Available online at www.sciencedirect.com

(2008) 189–195www.elsevier.com/locate/ypmed

Preventive Medicine 46

Multiple health risk behaviors in German first year university students

Stefan Keller a,b,⁎, Jason E. Maddock a, Wolfgang Hannöver c,J. René Thyrian d, Heinz-Dieter Basler b

a Department of Public Health Sciences, University of Hawaii at Manoa, Honolulu, HI, USAb Department of Medical Psychology, University of Marburg, Germany

c Department of Medical Psychology, University of Greifswald, Germanyd Department of Epidemiology and Social Medicine, University of Greifswald, Germany

Available online 9 October 2007

Abstract

Multiple health risk behaviors have been identified as a problem in young adults which includes university students. The goals of this studyincluded assessing the prevalence of major health risk behaviors in a cohort of German first year university students, analyzing the clustering ofthese behaviors and assessing readiness to change across multiple behaviors.

A total of 1262 students from the schools of law, teaching and medicine at a German university participated in a voluntary and anonymoussurvey in 2005. The study assessed indicators and readiness for change regarding fruit and vegetable consumption, exercise, smoking and bingedrinking as well as sociodemographic variables.

Confirming the hypotheses, prevalences for risk behaviors were high; over 95% ate less than five servings of fruits and vegetables, 60% did notexercise sufficiently, 31%were current smokers and 62% reported binge drinking. Only 2% had none, 10.5% had one, 34.5% had two, 34.8% had three,and 18.2% showed all four risk behaviors. Readiness for behavior change was very low across multiple risk behavior combinations, especially forreducing binge drinking and increasing fruit and vegetable consumption. Medical students showed slightly more positive patterns than other students.

The results indicate the need for addressing health behaviors in the student population of this university. If these findings can be replicated inother universities, programs that promote individual behavior change as well as changes in environmental conditions in the university environmentare necessary to address this urgent problem.© 2007 Elsevier Inc. All rights reserved.

Keywords: Health behavior; Multiple health behavior; Health promotion; Diet; Smoking; Exercise; Drinking behavior; Medical students

Introduction

Most of the causes for morbidity and mortality in Westernsocieties are behavior related. Smoking, physical inactivity,poor nutrition and immoderate alcohol use are the majorbehavioral contributors to premature morbidity and mortality inthe developed world (e.g. Mokdad et al., 2004; World HealthOrganisation [WHO], 2002). Traditionally, most studies havefocused on the significance of a single health risk behavior formorbidity or mortality. However, research has shown that thereis considerable clustering of health risk behaviors (e.g. Ma et al.,

⁎ Corresponding author. Department of Public Health Sciences and Epidemi-ology, University of Hawaii at Manoa, 1960 East-West Rd., Honolulu, HI96822, USA. Fax: +1 808 956 5818.

E-mail address: kellers@hawaii.edu (S. Keller).

0091-7435/$ - see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.ypmed.2007.09.008

2000; Glasgow et al., 2004; Chiolero et al., 2006; Poortinga,2007). For example, smokers seem to be at an elevated risk forother risk behaviors making smoking an indicator of an overallunhealthy lifestyle (e.g. Ma et al., 2000; Sherwood et al., 2000;Strine et al., 2005; Chiolero et al., 2006).

The prevalence of clustered risk behaviors is especiallyalarming among adolescents and young adults (Fine et al.,2004; Pronk et al., 2004; Sanchez et al., 2007). For young adultsin higher education, the transition into university is an importantphase. Changing social networks and environments as well asincreased freedom from parental control can have a strong effecton health behaviors (Borsari et al., 2007) that can develop intobehavior patterns (e.g. Škėmienė et al., 2007). Previous studieshave shown that large numbers of university students smoketobacco (e.g. Thompson et al., 2007), binge drink (e.g. Wechsleret al., 2002), do not eat enough fruits and vegetables (e.g. Unüsan,

Table 1Sample characteristics and individual health risk behaviors

Schools

Law Teaching Medicine Total Schooldifferences

n (%) 315(25)

404 (32) 543 (43) 1262(100)

