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BackgroundAdults who are homeless are more likely to have alcohol use disorders (AUDs) compared with domiciled adults. Although AUD treatments are commonly available, many factors (eg, transportation limitations and inability to schedule appointments) contribute to low treatment completion rates and low success rates of these interventions among adults experiencing homelessness. Most adults who are homeless own mobile phones; however, no interventions have been developed that use mobile devices to deliver and support AUD interventions for this population. Mobile phone–based AUD interventions may reduce barriers that have limited the use and utility of traditional interventions. ObjectiveThe aim of this study is to (1) identify variables (eg, affect, stress, geolocation, and cravings) that predict drinking among homeless adults (phase I), (2) develop a mobile intervention that utilizes an algorithm to identify moments of risk for drinking and deliver treatment messages that are tailored to the individual’s current needs in real time (phase II), and (3) pilot test the intervention app (phase III). MethodsIn phase I, adults experiencing homelessness with an AUD (N=80) will complete baseline, equipment, 2-week, and 4-week follow-up visits in person. Participants will be prompted to complete five daily ecological momentary assessments on a study-provided smartphone for 28 days. The smartphone app will collect GPS coordinates every 5 min for the entire 28-day study period. Participants will wear a transdermal alcohol sensor that will objectively measure alcohol use. In phase II, we will use phase I data to develop an algorithm that identifies moments of heightened risk for drinking and develop treatment messages that address risk factors for drinking. Phase III will pilot test the intervention in 40 adults experiencing homelessness with AUD. ResultsThis project was funded in June 2018. IRB approval was obtained in October 2018, and data collection for phase I began in February 2019. Phase III data collection is expected to conclude in 2020. To date, 80 participants have consented to the study, and data analysis for phase I will begin in early 2020. ConclusionsThis research will highlight intervention targets and develop a novel intervention for understudied and underserved adults experiencing homelessness with AUD. International Registered Report Identifier (IRRID)DERR1-10.2196/15610

BackgroundMobile assessment of the effects of acute marijuana on cognitive functioning in the natural environment would provide an ecologically valid measure of the impacts of marijuana use on daily functioning. ObjectiveThis study aimed to examine the association of reported acute subjective marijuana high (rated 0-10) with performance on 3 mobile cognitive tasks measuring visuospatial working memory (Flowers task), attentional bias to marijuana-related cues (marijuana Stroop), and information processing and psychomotor speed (digit symbol substitution task [DSST]). The effect of distraction as a moderator of the association between the rating of subjective marijuana high and task performance (ie, reaction time and number of correct responses) was explored. MethodsYoung adults (aged 18-25 years; 37/60, 62% female) who reported marijuana use at least twice per week were recruited through advertisements and a participant registry in Pittsburgh, Pennsylvania. Phone surveys and mobile cognitive tasks were delivered 3 times per day and were self-initiated when starting marijuana use. Completion of phone surveys triggered the delivery of cognitive tasks. Participants completed up to 30 days of daily data collection. Multilevel models examined associations between ratings of subjective marijuana high (rated 0-10) and performance on each cognitive task (reaction time and number of correct responses) and tested the number of distractions (rated 0-4) during the mobile task session as a moderator of the association between ratings of subjective marijuana high and task performance. ResultsParticipants provided 2703 data points, representing 451 reports (451/2703, 16.7%) of marijuana use. Consistent with slight impairing effects of acute marijuana use, an increase in the average rating of subjective marijuana high was associated with slower average reaction time on all 3 tasks—Flowers (B=2.29; SE 0.86; P=.008), marijuana Stroop (B=2.74; SE 1.09; P=.01), and DSST (B=3.08; SE 1.41; P=.03)—and with fewer correct responses for Flowers (B=−0.03; SE 0.01; P=.01) and DSST (B=−0.18; SE 0.07; P=.01), but not marijuana Stroop (P=.45). Results for distraction as a moderator were statistically significant only for certain cognitive tasks and outcomes. Specifically, as hypothesized, a person’s average number of reported distractions moderated the association of the average rating of subjective marijuana high (over and above a session’s rating) with the reaction time for marijuana Stroop (B=−52.93; SE 19.38; P=.006) and DSST (B=−109.72; SE 42.50; P=.01) and the number of correct responses for marijuana Stroop (B=−0.22; SE 0.10; P=.02) and DSST (B=4.62; SE 1.81; P=.01). ConclusionsYoung adults’ performance on mobile cognitive tasks in the natural environment was associated with ratings of acute subjective marijuana high, consistent with slight decreases in cognitive functioning. Monitoring cognitive functioning in real time in the natural environment holds promise for providing immediate feedback to guide personal decision making.

