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Purpose. To examine cross-sectional associations between credit card debt, stress, and health risk behaviors among college students, focusing particularly on ...
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Stress Management

Credit Card Debt, Stress and Key Health Risk Behaviors Among College Students Melissa C. Nelson, PhD, RD; Katherine Lust, PhD, MPH, RD; Mary Story, PhD, MPH, RD; Ed Ehlinger, MD, MSPH

Abstract Purpose. To examine cross-sectional associations between credit card debt, stress, and health risk behaviors among college students, focusing particularly on weight-related behaviors. Design. Random-sample, mailed survey. Subjects/Setting. Undergraduate and graduate students (n 5 3206) attending a large public university. Measures. Self-reported health indicators (e.g., weight, height, physical activity, diet, weight control, stress, credit card debt). Results. More than 23% of students reported credit card debt § $1000. Using Poisson regression to predict relative risks (RR) of health behaviors, debt of at least $1000 was associated with nearly every risk indicator tested, including overweight/obesity, insufficient physical activity, excess television viewing, infrequent breakfast consumption, fast food consumption, unhealthy weight control, body dissatisfaction, binge drinking, substance use, and violence. For example, adjusted RR [ARR] ranged from 1.09 (95% Confidence interval [CI]: 1.02–1.17) for insufficient vigorous activity to 2.17 (CI: 0.68–2.82) for using drugs other than marijuana in the past 30 days. Poor stress management was also a robust indicator of health risk. Conclusion. University student lifestyles may be characterized by a variety of coexisting risk factors. These findings indicate that both debt and stress were associated with wide-ranging adverse health indicators. Intervention strategies targeting at-risk student populations need to be tailored to work within the context of the many challenges of college life, which may serve as barriers to healthy lifestyles. Increased health promotion efforts targeting stress, financial management, and weight-related health behaviors may be needed to enhance wellness among young adults. (Am J Health Promot 2008;22[6]:400–407.) Key Words: Obesity, Diet, Exercise, Stress (Psychological), Socioeconomic Factors, College Health, Prevention Research. Manuscript format: research; Research purpose: modeling/relationship testing; Study design: nonexperimental; Outcome measure: behavioral; Setting: school (postsecondary, college/university); Health focus: fitness/physical activity, nutrition, weight control; Target population: college students

Melissa C. Nelson, PhD, RD; and Mary Story, PhD, MPH, RD are with the Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota. Katherine Lust, PhD, MPH, RD; and Ed Ehlinger, MD, MSPH are with the Boynton Health Service, University of Minnesota, Minneapolis, Minnesota. Send reprint requests to Melissa Nelson, PhD, RD, Division of Epidemiology & Community Health, University of Minnesota, 1300 South Second Street, WBOB Suite 300, Minneapolis, MN 55454-1015; [email protected]. This manuscript was submitted March 13, 2007; revisions were requested June 14, 2007; the manuscript was accepted for publication June 20, 2007. Copyright E 2008 by American Journal of Health Promotion, Inc. 0890-1171/08/$5.00 + 0

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PURPOSE The college years represent an important period in many individuals’ lives. The unique combination of stressors and risk factors that accompany traditional student lifestyles and many campus environments can have great impact on students’ short-term and long-term health. For many of the 17 million individuals currently enrolled in postsecondary institutions in the United States, the new responsibilities of college life increase the demands on one’s time, capabilities, and financial resources. Unhealthy behaviors (e.g., physical inactivity, poor dietary intake) are highly prevalent on college campuses,1–7 though little research to date has sought to explore the determinants of health behaviors or risk-behavior patterning among college students, particularly those related to weight status and long-term chronic disease prevention. In order to develop and target effective, campus-based, health promotion interventions and policies, a better understanding of important risk factors and relationships among a broad range of unhealthy lifestyle factors is needed. Previous research has consistently documented a strong association between financial or socioeconomic status (SES) and health.8–15 In particular, previous work suggests that socioeconomic disparities may, at least in part, underlie inequities in the populationwide distribution of weight status, weight gain and related health behaviors.8,10,13 However, scholars in the field have recently issued calls for research investigating other dimensions of financial well-being beyond traditional measures such as income

