Dieting behavior and alcohol use behaviors among national eating disorders screening program participants.
Subject: Compulsive eating (Surveys)
Anorexia nervosa (Surveys)
Alcohol and youth (Surveys)
Drinking of alcoholic beverages (Surveys)
Authors: Heidelberg, Natalie F.
Correia, Christopher J.
Pub Date: 12/01/2009
Publication: Name: Journal of Alcohol & Drug Education Publisher: American Alcohol & Drug Information Foundation Audience: Academic; Professional Format: Magazine/Journal Subject: Health; Psychology and mental health; Social sciences Copyright: COPYRIGHT 2009 American Alcohol & Drug Information Foundation ISSN: 0090-1482
Issue: Date: Dec, 2009 Source Volume: 53 Source Issue: 3
Accession Number: 218817577

Objective: Research has shown that college students have elevated rates of alcohol use and problematic eating behaviors. The current study focused on the relationships between dieting behaviors and alcohol use among a sample of undergraduates attending National Eating Disorder Screening Program. Method: All participants (n = 70, 100% female, average age 20.5) completed a packet of measures assessing alcohol use and eating behaviors. Results: Dieting was positively correlated with alcohol use and alcohol-related problems; dieting also increased the risk of alcohol-related problems among heavier drinkers. Conclusion: The results of this study show that dieting behaviors are associated with increased risk for alcohol use problems, and that students attending screening events may present with a range of negative health behaviors.

Keywords: dieting, alcohol use, college students, prevention


A significant percentage of college-aged women report dieting to lose weight (Chamay-Weber, Narring, & Michaud, 2005). Specific behaviors engaged in as part of a diet can include unhealthy and dangerous means of weight loss, that is, liquid and low-calorie diets, skipping meals and fasting, excessive exercising, and purging. Dieting has been associated with a range of negative physical and psychological outcomes, including an increased risk of developing an eating disorder (Chamay-Weber et al., 2005; Stice, 2002). Recent studies have highlighted the relationship between dieting and increased risk for problems related to substance use among college students (Krahn, Kurth, Gomberg, & Drewnowski, 2005).

Though the relation has been well documented in the literature, these studies focusing on alcohol use and dieting behaviors among college students have produced mixed results. Studies suggest that college-age women with full eating disorders and those that engage in dieting behaviors report more negative consequences as a result of alcohol use than do non-disordered eaters (Anderson, Martens, & Cimini, 2005; Dunn, Larimer, & Neighbors, 2002). However, the conflicting results are evident in whether or not individuals who engage in dieting behaviors report an increase in quantity and/or frequency of alcohol use. For example, among college-aged women, Chamay-Weber et al. (2005) reported that dieting behaviors are positively correlated with "increasing prevalence of alcohol, cigarette, and marijuana use, and with increasing frequency and intensity of alcohol use" (p. 422). Similarly, Anderson et al. stated that female college students who reported purging during the previous month also reported "more frequent alcohol use and more negative consequences of alcohol use than the comparison group" (p. 65). However, in a study that assessed for clinical diagnosis of eating disorders, Dunn et al. reported that the quantity and frequency of alcohol consumption are similar between college students with and without problematic eating behaviors, while those meeting criteria for eating disorders experience more negative consequences. The current study was designed to further examine the relationships between a specific type of problematic eating behavior, dieting, and alcohol use in a sample of college undergraduates. The current study focused on the relationship between dieting and alcohol use among a sample of undergraduates attending National Eating Disorder Screening Program (NEDSP). The NEDSP, an annual event on college campuses nationwide since 1996, has been sponsored by the Mental Health Screening Organization with the purpose of educating, screening, and providing help for at-risk college students (Becker, Franko, Nussbaum, & Herzog, 2004). NEDSP provides an excellent opportunity to investigate a range of issues related to disordered eating, including symptoms of anorexia and bulimia, dissatisfaction with body image, and unhealthy dieting behaviors. However, to date, no research conducted with NEDSP participants has looked beyond eating disorder symptoms. The current study took advantage of this event to look more closely at the relationship between dieting behaviors and alcohol use.

We hypothesized that NEDSP participants who engage in dieting behaviors will report elevated levels of alcohol use and related problems. We also sought to determine the degree to which dieting contributed to predictions of alcohol related problems.



The participants were female undergraduate students who participated in the NEDSP (n=70). All participants were at least 19 years old (M = 20.5). The majority of the sample identified itself as Caucasian (77%), although other ethnicities were represented in the sample (23% African American, 4% Asian American; numbers total to greater than 100% because participants could indicate multiple ethnicities).

