Log-Linear and Configural Analysis of Intra-Individual Time Series under Consideration of Serial Dependence

Serial dependence often prevents researchers from obtaining unbiased parameter estimates. In this article, we propose taking serial dependence into account, and exploiting the information that comes with serial dependence. This can be done in the form of shifted variables that are included in addition to the original variables, when models are specified. This way, models become more complex but relations can be considered that, otherwise, cannot be analyzed. Two fields of application are discussed. The first is log-linear modeling. This method is variable-oriented, but it has found applications in person-oriented research. The gain from including shifted variables in log-linear models is that new, specific variable relations can be analyzed. The second field is that of Configural Frequency Analysis. This method is person-oriented, and it allows researchers to detect local relations that, without consideration of shifted variables, cannot be detected. Application examples are given in the context of single-case analysis.

The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations

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.

Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1-3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9-12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses.

Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study

Meta-analysis using individual participant data (IPD) is an important methodology in intervention research because it (a) increases accuracy and precision of estimates, (b) allows researchers to investigate mediators and moderators of treatment effects, and (c) makes use of extant data. IPD meta-analysis can be conducted either via a one-step approach that uses data from all studies simultaneously, or a two-step approach, which aggregates data for each study and then combines them in a traditional meta-analysis model. Unfortunately, there are no evidence-based guidelines for how best to approach IPD meta-analysis for count outcomes with many zeroes, such as alcohol use. We used simulation to compare the performance of four hurdle models (3 one-step and 1 two-step models) for zero-inflated count IPD, under realistic data conditions. Overall, all models yielded adequate coverage and bias for the treatment effect in the count portion of the model, across all data conditions. However, in the zero portion, the treatment effect was underestimated in most models and data conditions, especially when there were fewer studies. The performance of both one- and two-step approaches depended on the formulation of the treatment effects, suggesting a need to carefully consider model assumptions and specifications when using IPD.

Do brief motivational interventions increase motivation for change in drinking among college students? A two-step meta-analysis of individual participant data

BACKGROUND

Brief motivational interventions (BMIs) are one of the most effective individually focused alcohol intervention strategies for college students. Despite the central theoretical role of motivation for change in BMIs, it is unclear whether BMIs increase motivation to change drinking behavior. We conducted a two-step meta-analysis of individual participant data (IPD) to examine whether BMIs increase motivation for change. N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually delivered motivational interviewing with personalized feedback (MI + PF), stand-alone personalized feedback (PF), and group-based motivational interviewing (GMI).

METHODS

We included 15 trials of BMI (N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually-delivered motivational interviewing with personalized feedback (MI + PF), stand-alone personalized feedback (PF), and group-based motivational interviewing (GMI). Different measures and responses used in the original trials were harmonized. Effect size estimates were derived from a model that adjusted for baseline motivation and demographic variables for each trial (step 1) and subsequently combined in a random-effects meta-analysis (step 2).

RESULTS

The overall intervention effect of BMIs on motivation for change was not statistically significant (standard mean difference [SMD]: 0.026, 95% CI: [-0.001, 0.053], p = 0.06, k = 19 comparisons). Of the three subtypes of BMIs, GMI, which tended to provide motivation-targeted content, had a statistically significant intervention effect on motivation, compared with controls (SMD: 0.055, 95% CI: [0.007, 0.103], p = 0.025, k = 5). By contrast, there was no evidence that MI + PF (SMD = 0.04, 95% CI: [-0.02, 0.10], k = 6, p = 0.20) nor PF increased motivation (SMD = 0.005, 95% CI: [-0.028, 0.039], k = 8, p = 0.75), compared with controls. Post hoc meta-regression analysis suggested that motivation sharply decreased each month within the first 3 months postintervention (b = -0.050, z = -2.80, p = 0.005 for k = 14).

CONCLUSIONS

Although BMIs provide motivational content and normative feedback and are assumed to motivate behavior change, the results do not wholly support the hypothesis that BMIs improve motivation for change. Changing motivation is difficult to assess during and following interventions, but it is still a theoretically important clinical endpoint. Further, the evidence cautiously suggests that changing motivation may be achievable, especially if motivation-targeted content components are provided.

A randomized pilot trial of a mobile delivered brief motivational interviewing and behavioral economic alcohol intervention for emerging adults

OBJECTIVE

Mobile health (mHealth) interventions show potential to broaden the reach of efficacious alcohol brief motivational interventions (BMIs). However, efficacy is mixed and may be limited by low participant attention and engagement. The present study examined the feasibility, acceptability, and preliminary efficacy of a live text-message delivered BMI in a pilot randomized clinical trial.

METHOD

Participants were 66 college students (63.6% women; 61.9% White; Mage = 19.95, SD = 1.66) reporting an average of 11.88 (SD = 8.74) drinks per week, 4.42 (SD = 3.59) heavy drinking episodes (HDEs), and 8.44 (SD = 5.62) alcohol-related problems in the past month. Participants were randomized to receive either (a) education or (b) an alcohol BMI plus behavioral economic substance-free activity session (SFAS), each followed by 4 weeks of mini sessions. All sessions were administered via live text-message. Participants completed assessments postintervention (after the 4th mini session) and at 3-month follow-up.

RESULTS

90.9% completed both initial full-length sessions and at least two of the four mini sessions with 87.9% retention at 3-month follow-up. Participants found the interventions useful, interesting, relevant, and effective, with no between-group differences. There were no statistically significant group differences in drinks per week or alcohol-related problems at follow-up, but BMI + SFAS participants reported fewer past-month HDEs than those who received education.

CONCLUSIONS

Live text-messaging to deliver the BMI + SFAS is feasible and well-received. The preliminary efficacy results should be interpreted cautiously due to the small sample size but support further investigation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cross-Tailoring Integrative Alcohol and Risky Sexual Behavior Feedback for College Students: Protocol for a Hybrid Type 1 Effectiveness-Implementation Trial

BACKGROUND

Underage drinking and related risky sexual behavior (RSB) are major public health concerns on United States college campuses. Although technology-delivered personalized feedback interventions (PFIs) are considered a best practice for individual-level campus alcohol prevention, there is room for improving the effectiveness of this approach with regard to alcohol-related RSB.