For individual use only. Duplication or distribution prohibited by law. and education. This is especially important among specific populations in which these traditional measures may not be particularly meaningful. One such population is college students, among whom health and well-being may be associated with other aspects of financial status (i.e., beyond current income or achieved educational status). For example, credit card debt is an important, yet understudied, aspect of financial well-being among college students, particularly given the widespread debt among U.S. undergraduate and graduate students.16,17 A recent report from the U.S. General Accounting Office concluded that credit card issuers have identified college students as ‘‘a profitable market over the long term,’’ and that ‘‘many issuers adjusted their underwriting standards for students, enabling college students with little or no employment income to obtain credit cards.’’18 Although there is a great concern on the part of university officials that students lack the financial savvy to effectively manage credit and debt, there are still a large number of U.S. postsecondary institutions that do not restrict or manage credit card issuers’ access to students or prohibit aggressive marketing techniques on campus.18 Previous literature has also suggested that mental health may be an important factor operating alongside the SES-health relationship. For example, research among adults has shown that SES and debt are associated with mental health, stress, and anxiety.8,12,14 Mental health characteristics also have been associated with weight status,19,20 though links with related health behaviors (e.g., dietary practices) have been less consistent.19,21 Issues surrounding stress and mental health may be particularly relevant on postsecondary campuses, with college students reporting substantial levels of perceived stress22,23 that may contribute to poor overall mental health profiles amongst a significant subset of this population.24,25 For many young adults, stress may originate from a variety of sources and may act as an important barrier to weight-related health. Overall, however, little research to date has examined the associations between key indicators of financial

health (e.g., credit card debt), stress, and health-risk behaviors.14,15 Even less is known about particularly high-debt groups such as college students.18 Using cross-sectional survey data from a large diverse sample of college students from one of the largest public universities in the United States, this research was conducted in order to describe the magnitude of credit card debt and the sociodemographic characteristics of those who carried debt versus those who did not and to examine the associations between debt, perceived stress, stress management, and health indicators such as overweight, obesity, weight-related behaviors and other health-risk attributes among university students. METHODS Setting, Sample, and Design In 2004, Boynton Health Service at the University of Minnesota (U of MN) conducted a mailed, random-sample survey of 6000 degree-seeking, feepaying students enrolled at the U of MN. The ten-page, health-risk survey has been conducted at the U of MN every 3 years since 1995. Surveys were returned anonymously, and participants separately returned a postcard (which was addressed, postage-paid, and included with each mailed survey) on which they indicated their consent to participate in this University needs assessment. Following the initial mailing, a second mailing was sent to those students who had not responded. As an incentive for their participation, students that responded to the mailing were entered into a random lottery drawing to receive either one of two $250 gift certificates or one of 40 $50 gift certificates for merchandize at the U of MN bookstores. Of the 6000 surveys sent out to students, 147 were undeliverable and 3206 were completed and returned. Thus, the final response rate was 54.8%. For completed surveys, the omission of data on key variables and covariates ranged from 0.5% (for gender) to 3.9% (for illegal drug use other than marijuana) of the total sample. Observations with missing values were excluded from analyses. Analyses of these data were approved

by the U of MN Institutional Review Board. Assessment of Primary Exposures The primary exposures of interest were indicators of credit card debt and stress. Students responded to the question: ‘‘Last month, how much total credit card debt did you carry? That is, what was the total unpaid balance on all of your credit cards?’’ Response options included ‘‘Not applicable—I don’t have a credit card’’; ‘‘None, I pay the full amount each month’’; and ten categories of monetary amounts ranging from $1–$99 to $6000 or greater. On a scale of 1 to 10, participants were also asked to rate their average stress level in the past month (1 5 not stressed at all; 10 5 very stressed) and their ability to manage their stress in the past month (1 5 ineffective, 10 5 very effective). These stress variables were treated as both continuous and dichotomous (categorized as above or below the median; median 5 7 for both indicators). Body Mass Body mass index (BMI, kg/m2) was calculated from the student’s self-reported height and weight. Risk-based BMI cut points of 25 and 30 kg/m2 were used to define overweight and obesity, respectively. 26,27 Physical Activity and Sedentary Behavior Using survey items similar to those included in national surveillance systems, students reported frequency of participation in vigorous physical activity, defined as days per week engaged in at least 20 minutes of exercise or physical activity ‘‘that made you breathe hard (e.g., running, swimming laps, fast bicycling, basketball, or similar activities’’) and moderate physical activity, defined as days per week engaged in at least 30 minutes of exercise or physical activity ‘‘that did not make you breathe hard (e.g., walking, slow bicycling, bowling or similar types of activities’’). Per national recommendations, physical activity was dichotomized using these levels of moderate activity on at least 5 days per week (vs. , 5 d/wk) and of vigorous activity at least 3 days per week (vs. , 3 d/wk).28 Students also self-reported the number of days in the

