Frank J. Schwebel

Prenatal substance use is associated with negative outcomes for both the pregnant person and child. Entering substance use treatment during pregnancy is associated with improved outcomes but there are many barriers to treatment such as stigma. To improve prevention and treatment outcomes, it is critical to understand the relationship between risk/protective factors for substance use during pregnancy and to look for temporal changes in these relationships. Pregnant people (N=32) with substance use disorder and a history of trauma exposure were included in this secondary analysis. Participants completed ecological momentary assessment (EMA), three times per day for 28 days. We used time-varying effects models (TVEMs) to examine the effects of craving on substance use, PTSD symptoms on craving, prenatal bonding on craving, and PTSD symptoms on prenatal bonding. Craving predicted substance use from gestational weeks 19.58 to 26.96. The relationship decreased over time and was not significant after week 27. PTSD symptoms predicted cravings at three times (weeks: 19.96-20.30, 23.36-31.68, 36.22-36.79). Maternal-fetal quality of attachment predicted craving from weeks 19.58-25.63. Maternal-fetal intensity of preoccupation predicted craving from weeks 19.58-22.42 and 31.31-31.87. PTSD symptoms predicted maternal fetal quality of attachment between weeks 25.06-26.20. There was no relationship between PTSD symptoms and maternal-fetal intensity of preoccupation over time. These findings support prior research on the relationship between risk/protective factors for substance use during pregnancy. However, these relationships appear to fluctuate over time indicating that other factors might impact substance use during pregnancy.

Background: Globally, approximately 8.6% of people will meet criteria for alcohol use disorder (AUD) in their lifetimes, with 2.2% meeting criteria for AUD in the past 12 months. In the United States, AUD prevalence is even greater, with 13.9% meeting criteria in the past 12 months. Effective treatments for AUD exist, although most people receive help through mutual support groups (ie, Alcoholics Anonymous [AA]). However, AA and other mutual support programs may not be desirable for all individuals, particularly those who do not seek abstinence-based approaches. Treatments that support reductions in drinking have been shown to be as effective as abstinence-based treatments in reducing alcohol-related harms, and may be more appealing to a broader range of people. Mindfulness-based interventions may be particularly effective in supporting long-term recovery, whole-person health, and functioning for those with abstinent and nonabstinent recovery goals. Objective: This study is designed to test a novel rolling group-based mindfulness-based relapse prevention (MBRP) delivered via videoconferencing, focused on drinking reduction goals and whole-person functioning. Methods: This study is a hybrid type 1 effectiveness-implementation design to prospectively test the effectiveness of MBRP and to identify barriers and facilitators of group participation to inform future implementation of MBRP as continuing care. Individuals with AUD (n=470) interested in stopping or reducing their drinking, meet criteria for AUD based on a symptom checklist, report heavy drinking at least once in the past 6 months, consent and understand study procedures in English, and provide a valid US mailing address are recruited nationwide via online sources in the United States. Participants are randomized to either MBRP groups via Zoom (Zoom Video Communications, Inc) or referral to online mutual support (eg, AA). All participants receive an individual orientation session delivered via Zoom that includes brief motivational interviewing and an overview of their assigned condition and next steps (MBRP condition: overview of the MBRP groups and instructions on participating in MBRP via Zoom; referral condition: referral to online mutual support groups). Participants provide blood samples for phosphatidylethanol testing at baseline and 3-year follow-up and complete self-report measures of psychosocial functioning, alcohol and other drug use, addiction cycle domains, and previously established predictors of recovery every 6 months for 3 years. Results: Recruitment began on September 13, 2023, and the last recruited participant was randomized on March 20, 2025. Follow-up data collection is ongoing with all 6-month follow-ups completed (86% retention). Conclusions: This study evaluates the effectiveness and mechanisms of MBRP delivered via videoconferencing, compared with referral to online mutual support groups, in supporting recovery among individuals with AUD. It also examines the reach, effectiveness, adoption, implementation, and maintenance of MBRP as an accessible, freely available continuing care option to support long-term recovery from AUD.

Traditionally, substance use treatment and research have prioritized abstinence as the primary goal or outcome while overlooking the broader spectrum of harm reduction goals and outcomes. However, emerging evidence suggests that substance use itself is neither necessarily the only nor the best indicator of treatment success, and other harm reduction goals and outcomes are increasingly accepted in treatment and research. This article reviews existing harm reduction-oriented measures and identifies gaps in current assessment approaches. Key measures discussed include those assessing substance-related harms (e.g., Alcohol Use Disorder Identification Test, Drug Abuse Screening Test), consumption-based metrics (e.g., Timeline Follow-Back), and quality of life indicators. We also highlight measures of protective behavioral strategies, harm reduction motives, harm reduction self-efficacy, and goal attainment scaling as important but underutilized approaches to assessing harm reduction outcomes. Given that harm reduction goals vary by individual and may evolve over time, we argue for the development of more reliable and valid measures that capture diverse treatment outcomes. Additionally, we emphasize the need to involve people with lived experience in all stages of the measure development process to ensure their relevance and applicability. Future research should focus on refining existing measures, expanding definitions of success in substance use treatment, and more thoroughly integrating harm reduction into clinical and research settings. By shifting the focus beyond abstinence alone, researchers and clinicians can better align treatment goals with patients’ needs and goals, enhance intervention effectiveness, and promote more inclusive and individualized pathways to recovery.

