In recent years, trajectory approaches to characterizing individual differences in the onset and course of substance involvement have gained popularity. Previous studies have sometimes reported four prototypic courses: (1) a consistently "low" group, (2) an "increase" group, (3) a "decrease" group, and (4) a consistently "high" group. Although not always recovered, these trajectories are often found despite these studies varying in the ages of the samples studied and the duration of the observation periods employed. We examined the consistency with which these longitudinal patterns of heavy drinking were recovered in a series of latent class growth analyses that systematically varied the age of the sample at baseline, the duration of observation, and the number and frequency of measurement occasions. Data were drawn from a four-year, eight-wave panel study of college student drinking (N=3720). Despite some variability across analyses, there was a strong tendency for these prototypes to emerge regardless of the participants' age at baseline and the duration of observation. These findings highlight potential problems with commonly employed trajectory-based approaches and the need to not over-reify these constructs.Historically, alcohol researchers have classified different types of problematic alcohol use on factors related to personal characteristics such as age-of-onset, gender, family history of alcoholism, personality traits, and comorbid psychopathology (e.g., Babor, 1996;Babor et al., 1992;Cloninger, 1987). One dimension that has been featured prominently in relatively recent classifications is that of developmental course (Zucker, 1987;Zucker, 1994;Zucker, Fitzgerald, & Moses, 1995). A rapidly evolving body of work has used various types of categorical trajectory-based approaches to model change in alcohol consumption, alcoholrelated problems, and alcohol use disorders (Bennett, McCrady, Johnson, & Pandina, 1999;Chassin, Flora, & King, 2004;Chassin, Pitts, & Prost, 2002;Chung, Maisto, Cornelius, & Martin, 2005;Chung, Maisto, Cornelius, Martin, & Jackson, 2005;Colder, Campbell, Ruel, Richardson, & Flay, 2002;D'Amico et al., 2001;Feldman, Masyn, & Conger, 2009;Flory, Lynam, Milich, Leukefeld, & Clayton, 2004;Greenbaum, Del Boca, Darkes, Wang, & Goldman, 2005;Guo et al., 2002;Hill, White, Correspondence regarding this manuscript should be sent to Kenneth J. Sher, 200 South Seventh Street, University of Missouri, Columbia, MO 65211, sherk@missouri.edu.. Publisher's Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available at www.apa.org/pubs/journals/abn...

Aims-We examined the alcohol-tobacco relationship using two prospective, ethnically diverse samples. Trajectories of alcohol and tobacco use are portrayed overall and by sex and ethnicity. Using prospective analyses, we examine directional influences between alcohol and tobacco use, and we characterize initiation versus persistence of drinking and smoking as a function of use of the other substance.Design, setting-Data were from the National Longitudinal Study of Adolescent Health (AddHealth) and the Adolescent Health Risk Study (AHRS). Follow-up intervals for AddHealth and AHRS were 1 and 5 years, respectively.Participants-AddHealth respondents (n = 4831) were on average 14.8 years old (48% male, 23% black, 61% white) and AHRS respondents (n = 1814) were on average 16.7 years old (47% male, 44% black, 49% white).Measurements-Two alcohol consumption variables and two smoking variables were used: drinking frequency and heavy drinking frequency, and regular (current) smoking and daily number of cigarettes.Findings-Alcohol and tobacco use exhibited monotonic increases over adolescence and young adulthood. Men and white respondents reported more use than women and black respondents. Alcohol and tobacco were moderately associated at both times. Analyses revealed that prior alcohol use predicted tobacco use more strongly than the converse. Initiation of smoking was a function of prior drinking; to a lesser extent, initiation of drinking was a function of prior smoking. Persistence of smoking was a function of prior drinking and persistence of drinking was a function of prior smoking.Conclusions-Provisional support exists for the claim that alcohol use predicts tobacco use more strongly than the converse. For both drinking and smoking, onset and persistence are predicted by prior use of the other substance, and these associations were robust across sex and ethnicity.

Aggregate-level prevalences and individual-level developmental trajectories of untreated problem gambling were examined in an 11-year, 4-wave longitudinal study spanning the adolescent through young adult years. The past-year prevalences, 3-4 year incidences, and lifetime prevalences of problem gambling from adolescence through young adulthood were relatively stable at 2%-3%, 1%-2%, and 3%-5%, respectively. Despite the stability of the prevalences at the aggregate level, problem gambling appeared to be more transitory and episodic than enduring and chronic at the individual level. The present study is consistent with the limited evidence available on the natural history of problem gambling in the community in suggesting that natural recovery may be the rule rather than the exception.

Cross-substance trajectory concordance was high, with parallel changes in substance use over emerging adulthood. This suggests similar developmental timing of use, perhaps due to the experience of developmental transitions that have a common influence on use of different substances. Prediction of combinations of comorbidity characterized by early onset and persistently high use suggests that to some extent, individuals use multiple substances because of a common vulnerability to each, rather than directional relations among substances (e.g., cross-tolerance, cueing).