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For individual use only. Duplication or distribution prohibited by law. past week in which they engaged in stretching and/or strengthening exercises. Finally students reported the average hours per week spent watching television and playing video or computer games. Based on national recommendations for youth, responses were categorized as at least 2 hours per day watching television or playing video/computer games, or less than 2 hours per day.29,30 Dietary Intake Students reported select dietary behaviors previously associated with weight and other important health indicators, including daily servings of fruits and vegetables in the past week, days of breakfast consumed in the past week, and the usual frequency of eating fast-food meals (never, once a year or less, a few times a year, one to two times per month, once per week, several times per week, daily, or several times per day). Unhealthy Weight Control Behaviors and Body Satisfaction Students self-reported how frequently in the past year they had engaged in unhealthy weight control behaviors (e.g., using laxatives, taking diet pills, binge eating, inducing vomiting). Students also reported whether they were never, sometimes, most of the time, or always satisfied with their body image/ size in the past 30 days. Other Risk Behaviors Students responded to survey items covering a wide range of additional risk behaviors. These included being in a physical fight in the past year; using tobacco, marijuana, or other illegal drugs in both the past 30 days and in the past year; binge drinking (§ 5 drinks at one time) of alcohol in the past 2 weeks; engaging in risky sexual behavior (having sex while intoxicated or having sex with a stranger or casual acquaintance). Sociodemographic Characteristics Covariates included in these analyses included gender, race (white or nonwhite), and year in school (a first through fifth year undergraduate or a graduate student). Although additional data on socioeconomic status (e.g., household income) was not included in this survey, students reported

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hours per week in which they worked for pay, which was used in these analyses as a proxy for other socioeconomic factors besides credit card debt.

ware, version 9.0 (STATA Corp, College Station, Texas).

Data Processing/Analysis Surveys were completed by students on scan-ready forms and were scanned to create an electronic database. Standard data-cleaning involved identifying a limited number of unlikely outlying values from open-ended response items (e.g., weight, height, age). Basic descriptive statistics were calculated, and differences in the prevalence of key variables across important covariates (e.g., gender, year in school, race) were tested using t-tests. Correlations between weight-related health indicators were also examined; analyses indicated that a large proportion of pairwise correlation coefficients were statistically significant, but a majority were weak in magnitude (with only approximately 6% of correlations § .25). Poisson regression was used to model the relationship between health-risk indicators or behaviors and credit card debt, perceived stress, and stress management. Although logistic regression is commonly used to model relationships in health research, the odds ratios yielded by these analyses substantially overestimates risk ratios that are greater than 1.0 (and underestimates those that are less than 1.0) when the outcome is not rare. Nearly all of the binomial outcomes included in this analysis were not rare (i.e., prevalence . 10%), so Poisson regression was used with robust variance estimates to generate valid estimates of the adjusted relative risks (ARRs), and the relatively conservative confidence intervals (CIs), instead of adjusted odds ratios.31 Where prevalence estimates were less than 10% (i.e., for fighting, drug use, intoxicated sex, casual sex), estimates from Poisson regression were compared with those of logistic regression. Estimates were comparable, so only those from the Poisson regression are presented here. Observations with missing data were dropped from models; thus, although n 5 3206 for the total sample, individual models vary slightly in sample size because of missing data (0.5%– 3.9% data missing). All statistical analyses were conducted using Stata soft-