OBJECTIVE

The aims of this study are to (1) evaluate the impact of a brief PFI that integrates content on alcohol use and RSB and is adapted to include a novel cross-tailored dynamic feedback (CDF) component for at-risk first-year college students and (2) identify implementation factors critical to the CDF's success to facilitate future scale-up in campus settings.

METHODS

This study uses a hybrid type 1 effectiveness-implementation design and will be conducted in 3 phases. Phase 1 is a stakeholder-engaged PFI+CDF adaptation guided by focus groups and usability testing. In phase 2, 600 first-year college students who drink and are sexually active will be recruited from 2 sites (n=300 per site) to participate in a 4-group randomized controlled trial to examine the effectiveness of PFI+CDF in reducing alcohol-related RSB. Eligible participants will complete a baseline survey during the first week of the semester and follow-up surveys at 1, 2, 3, 6, and 13 months post baseline. Phase 3 is a qualitative evaluation with stakeholders to better understand relevant implementation factors.

RESULTS

Recruitment and enrollment for phase 1 began in January 2022. Recruitment for phases 2 and 3 is planned for the summer of 2023 and 2024, respectively. Upon collection of data, the effectiveness of PFI+CDF will be examined, and factors critical to implementation will be evaluated.

CONCLUSIONS

This hybrid type 1 trial is designed to impact the field by testing an innovative adaptation that extends evidence-based alcohol programs to reduce alcohol-related RSB and provides insights related to implementation to bridge the gap between research and practice at the university level.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05011903; https://clinicaltrials.gov/ct2/show/NCT05011903.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43986.

Does abstaining from alcohol in high school moderate intervention effects for college students? Implications for tiered intervention strategies

Brief motivational intervention (BMI) and personalized feedback intervention (PFI) are individual-focused brief alcohol intervention approaches that have been proven efficacious for reducing alcohol use among college students and young adults. Although the efficacy of these two intervention approaches has been well established, little is known about the factors that may modify their effects on alcohol outcomes. In particular, high school drinking may be a risk factor for continued and heightened use of alcohol in college, and thus may influence the outcomes of BMI and PFI. The purpose of this study was to investigate whether high school drinking was associated with different intervention outcomes among students who received PFI compared to those who received BMI. We conducted moderation analyses examining 348 mandated students (60.1% male; 73.3% White; and 61.5% first-year student) who were randomly assigned to either a BMI or a PFI and whose alcohol consumption was assessed at 4-month and 15-month follow-ups. Results from marginalized zero-inflated Poisson models showed that high school drinking moderated the effects of PFI and BMI at the 4-month follow-up but not at the 15-month follow-up. Specifically, students who reported no drinking in their senior year of high school consumed a 49% higher mean number of drinks after receiving BMI than PFI at the 4-month follow-up. The results suggest that alcohol consumption in high school may be informative when screening and allocating students to appropriate alcohol interventions to meet their different needs.

Development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention for adults with alcohol misuse who are experiencing homelessness

BACKGROUND

Adults experiencing homelessness have much higher rates of alcohol misuse than housed individuals. This study describes the development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention (JITAI) to reduce alcohol use among adults experiencing homelessness.

METHODS

We conducted a pilot trial (N = 41; mean age [SD] = 45.2 [11.5]; 19.5% women) of the Smart-T Alcohol JITAI where participants completed brief ecological momentary assessments (EMAs) each day, received personalized treatment messages following each EMA, and accessed on-demand intervention content for 4 weeks. The prediction algorithm and treatment messages were developed based on an independent but similar sample as part of the trial. We examined three drinking outcomes: daily drinking (yes/no), drinks per day, and heavy episodic drinking, controlling for scores on the Alcohol Use Disorders Identification Test (AUDIT) at baseline, age, and sex using quadratic growth curve models.

RESULTS

Over the 4-week period, participants showed a decline in all alcohol use outcomes. Participants also reported high levels of satisfaction with the JITAI.

CONCLUSIONS

Use of the Smart-T Alcohol JITAI was well received and provided encouraging evidence that it may reduce any drinking, drinks per day, and heavy episodic drinking among adults experiencing homelessness.

Change in alcohol demand following a brief intervention predicts change in alcohol use: A latent growth curve analysis

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; M_age  = 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 O_max ) 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 O_max , significantly mediated the association between BAI and both outcomes-heavy drinking episodes (Est. = -0.23, SE = 0.08, p < 0.01) and alcohol-related problems (Est. = -0.15, SE = 0.07, p < 0.05)-at the 16-month follow-up.

CONCLUSIONS

Reducing high valuation of alcohol among heavy drinking emerging adults within the first month following BAI is critical for the long-term efficacy of the intervention. A two-session BAI was associated with enduring reductions in alcohol demand, and the change in demand intensity, but not O_max , was associated with sustained reductions in heavy drinking and alcohol-related problems.

Examining the viewing pattern of Web-delivered personalized feedback interventions aimed at reducing alcohol-related risky sexual behavior among young adults

OBJECTIVE

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).

METHOD

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.

RESULTS

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.

CONCLUSIONS

Most participants, especially the young adults who were at higher risk, utilized the intervention contents as intended.

Protective behavioral strategies are more helpful for avoiding alcohol-related problems for college drinkers who drink less

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.

A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.

A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes

Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero‐inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study‐level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta‐analysis. The current study proposes a novel statistical approach, the Zero‐inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero‐inflated Poisson regression model, thus it is attractive for meta‐analysis when individual participant‐level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero‐inflation bias in most situations.