BACKGROUND: The association between behavioral economic demand and various alcohol use outcomes is well established. However, few studies have examined whether changes in demand occur following a brief alcohol intervention (BAI), and whether this change predicts alcohol outcomes over the long term. METHODS: Parallel process piecewise latent growth curve models were examined in a sample of 393 heavy drinking emerging adults (60.8% women; 85.2% white; Mage  = 18.77). In these models, two linear slopes represented rates of change in alcohol use, heavy drinking episodes, alcohol-related problems, and demand (intensity and highest expenditure across all price points or Omax ) from baseline to 1 month (slope 1) and 1 month to 16 months (slope 2). Mediation analyses were conducted to estimate the effect of a BAI on 16-month alcohol outcomes through slope 1 demand. RESULTS: A two-session BAI predicted significant reductions in all five outcomes from baseline to 1-month follow-up. Although no further reduction was observed from the 1-month to the 16-month follow-up, there was no regression to baseline levels. Slope 1 demand intensity, but not Omax , significantly mediated the association between BAI and both outcomes-heavy drinking episodes (Est. = -0.23, SE = 0.08, p

Objective: To examine race, gender, and alcohol use level as moderators of the association between protective behavioral strategies (PBS) and alcohol-related problems. Participants: A sample of 12,011 participants who reported recent drinking (87.7% White, 61% Women) from Project INTEGRATE, a study that combined individual participant data (IPD) from 24 brief motivational intervention trials for college students. Methods: Hierarchical regressions were conducted to determine whether there was a moderated effect of PBS on alcohol problems across alcohol use levels, and whether the moderated protective effect of PBS by alcohol use differed by gender and race. Results: The protective association between PBS and alcohol-related problems was greater for those who drank less. This moderated effect did not differ across men and women or across racial groups. Conclusions: College drinking prevention programs should ensure that students are aware of the limits of PBS as a mitigator of alcohol problems.

Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesian versus ML estimators for LGMs by evaluating their accuracy via Monte Carlo (MC) simulations. For the MC study, longitudinal data sets were generated and estimated using LGM via both ML and Bayesian estimation with three different priors, and parameter recovery across the two estimators was evaluated to determine their relative performance. The findings suggest that ML estimation is a reasonable choice for most LGMs, unless it fails to converge, which can occur with limiting data situations (i.e., just a few time points, no covariate or outcome, modest sample sizes). When models do not converge using ML, we recommend Bayesian estimation with one caveat that the influence of the priors on estimation may have to be carefully examined, per recent recommendations on Bayesian modeling for applied researchers.

Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol‐related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero‐inflated, particularly compared with recently developed marginalized count regression approaches for such data. Methods: The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log‐transformed scales, respectively) and three prevailing count distribution‐based models (ie, Poisson, negative binomial, and zero‐inflated Poisson (ZIP) models). We also considered the marginalized zero‐inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero‐inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of the statistical models and approaches across data conditions that varied in sample size (N=100$$ N=100 $$ to 500), zero rate (0.2 to 0.8), and intervention effect sizes. Results: Under zero‐inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non‐zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of the linear model with a log‐transformed outcome variable was unsatisfactory. Conclusions: The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero‐inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros. [ABSTRACT FROM AUTHOR]

The present study examined young adults' completion of the Web-based intervention content in a randomized trial that tested two Personalized Feedback Interventions (PFIs) for alcohol use and risky sex behavior (RSB). Data are from a study that evaluated efficacy of two Web-delivered PFIs (combined PFI and integrated PFI) among 269 sexually active young adults between the ages of 18 and 25. We described the view patterns of the Web-based intervention and examined if baseline sociodemographic and alcohol- and sex-related behavioral factors were associated with the completion of PFI. Many participants viewed the intervention more than one time, and the majority finished all intervention pages in at least one session. Older participants and participants who drink more frequently prior to or during sex were more likely to complete. Most participants, especially the young adults who were at higher risk, utilized the intervention contents as intended. [ABSTRACT FROM AUTHOR]