Descriptive Characteristics of the Sample Approximately 39% of the study sample participants were male. In addition, 63% were undergraduates (22% first- or second-year, 16% thirdyear, and 25% fourth-year students or higher), and 36% were graduate students. Approximately 78% of the sample participants were Caucasian/white, and the remaining 22% were African American/black, American Indian, Asian Pacific Islander, Latino/Hispanic, mixed, or other race, which reflects the racial/ethnic composition of the campus (www.irr.umn.edu). The mean age of the overall sample was 24.2 6 5.9 years (median: 22 years). Reported risk indicators were prevalent in these data, and a majority of these indicators were reported by more than 10% of the sample. These estimates are similar to comparable measures of risk from other data sources (where available).32 For example, comparing these data with the 2006 American College Health Association National College Health Assessment (ACHA-NCHA) data respectively, the prevalence of a BMI of at least 25 kg/ m2 was 28.0% versus 31.4%, vigorous physical activity of at least 20 minute intervals on 3 or more days of the week was 42.1% versus 44.1% (in which reported estimates correspond with vigorous and moderate activity in the ACHA-NCHA dataset), reported physical fighting was 3.9% versus 6.2%, and marijuana use in the past month was 11.7% versus 14.4%.

RESULTS

Who Has Credit Card Debt? Table 1 illustrates the descriptive characteristics of the sample (gender, year in school, race, and perceived stress and stress management) by magnitude of credit card debt. Among the total sample, 21.8% of students did not own a credit card. More than 38% had at least one card on which they carried some balance in the past month, and 5.9% reported credit card debt of at least $6000 in the past month. More than 23% of students reported at least $1000 in credit card debt in the past month. Compared with males, females were significantly

For individual use only. Duplication or distribution prohibited by law. Table 1 Distribution of Self-Reported Credit Card Debt by Study Sample Characteristics Credit Card Debt in Past Month, % (N) Credit Card Balance ($) Characteristics

No Credit Card

0

1–999

1000–5999

§ 6000

22.7 (281) 21.1 (406)

42.7 (528) 37.8 (727)*

12.9 (159) 17.0 (327)*

16.8 (207) 17.6 (339)

4.9 (61) 6.5 (124)

59.6 40.9 24.1 17.5 7.0

27.4 35.2 34.9 29.3 53.7

10.4 17.0 18.7 20.5 11.1

2.5 5.9 17.8 25.4 19.4

0.0 1.0 4.5 7.3 8.8

Gender Male Female Year Undergraduate Undergraduate Undergraduate Undergraduate Graduate

year year year year

1 2 3 §4

(189) (159)* (122)* (138)* (81)*

(87) (137) (177) (232) (619)*

(33) (66) (95) (162) (128)*

(8) (23) (90)* (201)* (224)*

(0) (4) (23)* (58) (101)

Race Caucasian Non-Caucasian

21.6 (531) 22.4 (154)

39.7 (976) 39.5 (272)

15.4 (379) 15.1 (104)

17.3 (424) 17.6 (121)

6.0 (148) 5.5 (38)

Perceived stress High Low

19.4 (328) 24.7 (358)*

37.9 (642) 41.8 (607)

16.8 (285) 13.4 (195)*

19.0 (322) 15.4 (223)*

6.9 (117) 4.7 (68)*

Stress management Poor Good

21.4 (278) 22.1 (407)

36.8 (477) 41.8 (769)*

17.0 (221) 14.0 (258)

18.0 (233) 16.9 (310)

6.9 (89) 5.2 (96)

Total`

21.8 (690)

39.7 (1,257)

15.4 (487)

17.3 (548)

5.9 (186)

* The t-test within-group difference (p # 0.01) comparing variables in each category of credit-card debt: male and female; year of school and previous year (e.g., year 2 vs. 1, year 3 vs. 2); Caucasian and non-Caucasian; high and low stress; poor and good stress management.  Perceived stress and stress management were reported on a scale ranging from 1 (not stressed/ not effective) to 10 (very stressed/very effective), and scores were dichotomized at the median value (median 5 7 for both variables). ` Total sample 5 3206, though sample sizes for individual analyses vary slightly because of missing data (0.5%–3.9% data missing).