SexFemale (%) 57.0 56.5 62.0 59.0 ns

Age, M(SD) 20.23(1.52)

20.81(2.29)

20.93(2.48)

2.72(2.23)

F(2, 1246)=10.47; pb0.001;η2=0.017

Religion (%)Roman Catholic 34.4 35.0 27.6 31.7 χ2 (8)=46.18;

pb0.001Protestant 40.8 47.0 40.6 42.7Muslim 5.4 1.0 3.0 3.0Other 4.5 4.3 2.8 3.7No Church 15.0 12.8 26.1 19.0

Living situation (%)Dormitory 12.4 10.9 13.0 12.2 χ2 (8)=67.88;

pb0.001Shared apartment 36.5 38.3 38.5 37.9Alone 25.4 21.8 34.5 28.1With parent/partner

17.9 27.2 12.1 18.5

Fraternity house 7.8 1.8 1.7 3.3Fraternitymembership (%)

6.1 2.5 3.4 3.8 χ2 (8)=6.60;pb0.05

Military service (%) 8.0 9.5 9.2 9.0 nsb5 servings offruits andvegetables/day (%)

97.4 97.5 94.6 96.3 χ2 (2)=6.70;pb0.05

Less than 3×20 minof vigorousexercise/week (%)

68.6 55.7 57.2 59.5 χ2 (2)=14.27;pb0.01

Cigarette smoker 36.7 37.9 22.9 31.3 χ2 (2)=28.29;pb0.001

≥1 binge drinkingepisode/previous30 days (%)

70.8 66.9 53.3 62.1 χ2 (2)=31.33;pb0.001

Number of riskbehaviors, M(SD)

2.71(0.94)

2.65(0.97)

2.37(0.95)

2.57(0.97)

F(2, 1259)=21.41; pb0.001;η2=0.033

190 S. Keller et al. / Preventive Medicine 46 (2008) 189–195

2004) and do not exercise enough (e.g. Haase et al., 2004).Unfortunately, there does not seem to be a trend toward improve-ment (e.g. Steptoe et al., 2002). A number of sociodemographicvariables seem to be related to selected behaviors, e.g. livingconditions (Shaik and Deschamps, 2006) or fraternity member-ship (DeSimone, 2007). Very few studies look at multiplebehaviors and they confirm the results of population findings (e.g.Reed et al., 2007).

Little is also known about the readiness for changing unhealthylifestyle behaviors in this phase of transition. Many universitystudents intend to quit smoking before they graduate (Thompsonet al., 2007), but few studies examine intentions for otherbehavioral risk factors. This transition might also be an important‘teachable moment’ for primary prevention interventions butmore research is needed. The stages of change are an importantand widely used construct (e.g. Prochaska et al., 2004, 2005) thatcan assess readiness for change as an ordinal variable as well asthe dichotomous distinction between people at risk (precontem-plation, contemplation and preparation stages) and not at risk(action and maintenance stages). This tool can be used as adiagnostic instrument to assess whether to intervene on single ormultiple behaviors. Knowledge about clusters of behavioral riskfactors can lead to the development of tailored interventionsaddressing specific subgroups of the population, e.g. individualswho smoke and drink versus individuals who do not eat healthy.

Most studies on multiple risk behaviors and on student healthhave been performed in the United States, very little is knownabout multiple risk behaviors in German student populations. Asmall number of studies indicate high levels of health riskbehaviors among German university students but these studies donot specifically address multiple risk behaviors (e.g. Stock et al.,2001; Steptoe et al., 2002). In a recent study, Keller et al. (2007)found a low fruit and vegetable consumption and high smokingrates were related with binge drinking among German medicalstudents. These results indicate a high level of risk behaviors andclustering of behaviors that warrant further studies.

Consequently, the goals of this study are to assess theprevalence of the most relevant health risk behaviors (see above)in a cohort of German first year university students to analyze theinterrelation and clustering of health behaviors and the frequencyof multiple health risk behaviors, and to assess readiness tochange across behaviors. Based on previous results, we expect(a) high prevalence rates for the selected risk behaviors,(b) considerable clustering of risk behaviors, especially forsmokers, (c) an overall low readiness for behavior change and(d) somewhat more favorable results for medical students becauseof their self-selection into a health profession education.

