A motivational model of alcohol involvement (M. L. Cooper, M. R. Frone, M. Russell, & P. Mudar, 1995) was replicated and extended by incorporating social antecedents and motives and by testing this model cross-sectionally and longitudinally in a sample of college students. Participants (N = 388) completed a questionnaire battery assessing alcohol use and problems, alcohol expectancies, sensation seeking, negative affect, social influences, and drinking motives. Associations among psychosocial antecedents, drinking motives, and alcohol involvement differed from those found by M. L. Cooper et al. (1995). These findings point to the importance of social influences and of positive reinforcement motives but not to the centrality of drinking motives in this population.

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Purpose. Obesity accounts for approximately 300,000 deaths a year in the United States, and prevalence rates have been increasing over the past decade. The nutrition environment may be contributing to this epidemic. This study examined the relationship between fast food restaurants and obesity on a state-wide basis. Design. A one-time cross-sectional analysis of secondary data was used for this study. Setting. The setting for this study was the United States. Subjects. State-level data were used as the unit of analysis. Alaska was excluded as an outlier, and the District of Columbia was added (N ϭ 50). Measures. Measures included aggregate state-level means for square miles per fast food restaurant, population per fast food restaurant, population density, ethnicity, age, gender, physical inactivity, fruit and vegetable intake, and obesity rates. Data were obtained from the 2002 Behavioral Risk Factor and Surveillance Survey, the 2000 U.S. Census, and the 2002 U.S. Yellow Pages. Results. Multiple hierarchal regressions revealed that square miles per fast food restaurants and residents per restaurant accounted for 6% of the variance in state obesity rates after controlling for population density, ethnicity, age, gender, physical inactivity, and fruit and vegetable intake. The entire model explained 70% of the total variance in state obesity rates. Conclusions. These results indicate a correlational relationship between both the number of residents per fast food restaurant and the square miles per fast food restaurants with state-level obesity prevalence. Limitations include the use of correlational aggregate data.

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BackgroundCo-occurrence of different behaviors was investigated using the theoretical underpinnings of the Transtheoretical Model, the Theory of Triadic Influence and the concept of Transfer.PurposeTo investigate relationships between different health behaviors' stages of change, how behaviors group, and whether study participants cluster in terms of their behaviors.MethodRelationships across stages for different behaviors were assessed in three studies with N = 3,519, 965, and 310 individuals from the USA and Germany by telephone and internet surveys using correlational analyses, factor analyses, and cluster analyses.ResultsConsistently stronger correlations were found between nutrition and physical activity (r = 0.16–0.26, p < 0.01) than between non-smoking and nutrition (r = 0.08–0.16, p < 0.03), or non-smoking and physical activity (r = 0.01–0.21). Principal component analyses of investigated behaviors indicated two factors: a “health-promoting” factor and a “health-risk” factor. Three distinct behavioral patterns were found in the cluster analyses.ConclusionOur results support the assumption that individuals who are in a higher stage for one behavior are more likely to be in a higher stage for another behavior as well. If the aim is to improve a healthy lifestyle, success in one behavior can be used to facilitate changes in other behaviors—especially if the two behaviors are both health-promoting or health-risky. Moreover, interventions should be targeted towards the different behavioral patterns rather than to single behaviors. This might be achieved by addressing transfer between behaviors.