more likely to own a credit card and to carry a $0 balance or a balance of $1 to $999. There were no significant differences by gender in those having large amounts of debt (§ $1000). Students who were more advanced in their schooling (i.e., had completed more years at the university) were significantly less likely to not own any credit cards and were more likely to have substantial credit card debt. No significant differences in credit card debt were detected by race. Those with low perceived stress were both more likely to not have a credit card and less likely to carry any balance. There were fewer differences in debt by reported stress management, though those with favorable stress management were more likely to not carry a balance on their credit cards. Examining Health Risk Behavior: Associations With Debt and Stress Table 2 illustrates the associations between debt, stress, and weight-re-

lated health behaviors. Credit card debt of at least $1000 appeared to be a more robust indicator of unhealthy weight-related behaviors compared with either a high perceived stress or poor stress management. Although these stress indicators generally reflected the same directionality of effect as credit card debt, these associations did not consistently show statistical significance. In contrast, having at least $1000 of credit card debt was a significant predictor of every weight-related health indicator and behavior tested. Magnitudes of effect ranged from ARR 5 1.09 (95% CI: 1.02–1.17) for insufficient vigorous activity to ARR 5 1.83 (95% CI: 1.41–2.39) for BMI of at least 30, with the exception of eating less than five daily servings of fruits/ vegetables (ARR 5 1.04; 95% CI: .98– 1.11). Table 3 illustrates the associations between credit card debt, stress indicators, and other major health-risk

behaviors among students (e.g., physical violence, alcohol or tobacco/drug use, sexual behaviors). Credit card debt of at least $1000 and poor stress management were similarly indicative of these risk behaviors, significantly predicting engagement in physical fights, binge drinking, and use of tobacco, marijuana, and/or other drugs. Again, high perceived stress was a less consistent indicator of these behaviors. Neither substantial debt nor stress was a significant predictor of risky sexual behaviors. DISCUSSION Nearly one in four students in this 2004 sample reported credit card debt in excess of $1000. This is reflective of the extensive credit card debt among college students that has been reported elsewhere. For example, in 2003–2004 research by Nellie Mae, the average outstanding credit card bal-

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For individual use only. Duplication or distribution prohibited by law. Table 2 Associations Between Credit Card Debt, Perceived Stress, Stress Management and Unhealthy, Weight-Related Behaviors in University Students

Health risk

Crude Prevalence (%)

Body mass BMI§25 kg/m2 BMI§30 kg/m2 Physical activity 30 minutes moderate activity , 5 days/week 20 minutes vigorous activity , 3 days/week Stretching exercises , 2 times/week Strengthening exercises , 2 times/week Sedentary behavior TV, video, or computer games . 2 hours/day Dietary patterns , 5 servings fruits and vegetables/ day Breakfast , 5 days/week Eat fast food at least several times per week Weight control behaviors § 1 unhealthy behavior in past year|| Body satisfaction Not satisfied with body image/size most or all or the time

Credit Card Debt § $1000 in the Last Month

High Perceived Stress`

Poor Stress Management`

Adjusted Relative Risks§ (95% Confidence Intervals)

28.0 7.4

1.58 (1.40–1.77) 1.83 (1.41–2.39)

1.00 (0.89–1.12) 0.96 (0.74–1.24)

0.95 (0.85–1.07) 0.98 (0.75–1.27)

61.1

1.11 (1.04–1.18)

1.02 (0.97–1.08)

1.03 (0.97–1.09)

57.9

1.09 (1.02–1.17)

1.10 (1.04–1.17)

1.08 (1.02–1.15)

45.3 54.0

1.16 (1.07–1.27) 1.13 (1.05–1.22)

1.09 (1.01–1.18) 1.12 (1.04–1.19)

1.10 (1.02–1.19) 1.15 (1.08–1.23)

23.4

1.20 (1.03–1.40)

0.96 (0.84–1.09)

1.05 (0.92–1.20)

65.1

1.04 (0.98–1.11)

1.03 (0.97–1.08)

1.07 (1.01–1.12)

38.7 15.8

1.25 (1.12–1.39) 1.31 (1.09–1.58)

1.08 (0.99–1.18) 1.11 (0.94–1.31)

1.16 (1.07–1.27) 1.23 (1.05–1.45)

29.2

1.18 (1.04–1.34)

1.18 (1.05–1.32)

1.30 (1.17–1.45)

47.6

1.30 (1.20–1.41)

1.28 (1.18–1.38)

1.33 (1.24–1.43)

 Total sample 5 3206, though sample sizes for individual analyses vary slightly because of missing data (0.5%–3.9% data missing). ` Perceived stress and stress management were reported on a scale ranging from 1 (not stressed/ not effective) to 10 (very stressed/ very effective), and scores were dichotomized at the median value (median 5 7 for both variables). § Relative risks adjusted for gender, age, race, and hours worked per week. || Unhealthy weight control behaviors include using laxatives, taking diet pills, binge eating, and inducing vomiting.

