Search

This manuscript aims to contribute to the next phase of mechanisms of behavior change (MOBC) science on alcohol or other drug use. Specifically, we encourage the transition from a basic science orientation (i.e., knowledge generation) to a translational science orientation (i.e., knowledge application or Translational MOBC Science). To inform that transition, we examine MOBC science and implementation science and consider how these two research areas can intersect to capitalize on the goals, strengths, and key methodologies of each. First, we define MOBC science and implementation science and offer a brief historical rationale for these two areas of clinical research. Second, we summarize similarities in rationale and discuss two scenarios where one draws from the other-MOBC science on implementation strategy outcomes and implementation science on MOBC. We then focus on the latter scenario, and briefly review the MOBC knowledge base to consider its readiness for knowledge translation. Finally, we provide a series of research recommendations to facilitate the translation of MOBC science. These recommendations include: (1) identifying and targeting MOBC that are well suited for implementation, (2) use of MOBC research results to inform broader health behavior change theory, and (3) triangulation of a more diverse set of research methodologies to build a translational MOBC knowledge base. Ultimately, it is important for gains borne from MOBC science to affect direct patient care, while basic MOBC research continues to be developed and refined over time. Potential implications of these developments include greater clinical significance for MOBC science, an efficient feedback loop between clinical research methodologies, a multi-level approach to understanding behavioral change, and reduced or eliminated siloes between MOBC science and implementation science.

Objective: The Alcohol Addiction Research Domain Criteria (AARDoC) is an organizational framework for assessing heterogeneity in addictive disorders organized across the addiction cycle domains of incentive salience, negative emotionality, and executive functioning and may have benefits for precision medicine. Recent work found pretreatment self-report items mapped onto the addiction cycle domains and predicted 1- and 3-year alcohol use disorder treatment outcomes. Given the potential utility of the addiction cycle domains for predicting relevant treatment outcomes, this study sought to evaluate the longitudinal measurement invariance of the domains. Method: We conducted a secondary analysis of individuals with alcohol use disorder (n = 1,383, 30.9% female, 76.8% non-Hispanic White, 11.2% Hispanic) who participated in the COMBINE study. Eleven items assessed at pre- and posttreatment were included in exploratory structural equation modeling (ESEM) and longitudinal invariance analyses. Results: The pre- and posttreatment ESEM models had factor loadings consistent with the three addiction cycle domains and fit the data well. The ESEM factor structure was invariant from pre- to posttreatment (representing configural invariance) and metric invariance (factor loadings) was largely supported, but analyses failed to support scalar invariance (item-level thresholds) of the addiction cycle domains. Conclusions: A three-factor structure representing addiction cycle domains can be modeled using brief self-report measures pre- and posttreatment. Individuals demonstrated a downward shift in the level of item endorsement, indicating improvement with treatment. Although this 11-item measure might be useful at baseline for informing treatment decisions, results indicate the need to exercise caution in comparing the addiction cycle domains pre- to posttreatment within persons. Public Health Significance Statement: The addiction cycle domains of reward/incentive salience, relief/negative emotionality, and executive functioning, which are based on a neurobiological model of alcohol use disorder, may have clinical utility in predicting alcohol use disorder treatment response and recovery outcomes, but more work is needed before domain scores can be used to assess changes within individuals across time. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate the associations of jail-initiated medication for opioid use disorder (MOUD) and patient navigation (PN) with opioid use disorder (OUD) at 6 months post-release. Three randomized trials (combined N = 330) were combined to assess whether MOUD (extended-release naltrexone or interim methadone) initiated prior to release from jail with or without PN would reduce the likelihood of a DSM-5 diagnosis of OUD 6 months post-release relative to enhanced treatment-as-usual (ETAU). Across the three studies, assignment to MOUD compared to ETAU was not associated with an OUD diagnosis at 6 months post-release (69% vs. 75%, respectively, OR = 0.67, 95% CI: 0.42 to 1.20). Similarly, PN compared to MOUD without PN was not associated with an OUD diagnosis (63% vs 77%, respectively, OR = 0.61, 95% CI: 0.27 to 1.53). Results underscore the need to further optimize the effectiveness of MOUD for patients initiating treatment in jail, beginning with an emphasis on post-release treatment adherence. [ABSTRACT FROM AUTHOR]

