1. Risk Patterns Among College Youth: Identification and Implications for Prevention and Treatment.
- Author
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Luo, Juhua, Agley, Jon, Hendryx, Michael, Gassman, Ruth, and Lohrmann, David
- Subjects
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CHI-squared test , *COLLEGE students , *CONFIDENCE intervals , *DIET , *ALCOHOL drinking , *HEALTH behavior , *LATENT structure analysis , *QUESTIONNAIRES , *RISK-taking behavior , *SEX distribution , *SLEEP , *SMOKING , *SUBSTANCE abuse , *LOGISTIC regression analysis , *SOCIOECONOMIC factors , *PHYSICAL activity , *DATA analysis software , *DESCRIPTIVE statistics , *ODDS ratio - Abstract
Purpose. This study identified underlying subgroups among college students in terms of lifestyle characteristics and health risk behaviors and then investigated how demographic factors were associated with the underlying risk patterns to bolster health promotion efforts and interventions. Method. College students (N = 996) enrolled at Indiana University during 2009-2010 participated in a multidimensional online survey. Latent class analysis was used to identify underlying risk patterns based on seven lifestyle and health behaviors, including frequent alcohol use, binge drinking, smoking, low physical activity, low vegetable intake, low fruit intake, and poor sleep. Results. Four distinct risk behavior patterns were identified for both males and females including a “healthy” class, “low substance use but poor other health behaviors” class, “high substance use” (males)/“high alcohol use” (females) class, and a risk class characterized by elevated probability of all seven indicators. The highest risk class included 34% of the males and 22% of the females; they tended to be older or in more advanced undergraduate classes. Among males, compared with the “healthy” class, the “high substance use” class was more likely to contain non-Hispanic White students and students in advanced classes. Among females, the “low substance use but poor other health behaviors” class was associated with racial/ethnic minority status and lower levels of parental education. Conclusions. Our data suggest that risky health behaviors may tend to cluster in some students and that health promotion techniques might effectively be targeted to identifiable student subgroups. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
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