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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.Participants were 66 college students (63.6% women; 61.9% White;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.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) 2022 APA, all rights reserved).

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.

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.Parallel process piecewise latent growth curve models were examined in a sample of 393 heavy drinking emerging adults (60.8% women; 85.2% white; MA 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 OReducing 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

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.

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. CLINICALTRIAL ClinicalTrials.gov NCT05011903; https://clinicaltrials.gov/ct2/show/NCT05011903 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/43986

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.

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.

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., We examined 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) in adults experiencing homelessness. The concordance between EMA and SCRAM was promising while the concordance of TLFB with either EMA or SCRAM was weak. EMA for alcohol consumption is valid and encouraging, given its relatively low cost, low participant burden, ease of use, and less stigma.

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 the aggregate data using 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 zeros, 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.

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.

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., This study showed that physiological sensitivity to acute alcohol varied within a drinking episode depending on the environment, as did placebo response. Especially in the context of negative emotional and alcohol‐related cues, young adults with a family history of alcoholism, compared to those without, exhibited a low level of cardiovascular response to oral alcohol; this was not observed for neutral cues. Results highlight emotional and appetitive contextual influences on the expression of low sensitivity to alcohol and placebo response.

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.

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.

This paper provides a tutorial companion for the methodological approach implemented in Huh et al. (2015) that overcame two major challenges for individual participant data (IPD) meta-analysis. Specifically, we show how to validly combine data from heterogeneous studies with varying numbers of treatment arms, and how to analyze highly-skewed count outcomes with many zeroes (e.g., alcohol and substance use outcomes) to estimate overall effect sizes. These issues have important implications for the feasibility, applicability, and interpretation of IPD meta-analysis but have received little attention thus far in the applied research literature. We present a Bayesian multilevel modeling approach for combining multi-arm trials (i.e., those with two or more treatment groups) in a distribution-appropriate IPD analysis. Illustrative data come from Project INTEGRATE, an IPD meta-analysis study of brief motivational interventions to reduce excessive alcohol use and related harm among college students. Our approach preserves the original random allocation within studies, combines within-study estimates across all studies, overcomes between-study heterogeneity in trial design (i.e., number of treatment arms) and/or study-level missing data, and derives two related treatment outcomes in a multivariate IPD meta-analysis. This methodological approach is a favorable alternative to collapsing or excluding intervention groups within multi-arm trials, making it possible to directly compare multiple treatment arms in a one-step IPD meta-analysis. To facilitate application of the method, we provide annotated computer code in R along with the example data used in this tutorial.

Background Social support is a well-known protective factor against depressive symptoms and substance use problems, but very few studies have examined its protective effects among residents of permanent supportive housing (PSH), a housing program for people with a history of chronic homelessness. We utilized unconditional latent growth curve models (LGCMs) and parallel process growth models to describe univariate trajectories of social support, depressive symptoms, and substance use problems and to examine their longitudinal associations in a large sample of adults residing in PSH. Methods Participants were 653 adult PSH residents in North Texas (56% female; 57% Black; mean age: 51 years) who participated in a monthly health coaching program from 2014 to 2017. Their health behaviors were assessed at baseline and tracked every six months at three follow-up visits. Results Unconditional LGCMs indicated that over time, social support increased, whereas depressive symptoms and substance use problems decreased. However, their rates of change slowed over time. Further, in parallel process growth models, we found that at baseline, individuals with greater social support tended to have less severe depressive symptoms and substance use problems (coefficients: − 0.67, p Conclusions This study suggests that plausibly, increases in social support, though slowing over time, still positively impact depressive symptoms and substance use problems among PSH residents. Future PSH programs could emphasize social support as an early component as it may contribute to clients’ overall health.

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.