2.2. Self-Report Measures

EAT-26. The Eating Attitudes Test-26 (EAT-26) is a shortened version of the original 40 question Eating Attitudes Test (Garner, Olmstead, Bohr, & Garfinkel, 1982). The EAT-26 contains 26 statements and offers a 6 point Likert scale with responses ranging from Always to Never. The three responses most indicative of problematic eating behavior receive a score of 3, 2, and 1, while the other three responses are scored 0. For the current study, only the Dieting subscale was included in the analyses. The Dieting subscale is composed of thirteen questions related to the desire to be thinner and the avoidance of fattening foods. The EAT-26 has shown reliability and validity when used with undergraduate college students (Thome & Espilage, 2004). In the current study, the Dieting subscale had an internal reliability coefficient of .86. In addition to the items used to create the Dieting scale, EAT-26 includes items about the frequency of problematic eating behaviors, including binge eating, purging behaviors, and history of treatment for an eating disorder. The latter items were included to better describe the sample.

DDQ. The Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985) was used to assess the average quantity of alcohol consumed for each day of the week during the previous four weeks. The total DDQ score represents that amount of alcohol, in terms of standard drinks, consumed during a typical week. Research has shown self-reported alcohol use information to be valid when participants' confidentiality is ensured (Johnston & O'Malley, 1985).

RAPI. The Rutgers Alcohol Problem Index (RAPI) was used to assess negative consequences a participant has experienced over the previous 28 days as a result of alcohol use. The RAPI is a 23 item, self-report measure that requires participants to respond to statements based on how many times they have experienced a particular problem in the previous 28 days. Responses are indicated using a five-point Likert scale (0=never, 1=1-2 times, 2=3-5 times, 3=6-10 times, 4=more than 10 times). The RAPI was designed for use with adolescents and young adults, ages 12-21, which makes it an acceptable measure for use with college students. Internal consistency has been found in previous studies to be adequate (r = .77-.82; White & Labouvie, 1989), and in the current study (.89).


Participants were drawn from a larger sample of approximately 150 students who attended a campus-based NEDSP program. Students completed the recommended NEDSP activities, which included receipt of printed materials on issues related to eating disorders, completion of a screening measure, and a brief discussion with a clinician. The students were then offered an opportunity to participate in the current research project in exchange for entrance into a raffle to win one of two $50 gifts. The criteria for inclusion in the study were being female and at least 19 years old.


The descriptive data (mean, standard deviation, minimum, and maximum) for all variables used in the analyses are presented in Table 1. Of our sample, 26% of participants reported engaging in subjective binge eating episodes in the past 6 months; 10% reported self-induced vomiting to control their weight or shape; and 11% reported using laxatives, diet pills, or diuretics to control their weight or shape. Just under 3%, or 2 participants, reported that they had received treatment for an eating disorder in the past.

Correlations among Variables

The Dieting subscale was positively correlated with both the quantity of alcohol consumed (r = .24, p<.05) and the severity of alcohol-related problems (r = .31, p<.05).

Regression Modeling Alcohol-Related Problems

A hierarchical linear regression was conducted to determine if variables assessing alcohol use and dieting were predictive of alcohol related problems (Table 2). In the first step of the regression, the quantity of alcohol consumed during a typical week was predictive of alcohol-related problems [F(1, 66) = 50.20, p<.001] and accounted for 43% of the variance. The addition of the Dieting subscale during the second step increased the amount of variance accounted for by 2.7%, although the increase was not statistically significant [change statistic F(1, 65) = 3.19, p = .079]. The third and final step contained a significant quantity of alcohol consumption by Dieting subscale interaction term (p = .025) that led to a significant increase in the amount of variance accounted for in alcohol-related problems [change statistic F(1, 64) = 5.24, p = .025]. The final model, including the quantity of alcohol consumed, the Dieting subscale, and the interaction term, accounted for 48% of the variance, F(3, 64) = 21.3, p<.001.

A series of t-tests was used to further explore the significant interaction (Figure 1). Median splits were used to classify participants as either lighter drinkers (M = 0.26 drinks per week, SD = 0.61) or heavier drinkers (M = 12.74 drinks per week, SD = 6.72), and to classify participants as low dieters (Dieting M = 2.06, SD = 1.84) or high dieters (Dieting M = 11.62, SD = 6.72). For lighter drinking participants, the severity of alcohol related problems did not differ across the low dieters (M = 0.0, SD = 0.0) and high dieters (M = .14, SD = .36). For participants who reported heavier drinking, however, alcohol related problems did differ as a function of scores on the Dieting subscale, with low dieters reporting fewer alcohol-related problems (M = 4.58, SD = 3.60) than high dieters (M = 9.50. SD = 7.55), t(32) = .04.




