinking, unless discussed separately)?

Are there online activities that are more likely to be engaged in while drinking or under the influence of alcohol (e.g., interactive, social activities such as instant messaging)? Relatedly, do online drinkers demonstrate different internet use patterns in general, as compared to alcohol users who do not engage in online drinking?

What consequences are encountered when people combine drinking with internet use? The well-publicized case of a Morgan Stanley commodities trader who made $10 million in risky online trades while intoxicated (Hosking, 2009), suggests that the immediacy of online activities can lead to unique consequences for those who use the internet while under the disinhibiting effects of alcohol.

Given the association between social anxiety and problematic use of both alcohol and the internet, are individuals who combine alcohol use and internet activity more socially anxious than those who do not? It has been found that socially anxious individuals use alcohol with the expectation that drinking will reduce their anxiety in social situations (Carrigan & Randall, 2003). Are socially anxious individuals more likely to engage in interactive internet activities while drinking or under the influence of alcohol, as compared to their general involvement in interactive online activities?

Can the relationship between problematic use of both alcohol and the internet, as reported by Ko and colleagues, be replicated in an American population?

Finally, given that sex differences have been found for both the relationship between social anxiety and alcohol (e.g., Morris et al., 2005) and between problematic alcohol use and internet addiction (Ko et al., 2008), do males and females differ in terms of the above questions?

METHOD

Participants

Students (128 males, 169 females) at a regional state university in the southeast United States participated in the IRB-approved study in exchange for class credit, with an average age of 23.65 years (SD = 6.34). Caucasians (68%) and African-Americans (20%) comprised most of the sample. The majority were undergraduates (95%) and were divided equivalently (20-27%) across academic classifications and academic fields of study. Fifty-seven percent of the participants could be characterized as traditional college students (i.e., 17-22 years of age, single, and without children), which is reflective of the university enrollment as a whole (Armstrong Atlantic State University, 2010). Participants' reported internet use was typical, however, for a young adult population in that 89% reported using the internet either daily or several times daily, with an average of 13.18 (SD = 13.19) hours online per week estimated by participants. The majority (81%) reported their favorite location for internet use as their home. Participants reported that 60% of their online time is spent engaged in leisure activities (email, social networking, online research, listening to music, and general web surfing). Because this investigation focused on online drinking, only current drinkers were eligible to participate in the study. Drinking status was verified by participant response to item 1 on the AUDIT (i.e., How often do you have a drink containing alcohol?) and those few nondrinkers (n = 25) who erroneously volunteered for the study were excluded from data analyses.

Measures and Procedure

After providing informed consent via an informed consent document presented to participants at the outset of the survey, the following measures were administered using the questionnaire administration program, Survey Monkey:

Internet Use. A 6-item Internet Use Questionnaire was developed by the author to assess the frequency and locations for internet use. Participants were asked to report the locations in which they use the internet (e.g., home, school/work, internet cafe or other public location), their preferred location, their frequency of internet use, estimated hours per week of internet use, and their estimated hours per week of academic/work-related internet use versus leisure use. The Internet Activities Questionnaire (IAQ) was also developed by the author to assess self-reported likelihood of engaging in various activities while online. The items were selected based a literature review of research on internet use behavior. Participants used a Likert rating scale from 1 (very unlikely) to 5 (very likely) to indicate how likely it would be that they would engage in a variety of online activities (see Table 1 for the list of internet activities included in both the IAQ and the AIAQ, described below).

Alcohol and Internet Use. Participants were asked two items to assess if they had ever consumed alcohol while using the internet or had ever used the internet while under the influence of alcohol. Those who responded yes to at least one of the items went on to complete the Alcohol and Internet Activities Questionnaire (AIAQ) and the Alcohol and Internet Consequences Questionnaire (AICQ), both developed by the author. The AIAQ asked participants to rate the likelihood of engaging in the same 16 online activities as in the IAQ, during online drinking. By including the same activities on the IAQ and the AIAQ, comparisons could be drawn between the likelihood of engaging in each online activity when drinking or intoxicated, versus general likelihood of engaging in the activity. On this measure, only participants who ever engaged in that particular online activity were asked to rate their likelihood of engaging in the activity while drinking or intoxicated, from 1 (very unlikely) to 5 (very likely). The AICQ was used to assess whether participants had ever experienced 11 possible consequences related to online drinking (see Table 2). Since research has not explored online drinking consequences, items were selected based on a review of measures of general alcohol use consequences (such as the Drinker's Inventory of Consequences; Miller, Tonigan, & Longabaugh, 1995) and a literature review of problematic internet-related behaviors. Participants indicated "yes" or "no" to each item and total scores reflect the total number of consequences reported by participants.

