Objectives: To examine the association between depressed mood and clusters of health risk behaviors. Methods: A nationally representative sample of adolescents (N=15,214) was utilized to construct 10 pseudocontinuous health risk behaviors. Cluster analysis was performed to group adolescents, and subsequent multivariable logistic models were created. Results: Compared to non-risk takers, belonging to risk clusters significantly increased the odds of reporting depressed mood. African Americans in high-risk clusters appeared to be more vulnerable to depressed mood, when compared to remaining racial groups. Conclusions: Results suggest that adolescents engaging in multiple health-risk behaviors do so in the context of depressed mood.

Adolescents engage in a range of risk behaviors during their transition from childhood to adulthood. Identifying and understanding interpersonal and socio-environmental factors that may influence risk-taking is imperative in order to meet the Healthy People 2020 goals of reducing the incidence of unintended pregnancies, HIV, and other sexually transmitted infections among youth. The purpose of this study was to investigate gender differences in the predictors of HIV risk behaviors among South Florida youth. More specifically, this study examined how protective factors, risk factors, and health risk behaviors, derived from a guiding framework using the Theory of Problem Behavior and Theory of Gender and Power, were associated with HIV risk behavior. A secondary analysis of 2009 Youth Risk Behavior Survey data sets from Miami-Dade, Broward, and Palm Beach school districts tested hypotheses for factors associated with HIV risk behaviors. The sample consisted of 5,869 high school students (mean age 16.1 years), with 69% identifying as Black or Hispanic. Logistic regression analyses revealed gender differences in the predictors of HIV risk behavior. An increase in the health risk behaviors was related to an increase in the odds that a student would engage in HIV risk behavior. An increase in risk factors was also found to significantly predict an increase in the odds of HIV risk behavior, but only in females. Also, the probability of participation in HIV risk behavior increased with grade level. Post-hoc analyses identified recent sexual activity (past 3 months) as the strongest predictor of condom nonuse and having four or more sexual partners for both genders. The strongest predictors of having sex under the influence of drugs/alcohol were alcohol use in both genders, marijuana use in females, and physical fighting in males. Gender differences in the predictors of unprotected sex, multiple sexual partners, and having sex under the influence were also found. Additional studies are warranted to understand the gender differences in predictors of HIV risk behavior among youth in order to better inform prevention programming and policy, as well as meet the national Healthy People 2020 goals.

In recent years, tightening budgets have forced school districts to find new ways to save money. One way that has become increasingly popular is to shorten the traditional five-day school week to only four-days a week. This change is budget friendly and may act through efficiency wage theory as a recruitment tool for better teachers. Despite the increasing prevalence of districts running on four-day weeks, many of the effects of the shorter week on students are still unclear. Utilizing district-level panel data from the Colorado Department of Education, Study One took a difference-in-differences approach to determine the effect of the implementation of the four-day week on various delinquency measures, including suspensions, expulsions, and juvenile arrest rates. No significant results were found with robust standard errors, but trends suggested that juvenile crime may be increasing while school related incidents may be decreasing in areas with four-day school weeks. To offset these trends, this paper also proposes a second study aimed at investigating the effects of different types of fifth-day programs on adolescent well-being, academic achievement, and problem behaviors. Schools using the four-day week that agree to participate will be randomly assigned to receive either a career-prep program, an academic program, a recreational program, a social skills/mentoring program, or no program at all. Students (N=XXX) will fill out a survey at the beginning and at the end of the program or school year, reporting their subjective well-being and recent engagement in problem behaviors. It is expected that students who participate in the social skills/mentoring program will show the highest levels of well-being and little engagement in problem behaviors. This study will help to advance the research on after school programming, particularly in rural environments.

This chapter reviews the literature applying health behavior theories to indoor tanning. Few studies have tried to fit full versions of health behavior models to indoor tanning. Theoretical models from the family of theories referred to as the reasoned action approach (e.g., theory of planned behavior, behavioral alternative model, prototype willingness model, etc.) have been most commonly used to study indoor tanning. Results indicate that these models fit indoor tanning data moderately to extremely well. Two lesser known models, problem behavior theory and the terror management health model, have also demonstrated a reasonable fit. Two other common models, the health belief model and social cognitive theory, have never been fully tested with indoor tanning. However, key constructs from these models (e.g., perceived susceptibility and threat, modeling) have been used to understand indoor tanning. Empirical research conducted represents a solid start toward developing strong, comprehensive models of indoor tanning that can guide intervention efforts. This initial work needs to be expanded by conducting longitudinal studies and by including a broader age range in studies because the majority of existing work has focused on young adults. Incorporating findings related to tanning dependency, peer group affiliation, media influences and other constructs into these foundational models will also improve our understanding and ability to develop efficacious interventions to reduce engagement in this health risk behavior.