A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data: Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems

This paper introduces a meta-analytic mediation analysis approach for individual participant data (IPD) from multiple studies. Mediation analysis evaluates whether the effectiveness of an intervention on health outcomes occurs because of change in a key behavior targeted by the intervention. However, individual trials are often statistically underpowered to test mediation hypotheses. Existing approaches for evaluating mediation in the meta-analytic context are limited by their reliance on aggregate data; thus, findings may be confounded with study-level differences unrelated to the pathway of interest. To overcome the limitations of existing meta-analytic mediation approaches, we used a one-stage estimation approach using structural equation modeling (SEM) to combine IPD from multiple studies for mediation analysis. This approach (1) accounts for the clustering of participants within studies, (2) accommodates missing data via multiple imputation, and (3) allows valid inferences about the indirect (i.e., mediated) effects via bootstrapped confidence intervals. We used data (N = 3691 from 10 studies) from Project INTEGRATE (Mun et al. Psychology of Addictive Behaviors, 29, 34–48, 2015) to illustrate the SEM approach to meta-analytic mediation analysis by testing whether improvements in the use of protective behavioral strategies mediate the effectiveness of brief motivational interventions for alcohol-related problems among college students. To facilitate the application of the methodology, we provide annotated computer code in R and data for replication. At a substantive level, stand-alone personalized feedback interventions reduced alcohol-related problems via greater use of protective behavioral strategies; however, the net-mediated effect across strategies was small in size, on average.

Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness

Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of “imminent drinking” (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant’s current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness.

An epidemic model for non-first-order transmission kinetics

Compartmental models in epidemiology characterize the spread of an infectious disease by formulating ordinary differential equations to quantify the rate of disease progression through subpopulations defined by the Susceptible-Infectious-Removed (SIR) scheme. The classic rate law central to the SIR compartmental models assumes that the rate of transmission is first order regarding the infectious agent. The current study demonstrates that this assumption does not always hold and provides a theoretical rationale for a more general rate law, inspired by mixed-order chemical reaction kinetics, leading to a modified mathematical model for non-first-order kinetics. Using observed data from 127 countries during the initial phase of the COVID-19 pandemic, we demonstrated that the modified epidemic model is more realistic than the classic, first-order-kinetics based model. We discuss two coefficients associated with the modified epidemic model: transmission rate constant k and transmission reaction order n. While k finds utility in evaluating the effectiveness of control measures due to its responsiveness to external factors, n is more closely related to the intrinsic properties of the epidemic agent, including reproductive ability. The rate law for the modified compartmental SIR model is generally applicable to mixed-kinetics disease transmission with heterogeneous transmission mechanisms. By analyzing early-stage epidemic data, this modified epidemic model may be instrumental in providing timely insight into a new epidemic and developing control measures at the beginning of an outbreak.

Ecological Momentary Assessment of Alcohol Consumption and Its Concordance with Transdermal Alcohol Detection and Timeline Follow-Back Self-report Among Adults Experiencing Homelessness

Background

Studies of alcohol use presume valid assessment measures. To evaluate this presumption, we examined the concordance of alcohol use as measured by ecological momentary assessment (EMA) self‐reports, transdermal alcohol concentration readings via the Secure Continuous Remote Alcohol Monitor (SCRAM), and retrospective self‐reports via the Timeline Follow‐Back (TLFB) among adults experiencing homelessness.

Methods

Forty‐nine adults who reported alcohol misuse (mean age = 47, SD = 9; 57% Black; 82% men) were recruited from a homeless shelter. For 4 weeks, alcohol use was assessed: (i) 5 times or more per day by EMA, (ii) every 30 minutes by a SCRAM device worn on the ankle, and (iii) by TLFB for the past month at the end of the study period. There were 1,389 days of observations of alcohol use and alcohol use intensity for 49 participants.

Results

EMA and SCRAM alcohol use data agreed on 73% of days, with an interrater agreement Kappa = 0.46. A multilevel analysis of concordance of 3 measures for alcohol use yielded statistically significant correlations of 0.40 (day level) and 0.63 (person level) between EMA and SCRAM. Alcohol use was detected on 49, 38, and 33% of days by EMA, SCRAM, and TLFB, respectively. For alcohol use intensity, EMA and SCRAM resulted in statistically significant correlations of 0.46 (day level) and 0.78 (person level). The concordance of TLFB with either EMA or SCRAM was weak, especially at the day level.

Conclusions

This is the first study to examine concordance of alcohol use estimates using EMA, SCRAM, and TLFB methods in adults experiencing homelessness. EMA is a valid approach to quantifying alcohol use, especially given its relatively low cost, low participant burden, and ease of use. Furthermore, any stigma associated with wearing the SCRAM or reporting alcohol use in person may be attenuated by using EMA, which may be appealing for use in studies of stigmatized and underserved populations.

A cross-sectional study of latent tuberculosis infection, insurance coverage, and usual sources of health care among non-US-born persons in the United States

More than 70% of tuberculosis (TB) cases diagnosed in the United States (US) occur in non-US-born persons, and this population has experienced less than half the recent incidence rate declines of US-born persons (1.5% vs 4.2%, respectively). The great majority of TB cases in non-US-born persons are attributable to reactivation of latent tuberculosis infection (LTBI). Strategies to expand LTBI-focused TB prevention may depend on LTBI positive non-US-born persons' access to, and ability to pay for, health care.To examine patterns of health insurance coverage and usual sources of health care among non-US-born persons with LTBI, and to estimate LTBI prevalence by insurance status and usual sources of health care.Self-reported health insurance and usual sources of care for non-US-born persons were analyzed in combination with markers for LTBI using 2011-2012 National Health and Nutrition Examination Survey (NHANES) data for 1793 sampled persons. A positive result on an interferon gamma release assay (IGRA), a blood test which measures immunological reactivity to Mycobacterium tuberculosis infection, was used as a proxy for LTBI. We calculated demographic category percentages by IGRA status, IGRA percentages by demographic category, and 95% confidence intervals for each percentage.Overall, 15.9% [95% confidence interval (CI) = 13.5, 18.7] of non-US-born persons were IGRA-positive. Of IGRA-positive non-US-born persons, 63.0% (95% CI = 55.4, 69.9) had insurance and 74.1% (95% CI = 69.2, 78.5) had a usual source of care. IGRA positivity was highest in persons with Medicare (29.1%; 95% CI: 20.9, 38.9).Our results suggest that targeted LTBI testing and treatment within the US private healthcare sector could reach a large majority of non-US-born individuals with LTBI. With non-US-born Medicare beneficiaries' high prevalence of LTBI and the high proportion of LTBI-positive non-US-born persons with private insurance, future TB prevention initiatives focused on these payer types are warranted.

