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Studies investigating the impact of medical marijuana legalization have found no significant changes in adolescent use. In one of the few studies focused on recreational marijuana, we investigated how recreational marijuana legalization and community sales policy influenced factors that likely impact youth use (youth willingness and intent to use, parent use) as well as youth use. Legalization of recreational marijuana in Oregon coincided with our study on adolescent substance use. Cohort 1 transitioned from 8th to 9th grade prior to legalization and Cohort 2 made this transition during legalization (N = 444; 53% female). Communities were allowed to opt out of sales. Multivariate linear regression models estimated the impact of legalization and community sales policy on changes in attitudes and parent use (2 time points 1 year apart). Zero-inflated Poisson growth curve models estimated the effects on initial levels and rate of change from 8th through 9th grade (4 time points). In communities opting out of sales, the prior-to-legalization cohort was less likely to increase their willingness and intent to use marijuana, and the legalization cohort was more likely to increase intent to use. For youth who used marijuana, legalization was associated with increased use, and those in communities opting out of sales had greater growth in marijuana use. Community policy appears to impact youth attitudes toward, and use of, marijuana. Results suggest that legalization of recreational marijuana did not increase marijuana use for youth who did not use marijuana but did increase use in youth who were already using. (PsycINFO Database Record [ABSTRACT FROM AUTHOR]

Purpose The aim of this study was to examine the use of electronic cigarettes (e-cigarettes) among adolescents over time, including correlates of lifetime use by eighth grade and trajectories of current use across ninth grade. Methods Participants (N = 1,091) from seven school districts in Oregon, United States, completed four self-report surveys on substance use, from the spring of eighth grade (M age = 14.4 years old; standard deviation = .50) through the spring of ninth grade. Results Overall, 27.7% of eighth graders had used e-cigarettes, and 16.8% were current e-cigarette users (used in the past 30 days); use did not significantly differ by gender or ethnicity. Correlates of e-cigarette lifetime use by eighth grade included lifetime and current use of marijuana, alcohol, cigarettes, and chewing tobacco. Five percent of students were “accelerators,” on average using e-cigarettes 14 of the last 30 days in eighth grade, increasing to daily use (30/30 days) by the end of ninth grade. Across all substances, those in the accelerator group were more likely to have reported lifetime substance use by eighth grade and current substance use in ninth grade, compared to the “infrequent/no use” group. Conclusions A sizeable proportion of young adolescents are using e-cigarettes, and e-cigarette use is highly correlated with use of other substances, including marijuana. Adolescents who progress to daily e-cigarette use in high school are more likely to use other substances compared to low or nonusers. E-cigarettes may be a relatively new addition to a constellation of substances being actively used by a segment of the youth population. [ABSTRACT FROM AUTHOR]

Background: Many best practice smoking cessation programs use fully automated internet interventions designed for nonmobile personal computers (desktop computers, laptops, and tablets). A relatively small number of smoking cessation interventions have been designed specifically for mobile devices such as smartphones.Objective: This study examined the efficacy and usage patterns of two internet-based best practices smoking cessation interventions.Methods: Overall, 1271 smokers who wanted to quit were randomly assigned to (1) MobileQuit (designed for-and constrained its use to-mobile devices, included text messaging, and embodied tunnel information architecture) or (2) QuitOnline (designed for nonmobile desktop or tablet computers, did not include text messages, and used a flexible hybrid matrix-hierarchical information architecture). Primary outcomes included self-reported 7-day point-prevalence smoking abstinence at 3- and 6-month follow-up assessments. Program visits were unobtrusively assessed (frequency, duration, and device used for access).Results: Significantly more MobileQuit participants than QuitOnline participants reported quitting smoking. Abstinence rates using intention-to-treat analysis were 20.7% (131/633) vs 11.4% (73/638) at 3 months, 24.6% (156/633) vs 19.3% (123/638) at 6 months, and 15.8% (100/633) vs 8.8% (56/638) for both 3 and 6 months. Using Complete Cases, MobileQuit's advantage was significant at 3 months (45.6% [131/287] vs 28.4% [73/257]) and the combined 3 and 6 months (40.5% [100/247] vs 25.9% [56/216]) but not at 6 months (43.5% [156/359] vs 34.4% [123/329]). Participants in both conditions reported their program was usable and helpful. MobileQuit participants visited their program 5 times more frequently than did QuitOnline participants. Consistent with the MobileQuit's built-in constraint, 89.46% (8820/9859) of its visits were made on an intended mobile device, whereas 47.72% (691/1448) of visits to QuitOnline used an intended nonmobile device. Among MobileQuit participants, 76.0% (459/604) used only an intended mobile device, 23.0% (139/604) used both mobile and nonmobile devices, and 0.1% (6/604) used only a nonmobile device. Among QuitOnline participants, 31.3% (137/438) used only the intended nonmobile devices, 16.7% (73/438) used both mobile and nonmobile devices, and 52.1% (228/438) used only mobile devices (primarily smartphones).Conclusions: This study provides evidence for optimizing intervention design for smartphones over a usual care internet approach in which interventions are designed primarily for use on nonmobile devices such as desktop computers, laptops. or tablets. We propose that future internet interventions should be designed for use on all of the devices (multiple screens) that users prefer. We forecast that the approach of designing internet interventions for mobile vs nonmobile devices will be replaced by internet interventions that use a single Web app designed to be responsive (adapt to different screen sizes and operating systems), share user data across devices, embody a pervasive information architecture, and complemented by text message notifications.Trial Registration: ClinicalTrials.gov NCT01952236; https://clinicaltrials.gov/ct2/show/NCT01952236 (Archived by WebCite at http://www.webcitation.org/6zdSxqbf8). [ABSTRACT FROM AUTHOR]