BACKGROUND Globally, approximately 8.6% of people will meet criteria for alcohol use disorder (AUD) in their lifetimes, with 2.2% meeting criteria for AUD in the past 12-months. In the United States, AUD prevalence is even greater with 13.9% meeting criteria in the past 12-months. Effective treatments for AUD exist, although most people receive help through mutual support groups (i.e., Alcoholics Anonymous (AA)). However, AA and other mutual support programs may not be desirable for all individuals, particularly those who do not seek abstinence-based approaches. Treatments that support reductions in drinking have been shown to be as effective as abstinence-based treatments in reducing alcohol-related harms and may be more appealing to a broader range of people. Mindfulness-based interventions may be particularly effective in supporting long-term recovery and whole person health and functioning for those with abstinent and non-abstinent recovery goals. OBJECTIVE This study is designed to test a novel rolling group-based mindfulness-based relapse prevention (MBRP) delivered via video conferencing, focused on drinking reduction goals and whole-person functioning. METHODS This study is a hybrid Type 1 effectiveness-implementation design to prospectively test the effectiveness of MBRP, and to identify barriers and facilitators of group participation to inform future implementation of MBRP as continuing care. Individuals with AUD (n=470) interested in stopping or reducing their drinking, meet criteria for AUD based on a symptom checklist, report heavy drinking at least once in the past 6 months, consent and understand study procedures in English, and provide a valid United States mailing address are recruited nationwide via online sources in the United States. Participants are randomized to either MBRP groups via Zoom or referral to online mutual support (e.g., AA). All participants receive an individual orientation session delivered via Zoom that includes brief motivational interviewing and an overview of their assigned condition and next steps (MBRP condition: overview of the MBRP groups and instructions on participating in MBRP via zoom; Referral condition: referral to online mutual support groups). Participants provide blood samples for phosphatidylethanol testing at baseline and 3-year follow-up and complete self-report measures of psychosocial functioning, alcohol and other drug use, addiction cycle domains, and previously established predictors of recovery every 6 months for 3 years. RESULTS Recruitment began on September 13, 2023 and the last recruited participant was randomized on March 20, 2025. Follow-up data collection is ongoing with all 6-month follow-ups completed (86.0% retention). CONCLUSIONS This study evaluates the effectiveness and mechanisms of MBRP delivered via video conferencing, compared with referral to online mutual support groups, in supporting recovery among individuals with AUD. It also examines the reach, effectiveness, adoption, implementation, and maintenance of MBRP as an accessible, freely available continuing care option to support long-term recovery from AUD. CLINICALTRIAL This trial is registered on ClinicalTrials.gov under study record NCT06074341 with initial registration date of October 06, 2023.

Machine learning algorithms hold promise for developing precision medicine approaches to addiction treatment yet have been used sparingly to identify predictors of alcohol-related problems. Recursive partitioning, a machine learning algorithm, can identify salient predictors and clinical cut points that can guide treatment. This study aimed to identify predictors and cut points of alcohol-related problems and to examine result stability in two separate, large data sets of college student drinkers (n = 5,090 and 2,808). Four regression trees were grown using the "rpart" package in R. Seventy-one predictors were classified as demographics (e.g., age), alcohol use indicators (e.g., typical quantity/frequency), or psychosocial indicators (e.g., anxiety). Predictors and cut points were extracted and used to manually recreate the tree in the other data set to test result stability. Outcome variables were alcohol-related problems as measured by the Alcohol Use Disorder Identification Test and Brief Young Adult Alcohol Consequences Questionnaire. Coping with depression, conformity motives, binge drinking frequency, typical/heaviest quantity, drunk frequency, serious harm reduction protective behavioral strategies, substance use, and psychosis symptoms best predicted alcohol-related problems across the four trees; coping with depression (cut point range: 1.83-2.17) and binge drinking frequency (cut point range: 1.5-2.5) were the most common splitting variables. Model fit indices suggest relatively stable results accounting for 17%-30% of the variance. Results suggest the nine salient predictors, particularly coping with depression motives scores around 2 and binge drinking frequency around two to three times per month, are important targets to consider when treating alcohol-related problems for college students. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individuals with a substance use disorder complete ecological momentary assessments (EMA) at lower rates than community samples. Previous research in tobacco users indicates that early log-in counts to smoking cessation websites predicted subsequent smoking cessation website usage. We extended this line of research to examine individuals who are seeking to change their drinking behaviors through mutual support groups. We examined whether adherence in the first 7 days (1487 observations) of an intensive longitudinal study design could predict subsequent EMA protocol adherence (50% and 80% adherence separately) at 30 (5700 observations) and 60 days (10,750 observations). Participants (n = 132) attending mutual-help groups for alcohol use completed two assessments per day for 6 months. We trained four classification models (logistic regression, recursive partitioning, support vector machines, and neural networks) using a training dataset (80% of the data) with each of the first 7 days' cumulative EMA assessment completion. We then tested these models to predict the remaining 20% of the data and evaluated model classification accuracy. We also used univariate receiver operating characteristic curves to examine the minimal combination of days and completion percentage to best predict subsequent adherence. Different modeling techniques can be used with early assessment completion as predictors to accurately classify individuals that will meet minimal and optimal adherence rates later in the study. Models ranged in their performance from poor to outstanding classification, with no single model clearly outperforming other models. Traditional and machine learning approaches can be used concurrently to examine several methods of predicting EMA adherence based on early assessment completion. Future studies could investigate the use of several algorithms in real time to help improve participant adherence rates by monitoring early adherence and using early assessment completion as features in predictive modeling.