Do Brief Alcohol Interventions Reduce Driving After Drinking Among College Students? A Two-step Meta-analysis of Individual Participant Data

Aims

College students who drink are at an increased risk of driving after drinking and alcohol-involved traffic accidents and deaths. Furthermore, the persistence of driving after drinking over time underscores a need for effective interventions to prevent future drunk driving in adulthood. The present study examined whether brief alcohol interventions (BAIs) for college students reduce driving after drinking.

Methods

A two-step meta-analysis of individual participant data (IPD) was conducted using a combined sample of 6801 college students from 15 randomized controlled trials (38% male, 72% White and 58% first-year students). BAIs included individually delivered Motivational Interviewing with Personalized Feedback (MI + PF), Group Motivational Interviewing (GMI), and stand-alone Personalized Feedback (PF) interventions. Two outcome variables, driving after two+/three+ drinks and driving after four+/five+ drinks, were checked, harmonized and analyzed separately for each study and then combined for meta-analysis and meta-regression analysis.

Results

BAIs lowered the risk of driving after four+/five+ drinks (19% difference in the odds of driving after drinking favoring BAIs vs. control), but not the risk of driving after two+/three+ drinks (9% difference). Subsequent subgroup analysis indicated that the MI + PF intervention was comparatively better than PF or GMI.

Conclusions

BAIs provide a harm reduction approach to college drinking. Hence, it is encouraging that BAIs reduce the risk of driving after heavy drinking among college students. However, there may be opportunities to enhance the intervention content and timing to be more relevant for driving after drinking and improve the outcome assessment and reporting to demonstrate its effect.

A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome

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.

Reducing Drinking Among People Experiencing Homelessness: Protocol for the Development and Testing of a Just-in-Time Adaptive Intervention

Background

Adults 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.

Objective

The 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).

Methods

In 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.

Results

This 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.

Conclusions

This 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

Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study

Background

Mobile 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.

Objective

This 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.

Methods

Young 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.

Results

Participants 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).

Conclusions

Young 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.

Getting to the Heart of Low Sensitivity to Alcohol: Context Moderates Low Cardiovascular Response to Alcohol in Persons With a Family History of Alcohol Use Disorder

Background

Low sensitivity to alcohol in persons with a family history of alcoholism (FH+), compared to those without (FH−), contributes to risk for alcohol use disorder (AUD). However, sensitivity of FH+ cardiovascular response to alcohol is not well understood. This gap is significant because cardiovascular processes contribute to emotional regulation and stress response problems theorized to be central to the development and persistence of AUD. This study compared changes in heart rate (HR) and HR variability (HRV) between FH groups after consuming alcohol and control beverages and examined how these changes were moderated by emotional and alcohol‐related contexts.

Methods

Young adults (N = 165) with FH+ (n = 110) or FH− (n = 55) each completed 2 sessions, separated by 1 week. They received one of 3 different beverages (alcohol, placebo, and told‐no‐alcohol) in each session. Electrocardiogram data were recorded during pre–beverage consumption and post–beverage consumption baselines, and then during 4 picture cue tasks (neutral, positive, negative, and alcohol‐related). Generalized estimating equations were used to examine differences in cardiovascular reactivity (changes in HR and HRV power at ~ 0.1 Hz) across FH groups, beverage conditions, and picture cue tasks.

Results

A significant beverage condition × cue task × FH interaction effect on HRV was observed. The FH+ group, compared to the FH− group, showed (a) significantly less HRV suppression in specific cue contexts following alcohol, (b) a mixed pattern of more and less HRV suppression across cue contexts following placebo, and (c) a similar HRV reactivity pattern in the told‐no‐alcohol condition across cue tasks. For HR, there were no significant effects involving FH.

Conclusions

Diminished cardiovascular sensitivity to oral alcohol in FH+ persons varied within a given drinking episode depending on emotional and alcohol‐related features of the context, suggesting that environmental characteristics play a role in the expression of low sensitivity to alcohol among FH+ individuals.

A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials

Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach. However, simultaneously combining multiple related parameters across heterogeneous studies is challenging because each parameter from each study has a specific interpretation within the context of the study and other covariates in the model. This paper proposes a novel mapping method to combine study-specific estimates of multiple related parameters across heterogeneous studies, which ensures valid inference at all inference levels by combining sample-dependent functions known as Confidence Distributions (CD). We describe the "CD-based mapping method" and provide a data application example for a multivariate random-effects meta-analysis model. We estimated up to 13 study-specific regression parameters for each of 14 individual studies using IPD in the first step, and subsequently combined the study-specific vectors of parameters, yielding a full vector of hyperparameters in the second step of meta-analysis. Sensitivity analysis indicated that the CD-based mapping method is robust to model misspecification. This novel approach to multi-parameter synthesis provides a reasonable methodological solution when combining complex evidence using IPD.

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis

The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = 20.5, SD = 2.21; 52% women) between the ages of 18 and 30 following a mildly stressful experiment. We used a total of 30 features of demographic variables, lifestyle variables, alcohol and other drug use behaviors and problems, state anxiety and depressive symptoms, and physiological indicators that were lab assessed or self-reported. A random forest regression analysis with 10-fold cross-validation resulted in accurate prediction of post-experiment fatigue (R² equivalent = 0.93) with the average "out-of-bag" (OOB) R² = 0.52. Not surprisingly, self-reported pre-experiment fatigue was the most important variable (54%) in the prediction of post-experiment fatigue. Feeling anxious (state anxiety) pre- and post-experiment (3%, 7%), feeling less vigorous post experiment (3%), systolic and diastolic blood pressure (3%, 2%) and LF HRV (2%) assessed at baseline, and self-reported alcohol-related problems (3%) and sleep (2%) additionally contributed to the prediction of post-experiment fatigue. Other remaining input variables had relatively minimal importance. Substantively, this study suggests that complex interactions across multiple systems domains that support regulation may be linked to fatigue. A random forest regression analysis can relatively easily be implemented with a built-in cross-validation function and reveal a web of connections undergirding health behavior and risks.

Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries.

What happens to agreement over time? A longitudinal study of self-reported substance use compared to saliva toxicological testing among subsidized housing residents

The agreement between self-reported and toxicologically verified substance use provides important information about the validity of self-reported use. While some studies report aggregate agreement across follow-up points, only a few have examined the agreement at each time point separately. An overall rate of agreement across time may miss changes that occur as people progress through a research study. In this study, a sample of 644 adults (43.8% male, 32.6% White, 57.0% Black, 90.2% ages 36+) residing in subsidized housing was used to determine the agreement between self-reported use and saliva toxicological testing for marijuana, cocaine, PCP, amphetamine, and methamphetamine at three different time points. Agreement between saliva toxicological testing and self-report ranged between 84.2% and 94.3% for different substances over time. Higher rates of agreement were found for cocaine than had been reported by previous studies. Statistically significant differences in the odds ratios of concordance over time (baseline, 6-month, and 12-month follow-up) were found for marijuana and the combined category for PCP, amphetamine, and methamphetamine. Our findings suggest that oral fluid drug tests generally withstand community field assessments and result in relatively high levels of agreement for marijuana, cocaine, PCP, amphetamine, and methamphetamine use, when compared to self-report. Because of the ease of sample collection and low chance of adulteration, we conclude that saliva testing is a viable method for toxicological confirmation of substance use behavior in this setting.

Do drinking motives distinguish extreme drinking college students from their peers?

OBJECTIVE

The literature highlights the need to move beyond the traditional heavy episodic ("binge") drinking criteria when trying to identify at-risk college drinkers. Thus, recent attention has focused on more extreme levels of drinking. This study examines whether drinking motives can distinguish college student extreme drinkers from lighter drinkers.

METHOD

We used data from 3518 college student current drinkers (63.4% women) who participated in eight different studies at five different college campuses across the United States; a subsample of these students was followed up at 6months post-baseline. At baseline and follow-up, drinkers were divided into three groups: nonbinge drinkers (<4 drinks for women and 5 for men on their maximum drinking occasion), binge drinkers (4-7 drinks for women; 5-9 for men), and extreme drinkers (8+ for women and 10+ for men).

RESULTS

At baseline, extreme drinkers, compared to nonbinge and binge drinkers, reported greater social, enhancement, and coping motives, as well as greater quantity and frequency of drinking per week and more alcohol-related problems. Those who were not extreme drinkers at baseline and later became extreme drinkers at follow-up reported significantly greater increases in social and enhancement motives, compared to those who remained nonextreme drinkers. Those who were extreme drinkers at baseline and reduced their drinking 6months later, compared to those who remained extreme drinkers, reported greater reductions in enhancement and coping motives.

CONCLUSIONS

Focusing on drinking motives might be an efficacious target for preventive intervention programs to reduce extreme drinking among college students.

Brief motivational interventions for college student drinking may not be as powerful as we think: an individual participant-level data meta-analysis

BACKGROUND

For over 2 decades, brief motivational interventions (BMIs) have been implemented on college campuses to reduce heavy drinking and related negative consequences. Such interventions include in-person motivational interviews (MIs), often incorporating personalized feedback (PF), and stand-alone PF interventions delivered via mail, computer, or the Web. Both narrative and meta-analytic reviews using aggregate data from published studies suggest at least short-term efficacy of BMIs, although overall effect sizes have been small.

METHODS

This study was an individual participant-level data (IPD) meta-analysis of 17 randomized clinical trials evaluating BMIs. Unlike typical meta-analysis based on summary data, IPD meta-analysis allows for an analysis that correctly accommodates the sampling, sample characteristics, and distributions of the pooled data. In particular, highly skewed distributions with many zeroes are typical for drinking outcomes, but have not been adequately accounted for in existing studies. Data are from Project INTEGRATE, one of the largest IPD meta-analysis projects to date in alcohol intervention research, representing 6,713 individuals each with 2 to 5 repeated measures up to 12 months postbaseline.

RESULTS

We used Bayesian multilevel over dispersed Poisson hurdle models to estimate intervention effects on drinks per week and peak drinking, and Gaussian models for alcohol problems. Estimates of overall intervention effects were very small and not statistically significant for any of the outcomes. We further conducted post hoc comparisons of 3 intervention types (individual MI with PF, PF only, and group MI) versus control. There was a small, statistically significant reduction in alcohol problems among participants who received an individual MI with PF. Short-term and long-term results were similar.

CONCLUSIONS

This study questions the efficacy and magnitude of effects of BMIs for college drinking prevention and intervention and suggests a need for the development of more effective intervention strategies.

Are there secondary effects on marijuana use from brief alcohol interventions for college students?

OBJECTIVE

This study examined whether brief motivational interventions (BMIs) designed for reducing heavy drinking among college students have secondary effects on reducing marijuana use.

METHOD

The data came from Project INTEGRATE, which combined data from 24 independent trials of BMIs and other individual-focused interventions designed to reduce heavy drinking and related problems among college students. We analyzed data from 10 samples across nine studies that used random assignment of participants into either a BMI or a control group and assessed marijuana use outcomes (N = 6,768; 41.5% men; 73.2% White; 57.7% first-year students; 19.2% current marijuana users at baseline). We derived three marijuana use groups within studies by cross-tabulating baseline and follow-up data: Nonusers, Reducers, and Stayers/Increasers.