Background: Social support is a well-known protective factor against depression and substance use problems, but very few studies have examined its protective effects among Permanent Supportive Housing (PSH) residents. We utilized unconditional latent growth curve models (LGCMs) and parallel process growth models to describe univariate trajectories of social support, depression, and substance use problems, and to examine their longitudinal associations in a large sample of adults residing in PSH.Methods: Participants were 653 adult PSH residents in North Texas (56% female; 57% Black, 35% White, 8% other; mean age: 51 years) who participated in a monthly health coaching program from 2014 to 2017. Their health behaviors were assessed at baseline and tracked every six months at three follow-up visits.Results: Unconditional LGCMs indicated that over time, social support increased, whereas depressive symptoms and substance use problems decreased. However, their rates of change slowed over time. Further, in parallel process growth models, we found that at baseline, individuals with greater social support tended to have less severe depression and substance use problems (coefficients: –0.67, pConclusions: This study suggests that increases in social support, though slowing over time, still positively impact depression and substance use problems among PSH residents. Future PSH programs could emphasize social support as an early component as it may contribute to clients’ overall health.

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.

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.

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.

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

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.

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

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.

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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.

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. Language: en

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.

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.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.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.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.

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 (MVV) 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.

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.

Alcohol cue reactivity, operationalized as a classically conditioned response to an alcohol related stimulus, can be assessed by changes in physiological functions such as heart rate variability (HRV), which reflect real time regulation of emotional and cognitive processes. Although ample evidence links drinking histories to cue reactivity, it is unclear whether in-the-moment cue reactivity becomes coupled to a set of consolidated beliefs about the effects of alcohol (i.e., expectancies) and whether treatment helps dissociate the relation of positive versus negative expectancies to cue reactivity. This study examined the relationship between reactivity to alcohol picture cues and alcohol expectancies in two groups of emerging adults: an inpatient sample with alcohol use disorders ( n = 28) and a college student sample who previously were mandated to a brief intervention for violating university policies about alcohol use in residence halls ( n = 43). Sequential regression analysis was conducted using several HRV indices and self-report arousal ratings as cue reactivity measures. Results indicated that the relationship between cue reactivity and negative alcohol outcome expectancies differed for the two groups. Greater cue reactivity, assessed using HRV indices, was associated with more negative expectancies in the inpatient sample but with less negative expectancies in the mandated student sample, while an opposite trend was found for subjective arousal. The present findings highlight the importance of characterizing cue reactivity through multi-dimensional assessment modalities that include physiological markers such as HRV.

Multi-group latent growth modeling in the structural equation modeling 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 modeling 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 modeling 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 modeling. AMonte 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 the results that were valid and reliable under the mentioned problematic data conditions. We also presented a real data example and demonstrated 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.

Moderator analysis in developmental research is concerned with the conditionals of development. When conditionals are known, developmental trajectories can be predicted, and the conditionals can be interpreted as moderators of development. In this article, we start from a standard definition of the term moderator, and present configural models that allow researchers to test hypotheses that are compatible with moderation. The methods that are discussed enable researchers to take person- as well as variable-oriented perspectives by using configural frequency analysis or logit models, respectively. We then proceed and propose a hierarchy of moderator models for configural analysis. The three levels of this hierarchy differ in the roles played by the variables under study and type as well as amount of information that decisions about moderation are based on. At the highest level, moderation hypotheses are analysed in the Baron and Kenny sense. Application examples use data from a study on the development of a...

Many developmental pathways and trajectories are far from universal. In this article, configural methods are proposed that allow one to test hypotheses about differential developmental trajectories. Trends in categorical variables are discussed. A simulation study is presented in which it is shown that (1) all conceivable patterns of trajectories can be characterized configurally, and (2) even when the null hypothesis of independence of repeated observations holds, descriptors of growth trends can be correlated. Implications of these results for the specification of base models are discussed. Models with single and multiple categorical predictors are discussed, as are models that include multiple descriptors for the shape of trajectories. A data example is given in which trajectories of depression are predicted for women who did versus did not experience intimate partner violence. The data are analyzed from both person- and variable-oriented perspectives.