Methods

Participant recruitment

In a cross-sectional study design, a convenience sample of n=1319university students participated by voluntarily and anonymously filling out aquestionnaire during an introductory lecture within the first 8 weeks of the firstsemester at their respective schools. University students in Germany enterspecializations during their undergraduate years. The study included studentsfrom the Departments of Law (n=315), Education (n=404), and Medicine

(n=543) from the University of Marburg, Germany. A total of 44 studentquestionnaires were excluded because students indicated they were not in theirfirst semester. Questionnaires were screened for plausibility. Data fromindividuals who indicated that they drank more than 49 drinks per week (top1% of the sample, n=13) were screened for plausibility. The data from theseparticipants showed either highly implausible values in other variables orextreme values (e.g. outliers with 120 drinks/week as ‘normal’ consumption)and were consequently excluded from the analysis because it was assumed thatthese students had deliberately provided dishonest answers.

The sample reflected approximately 50% of the law students, 65% of theteaching students and 80% of the medical students in that year's cohort. Thesenumbers reflect the attendance in the lectures where the data were collected. Theestimated response rate, i.e. percentage of students who voluntarily filled out thequestionnaire during each of the respective lectures where questionnaires weredistributed, was N90%. The study followed the local ethics protocol for studiesof this type at the University of Marburg.

Sample

The final sample consisted of n=1262 students. Overall sample characteristicsare presented in Table 1. There were no differences in gender distributions or meanage between students from the three included schools. Medical students were

191S. Keller et al. / Preventive Medicine 46 (2008) 189–195

somewhatmore likely not to belong to any church (χ2 (8)=46.2; pb0.001) and lawstudents were slightly more likely to be members of a fraternity (χ2 (2)=6.6;pb0.05). Medical students were slightly more likely to live alonewhereas teachingstudents were somewhat more likely to live with their parents (χ2 (12)=71.9;pb0.001).

Measures

Fruit and vegetable consumptionReadiness to increase fruit and vegetable intake was measured by a staging

algorithm that categorized participants into the five stages of change (Prochaskaet al., 1992). If individuals ate less than five servings of fruit and vegetables ona typical day, they were asked if they planned on increasing the number to five.If they answered ‘yes’ in the next 30 days, they were categorized as being in thepreparation stage. The answer ‘yes’ in the next 6 months represented thecontemplation stage. Precontemplators were not planning on eating five ormore servings in the next 6 months. If they ate five or more servings,participants were asked if they had done so for less than 6 months (actionstage), or for more than 6 months (maintenance stage). Additionally, weassessed intake (number of servings) of fresh fruit, salad, vegetables for lunchand dinner and juice intake for the previous day and calculated a sum score forthe number of fruit and vegetable servings. Both measures had been usedpreviously in German samples and had shown good reliability and validity(Keller et al., 2001).

ExerciseWhile the benefits of moderate activity are well known, this study focused on

vigorous exercise defined as ‘exercising vigorously for a minimum of three timesper week for at least 20 min per occasion’, following the recommendations of theAmerican College of Sports Medicine (1998). Pilot studies had indicated thatasking for this specific target behavior also produced more valid answers thanasking for moderate activity (unpublished data). Students were asked for thefrequency and time per week spent on endurance sports, aerobic exercise, gamesports and other vigorous physical activity. Total exercise minutes werecombined to a sum score. Readiness to increase exercise was assessed by astaging algorithm that was structurally similar to the one for fruit and vegetableconsumption. This exercise measure and the corresponding staging algorithmhad been used in previous studies and had been found to be of good reliability andvalidity (Basler et al., 1999).