RESULTS

Peto's one-step odds ratio analyses for meta-analysis revealed no significant intervention effects on marijuana use at either short-term (1-3 month) or long-term (6-12 month) follow-up. Subsequent exploratory analyses showed that those who reduced drinking were more likely to be a marijuana Reducer or Nonuser, compared with a Stayer/Increaser, at both follow-ups.

CONCLUSIONS

The BMIs to reduce heavy drinking evaluated in this study did not reduce marijuana use. However, our exploratory results suggest that if we can develop interventions for college students that effectively reduce drinking, we may also reduce their marijuana use. Furthermore, as recreational use of marijuana becomes legal or decriminalized and marijuana becomes more readily available, it may be necessary to develop interventions specifically targeting marijuana use among college students.

A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis

The present paper proposes a hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model extended for a large number of groups. The proposed model was motivated by a large-scale integrative data analysis (IDA) study which combined data (N = 24,336) from 24 independent alcohol intervention studies. IDA projects face unique challenges that are different from those encountered in individual studies, such as the need to establish a common scoring metric across studies and to handle missingness in the pooled data. To address these challenges, we developed a Markov chain Monte Carlo (MCMC) algorithm for a hierarchical 2PL-MUIRT model for multiple groups in which not only were the item parameters and latent traits estimated, but the means and covariance structures for multiple dimensions were also estimated across different groups. Compared to a few existing MCMC algorithms for multidimensional IRT models that constrain the item parameters to facilitate estimation of the covariance matrix, we adapted an MCMC algorithm so that we could directly estimate the correlation matrix for the anchor group without any constraints on the item parameters. The feasibility of the MCMC algorithm and the validity of the basic calibration procedure were examined using a simulation study. Results showed that model parameters could be adequately recovered, and estimated latent trait scores closely approximated true latent trait scores. The algorithm was then applied to analyze real data (69 items across 20 studies for 22,608 participants). The posterior predictive model check showed that the model fit all items well, and the correlations between the MCMC scores and original scores were overall quite high. An additional simulation study demonstrated robustness of the MCMC procedures in the context of the high proportion of missingness in data. The Bayesian hierarchical IRT model using the MCMC algorithms developed in the current study has the potential to be widely implemented for IDA studies or multi-site studies, and can be further refined to meet more complicated needs in applied research.

Is motivational interviewing effective at reducing alcohol misuse in young adults? A critical review of Foxcroft et al. (2014)

Foxcroft, Coombes, Wood, Allen, and Almeida Santimano (2014) recently conducted a meta-analysis evaluating the effectiveness of motivational interviewing (MI) in reducing alcohol misuse for youth up to Age 25. They concluded that the overall effect sizes of MI in this population were too small to be clinically meaningful. The present article critically reviews the Foxcroft et al. meta-analysis, highlighting weaknesses such as problems with search strategies, flawed screening and reviews of full-text articles, incorrect data abstraction and coding, and, accordingly, improper effect size estimation. In addition, between-study heterogeneity and complex data structures were not thoughtfully considered or handled using best practices for meta-analysis. These limitations undermine the reported estimates and broad conclusion made by Foxcroft et al. about the lack of MI effectiveness for youth. We call for new evidence on this question from better-executed studies by independent researchers. Meta-analysis has many important utilities for translational research. When implemented well, the overall effectiveness, as well as different effectiveness for different populations, can be examined via meta-analysis. Emerging methods utilizing individual participant-level data, such as integrative data analysis, may be particularly helpful for identifying the sources of clinical and methodological heterogeneity that matter. The need to better understand the mechanisms of alcohol interventions has never been louder in the addiction field. Through more concerted efforts throughout all phases of generating evidence, we may achieve large-scale evidence that is efficient and robust and provides critical answers for the field.

The association between protective behavioral strategies and alcohol-related problems: An examination of race and gender differences among college drinkers

This study examined race and gender differences in use of specific types of protective behavioral strategies (PBS) and the moderating effects of race and gender on the relationship between PBS use and alcohol problems, controlling for alcohol use, among a large sample of Asian, Black, and White college drinkers. There were significant racial and gender differences in the types of PBS used. Moderation analyses indicated that PBS were more protective for women than men against experiencing alcohol-related problems. There were no significant race effects or race-by-gender interaction effects on alcohol problems. Implementing PBS may be beneficial for all college students.

Drinking game play among first-year college student drinkers: an event-specific analysis of the risk for alcohol use and problems

BACKGROUND

College students who play drinking games (DGs) more frequently report higher levels of alcohol use and experience more alcohol-related harm. However, the extent to which they are at risk for increased consumption and harm as a result of DG play on a given event after accounting for their typical DG participation, and typical and event drinking, is unclear.

OBJECTIVES

We examined whether first-year students consumed more alcohol and were more likely to experience consequences on drinking occasions when they played DGs.

METHODS

Participants (n = 336) completed up to six web-based surveys following weekend drinking events in their first semester. Alcohol use, DG play, and consequences were reported for the Friday and Saturday prior to each survey. Typical DG tendencies were controlled in all models. Typical and event alcohol use were controlled in models predicting risk for consequences.

RESULTS

Participants consumed more alcohol on DG versus non-DG events. All students were more likely to experience blackout drinking consequences when they played DGs. Women were more likely to experience social-interpersonal consequences when they played DGs.

CONCLUSION

DG play is an event-specific risk factor for increased alcohol use among first-year students, regardless of individual DG play tendencies. Further, event DG play signals increased risk for blackout drinking consequences for all students, and social-interpersonal consequences for women, aside from the amount of alcohol consumed on those occasions as well as typical drinking behaviors. Prevention efforts to reduce high-risk drinking may be strengthened by highlighting both event- and person-specific risks of DG play.

Project INTEGRATE: An integrative study of brief alcohol interventions for college students

This article provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed 2 or more times from baseline up to 12 months, with varying assessment schedules across studies. This article describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo (MCMC) algorithms for 2-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single intervention studies.