Aims Among individuals with alcohol use disorder (AUD), sleep disturbances are pervasive and contribute to the etiology and maintenance of AUD. However, despite increased attention toward the relationship between alcohol use and sleep, limited empirical research has systematically examined whether reductions in drinking during treatment for AUD are associated with improvements in sleep problems. Methods We used data from a multisite, randomized, controlled trial that compared 6 months of treatment with gabapentin enacarbil extended-release with placebo for adults with moderate-to-severe AUD (N  = 346). The Timeline Follow-back was used to assess WHO risk drinking level reductions and the Pittsburgh Sleep Quality Index was used to assess sleep quality over the prior month at baseline and the end of treatment. Results Sleep problem scores in the active medication and placebo groups improved equally. Fewer sleep problems were noted among individuals who achieved at least a 1-level reduction (B  = −0.99, 95% confidence interval (CI) [−1.77, −0.20], P  = .014) or at least a 2-level reduction (B  = −0.80, 95% CI [−1.47, −0.14], P  = .018) in WHO risk drinking levels at the end of treatment. Reductions in drinking, with abstainers excluded from the analysis, also predicted fewer sleep problems at the end of treatment (1-level: B  = −1.01, 95% CI [−1.83, −0.20], P  = .015; 2-level: B  = −0.90, 95% CI [−1.59, −0.22], P  = .010). Conclusions Drinking reductions, including those short of abstinence, are associated with improvements in sleep problems during treatment for AUD. Additional assessment of the causal relationships between harm-reduction approaches to AUD and improvements in sleep is warranted. [ABSTRACT FROM AUTHOR]

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials.

Background: Abstinence has historically been considered the preferred goal of alcohol use disorder (AUD) treatment. However, most individuals with AUD do not want to abstain and many are able to reduce their drinking successfully. Craving is often a target of pharmacological and behavioral interventions for AUD, and reductions in craving may signal recovery. Whether reductions in drinking during AUD treatment are associated with reductions in craving has not been well examined. Methods: We conducted secondary analyses of data from three AUD clinical trials (N's= 1327, 346, and 200). Drinking reductions from baseline to the end of treatment were measured as changes in World Health Organization (WHO) risk drinking levels; alcohol craving was measured using validated self‐report measures. Regression analyses tested whether drinking reductions were associated with end‐of‐treatment craving reductions; moderation analyses tested whether associations between drinking reduction and end‐of‐treatment craving differed across AUD severity. Results: Reductions of at least 1 or at least 2 WHO risk drinking levels were associated with lower craving (all p's < 0.05). Results were substantively similar after removing abstainers at the end‐of‐treatment. Associations between drinking reductions and craving were generally not moderated by AUD severity. Conclusions: Individuals with WHO risk drinking level reductions reported significantly lower craving, as compared to those who did not achieve meaningful reductions in drinking. The results demonstrate the utility of WHO risk drinking levels as AUD clinical trial endpoints and provide evidence that drinking reductions mitigate craving. [ABSTRACT FROM AUTHOR]

Background: The Alcohol Use Disorders Identification Test‐Consumption (AUDIT‐C) is a three‐item screening measure of unhealthy alcohol use that is widely used in healthcare settings. Evidence shows high test–retest reliability of the AUDIT‐C in research samples, but most studies had limited external validity and used small samples that could not be used to evaluate reliability across demographic subgroups and/or screening modalities. This study evaluates the test–retest reliability of the AUDIT‐C completed in routine care in a large primary care sample, including across demographic subgroups defined by age, sex, race, ethnicity, and screening modality (i.e., completed in‐clinic or online). Methods: We used electronic health record (EHR) data from Kaiser Permanente Washington. The sample included 18,491 adult primary care patients who completed two AUDIT‐C screens 1–21 days apart as part of routine care in 2021. Test–retest reliability was evaluated for AUDIT‐C total scores (0–12) and for a binary measure indicating unhealthy alcohol use (scores ≥3 women, ≥4 men). Using previously established cutoffs, we interpreted reliability coefficients >0.75 as indicating "excellent" reliability. Results: AUDIT‐C screens completed in routine care and documented in EHRs had excellent test–retest reliability for total scores (ICC = 0.87, 95% CI: 0.87–0.87) and the binary indicator of unhealthy alcohol use (κ = 0.79, 95% CI: 0.78–0.80). Reliability coefficients were good to excellent across all demographic groups and for in‐clinic and online modalities. Higher reliability was seen when both screens were completed through online patient portals (ICC = 0.93, 95% CI: 0.93–0.93) versus in‐clinic (ICC = 0.81, 95% CI: 0.79–0.82) or when one screen was completed using each modality (ICC = 0.83, 95% CI: 0.82–0.83). Lower reliability was seen in American Indian/Alaska Native (ICC = 0.82, 95% CI: 0.75–0.87) and multiracial individuals (ICC = 0.82, 95% 0.80–0.84). Conclusions: In real‐world routine care conditions, AUDIT‐C screens have excellent test–retest reliability across demographic subgroups and modalities (online and in‐clinic). Future research should examine why reliability varies slightly across modalities and demographic subgroups. [ABSTRACT FROM AUTHOR]