Although longitudinal evidence has linked alcohol use with aggressive behavior during adolescence, most studies have failed to adequately control for the numerous between-individual differences that may underlie this association. In addition, few studies of adolescents have examined whether the nature of the within-individual association between alcohol use and aggression depends on individual and contextual factors. To address these limitations, this study examined the association between within-individual changes in alcohol use and aggressive behavior across adolescence and determined whether impulsive behavior, positive attitudes toward violence, violent peers, neighborhood crime, and race moderated this association. Data from 971 adolescent males assessed annually from ages 13 to 18 were analyzed using a within-individual regression panel model that eliminated all stable between-individual factors as potential confounds. Findings indicated that within-individual increases in alcohol use quantity from one's own typical levels of drinking were concurrently associated with within-individual increases in aggressive behavior, and vice versa. However, increases in alcohol were more strongly linked to increases in aggressive behavior among boys with attitudes favoring violence and those who lived in high-crime neighborhoods. The association between alcohol and aggressive behavior was similar for White and Black young men. Interventions designed to reduce aggressive behaviors should consider targeting not only alcohol use, but also individual and environmental risk factors that contribute to this link.

This study examined racial differences in the consequences of childhood maltreatment for depression, heavy drinking, and violence during adolescence and young adulthood among black and white young men.Data were obtained from the Pittsburgh Youth Study, a prospective longitudinal study of urban males (N = 971, 56% black). Childhood maltreatment was defined as substantiated physical or sexual abuse, physical neglect, emotional maltreatment, or moral/legal/educational maltreatment, with the first referral before 12 years of age. Self-reports of depressive symptoms and heavy drinking (consuming more than six drinks on a single occasion) and official, parent, and self-reports of violent offending were assessed between 12 and 17 years of age (adolescence) and at 24/25 years of age (young adulthood). Regression analyses were conducted to examine childhood maltreatment and race, as well as maltreatment-by-race interactions, as predictors of the three outcomes.Prevalence of childhood maltreatment was higher for black than for white boys; however, there were no racial differences in timing, type, severity, and chronicity of maltreatment. When socioeconomic status and cohort were controlled, childhood maltreatment significantly predicted depressive symptoms and violence in adolescence but none of the outcomes in young adulthood. Race was a significant predictor of heavy drinking and violence during adolescence, and of all three outcomes in young adulthood. No significant race-by-maltreatment interaction effects were found.Childhood maltreatment has similar negative consequences for black and white male youth during adolescence. Extending intervention efforts through adolescence is important to alleviate these problems among victims.

This study examined the association of alcohol use with the persistence and desistance of serious violent offending among African American and Caucasian young men from adolescence into emerging adulthood. Five violence groups were defined: nonviolent, late-onsetters, desisters, persisters, and one-time offenders. We examined alcohol use trajectories for these groups spanning 12 through 24/25 years of age using a four-piecewise linear growth model s 12-14, 14-18, 18-21, and 21-24/25 years of age. The persisters and desisters reported the highest levels of drinking at 13 years of age. From 14 to 18 years old, however, the late-onsetters showed a higher rate of increase in drinking, compared with the persisters and desisters. Starting at 18 years of age, the desisters' drinking trajectory started to resemble that of the nonviolent group, who showed the highest rate of increase in drinking during emerging adulthood. By 24/25 years of age, the persisters could not be distinguished from the late-onsetters, but they were lower than the nonviolent and one-timer groups in terms of their drinking. At 24/25 years old, the desisters were not significantly different from the other violence groups, although they seemed most similar to the nonviolent and one-timer groups. We found no evidence that the association between drinking and violence differed for African Americans and Caucasians. The findings suggest that yearly changes in alcohol use could provide important clues for preventing violent offending. Language: en

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.

This article describes log-linear models as special cases of generalized linear models. Specifically, log-linear models use a logarithmic link function. Log-linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns. Three types of log-linear models are discussed, hierarchical models, nonhierarchical models, and nonstandard models. Emphasis is placed on parameter interpretation. It is demonstrated that parameters are best interpretable when they represent the effects specified in the design matrix of the model. Parameter interpretation is illustrated first for a standard hierarchical model, and then for a nonstandard model that includes structural zeros. In a data example, the relationships among race of defendant, race of victim, and death penalty sentence are examined using a log-linear model with all three two-way interactions. Recent developments in log-linear modeling are discussed. WIREs Comput Stat 2012, 4:218–223. doi: 10.1002/wics.203