Table 2Stage distributions and indicators of relevant health behaviors across stages of chan

Variable Stages of change a

PC C PR A M

Fruit and vegetable intake N 813 188 180 6 40% 66.3 15.3 14.7 0.5 3.3

Servings previous day M 3.49 3.78 4.33 8.00 6.98SD 2.04 1.94 2.14 1.41 2.31

Exercise N 251 289 200 141 362% 20.2 23.3 16.1 11.3 29.1

Minutes per week M 83.98 97.20 159.24 222.52 293.09SD 112.51 107.0 153.38 154.29 185.88

7Smoking N 213 133 41 73 131

% 36.0 22.5 6.9 12.4 22.2Cigarettes per day M 11.17 10.53 8.66 0.03 0.01

SD 7.69 6.89 7.30 0.25 0.09Binge drinking N 725 35 166 47 43

% 71.4 3.4 16.3 4.6 4.2Number of drinks per week M 7.75 9.43 8.31 2.56 2.19

SD 7.26 6.29 7.68 2.24 3.37

PC=precontemplation, C=contemplation, PR=preparation, A=action, M=maintena Analysis based on maximum available n for each behavior.b Only current smokers.⁎ Tukey post hoc, pb0.05.

SmokingStudents were asked if they ever smoked cigarettes and the current number

of cigarettes per day if they were ever-smokers. The algorithm did notdifferentiate between regular and occasional smokers. The stages of change forsmoking cessation were assessed by the algorithm introduced by Prochaska et al.(1992). This algorithm had been shown to be reliable and valid for Germanpopulations (Keller et al., 1999).

Binge drinkingBinge drinkingwas defined as four drinks forwomen and five drinks formen as

described by Wechsler et al. (1995). We asked students how many binge drinkingepisodes they had ever, in the previous 30 days, the previous 6 months and theprevious 12 months. According to their binge drinking frequency, students werecategorized into five groups: non-drinkers (no alcohol in the previous 30 days),non-bingers (had alcohol in the previous 30 days but did not binge), infrequentbingers (had 1–2 binge episodes in 30 days), bingers (had 3–5 binge episodes) andfrequent bingers (had 6 ormore binge episodes in 30 days). Readiness to stop bingedrinking was assessed by a staging algorithm proposed by Laforge et al. (1998).The categorization was limited to those students who indicated that they had had atleast one binge drinking episode in the past. Staging questions referred to planningto ‘limit the number of drinks at any one occasion to less than 5’ (4 for women) inthe next 30 days (preparation stage) or 6 months (contemplation stage). Those withno such plans were in the precontemplation stage. Individuals who had bingeepisodes in the previous 12months but not in the past 6monthswere categorized asbeing in the action stage. Those who did not binge in the previous 12 months werecategorized as being in the maintenance stage.

DemographicsSeveral exploratory demographic variables including living situation,

fraternity membership, military service, religion, age and gender were included.

Results

Individual risk behaviors

Medical students had the lowest smoking and binge drinkingrates while law students had the lowest exercise and the highest

ge

ANOVAa Post hoc ⁎

Total

F(4;1209)=37.97; pb0.01; η2=0.112 PC, C, PRbA, M PCbPR

3.802.17

F(4;1238)=106.15; pb0.01; η2=0.255 PC, CbPRbAbM

175.78170.72

F(4;550)=99.09; pb0.01; η2=0.419 PC, C, PRNA, M

10.47 b

7.44F(4;1011)=14.08; pb0.001; η2=0.053 PC, C, PRNA, M

7.417.21

ance.

Table 3Multiple risk behavior combinations and readiness to change

Riskfactors

Fruit andvegetableconsumption

Exercise Smoking Bingedrinking

n Percentage oftotal sample

Percentage in PC or Cfor all combined factors a

Percentage in PR for atleast one behavior a

Percentage in PR for allcombined behaviors a

4 + + + + 230 18.2 51.3 48.7 0.03 + + + − 24 1.9 58.3 41.7 0.0

+ + − + 309 24.5 52.1 47.8 1.0+ − + + 104 8.2 57.7 42.3 2.9− + + + 2 0.2 – – –

2 + + − − 149 11.8 64.4 35.6 7.4+ − + − 16 1.3 93.8 6.3 0.0+ − − + 250 19.8 69.2 30.8 4.4− + + − 3 0.2 – – –− + − + 12 1.0 – – –− − + + 5 0.4 – – –

1 + − − − 99 7.8 81.8 18.2 18.2− + − − 11 0.9− − + − 3 0.2 – – –− − − + 20 1.6 75.0 25.0 25.0

0 − − − − 25 2.0 – – –Total 1262 100.0 M=67.1 M=32.9% M=6.5%

At-risk defined as being in precontemplation (PC), contemplation (C) or preparation (PR) for the respective behaviors.Non-drinkers and non-smokers were set to ‘not at risk’.a Only calculated for cells with nN13 (1%).