ance among undergraduates was $2169 and among graduate students was $7831.16,17 In addition, our findings indicate that students who reported debt in excess of $1000 were also more likely to report nearly every health-risk indicator tested including overweight/ obesity, insufficient moderate and vigorous physical activity, excess television viewing, infrequent breakfast consumption, fast-food consumption, unhealthy weight control, low body satisfaction, binge drinking, substance use, and violence. Poor stress management was also a significant and robust indicator of many of these wide-ranging risk behaviors. Perceived stress level was a less consistent indicator of adverse risk behavior patterns. Al-

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though most of these associations yielded the same directionality of effect, they were not necessarily statistically significant. These findings indicate that university students are at risk for a variety of adverse co-occurring factors, which may increasingly yield barriers to engaging in beneficial weight-related health behaviors. Although it is possible that credit card debt and stress may lead to compromised health behaviors among students, causality cannot be inferred from our cross-sectional data. In fact, it is also possible that adverse health-risk behaviors may in some way increase stress or that credit card use may be employed as a stress reduction mechanism by some students. Addi-

tional longitudinal data or interventional studies are needed to help decipher these issues of causality. Regardless of causal mechanisms, however, the most important message underlying our findings is perhaps that students who report a single risk behavior (such as financial risk or poor stress management) are significantly more likely to report many other types of adverse risk behaviors (e.g., poor diets, inactivity, substance use), which together may have particularly deleterious long-term effects. Ultimately, understanding the cooccurrence and patterning of risk factors may be essential in conceptualizing the overall culture of college life, including the broad context in which

For individual use only. Duplication or distribution prohibited by law. Table 3 Credit Card Debt, Perceived Stress, Stress Management, and Other Major Risk Behaviors in University Students

Crude Prevalence (%)

Health Risk Been in a physical fight in past 12 months Reported binge drinking (§ 5 drinks at one time) in past 2 weeks Used tobacco in the past 30 days Used marijuana in the past 30 days Used other drugs in the past 12 months|| Was intoxicated last time had sex Most recent sexual partner was causal acquaintance or stranger

Credit Card Debt § $1000 in the Last Month

High Perceived Stress

Poor Stress Management`

Adjusted Relative Risks§ (95% Confidence Intervals)

3.9 31.4

1.78 (1.16–2.73) 1.30 (1.15–1.47)

1.52 (1.05–2.19) 1.06 (0.96–1.17)

1.84 (1.28–2.64) 1.23 (1.11–1.36)

24.8 11.7 8.9 8.1 5.2

1.43 1.31 2.17 1.08 1.24

1.15 1.01 1.24 0.98 1.02

1.31 1.37 1.71 1.09 1.25

(1.24–1.64) (1.03–1.65) (1.68–2.82) (0.79–1.46) (0.87–1.77)

(1.02–1.31) (0.84–1.23) (0.98–1.57) (0.77–1.26) (0.75–1.39)

(1.16–1.48) (1.13–1.66) (1.35–2.15) (0.85–1.39) (0.92–1.70)

 Total sample 5 3206, though sample sizes for individual analyses vary slightly because of missing data (0.5%–3.9% data missing). ` Relative risks adjusted for gender, age, race, and hours worked per week. § Perceived stress and stress management were reported on a scale ranging from 1 (not stressed/ not effective) to 10 (very stressed/ very effective), and scores were dichotomized at the median value (median 5 7 for both variables). || Other drugs include cocaine, amphetamines, sedatives, hallucinogens, opiates, inhalants, ecstasy or other designer drugs, performance enhancing steroids, GHB, and rohypnol.