Internet Addiction Test. The IAT was developed by Young (1996, 1998) based on DSM-IV-IV criteria for impulse control disorders such as compulsive gambling. Participants are asked how often they have experienced 20 situations related to their internet use, capturing the effect of internet use on their relationships, emotions, sleeping, and daily activities, among others. Scores range from 20-100, with scores greater than 40 indicating frequent problems and scores greater than 70 indicating significant problems related to internet use. Factor analysis of the IAT has yielded six subscales, with good internal consistency. Concurrent validity has also been established (Widyanto & McMurran, 2004).

Alcohol Outcome Expectancy Scale. The AOES was administered to assess the outcome expectancies that participants have for their alcohol use. The measure was developed by Leigh and Stacy (1993) and asks participants to rate the likelihood that 34 different outcomes would occur to them if they were to consume alcohol. The outcomes include negative outcome categories (negative social, emotional, physical, and cognitive/performance effects), and positive outcome categories (social facilitation, fun, sex, and tension reduction). The subscales and the positive and negative dimensions have been found to have good internal consistency (Leigh & Stacy, 1993). While both positive and negative expectancies have been found to be associated with drinking behavior, endorsement of stronger positive expectancies has been found to account for greater variability in alcohol use (Leigh & Stacy, 1993). A particular interest in this investigation was the expectation that alcohol will facilitate social interaction and reduce tension.

Social Anxiety and Distress Scale. The SAD is a well-established measure of anxiety in social situations. It consists of 28 true-false items and yields a total score with excellent internal consistency and good one-month test-retest reliability (Watson & Friend, 1969). There is no established cut-off score (higher scores indicate greater social anxiety), although past research has yielded mean scores and standard deviations for college males and females.

Alcohol Use Disorders Identification Test. The AUDIT is an established screening measure for alcohol problems and was developed in collaboration with the World Health Organization to provide a gender and culture-neutral screening measure for the early detection of alcohol problems (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). It contains ten items that assess both quantity and frequency of alcohol use, as well as dependence symptoms and the occurrence of problems related to alcohol use. The AUDIT yields a total score of up to 40, with a criterion of 8 typically used to identify individuals with potential alcohol problems.

Demographic Questionnaire. A questionnaire was used to gather demographic information on participants, including their age, sex, race, class standing, major program of study, marital status, and residence status.

Preliminary Analyses

As research on alcohol use during online activity is a new area of investigation, several questionnaires were developed by the author due to an absence of established measures of the constructs of interest. Specifically, the IAQ assessed reported likelihood of engaging in a variety of internet activities when online, the AIAQ assessed reported likelihood of engaging in those same activities during online drinking, and the AICQ assessed whether a variety of consequences had ever occurred as a result of online drinking. Activities surveyed by the IAQ and AIAQ are listed in Table 1 and consequences that were included in the AICQ are listed in Table 2. The Cronbach's Alphas for the IAQ, AIAQ, and AICQ are presented in Table 3, along with preliminary validation in the form of correlations with other measures used in the study that were expected to yield associations with the constructs of interest. As can be seen in the Table, internal reliability is marginally acceptable for the IAQ and AIAQ, but good for the AICQ. However, with regard to the IAQ and AIAQ, it has been noted that internal consistency in the .6 to .7 range is acceptable for exploratory studies (Garson, 2011), which accurately describes the nature of this investigation. Total scores for the IAQ and AIAQ were not a focus of this investigation and were not used in the principal analyses. However, IAQ and AIAQ total scores were examined relative to other potentially related measures for the purpose of exploring construct validity. Expected correlations between the newly developed measures and more established measures of related constructs were found, providing preliminary support for their validity. For example, greater likelihood of engaging in a variety of online activities on the IAQ was significantly positively associated with scores on the IAT, whereas greater likelihood of engaging in various online activities while drinking and reports of greater consequences of online drinking were significantly positively associated with both IAT scores and AUDIT scores.