A computational physiology approach to personalized treatment models: the beneficial effects of slow breathing on the human cardiovascular system

Heart rate variability biofeedback intervention involves slow breathing at a rate of ∼6 breaths/min (resonance breathing) to maximize respiratory and baroreflex effects on heart period oscillations. This intervention has wide-ranging clinical benefits and is gaining empirical support as an adjunct therapy for biobehavioral disorders, including asthma and depression. Yet, little is known about the system-level cardiovascular changes that occur during resonance breathing or the extent to which individuals differ in cardiovascular benefit. This study used a computational physiology approach to dynamically model the human cardiovascular system at rest and during resonance breathing. Noninvasive measurements of heart period, beat-to-beat systolic and diastolic blood pressure, and respiration period were obtained from 24 healthy young men and women. A model with respiration as input was parameterized to better understand how the cardiovascular processes that control variability in heart period and blood pressure change from rest to resonance breathing. The cost function used in model calibration corresponded to the difference between the experimental data and model outputs. A good match was observed between the data and model outputs (heart period, blood pressure, and corresponding power spectral densities). Significant improvements in several modeled cardiovascular functions (e.g., blood flow to internal organs, sensitivity of the sympathetic component of the baroreflex, ventricular elastance) were observed during resonance breathing. Individual differences in the magnitude and nature of these dynamic responses suggest that computational physiology may be clinically useful for tailoring heart rate variability biofeedback interventions for the needs of individual patients.

When less is more and more is less in brief motivational interventions: characteristics of intervention content and their associations with drinking outcomes

Brief motivational interventions (BMIs) that aim to reduce alcohol use and related problems have been widely implemented in college settings. BMIs share common principles, but vary in specific content. Thus far, the variation in content has not been thoroughly understood in relation to intervention outcomes. The present study addressed this gap by examining variation in breadth of BMI content (i.e., total number of components covered), the extent to which content was personalized to participants, and the interaction between breadth and personalization in relation to treatment outcomes. Data (N = 6,047 participants across 31 separate BMI conditions) came from an integrative data analysis (IDA) study featuring individual-level data from a broad sample of 24 BMI studies of college students. Participants were assessed at baseline and at least 1 follow-up point, conducted up to 12 months postbaseline. Structural equation modeling revealed a significant interaction effect between breadth and personalization of BMI content on alcohol use and related problems at the long-term follow-up (6-12 months) but not at the short-term follow-up (1-3 months). Results indicated that "more is better" for reducing both alcohol use and related problems when BMIs were highly personalized to participants. For less personalized BMIs, coverage of more components was associated with increases in both alcohol use and problems. Findings point to the importance of strategically designing BMIs to maximize their impact on drinking outcomes in college students.

Associations between alcohol use and alcohol-related negative consequences among black and white college men and women

OBJECTIVE

The gender gap in alcohol use has been narrowing among young adults, while race differences in alcohol problems change throughout the life course, with Whites experiencing more problems before middle adulthood and Blacks experiencing more after. Yet, there is a paucity of research on the intricate relationship among gender, race, alcohol use, and alcohol problems in emerging adults. The present study addressed this gap in the literature.

METHOD

The sample included White (n = 14,772) and Black (n = 458) college students from multiple colleges across the United States (59% female; 51% freshmen; Mage = 20 years).

RESULTS

With alcohol use levels adjusted for, women were more likely to report consequences related to damage to self and dependence-like symptoms than men. There were no significant race differences in either the type or the number of alcohol problems. Further, there was no Race × Alcohol Use interaction in relation to alcohol problems. We found a statistically significant interaction between gender and alcohol use in predicting alcohol problems, suggesting that, at higher levels of drinking, the risk for women to experience alcohol problems was significantly greater than that for men.

CONCLUSIONS

The reverse race gap in alcohol use and problems may not surface until young adulthood or may not be relevant for those who attend college. College interventions should help both Black and White students reduce problems associated with drinking and focus on limiting harm among female students.

Using mixture models with known class membership to address incomplete covariance structures in multiple-group growth models

Multi-group latent growth modelling in the structural equation modelling framework has been widely utilized for examining differences in growth trajectories across multiple manifest groups. Despite its usefulness, the traditional maximum likelihood estimation for multi-group latent growth modelling is not feasible when one of the groups has no response at any given data collection point, or when all participants within a group have the same response at one of the time points. In other words, multi-group latent growth modelling requires a complete covariance structure for each observed group. The primary purpose of the present study is to show how to circumvent these data problems by developing a simple but creative approach using an existing estimation procedure for growth mixture modelling. A Monte Carlo simulation study was carried out to see whether the modified estimation approach provided tangible results and to see how these results were comparable to the standard multi-group results. The proposed approach produced results that were valid and reliable under the mentioned problematic data conditions. We also present a real data example and demonstrate that the proposed estimation approach can be used for the chi-square difference test to check various types of measurement invariance as conducted in a standard multi-group analysis.

A Peer-Led High School Transition Program Increases Graduation Rates Among Latino Males

The present study 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 twelfth 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 ninth grade were significantly more likely to graduate from high school within four years than male students in the control group (81% versus 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 the ninth grade to influence the probability of high school graduation.

Negative mood and alcohol problems are related to respiratory dynamics in young adults

This study examined the relationship of negative affect and alcohol use behaviors to baseline respiration and respiratory response to emotional challenge in young adults (N = 138, 48 % women). Thoracic-to-abdominal ratio, respiratory frequency and variability, and minute volume ventilation were measured during a low-demand baseline task, and emotional challenge (viewing emotionally-valenced, emotionally-neutral, and alcohol-related pictures). Negative mood and alcohol problems principal components were generated from self-report measures of negative affect and mood, alcohol use, and use-related problems. The negative mood component was positively related to a thoracic bias when measured throughout the study (including baseline and picture exposure). There was generally greater respiratory activity in response to the picture cues, although not specifically in response to the content (emotional or alcohol-related) of the picture cues. The alcohol problems component was positively associated with respiratory reactivity to picture cues, when baseline breathing patterns were controlled. Self-report arousal data indicated that higher levels of negative mood, but not alcohol problems, were associated with greater arousal ratings overall. However, those with alcohol problems reported greater arousal to alcohol cues, compared to emotionally neutral cues. These results are consistent with theories relating negative affect and mood to breathing patterns as well as the relationship between alcohol problems and negative emotions, suggesting that the use of respiratory interventions may hold promise for treating problems involving negative affect and mood, as well as drinking problems.