risk behaviors occur, and in tailoring intervention strategies that target specific subgroups of students. Our findings suggest, for example, that the initiation of health-behavior change (such as improving dietary intake or activity) may be substantially more difficult among a significant subset of the college population who are not only engaging in unhealthy weightrelated practices but also have many other serious issues of concern in their lives. To target these individuals, multifaceted health-promotion strategies are needed. For example, health-promotion strategies aimed at building skill sets, support, and motivation for making healthy lifestyle choices may need to be supplemented with additional efforts, such as individual-level stress-management promotion and environmental change to improve healthy-food availability or pricing incentives. Such strategies may improve the ease with which healthy decisions can be made on campus, even among a subset of students who are coping with a wide array of immediate and pressing challenges, such as debt and stress. This study is among the first of its kind to examine the co-occurrence of risky financial behaviors, stress management, and health-risk indicators

among university students. Although much additional research is needed in this area to understand the complex patterning and colinearity of risk behavior, it is clear that students cope with a wide array of new and difficult challenges. Campus administrators must acknowledge the many facets of college life that can serve as barriers or challenges to healthy lifestyle characteristics. When possible, campus promotion strategies need to embrace a broad-based view of wellness initiatives, recognizing that successfully addressing issues such as poor stress management or financial strain may move students into a position in which they are better able to respond to other health promotion efforts (e.g., to make healthy diet and activity choices). Postsecondary institutions can provide a number of important resources to help assist students in avoiding credit card debt. Whereas one study of 13 schools showed no effect on overall levels of students’ debt by institutional provision of information on topics such as avoiding credit card debt and maintaining financial health, this research indicates that a narrow approach to prevention may not be suitable.33 A multilevel approach may be more appropriate. For example, providing information on strategies for

maintaining financial well-being may in fact be effective if they are combined with other institutional strategies, such as implementing school-level policy changes alongside the provision of individual credit-counseling services and other educational opportunities. An example of such an educational strategy is an online course recently developed for students at the U of MN that covers topics on basic moneymanagement strategies, with a special emphasis on credit cards (fsos.che. umn.edu/img/assets/7574/ 06spring1301.pdf). In addition, it may be important to expand this approach by providing educational opportunities for students and parents. Previous research has demonstrated that students whose parents had provided them with information on credit cards had lower credit card debt.33 Thus, at the U of MN in September 2006, a similar online course was made available to parents of students, following a programmatic model originally developed to prevent student alcohol use by facilitating parent-child communication on these issues even through the college years (www.parent.umn.edu/ news/onlinecourses.html). In addition, policy changes may be used to supplement and enhance these efforts to increase students’ financial

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For individual use only. Duplication or distribution prohibited by law. health and to reduce credit card debt. Institutional policy changes can help in reducing the adverse impact of credit cards on students. Examples of such policies include eliminating the option to make tuition payments with credit cards and prohibiting solicitation by credit card issuers in campus buildings (e.g., banning promotional tables of credit card–company giveaways, eliminating the practice of stuffing campus bookstore bags with credit card applications). Alternative strategies may include restricting the release of student data to credit card issuers for use in mail solicitation, the means by which approximately 36% to 37% of students currently acquire credit cards.18 Beyond institutional policy, numerous states have proposed legislation to limit the ability of credit card companies to use such strategies listed above. A number of these bills have incorporated particular emphasis on students under the age of 21 and those attending public postsecondary institutions. Although many bills have failed to gain the support needed to pass, there are several examples of successful legislation18 prohibiting or limiting the presence of credit card companies on campus. In addition, legislative resolutions have been approved in several states to bring increasing attention to this issue or urge postsecondary institutions take action towards addressing these problems.18 Although the findings from this research are robust, they are not without limitation. The U of MN is the second-largest public university in the United States, which may limit the ability to generalize these findings to students attending small or private postsecondary institutions across the country. However, of students currently enrolled in U.S. postsecondary institutions, nearly 77% attend public institutions and more than half of these (almost 6.5 million) attend large public universities with more than 15,000 students.34 In addition, previous research has indicated that exposure to credit card solicitation is similar in both public and private institutions, even with widely ranging student enrollment. Therefore, these findings are likely applicable to students attending many different types of institutions.18 Although credit card usage is similar