In order to verify the internal consistency of more established measures used in the primary analyses, Cronbach's Alphas for these measures and subscales (e.g., tension reduction scale of the AOES) were also computed. Internal consistency was high in all cases, ranging from .79 for the AUDIT to .93 for the SAD.

Primary Analyses

What percent of college student drinkers consume alcohol during online activities, or use the internet while under the influence of alcohol? Forty percent of participants (47% males, 36% females) reported using alcohol while engaged in online activity and 52% (59% males; 47% females) reported going online while under the influence of alcohol. Perhaps not surprisingly, online drinkers had significantly higher AUDIT scores (M = 9.27, SD = 5.21) than online abstainers (M = 4.93, SD = 4.08), [t(295) = -7.87, p < .01].

Are there online activities that are more likely to be engaged in while drinking or under the influence of alcohol? As shown in Table 1, the online activities most often engaged in when participants use alcohol are social or entertainment-based. Independent samples t-tests comparing males and females indicated that males were significantly more likely to go online to watch or download music or videos, use regular or interactive cybersex sites, or play interactive games during online drinking, whereas females were significantly more likely to use social networking sites (all p's < .01). These sex differences were similar to those evidenced by participants when asked about their general (non-alcohol related) online activities.

Do online drinkers demonstrate different internet use patterns in general, as compared to alcohol users who do not engage in online drinking? With regard to general online activities, individuals who engage in online drinking were found to be significantly more likely than alcohol users who abstain from online drinking to spend more time online and more time engaged in online leisure activities (see Table 4). As can be seen in the Table, they were also significantly more likely to report spending their online time engaged in general web surfing and entertainment-oriented activities, including shopping and visiting cybersex sites.

Are there unique consequences associated with mixing alcohol and internet use? Participants acknowledged experiencing a range of consequences as a result of online drinking. As shown in Table 2, the most common consequence was spending too much time online and neglecting other tasks. However, approximately 30-40% of participants indicated that they said, wrote, or did something they later regretted; got into an argument; felt more comfortable/less anxious during the online activity; and gave out too much information about themselves as a result of their alcohol use.

Can the relationship between problematic use of both alcohol and the internet, as reported by Ko and colleagues, be replicated in an American population? Using the recommended IAT criterion score of 40, 20% of our sample evidenced a problematic pattern of internet use (23% males, 17% females). Using the recommended AUDIT criterion score of 8, 40% of our sample responded to the measure in such a way as to indicate potential problems with alcohol use (53% males, 31% females). A significant positive correlation was found between IAT and AUDIT scores (r = .22, p < .001). However, when computed for males and females separately, it was discovered that the relationship was only present for female participants (males: r = .09, p = .31; females: r = .29, p < .001). Finally, participants who reported consuming alcohol during online activity (M = 30.98, SD = 14.3) had significantly higher IAT scores than participants who denied drinking during online activity (M = 27.71, SD = 11.89), [t(292) = 2.13, p < .05]. Similarly, participants who reported going online when under the influence of alcohol (M = 30.95, SD = 13.9) had significantly higher IAT scores than participants who denied online intoxication (M = 26.94, SD = 11.63), [t(292) = 2.68, p < .01]. This pattern was similar for male and female participants.

Are individuals who combine alcohol use and internet activity more socially anxious than those who do not? The mean score for participants on the SAD was 8.07 (SD = 7.04), which is similar to the scores (M = 9.1, SD = 8) for university students reported by the measure developers (Watson & Friend, 1969). As expected, SAD scores were significantly positively correlated with IAT scores (r = .284, p < .001) for participants as a whole, and when examining scores separately for males (r = .342, p < .001) and females (r = .253, p = .001). Contrary to expectations, there was no correlation between AUDIT and SAD scores (r = .037, p > .05). T-tests revealed that participants who reported using the internet while drinking [t(295) = -.169, p > .05] or intoxicated [t(295) = .01, p > .05] reported similar social anxiety and avoidance levels on the SAD as compared to participants who denied such involvement.