The current study is the first to investigate the relationship between dieting behaviors and alcohol use among a sample of NEDSP participants. The results are consistent with previous studies in suggesting that dieting is related to alcohol use and use-related problems (Krahn et al., 2005). Dieting increases predictions of alcohol related problems after first accounting for the effects of the amount of alcohol consumed during a typical week. The results also suggest that dieting might exacerbate the positive relationship between alcohol use and alcohol-related problems, with dieting increasing the risk among women who engage in patterns of heavier alcohol use. These findings highlight the fact that dieting may be associated with a wide range of negative health behaviors, and that multiple behaviors serve as risk factors for alcohol-related problems.

Several theories have been used to help explain the pattern of comorbidity. The dysregulation hypothesis suggests that the comorbidity between dieting and alcohol use is the result of an overall dysfunction in an individual's behavioral regulation (Stewart, Angelopoulos, Baker, & Boland, 2000). This hypothesis is supported by a study that showed that, the more dysfunctional the dieting behavior individuals engage in, the more likely they are to engage in risky alcohol use (Krahn et al., 2005). Researchers have also investigated the role of restraint in both dieting and alcohol use, suggesting that individuals who restrict what they eat are more likely to consume higher levels of alcohol on drinking occasions than non-restrained eaters (Stewart et al.). Similarly, Polivy and Herman (1985) observed that individuals who attempt to limit caloric intake (i.e., dieters) are more likely to engage in binge eating episodes. This phenomenon has also been observed in social drinkers; Muraven et al. (2005) observed that individuals who set limits on alcohol consumption were more likely to drink in excess once their limits were violated. Although research has been conducted to investigate the effects of limit violations in both dieters and social drinkers, no research has simultaneously investigated both behaviors to determine if certain individuals are prone toward responding to food and alcohol-related violations with excessive consumption. Future research should continue to investigate this relationship and the mechanisms responsible for the comorbidity between dieting and alcohol use, and related problems.

Clinical Implications

National Eating Disorder Screening Program, along with National Depression Screening Day, National Anxiety Screening Day, and National Alcohol Screening Day, are four screening programs conducted by the Mental Health Screening Organization on college campuses across the United States each year. While these screening events are designed to target individual disorders, data suggest that comorbidity among disorders is the norm rather than the exception. In addition to being at risk for alcohol related problems, individuals with eating disorders are also at an elevated risk for experiencing mood disorders (prevalence rates = 25-50%), anxiety disorders (20-70%), substance abuse disorders (10-55%), and personality disorders (30-90%; Steiger & Seguin, 1999). A combined "Mental Health Screening Day" might better address the clinical reality that individuals with one disorder likely experience symptoms of additional disorders. Alternatively, screening day organizers and clinicians at diagnosis-specific screening events should be prepared to discuss and screen for conditions that commonly co-occur with the target diagnosis. Additional research aimed at developing interventions for multiple negative health behaviors is also warranted.

Limitations and future directions

This study is limited by a small, homogeneous self-selected sample of female college students. Given the prevalence of problematic eating and alcohol use in this population the sample seems appropriate, but additional studies will be needed to determine how well the results generalize to more diverse samples. The results of this study demonstrate the potential value of assessing for comorbid negative health behaviors during mental health screening events. Additional research will be needed to determine the clinical utility of screening for multiple disorders and to develop interventions that can be delivered at diagnosis-specific or general mental health screening events. For example, we have shown in previous research the feasibility and effectiveness of incorporating personalized feedback regarding alcohol use into National Alcohol Screening Day (Benson, Ambrose, Mulfinger, & Correia, 2004; Henslee et al., 2006). Research in incorporating empirically supported interventions into NEDSP is clearly warranted.

Correspondence concerning this article should be addressed to: Christopher J. Correia, Ph.D., Department of Psychology, Auburn University, Thach Hall 226, Auburn University, AL 36849, Phone: (334) 844-6480, Fax: (334) 844-4447, Email:


Anderson, D. A., Martens, M. P., & Cimini, M. D. (2005). Do female college students who purge report greater alcohol use and negative alcohol-related consequences? International Journal of Eating Disorders, 37, 65-68.

Becker, A. E., Franko, D. L., Nussbaum, K., & Herzog, D. B. (2004). Secondary prevention for eating disorders: The impact of education, screening, and referral in a college-based screening program. International Journal of Eating Disorders, 36, 157-162.