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across different regions of the United States, students in the Midwest have high average credit card debt ($2498, compared with the national average of $2169) and have a large number of high-debt owners (28% with balances § $3000, compared with the national average of 23%).16 Finally, the survey tools used to collect our data may limit our findings. To maximize the efficiency of our needs-assessment efforts, much of the survey has been based on single-item indicators of risk. More in-depth assessments likely would reduce error and provide higher validity. In addition, although the survey items that were used here to assess stress and stress management may capture important dimensions of stress and mental health, other measures of stress (e.g., source-specific stress) may provide additional insight in future research. Overall these findings highlight the fact that students are at high risk for a variety of co-occurring risk factors, including financial, mental, and weight-related health issues. Although our research does not allow us to draw conclusions regarding the causal nature of these associations, enhanced promotion efforts targeting the financial and mental health of university students may be needed as one way to reduce barriers to healthy living and to help students cope with the many challenges of college life. For those students who are not only engaging in unhealthy weight-related practices but also have many other serious concerns in their lives, the initiation of healthy dietary and activity change may be particularly difficult. Campuses seeking to target this at-risk population likely need to take a broad approach to health promotion, recognizing the many challenges with which students must cope. Through a combination of institutional policy change (e.g., limiting the presence of credit card solicitation on campus) and individual-level intervention strategies (e.g., focusing on the promotion of stress management techniques and education on issues relevant to financial, mental, and physical health), college and university campuses may be better equipped at successfully promoting healthy lifestyle characteristics among students.

SO WHAT? Implications for Health Promotion Practitioners and Researchers These findings indicate that, among college students, credit card debt and stress management are associated with a wide range of health risks, particularly adverse weight-related indicators (e.g., dietary intake, physical inactivity), and may act as barriers to healthy lifestyles on college campuses. Intervention strategies need to recognize the co-occurrence of such risk factors to successfully target groups of at-risk students. Increased health promotion efforts on college campuses that address important issues such as stress and financial management are needed to help students cope with the many challenges of college life and to encourage beneficial health behaviors and healthy lifestyles.

Acknowledgments Data collection in this project was funded by the Boynton Health Service at the University of Minnesota (www.bhs. umn.edu). Salary support for data analysis of these data was funded by the University of Minnesota Obesity Prevention Center (www.obesityprevention.umn.edu).

References

1. Youth Risk Behavior Surveillance: National College Health Risk Behavior Survey— United States, 1995. MMWR CDC Surveill Summ. 1997;46(6):1–56. 2. Haberman S, Luffey D. Weighing in college students’ diet and exercise behaviors. J Am Coll Health. 1998;46(4):189–191. 3. Huang TT, Harris KJ, Lee RE, et al. Assessing overweight, obesity, diet, and physical activity in college students. J Am Coll Health. 2003;52(2):83–86. 4. Debate RD, Topping M, Sargent RG. Racial and gender differences in weight status and dietary practices among college students. Adolescence. 2001;36(144):819–833. 5. Butler SM, Black DR, Blue CL, Gretebeck RJ. Change in diet, physical activity, and body weight in female college freshman. Am J Health Behav. 2004;28(1):24–32. 6. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. J Am Coll Health. 2005;53(6):245–251. 7. Buckworth J, Nigg C. Physical activity, exercise, and sedentary behavior in college students. J Am Coll Health. 2004;53(1):28–34. 8. Everson SA, Maty SC, Lynch JW, Kaplan GA. Epidemiologic evidence for the relation between socioeconomic status and

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Editor in Chief Michael P. O’Donnell, PhD, MBA, MPH Associate Editors in Chief Margaret Schneider, PhD Jennie Jacobs Kronenfeld, PhD Shirley A. Musich, PhD Kerry J. Redican, MPH, PhD, CHES SECTION EDITORS Interventions Fitness Barry A. Franklin, PhD Medical Self-Care Donald M. Vickery, MD Nutrition Karen Glanz, PhD, MPH Smoking Control Michael P. Eriksen, ScD Weight Control Kelly D. Brownell, PhD Stress Management Cary Cooper, CBE Mind-Body Health Kenneth R. Pelletier, PhD, MD (hc) Social Health Kenneth R. McLeroy, PhD Spiritual Health Larry S. Chapman, MPH Strategies Behavior Change James F. Prochaska, PhD Culture Change Daniel Stokols, PhD Health Policy Kenneth E. Warner, PhD Population Health David R. Anderson, PhD Applications Underserved Populations Ronald L. Braithwaite, PhD Health Promoting Community Design Bradley J. Cardinal, PhD The Art of Health Promotion Larry S. Chapman, MPH Research Data Base Troy Adams, PhD Financial Analysis Ron Z. Goetzel, PhD From Evidence-Based Practice to Practice-Based Evidence Lawrence W. Green, DrPH Qualitative Research Marjorie MacDonald, BN, PhD Measurement Issues Shawna L. Mercer, MSc, PhD