Are socially anxious individuals more likely to engage in particular internet activities (e.g., interactive activities) while drinking or under the influence of alcohol? Correlation analyses were run to determine if SAD scores were associated with a greater likelihood of drinking during particular types of online activities as reported on the AIAQ. Results indicated that SAD scores were not significantly associated with likelihood of involvement in most types of online activities while drinking. An exception was reported likelihood of online gambling while drinking, which was significantly positively associated with SAD scores (r = .316, p < .05) for participants as a whole, and drinking during role play gaming, which was significantly positively associated with SAD scores for female participants (r = .392, p < .05). Finally, hierarchical linear regression was used to examine whether social anxiety and avoidance, alcohol outcome expectancies, or the interaction between these variables, might predict likelihood of engaging in various online activities while drinking. Because males and females differed in their reported likelihood of engaging in many online activities (in general, and while drinking), participant sex was first entered in the regression model. In a second step, SAD, AOEQ tension reduction, and AOEQ social facilitation scores were entered in a stepwise (forward) manner, along with interaction terms combining SAD x AOEQ tension reduction scores and SAD x AOEQ social facilitation scores. Results indicated that social anxiety, alcohol outcome expectancies, and their combination failed to predict reported likelihood of engaging in the various online activities when drinking (regression results available on request for the 16 online activities assessed on the AIAQ).

DISCUSSION

Just over half of current drinkers (55%), acknowledged either using the internet while drinking or going online while under the influence of alcohol. Thus, combining drinking and online activity is a commonplace, though not universal, phenomenon among the college students in our sample. Participants reported they were most likely to drink while using the internet for entertainment or social purposes. In this way, the online world appears to mirror the offline world in terms of the settings (e.g., social networking sites) and activities in which alcohol use is most common. However, consequences can still arise in these situations. Over half of participants reported their alcohol use led them to spend too much time online and to neglect something important as a result. Over a third of participants acknowledged saying (posting) something they later regretted or getting into an argument as a result of combining alcohol and online activity, while a third of females reported giving out too much information about themselves due to online drinking. Because many people go online when they are alone (and unmonitored), and since online behaviors can occur so quickly (e.g., spending money with a few key strokes or clicks of the mouse), the disinhibiting effects of alcohol can be particularly problematic when combined with online activity. Indeed, participants who reported online drinking had significantly higher IAT scores than their sober web surfing peers. Given the correlational nature of this study, the direction of the relationship between online drinking and problematic (e.g., compulsive, excessive) internet use is unclear. It is possible that online drinking leads to increased risk for the development of problematic internet behaviors, such as spending too much time and neglecting other activities when online. Individuals with problems related to their internet use may also be more inclined to drink during online activity, as a means of assuaging feelings of guilt or as a way of heightening pleasure from the activity. The top three most frequently identified consequences of online drinking by participants (spending too much time online, neglecting something important, and enjoying the online activity more) are consistent with both possibilities. Further research is needed to better understand the nature of the relationship found here between online drinking and internet addiction.

Online drinkers also reported different general internet use patterns than online abstainers. They reported spending more time online, particularly engaged in leisure activities such as those that could be considered escapist in nature (listening to music and videos, shopping, general web surfing, and visiting cybersex sites). While an acquiescence response bias could account for this difference, the lack of a relationship between online drinking and social anxiety, as well as lower reported likelihood of engaging in some forms of online activity (e.g., online research) are not consistent with this interpretation. A third variable, such as depression or sensation seeking, could account for the greater likelihood of some drinkers using the internet more often, and for particular means, even when not combining alcohol and internet use.

While online drinking was associated with increased internet addiction scores for both males and females in our sample, the association between problematic alcohol use and internet addiction that has been identified by others (Ko et al., 2008; Yen et al., 2009) was only found for female participants in this study. When a sex difference has been found in Asian samples, it has supported a stronger association between alcohol and internet addiction for males. Ko et al. found that co-occurring alcohol problems and internet addiction were associated with other variables that suggested a pattern of family disruption and conduct problems. It may be that in an American college population, the risk pattern is different. Although social anxiety did not appear to play a significant role in online drinking in this study, other variables (e.g., depression) that may be relevant for understanding the relationship between alcohol use and internet addiction were not assessed. American college student females may be using both alcohol and internet activity to help cope with aversive emotions, to a point that both become excessive or problematic. Further research is needed to explore mechanisms underlying the development of problematic patterns of alcohol use and internet use for female college students.