192 S. Keller et al. / Preventive Medicine 46 (2008) 189–195

binge drinking rate. Teaching students had the highest smokingrate and tied the law students for the lowest fruit and vegetableconsumption (see Table 1).

Only 3.8% of all students reported eating the recommendedfive servings of fruits and vegetables per day (see Table 2).Women ate slightly more servings than men (F(1, 1238)=8.70;pb0.01; η2 =0.007) and were more likely to be in later stages(χ2 (4)=51.3; pb0.001). Regarding physical activity, 59.5% ofall students reported not reaching the criterion of exercisingvigorously three times a week for a minimum of 20 min (seeTable 2). Sixteen percent (n=206) reported zero exerciseminutes per week. Men spent more minutes per week exercisingthan women (F(1, 1251)=12.49; pb0.001; η2 =0.01) and weremore frequently in later stages than women (χ2 (4)=22.8;pb0.001). Approximately 47.7% of all students reported havingbeen a cigarette smoker at some point in their lives and 31.3%(n=386) were current smokers at the time of assessment. Mensmoked more cigarettes per day than women (F(1, 777)=10.77;pb0.01; η2 =0.014) and teaching students smoked more thanthe other students (F(2, 778)=18.72; pb0.001; η2 =0.046). Ofall students, 9.1% were non-drinkers, 28.8% were non-bingers,42.4% were infrequent bingers, 9.1% regular bingers and 10.6%

Table 4Correlations of behavior indicators

Servings of fruits andvegetables on previous day

Servings of fruits and vegetables on previous day –Exercise minutes/week –Cigarettes/day –Binge drinking episodes in previous 2 weeks –

⁎ pb0.05.⁎⁎ pb0.01.

frequent bingers with 6 or more binge drinking episodes in the30 days preceding assessment. As expected, men had moredrinks per week than women (F(1, 1235)=130.64; pb0.001;η2 =0.096) and law students drank more than teaching studentswho in turn drank more than medical students (F(2, 1239)=23.87; pb0.001; η2 =0.037; Tukey post hoc pb0.05).

Multiple risk behaviors

Table 3 lists the possible combinations of all four riskbehaviors. Only 2% of all students had none of the riskbehaviors, 10.5% had one risk behavior, 34.5% had two, 34.8%had three and 18.2% of the sample showed all four riskbehaviors. Overall, the readiness to change behaviors was low.Averaged across all relevant behavior combinations, approxi-mately two thirds were in the precontemplation or contempla-tion stages, i.e. not ready for immediate behavior change forany of their respective risk behaviors. Across all behaviorcombinations, only 6.5% of students were ready to change alltheir risk behaviors in the near future. On average, almost onethird of all students were in the preparation stage for changing atleast one of their risk behaviors.

Exercise minutes/week Cigarettes/day Binge drinking episodesin previous 2 weeks

0.14 ⁎⁎, n=1244 −0.11 ⁎⁎, n=779 −0.06 ⁎, n=1163– −0.09 ⁎, n=781 0.05, n=1173– – 0.35 ⁎⁎, n=772– – –

Table 5Sociodemographic variables by number of risk behaviors

Variable Number of risk behaviors Differences

0 1 2 3 4 Total

n 25 133 435 439 230 1262

SexFemale (%) 76.2 67.4 55.4 58.4 60.4 59.0 ns

Age (years), M(SD) 20.10 (1.53) 20.82 (2.95) 20.77 (2.41) 20.64 (2.01) 20.78 (1.87) 20.72 (2.23) nsSchool (%)