Benson, T.A., Ambrose, C.A., Mulfinger, A.M.M., & Correia, C.J. (2004). Integrating mailed personalized feedback and alcohol screening events: A feasibility study. Journal of Drug Education, 34, 327-334..

Chamay-Weber, C., Narring, F., & Michaud, P. A. (2005). Partial eating disorders among adolescents: A review. Journal of Adolescent Health, 37, 417-427.

Collins, R. L., Parks, G. A., & Marlatt, G. A. (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53, 189-200.

Dunn, E. C., Larimer, M. E., & Neighbors, C. (2002). Alcohol and drug-related negative consequences in college students with bulimia nervosa and binge eating disorder. International Journal of Eating Disorders, 32, 171-178.

Garner, D. M., Olmsted, M. P., Bohr, Y., & Garfinkel, P. E. (1982). The eating attitudes test: Psychometric features and clinical correlates. Psychological Medicine, 12, 871-878.

Henslee, A.M., Irons, J.G., Day, J.M., Butler, L., Benson, T.A., & Correia, C.J. (2006). Using National Alcohol Screening Day to deliver personalized feedback: A pilot study. Journal of Drug Education, 36, 271-278.

Johnston, L. D., & O'Malley, P. M. (1985). Issues of validity and population coverage in student surveys of drug use. In B. A. Rouse, N. J. Kozel, & L. G. Richards (Eds.), Self report methods of estimating drug use: Meeting current challenges to validity (National Institute on Drug Abuse Research Monograph No. 57, ADM 85-1402). Washington, DC: National Institute on Drug Abuse.

Krahn, D. D., Kurth, C. L., Gomberg, E., & Drewnowski, A. (2005). Pathological dieting and alcohol use in college women--a continuum of behaviors. Eating Behaviors, 6, 43-52.

Muraven, M., Collins, R. L., Morsheimer, E. T., Shiffman, S., & Paty, J. A. (2005). The morning after: Limit violations and the self-regulation of alcohol consumption. Psychology of Addictive Behaviors, 19, 253-262.

Polivy, J., & Herman, C. P. (1985). Dieting and binging: A causal analysis. American Psychologist, 40, 193-201.

Steiger, H., & Seguin, J. R. (1999). Eating disorders: Anorexia Nervosa and Bulimia Nervosa. In T. Millon, P. H. Blaney, & R. D. Davis (Eds.), Oxford textbook of psychopathology (pp. 365-389). New York, NY: Oxford University Press.

Stewart, S. H., Angelopoulos, M., Baker, J. M., & Boland, F. J. (2000). Relations between dietary restraint and patters of alcohol use in young adult women. Psychology of Addictive Behaviors, 14, 77-82.

Stice, E. (2002). Risk and maintenance factors for eating pathology: a meta-analytic review. Psychological Bulletin, 128, 825-848.

Thome, J., & Espelage, D. L. (2004). Relations among exercise, coping, disordered eating, and psychological health among college students. Eating Behaviors, 5, 337-351.

White, H. R., & Labouvie, E. W. (1989). Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol, 50, 30-37.

Natalie F. Heidelberg, M.S. & Christopher J. Correia, Ph.D.

Auburn University
Summary of Descriptive Data for Sample

                     Minimum   Maximum   Mean    SD

Eating variables
  Dieting               0       27.00    7.10   6.96
Drinking variables
  DDQ Total             0       29.00    3.91   6.16
  RAPI Total            0       38.00    6.50   8.34

Note. Total n=70

Dieting: Dieting Subscale of the EAT-26

DDQ Total: Daily Drinking Questionnaire, number of drinks per

RAPI Total: Rutger's Alcohol Problem Index

Summary of Regression Analyses for Alcohol-Related Problems

Variable         [beta]   SE [beta]    B       T       [R.sup.2]

Step 1                                                  .43 **
 DDQ                .48       .07      .66   7.09 **

Step 2                                                  .44 **
 DDQ                .45       .07      .62   6.58 **
 Dieting            .15       .08      .17   1.78

Step 3                                                  .50 **
 DDQ                .26       .11      .36   2.49 *
 Dieting           -.04       .12     -.05   -.38
 DDQ x Dieting      .02       .01      .43   2.29 *

Note. Total n=70

Dieting: Dieting Subscale of the EAT 26

DDQ Total: Daily Drinking Questionnaire, number of drinks per

* p < .05, ** p <.001
Gale Copyright: Copyright 2009 Gale, Cengage Learning. All rights reserved.