Interestingly, although social anxiety was associated with internet addiction in this study, it added little to understanding online drinking. It could be that, unlike face-to-face interactions, online communication provides enough of a buffer for those with social anxiety that drinking during online activity is not needed. Future research might test this directly by using an experimental design in which participants high versus low in social anxiety are asked to consume alcohol during a "taste rating task" prior to engaging in either in vivo or internet-based social interactions. Future research might also ask participants to monitor their alcohol use during online activities in order to address a relative limitation of this research design, which is a reliance on participants' retrospective reports on their online drinking behavior and an absence of information about the amount of alcohol consumed during online drinking.

Another limitation of this study is that the participant sample may not represent American college students more generally. However, national data suggest that our sample is in line with changes in college enrollment and that our participants reflect in many ways this changing face of today's college student (e.g., older, more likely to have children; Kennedy & Ishler, 2008). Despite these limitations, this study advances the alcohol literature by examining online drinking rates, behaviors, and consequences, along with the association between alcohol and internet use problems. As internet activity becomes increasingly integrated in our lives, it is important to understand how other behaviors, such as drinking, are manifested in, alter, or are altered by our online existence. While correlational and exploratory in nature, this investigation presents initial descriptive information about drinking and online activity, and reveals some associations that can be further explored by longitudinal and experimental research in this area.

Acknowledgements: Thank you to Forrest Files and Shrinidhi Subramaniam for assistance with measure development and data collection. Thank you to Vann Scott and Bradley Sturz for providing feedback and editing assistance with this manuscript.

REFERENCES

Andrews, W. (2010). Online drinking parties for younger Japanese. Retrieved from http://www.japantrends.com/younger-japanese-drinkers-enjoy-nomi kai-parties-online/.

Armstrong Atlantic State University, Office of Institutional Research (2010). 2010 Fact Book. Retrieved June 3, 2011, from http://www.armstrong.edu/images/institutional research/Armstrong Atlantic State University 2010 Fact Book.pdf.

Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (1992). The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care (2nd ed.). Geneva, Switzerland: World Health Organization, Department of Mental Health and Substance Dependence (document number WHO/PSA/92.4).

Caplan, S. E. (2010). Theory and measurement of generalized problematic internet use: A two-step approach. Computers in Human Behavior, 26, 1089-1097.

Carrigan, M. H., & Randall, C. L. (2003). Self-medication in social phobia: A review of the alcohol literature. Addictive Behaviors, 28, 269-284.

Chak, K., & Leung, L. (2004). Shyness and locus of control as predictors of internet addiction and internet use. CyberPsychology & Behavior, 7, 559-570.

Crum, R. M., & Pratt, L. A. (2001). Risk of heavy drinking and alcohol use disorders in social phobia. American Journal of Psychiatry, 158, 1693-1700.

Epstein, J. A. (2011). Adolescent computer use and alcohol use: What are the role of quantity and content of computer use? Addictive Behaviors, 36, 520-522.

Garson, G. D. (2011). Reliability analysis. from Statnotes: Topics in Multivariate Analysis. Retrieved from http://faculty.chass.ncsu.edu/garson/ PA765/ statnote.htm.

Hosking, P. (2009). Trader banned for $10m bet after 'boozy' lunch. Retrieved from http://business.timesonline.co.uk/tol/business/industry sectors/ banking and finance/article6326340.ece.

Kennedy, K., & Ishler, J. C. (2008). The changing college student. In V. N. Gordon, W. R. Habley, & T. J. Grites (Eds.), Academic advising: A comprehensive handbook (2nd ed., pp. 123-141). San Francisco: JosseyBass.

Ko, C., Yen, J., Yen, C., Chen, C., Weng, C., & Chen, C. (2008). The association between internet addiction and problematic alcohol use in adolescents: The problem behavior model. CyberPsychology & Behavior, 11, 571-576. doi: 10.1089/cpb.2008.0199.

Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53, 1017-1031.

Kushner, M. G., Sher, K. J., & Beitman, B. D. (1990). The relation between alcohol problems and the anxiety disorders. American Journal of Psychiatry, 147, 685-695.

Leigh, B. C., & Stacy, A. W. (1993). Alcohol outcome expectancies: Scale construction and predictive utility in higher order confirmatory models. Psychological Assessment, 5, 216-229.

Miller, W. R., Tonigan, J. S., & Longabaugh, R. (1995). The Drinker Inventory of Consequences (DrInC): An instrument for assessing adverse consequences of alcohol abuse. Rockville, MD: NIAAA.

Morris, E. P., Stewart, S. H., & Ham, L. S. (2005). The relationship between social anxiety disorder and alcohol use disorders: A critical review. Clinical Psychology Review, 25, 734-760.

Pew Research Center: Internet & American Life Project (2009). Generational differences in online activities. Retrieved from http://www. Pew internet.org/Infographics/Generational-differences-in-online activities. aspx. Pew Research Center: Internet & American Life Project (2010). Who's online:

Internet demographics. Retrieved from http://www.pewinternet.org/Trend Data/Whos-Online.aspx.

Thomas, S. E., Randall, C. L., & Carrigan, M. H. (2003). Drinking to cope in socially anxious individuals: A controlled study. Alcoholism: Clinical and Experimental Research, 27, 1937-1943.

Watson, D., & Friend, R. (1969). Measurement of social-evaluation anxiety, Journal of Consulting and Clinical Psychology, 33, 448-457.

Widyanto, L., & McMurran, M. (2004). The psychometric properties of the internet addiction test. CyberPsychology & Behavior, 7, 443-450.

Yen, J., Ko, C., Yen, C., Chen, C., & Chen, C. (2009). The association between harmful alcohol use and internet addiction among college students: Comparison of personality. Psychiatry and Clinical Neurosciences, 63, 218-224.

Young, K. (1996). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 3, 237-244.

Young, K. (1998). Caught in the net. New York, NY: John Wiley & Sons.

Wendy L. Wolfe

Armstrong Atlantic State University

Author info: Correspondence should be sent to: Wendy L. Wolfe, Department of Psychology, Armstrong Atlantic State University, 11935 Abercorn St., Savannah, GA 31419. Phone: 912-344-2955; Email: wendy.wolfe@armstrong.edu.
TABLE 1 Likelihood of Engaging in Various Internet Activities
During Online Drinking

Activity                         n          M          SD

Social networking sites         135        3.71       1.27
Watch/downloading video         149        3.48       1.31
Listen to/downloading music     151        3.38       1.44
General web surfing             151        3.21       1.38
Interactive chat                 99        2.75       1.51
Reading/Responding to Email     148        2.59       1.45
Cybersex websites                82        2.51       1.39
Reading/Posting to Blogs        101        2.33       1.40
Gaming                           72        2.13       1.15
Interactive Gaming               59        2.12       1.35
Shopping                        116        2.05       1.28
Research                        143        1.95       1.19
Interactive Cybersex             38        1.95       1.37
Dating Websites                  38        1.84       1.24
Roleplay Gaming                  46        1.78       1.26
Gambling                         32        1.44       .91

Note. Only participants who reported using the internet while
drinking or intoxicated are reflected in the table. For each
activity, ratings are only included for participants who reported
ever engaging in that particular online activity. Ratings were
assigned using a 1 (Very Unlikely) to 5 (Very Likely) scale in
response to the question, "How likely are you to engage in the
following internet activities while drinking alcohol or while
under the influence of alcohol?".

TABLE 2 Consequences of Combining Alcohol and Online Activity

                                         %
                                     Reporting      %         %
Consequence                             Yes       Males    Females

Spent too much time online               61         54        67
Neglected something important            53         52        54
Found activity more enjoyable            51         61        42
Said/wrote something later               44         44        44
regretted
Felt more comfortable being myself       41         46        36
Got into an argument                     38         34        42
Felt less anxious when online            30         32        28
Did something I later regretted          29         27        31
Gave too much info about self            29         24        33
Spent too much money                     23         24        22
Drank more than intended                 14         18        11

Note. Above reflects percentage of online drinkers who reported
the consequence has ever occurred to them as a result of their
use of the internet while drinking or intoxicated.