Law 12.0 19.5 18.9 28.9 33.5 25.0 χ2 (8)=50.95; pb0.001Teaching 20.0 27.8 32.9 28.9 40.0 32.0Medicine 68.0 52.6 48.3 42.1 26.5 43.0

Religion (%)Roman Catholic 23.8 27.3 28.2 35.6 33.9 31.7 nsProtestant 33.3 43.2 45.5 42.3 38.7 42.7Muslim 4.8 5.3 3.5 2.1 2.2 3.0No church 14.3 3.0 3.51 3.4 3.9 3.7Other 23.8 21.2 19.4 16.6 21.3 19.0

Living situation (%)Dormitory 19.0 15.5 14.7 10.1 8.9 12.2 χ2 (20)=34.26; pb0.05Shared apartment 28.6 21.7 36.7 41.3 44.0 37.9Alone 38.1 35.7 28.7 26.3 25.3 28.1With partner 9.5 11.6 7.1 9.4 8.9 8.7With parents 0 13.2 10.2 9.4 8.4 9.7Fraternity house 4.8 2.3 2.6 3.5 4.4 3.3

Fraternity member (%) 4.8 3.0 5.1 3.2 2.6 3.8 nsMilitary service (%) 4.8 7.7 11.3 8.5 6.6 9.0 ns

193S. Keller et al. / Preventive Medicine 46 (2008) 189–195

The correlations between behavioral indicators were low tomedium high. The highest correlation was found for number ofcigarettes and number of drinks per week (see Table 4).

Medical students were more likely to have fewer risk be-haviors than teaching and law students. Individuals sharing anapartment with others showed the highest number of risks.Gender, religion, age, being a fraternity member and havingserved in the army were not systematically related to the numberof multiple risk behaviors (see Table 5).

From a behavioral perspective, smokers were more likely tohave multiple problem behaviors. While only one smoker wasnot at risk for any of the three other health behaviors, 94% of allsmokers had at least two other health risks. Smokers ate 1/2serving of fruits and vegetables per day less than non-smokers(M=3.45 [SD=2.19] vs. M=3.96 [SD=2.17]; F(1, 1187)=14.00; pb0.001; η2 =0.011), exercised 1/2 h per week less thannon-smokers (M=151 min [SD=170] vs. M=186 min[SD=172]; F(1, 1187)=10.45; pb0.01; η2 =0.009) and hadfour more drinks in a typical week than non-smokers (M=9.00[SD=8.29] vs. M=5.09 [SD=6.01]; F(1, 1187)=84.53;pb0.001; η2 =0.066). Additionally, smokers reported seventimes the number of days of using cannabis during the previousyear compared to non-smokers (M=26.85 [SD=70.9] vs. M=3.75 [SD=21.92]; F(1, 1187)=71.73; pb0.001; η2 =0.096).

Discussion

As expected, this study documents that low fruit and vege-table consumption, sedentary life style, smoking and bingedrinking are highly prevalent among German university stu-dents. The results show considerable clustering of health risk

behaviors in this sample and thus replicate findings from otherinternational studies. However, with only 2% of the totalsample without risk behaviors and more than 18% with all four,multiple risk behaviors in this sample are more frequent than incomparable studies (Schuit et al., 2002; Fine et al., 2004;Poortinga, 2007) even though a direct comparison to thesestudies is limited by differences in the selected indicatorvariables and by different characteristics of the studiedpopulations. For example, Fine et al. (2004) reported a meannumber of 1.67 risk factors versus the 2.57 found in this sample;however, that study included being overweight as a variableinstead of fruit and vegetable consumption and included riskydrinking instead of binge drinking. Poortinga (2007) usedsimilar operationalizations in a population sample but reportedonly around 5% with four risk behaviors. Especially smokersseem to be at a high risk in this student sample: This studyconfirmed previous results that showed the close relationshipbetween smoking and drinking alcohol (e.g. Reed et al., 2007;Schuit et al., 2002). Overall, almost all smokers had two or threeadditional risk behaviors. Several previously identified pre-dictors (sex, age, socioeconomic status) for multiple riskbehavior could not be confirmed because of minimal varianceor because they were not relevant in this homogeneous sample.Overall, medical students showed a slightly more favorablebehavior pattern than teaching and law students.