Understanding the efficacy and relative effectiveness of a brief alcohol intervention (BAI) relies on obtaining a credible intervention effect estimate. Outcomes in BAI trials are often count variables, such as the number of drinks consumed, which may be overdispersed (i.e., greater variability than expected based on a given model) and zero‐inflated (i.e., greater probability of zeros than expected based on a given model). Ignoring such distribution characteristics can lead to biased estimates and invalid statistical conclusions. In this critical review, we identified and reviewed 64 articles that reported count outcomes from a systematic review of BAI trials for adolescents and young adults from 2013 to 2018. Given many statistical models to choose from when analyzing count outcomes, we reviewed the models used and reporting practices in the BAI trial literature. A majority (61.3%) of analyses with count outcomes used linear models despite violations of normality assumptions; 75.6% of outcome variables demonstrated clear overdispersion. We provide an overview of available count models (Poisson, negative binomial, zero‐inflated or hurdle, and marginalized zero‐inflated Poisson regression) and formulate practical guidelines for reporting outcomes of BAIs. We provide a visual step‐by‐step decision guide for selecting appropriate statistical models and reporting results for count outcomes. We list accessible resources to help researchers select an appropriate model with which to analyze their data. Recent advances in count distribution‐based models hold promise for evaluating count outcomes to gauge the efficacy and effectiveness of BAIs and identify critical covariates in alcohol epidemiologic research. We recommend that researchers report the distributional properties of count outcomes, such as the proportion of zero counts, and select an appropriate statistical analysis for count outcomes using the provided decision tree. By following these recommendations, future research may yield more accurate, transparent, and reproducible results. [ABSTRACT FROM AUTHOR]

The authors investigated the impact of a manualized high school transition program, the Peer Group Connection (PGC) program, on the graduation rate at a low-income, Mid-Atlantic high school. The program utilized 12th-grade student peer leaders to create a supportive environment for incoming ninth-grade students. Results of a randomized control trial demonstrated that male students who participated in the program during Grade 9 were significantly more likely to graduate from high school within 4 years than male students in the control group (81% vs. 63%). Findings suggest that peers can be effective in delivering a school-based, social emotional learning intervention and that it is possible to intervene in Grade 9 to influence the probability of high school graduation.

Little is known about individual and situational factors that moderate the efficacy of personalized feedback interventions (PFIs). Mandated college students (N = 348) were randomly assigned either to a PFI delivered in the context of a brief motivational interview (BMI; n = 180) or to a written PFI only (WF) condition and were followed up at 4 months and 15 months postintervention. The authors empirically identified heterogeneous subgroups utilizing mixture modeling analysis based on heavy episodic drinking and alcohol-related problems. The 4 identified groups were dichotomized into an improved group (53.4%) and a nonimproved group (46.6%). Logistic regression results indicated that the BMI was no more efficacious than the WF across all mandated students. However, mandated students who experienced a serious incident requiring medical or police attention and those with higher levels of alcohol-related problems at baseline benefited more from the BMI than from the WF. It may be an efficacious and cost-effective approach to provide a written PFI for low-risk mandated students and an enhanced PFI with a BMI for those students who experience a serious incident or have higher baseline alcohol-related problems. (Contains 5 figures, 3 tables and 12 footnotes.)

This study analyzes latent change scores using latent curve models (LCMs) for evaluation research with pre-post-post designs. The article extends a recent article by Willoughby, Vandergrift, Blair, and Granger (2007) on the use of LCMs for studies with pre-post-post designs, and demonstrates that intervention effects can be better tested using different parameterizations of LCMs. This study illustrates how to test the overall mean of a latent variable at the time of research interest, not just at baseline, as well as means of latent change variables between assessments, and introduces how individual differences in the referent outcome (i.e., Level 2 random effects) and measurement-specific residuals (i.e., Level 1 residuals) can be modeled and interpreted. Two intervention data examples are presented. This LCM approach to change is more advantageous than other methods for its handling of measurement errors and individual differences in response to treatment, avoiding unrealistic assumptions, and being more powerful and flexible. (Contains 6 figures, 4 tables and 8 footnotes.)

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women on the basis of their baseline heart rate and heart rate variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled the high alcohol risk and normative groups. Compared to the normative group, individuals in the high alcohol risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The high alcohol risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the normative group showed a significant HRV change only to negative cues. Findings suggest that individuals with autonomic self-regulatory difficulties may be more susceptible to heavy alcohol use and use of alcohol for emotional regulation.

This article reviews the premises of configural frequency analysis (CFA), including methods of choosing significance tests and base models, as well as protecting [alpha], and discusses why CFA is a useful approach when conducting longitudinal person-oriented research. CFA operates at the manifest variable level. Longitudinal CFA seeks to identify those temporal patterns that stand out as more frequent (CFA types) or less frequent (CFA antitypes) than expected with reference to a base model. A base model that has been used frequently in CFA applications, prediction CFA, and a new base model, auto-association CFA, are discussed for analysis of cross-classifications of longitudinal data. The former base model takes the associations among predictors and among criteria into account. The latter takes the auto-associations among repeatedly observed variables into account. Application examples of each are given using data from a longitudinal study of domestic violence. It is demonstrated that CFA results are not redundant with results from log-linear modeling or multinomial regression and that, of these approaches, CFA shows particular utility when conducting person-oriented research.