TABLE 3 Internal Consistency and Concurrent Validity of New
Measures

                             Cronbach's   Correlation with
Measures                     Alpha        Measures

Internet Use Questionnaire   Not
  --online frequency         assessed     IAT (r = .15 *)
  --weekly hrs online                     IAT (r = .30 ***)
IAQ                          .63          IAT (r = .42 ***)
AIAQ                         .66          AUDIT (r = .26 ***),
                                          IAT (r = .31 ***)
AICQ                         .77          AUDIT (r = .34 ***),
                                          IAT (r = .41 ***)

Note: Other than Internet Use Questionnaire, analyses above were
based on total scores for the measures. * p < .05, *** p < .001. IAQ
= Internet Activities Questionnaire, AIAQ = Alcohol and Internet
Activities Questionnaire, AICQ = Alcohol and Internet Consequences
Questionnaire, IAT = Internet Addiction Test, AUDIT = Alcohol Use
Disorders Identification Test.

TABLE 4 Differences in General Reported Internet Use Between
Online Drinkers and Online Abstainers

                               Drinkers:       Abstainers:
Internet Use/Activity          M (SD)          M (SD)

Weekly hours online            15.68 (15.73)   10.17 (8.4)
Weekly hours online leisure    9.72 (13.25)    5.57 (5.82)
Weekly hours online non-       5.96 (6.03)     5.1 (5.89)
leisure
Reading/Responding to Email    3.8 (1.23)      3.7 (1.26)
Reading/Posting to Blogs       2.22 (1.35)     1.96 (1.27)
Interactive chat               2.07 (1.42)     1.74 (1.17)

Social networking              3.56 (1.5)      3.65 (1.53)
Gambling                       1.13 (.45)      1.11 (.49)
Shopping                       2.49 (1.2)      2.2 (1.08)
Gaming                         1.8 (1.14)      1.67 (1.05)
Interactive Gaming             1.54 (1.07)     1.45 (.98)
Roleplay Gaming                1.34 (.9)       1.24 (.72)
Cybersex                       1.79 (1.13)     1.5 (.92)
Interactive Cybersex           1.17 (.54)      1.15 (.6)
Dating websites                1.21 (.6)       1.19 (.73)
Watching or downloading        3.27 (1.24)     2.65 (1.37)
video
Listening to or downloading    3.6 (1.29)      3.28 (1.35)
music
Research                       3.7 (1.07)      3.88 (1.02)
General web surfing            3.82 (1.07)     3.49 (1.19)

Internet Use/Activity          T-test

Weekly hours online            t(295)=-3.66 **
Weekly hours online leisure    t(295)=-3.38 **
Weekly hours online non-       t(295)= -1.24
leisure
Reading/Responding to Email    t(294)= -.67
Reading/Posting to Blogs       t(294)= -1.70
Interactive chat               t(293)= -2.14 *

Social networking              t(293)= .27
Gambling                       t(293)= -.34
Shopping                       t(293)= -2.16 *
Gaming                         t(294)= -1.02
Interactive Gaming             t(295)= -.76
Roleplay Gaming                t(291)= -.98
Cybersex                       t(292)= -2.32 *
Interactive Cybersex           t(292)= -.29
Dating websites                t(291)= -.31
Watching or downloading        t(292)=-4.02 **
video
Listening to or downloading    t(294)= -2.05 *
music
Research                       t(292)= 1.42
General web surfing            t(294)= -2.55 *

Note. * p < .05, ** p < .01. Participants who reported using the
internet when drinking or when under the influence of alcohol
comprise the "Drinkers" group above. Comparing these two groups
separately to alcohol users who denied online drinking yielded
similar findings as above, with additional significant group
differences for participants who reported drinking while online
(compared to online abstainers), who were more likely to post to or
read discussion boards/blogs (p<.05) and to engage in roleplay
gaming (p<.05), but less likely to spend time doing online research
(p<.01).
Gale Copyright:
Copyright 2012 Gale, Cengage Learning. All rights reserved.