The transition into the university environment can poten-tially offer new ‘cues to action’ and different social norms thatcan influence an individual's readiness for behavior change.However, in this sample only 6.5% of all students were ready towork on all their health risk behaviors indicating that moststudents are not ready for action-oriented interventions that aim

194 S. Keller et al. / Preventive Medicine 46 (2008) 189–195

at simultaneously changing all their health risk behaviors.Competing social and performance demands in the newenvironment may affect students' readiness to deal with healthbehaviors. On the other hand, one third of the sample is ready tochange one of their risk behaviors which indicates that there is apotential to reach students with adequate offers for healthpromotion, especially with programs that are tailored to theindividual's readiness for change (e.g. Emmons et al., 1994,1999; Prochaska et al., 2004, 2005).

This study is among the first to report on multiple riskbehaviors in a German population. The studied variables repre-sent the most relevant behavioral contributors to morbidity andmortality (Mokdad et al., 2004), the indicators are clearly definedand assessed by validated measures. The study is also among thefirst to report on individuals' readiness to change multiple riskbehaviors and thus adds to the existing literature and providesimportant information for future intervention planning.

Limitations of the study include the reliance on self-reportdata, a potential social desirability bias caused by the ques-tioning format, the selectiveness of the sample (universitystudents who attended a core lecture) and the restriction tostudents from only three of many potential schools. The time ofassessment at the very beginning of the first school year whenstudents establish themselves in their new environments maynot be fully representative of their usual behavior patterns.However, studies have shown that some of the behavioralpatterns remain stable during the time in a professional school(e.g. Škėmienė et al., 2007). In the future, longitudinal studiesor cohort studies including different years in school will benecessary to address some of these limitations; such studies arecurrently performed by the authors of this paper. Additionalmethodological limitations include the validity of the stagingalgorithms. While their basic usefulness had been shown earlier,they still produce stage misclassifications that can affect theresults when stage is used as determinant of being at risk.Finally, different cut-off scores for the behavioral criteria wouldalso affect the results, e.g. the restriction to vigorous exercisemay underestimate total activity by ignoring moderate physicalactivity.

This study has important implications. In Germany, theproblem of multiple risk behaviors among students has not yetbeen sufficiently recognized and requires attention fromuniversity administrators, political decision makers, healthcare services and prevention specialists. Most German univer-sities, including the one where these data were collected, do notoffer health services to their students except for individual crisiscounseling. With health insurance coverage of almost 100% ofthe population, universities rely on the medical system to takeresponsibility. However, there is no strong culture of preventionin the German medical system.

The results regarding the low readiness of students to changetheir behaviors also indicate, that action-oriented interventions(like group programs) will not directly reach the majority ofindividuals in need of interventions. Clearly, an effort needs tobe made to move individuals from earlier to later stages ofchange by raising awareness, informing about the significanceof multiple risk behaviors, and by creating environments that

are conducive to change. For example, student cafeterias couldoffer more choices of fruit and vegetables; student tutors couldpoint out opportunities for healthy eating or exercise in theorientation week; information on the risks of binge drinking andother health risk behaviors could be provided to students withtheir registration material. While a public discussion is currentlyunder way in Germany regarding the raising of the legaldrinking age, and recent changes in legislature will lead to asignificant restriction of smoking in public, sedentary lifestyleand low fruit and vegetable intake as risk factors have not yetreceived much attention. The university environment may beideal to facilitate an increase in students' readiness for healthbehavior change. The fact that many of these students will laterbe multiplicators and role models (e.g. physicians and teachers)makes this an even more important task.

Conclusions

Multiple risk behaviors are highly prevalent in law, teachingand medical students in this German university. The results ofthis study indicate that education and prevention programsaddressing multiple risk behavior may be important in thissetting. Future studies need to examine to what extent thesefindings can be replicated in other German universities and whatprograms are most effective and efficient in addressing multiplerisk behaviors in students as well as in general and high riskpopulations.

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