Extant developmental research distinguishing young adults who moderate versus persist in alcohol consumption has not consistently evaluated the domain of alcohol involvement being modeled, making it difficult to compare findings across studies. In the present study, the authors characterized the developmental course of 5 indices of alcohol involvement using a prospective (6-wave) sample of 377 young adults (Year 1 age = 18.52 years; 55% female; 51% with family history of alcoholism) over 11 years. Growth mixture models were applied to each measure. Despite similarity in trajectory shape, predicted prevalences varied, and the consistency of trajectory classifications across alternate indices revealed low agreement. Correlates of drinking course, however, were somewhat robust across alcohol index. The finding that trajectories are conditional on the specific indices used suggests that it may be hazardous to generalize across alternate indices of alcohol involvement. Keywordsalcohol; drinking; trajectory; course; developmental Early young adulthood (ages 18-25 years) represents the period of peak prevalence for alcohol use (Gallup Organization, 1987) and alcohol use disorders (AUDs) (Grant, 1997), and the transition from high school to college is a time of risk for heavy drinking (Baer, Kivlahan, & Marlatt, 1995;Johnston, O'Malley, & Bachman, 2002a, 2002b. As young adults age beyond college, however, many exhibit a tendency to moderate heavy alcohol involvement (Donovan, Jessor, & Jessor, 1983;Perkins, 1999). In the population, alcohol consumption (particularly heavy drinking), problem drinking, and AUDs tend to peak in the early 20s and then show a decline over the third decade of life (Dawson, Grant, Stinson, & Chou, 2004; Fillmore, 1988;Jessor, Donovan, & Costa, 1991;Johnston et al., 2002aJohnston et al., , 2002bMuthén & Muthén, 2000). Against this normative decrease, many young adults continue to drink heavily (Windle, 1988), continue to have drinking problems (Fillmore, 1988;Tubman, Vicary, von Eye, & Lerner, 1990;Windle, 1988), and manifest AUDs (Zucker, Fitzgerald, & Moses, 1995). Thus, a central question of research into the etiology of alcoholism concerns factors that distinguish young adults who moderate their alcohol consumption from those who persist in heavy drinking. Yet, until recently, relatively little research has addressed variability in typical courses of alcohol involvement during this rapidly changing period of life of early young adulthood (recently termed "emerging adulthood"; Arnett, 2000). In the present study, we addressed three central research questions: (a) whether there exist prototypical courses of Copyright 2005 by the American Psychological AssociationCorrespondence concerning this article should be addressed to Kristina M. Jackson, who is now at the Center for Alcohol and Addiction Studies, Brown University, Box G-BH, Providence, RI 02912. kristina_jackson@brown.edu. NIH Public Access Author ManuscriptPsychol Addict Behav. Author manuscript; available in PMC 2010 July 7. ...

An appearance-based sun-protection intervention program was developed, implemented, and evaluated in a sample of 211 Caucasian women (ages 18-25) randomly assigned to the sun-protection program or to a stress management (control) program. The sun-protection program incorporated a novel construct of image norms of aspirational peers (i.e., female media figures, fashion models) approving paleness. The authors targeted these image norms as well as the advantages of tanning, health beliefs about photoaging and skin cancer, and self-efficacy for sun protection. The intervention produced significant differences across conditions favoring sun protection on all constructs but severity of skin cancer and barriers to sun protection. At follow-up, treatment participants exceeded controls both in intention to sun protect and sun-protective behavior and reported lower intention to sunbathe and fewer hours of sunbathing. A mediational model of intervention outcomes revealed distinct mediators for sun protection versus sunbathing.

College students are an at-risk population based on their heavy alcohol consumption and associated consequences. First-year students are at particular risk due to greater freedom and access to alcohol on campus. Web-based (electronic) interventions (e-interventions) are being rapidly adopted as a universal approach to prevent high-risk drinking, but have not been well evaluated. The objective of this study was to investigate the effectiveness of the two most widely adopted EIs, AlcoholEdu and The Alcohol eCHECKUP TO GO (e-Chug), in reducing both alcohol use and alcohol-related consequences in incoming college students. To do so, we conducted a 3-group randomized trial (N = 82) comparing AlcoholEdu and e-Chug to an assessment-only control group. Compared to the assessment-only control group, participants in the AlcoholEdu and e-Chug groups reported lower levels of alcohol use across multiple measures at 1-month follow up. Participants who received AlcoholEdu showed significantly fewer lower alcohol-related consequences than assessment-only controls, while there was a trend for reduced consequences in participants who received e-Chug versus assessment-only. Findings indicate that e-intervention is a promising prevention approach to address the problem of college student alcohol consumption, especially for campuses that have limited resources.

Results suggest that social media plays a unique role in contributing to peer influence processes surrounding alcohol use and highlight the need for future investigative and preventive efforts to account for adolescents' changing social environments.