Treatment outcomes of a combined cognitive behavior therapy and pharmacotherapy for a sample of women with and without substance abuse histories on an acute psychiatric unit: do therapeutic alliance and motivation matter?

BACKGROUND AND OBJECTIVES

Women with comorbid psychiatric and substance abuse problems (PwSA) experience more consequences from their use and typically have the poorest prognosis and outcome, compared to those with psychiatric problems but without substance abuse problems (PwoSA). The present study examined outcomes of a combined intensive inpatient cognitive behavior therapy (CBT) and pharmacotherapy program for women with PwSA and PwoSA.

METHODS

Sample consisted of 117 women on a women-only acute inpatient unit (PwSA = 50, PwoSA = 67).

RESULTS

Women in both groups made significant improvements in psychological functioning. High motivation at admission and therapeutic alliance at discharge were associated with improved psychological functioning at discharge for both groups.

SCIENTIFIC SIGNIFICANCE

Findings provide preliminary support for the efficacy of a combined CBT and pharmacotherapy program for women with psychiatric diagnoses on a women-only acute inpatient unit, and for pre-treatment motivation and therapeutic alliance as important correlates of better treatment outcomes.

Potential side effects of unhealthy lifestyle choices and health risks on basal and reactive heart rate variability in college drinkers

OBJECTIVE

Emerging adults often begin making independent lifestyle choices during college, yet the association of these choices with fundamental indicators of health and adaptability is unclear. The present study examined the relationship between health risks and neurocardiac function in college drinkers.

METHOD

Heart rate variability (HRV) was assessed at baseline and in reaction to a paced breathing challenge in 212 college drinkers (53.8% women). Basal HRV served as a general indicator of health. Reactive HRV (during paced breathing) was used as a marker of an individual's adaptability to challenge. The relationship of HRV to alcohol use, cigarette use, exercise, sleep, and body mass index (BMI) was assessed.

RESULTS

Greater alcohol use and less exercise were associated with lower basal HRV. BMI was unrelated to basal HRV but was negatively associated with reactive HRV during the breathing challenge.

CONCLUSIONS

High levels of alcohol use and lack of exercise are negative correlates of cardiovascular and general health, even in apparently healthy college drinkers. The negative relationship between BMI and reactive HRV suggests that overweight individuals have reduced ability to psychophysiologically adapt to challenges; understanding the temporal course of this relationship is needed. This study highlights the importance of examining HRV at baseline and in response to a challenge to capture the active neurocardiac processes that contribute to health and adaptive responding. The suppressive effects of health risks on HRV are modifiable; thus, HRV may be useful in evaluating the health benefits of lifestyle change and in promoting change behaviors in college drinkers.

Granger causality--statistical analysis under a configural perspective

The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger causation by adopting a configural perspective. This perspective allows researchers to assume that effects exist for specific categories only or for specific sectors of the data space, but not for other categories or sectors. Configural Frequency Analysis (CFA) is proposed as the method of analysis from a configural perspective. CFA base models are derived for the exploratory analysis of Granger causation. These models are specified so that they parallel the regression models used for variable-oriented analysis of hypotheses of Granger causation. An example from the development of aggression in adolescence is used. The example shows that only one pattern of change in aggressive impulses over time Granger-causes change in physical aggression against peers.

Memory for emotional picture cues during acute alcohol intoxication

OBJECTIVE

Memory affects behavior by allowing events to be anticipated and goals to be planned based on previous experiences. Emotional memory, in particular, is thought to play a central role in behavior in general and in drinking behavior in particular. Alcohol intoxication has been shown to disrupt intentional, conscious memory, but not unintentional, implicit memory for neutral stimuli; however, its effects on emotional memory are not well understood. This study examined whether alcohol intoxication affected memory for emotionally valenced stimuli by testing explicit recall and implicit repetition priming of emotional picture cues.

METHOD

Participants were 36 young adults (21-24 years old, 16 women) who received an alcohol, placebo, or no-alcohol beverage. Both cue exposure and memory testing occurred after beverage consumption (i.e., during intoxication for the alcohol group).

RESULTS

Alcohol intoxication impaired explicit recall of all cue types but did not impair implicit repetition priming. Emotionally negative and positive cues were more often recalled compared with neutral cues across all beverage groups, and emotionally negative cues demonstrated more priming than emotionally positive or neutral cues in all beverage groups.

CONCLUSIONS

Alcohol intoxication disrupted effortful recall of all cues, although the relative memory advantage of emotionally valenced over-neutral stimuli remained even after drinking. The effects of alcohol on unintentional memory priming were not statistically significant, but the effects of emotionally negative cues were. Further research is needed to better understand alcohol intoxication and emotional valence effects on memory processes during implicit memory tasks and the possibility that negative mood facilitates memory priming of negative emotional stimuli.

Exploring the intersection of neighborhood racial and economic composition and individual race on substance use among male adolescents

This article examines how Blacks and Whites living in neighborhoods with divergent racial and income profiles differed in early onset (by age 14 years) and adolescent lifetime prevalence (by age 18 years) of substance use, with longitudinal data from 473 high-risk boys (58% Black). A latent profile analysis identified four neighborhood classes: Black, lower-income; racially mixed, middle-income; White, middle-income; and White, upper-income. Bivariate analyses showed that Blacks living in racially mixed, middle-income neighborhoods reported the lowest rates of tobacco and marijuana use. Whites living in White, upper-income neighborhoods reported higher substance use prevalence, particularly marijuana. Findings suggest that substance use prevention efforts are critical for Whites in upper-income communities.