Temporal associations linking alcohol and cannabis use to cigarette smoking in young adults engaged in a tobacco cessation and relapse monitoring study.

Young adulthood remains a developmental period in which cigarette smoking initiation and progression to dependence and regular use is common. Moreover, co-use of alcohol and/or cannabis with tobacco is common in this age group and may have detrimental effects on tobacco use rates and cessation outcomes. Although young adults are interested in quitting smoking, achieving abstinence remains difficult, even with evidence-based treatment strategies. Understanding proximal associations between other substance use (e.g., alcohol and cannabis) and smoking may have important treatment implications. This exploratory analysis investigated the role of alcohol and/or cannabis use in contributing to smoking events on the same day or next day among young adults engaged in a smoking cessation and relapse monitoring study. We used ecological momentary assessment (EMA) data from 43 young adults (ages 18-25; 932 observations) who smoked cigarettes daily and agreed to participate in a 5-week study that included a 2-day smoking quit attempt and provision of tobacco treatment in the form of nicotine replacement therapy, brief cessation counseling, and financial incentives for abstinence (incentives were provided only during the 2-day quit attempt). We tested multilevel time-series models of daily associations between alcohol use, cannabis use, and smoking. Consistent with hypotheses, days on which participants were more likely to drink alcohol predicted increased likelihood of smoking the next day (OR = 2.27, p =.003). This effect was significant after controlling for both the one-day lagged effect of smoking (i.e., autoregression) and the concurrent (i.e., same day) effects of drinking and cannabis use. Although there was a positive concurrent effect of cannabis use on smoking (OR = 12.86, p =.003), the one-day lagged effect of cannabis use and the concurrent effect of drinking was not significant, contrary to hypotheses. Results indicate that alcohol use presents a potential threat to successful smoking cessation that extends to the following day. This suggests a risk-window in which treatment could be supplemented with just-in-time interventions and extending the focus on co-use to include this lagged impact on cessation outcomes.

Trial-level bias score versus mean bias score: Comparison of the reliability and external validity using dot-probe task among daily smokers.

INTRODUCTION: Attentional bias (AB) is an individual difference risk factor that represents the extent to which cigarette cues capture one's attention. AB is typically indexed by mean bias score (MBS), theoretically assuming that AB is static. However, poor reliability of MBS has threatened valid interpretation of the results on AB. Based on observed trial-by-trial temporal fluctuation and variability of attentional allocation, trial-level bias score (TLBS) has been introduced as an alternative index with evidence of better psychometric properties in various populations, as compared to MBS. However, such evidence is limited among daily smokers. The current study aimed to replicate and extend extant findings in a sample of daily smokers by hypothesizing that TLBS, as compared to MBS, would demonstrate superior reliability and external validity. METHODS: Forty-eight daily smokers completed self-reports, ad-libitum smoking, and a dot-probe task three times, which was comprised of 36 pairs of pictorial stimuli of cigarette and neutral cues, yielding 144 total trials. RESULTS: The TLBS demonstrated superior internal (range intra class correlation [ICC] = 0.79-0.95) and test-retest reliability (range ICC = 0.64-0.88) compared to MBS (range ICC = 0.31-0.40 and 0.06-0.16, respectively). However, few significant relations between either the MBS or TLBS and measures of biobehavioral and self-report indices of smoking reinforcement were observed. CONCLUSIONS: The current findings demonstrate that TLBS, as compared to MBS, is a more reliable measure of AB among daily smokers, while evidence of its external validity is limited.

Using machine learning to examine predictors of treatment goal change among individuals seeking treatment for alcohol use disorder.

INTRODUCTION: The goals of individuals seeking treatment for alcohol use disorder (AUD) are typically quantified as abstinent or nonabstinent (e.g., moderate drinking) goals. However, treatment goals can vary over time and be influenced by life circumstances. This study aims to identify predictors of treatment goal change and direction of change from baseline to six-month follow-up among individuals seeking treatment for AUD. METHODS: This study is a secondary analysis of data from the Relapse Replication and Extension Project. The study included participants who completed assessments at baseline and six-month follow-up in the analysis (n = 441). We used decision trees to examine 111 potential predictors of treatment goal change. The study cross-validated results using random forests. The team examined changes in goal between baseline and follow-up (Decision Tree 1) and quantified them as being toward or away from a complete abstinence goal (Decision Tree 2). RESULTS: Nearly 50 % of the sample changed their treatment goal from baseline to 6 months, and 68.7 % changed from a nonabstinence goal toward a complete abstinence goal. The study identified seven unique predictors of goal change. The most common predictors of changing a treatment goal were number of recent treatment sessions prior to enrolling in the study, other substance use, negative affect, anxiety, social support, and baseline drinks per drinking day. Participants with a greater number of recent treatment sessions and who sought social support were most likely to change their goal. Additionally, individuals with more substance use tended to change away from complete abstinence goals. Cross-validation supported baseline drinks per drinking day, social support, baseline maximum blood alcohol concentration (BAC), lifetime tobacco use, baseline average BAC, lifetime cocaine use, Inventory of Drinking Situations total score, and Situational Confidence Questionnaire average score as important predictors. CONCLUSIONS: This study identified seven unique predictors of treatment goal change while in AUD treatment. Prior treatment, drinking to cope, and social support were most associated with goal changes. This information can inform providers who seek to understand factors associated with treatment goal selection and changes in goals during treatment.

Why don't they stop? Understanding unplanned marijuana use among adolescents and young adults.

Difficulty regulating substance use is a core feature of addiction that can manifest as unplanned use. This study sought to identify internal and situational influences on unplanned marijuana use among youth ages 15 to 24 years (N = 85; 48% female; 27% age <18 years). Additionally, we disentangled person-level associations from within-person day-to-day influences. Ecological momentary assessment methods captured affective (positive: energized, excited, sociable, happy, relaxed; negative: bored, tense, sad, stressed) and situational factors in real-world settings during a 1-week monitoring period. Participants reported no plan to use on 51% of days (269/527), and youth ultimately used marijuana on 35% of these unplanned days. At the day level, on days when youth spent more time in the presence of marijuana-related cues than they typically do, they used more grams on planned days and less on unplanned days. Regardless of use plans, youth were more likely to use on days when they spent more time with using friends and if they reported greater availability of marijuana in general across the monitoring period. At the person level, youth who generally reported higher positive affect, relative to other participants, used more on planned days and less on unplanned days. Regardless of use plans, youth who generally reported greater craving and time in the presence of marijuana-related cues used more grams, whereas youth who generally reported greater negative affect used less. Together, findings revealed several factors, with clear clinical relevance, which may explain why some youth struggle to control their marijuana use. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A reinforcement sensitivity model of affective and behavioral dysregulation in marijuana use and associated problems.

This study tested a model linking sensitivity to punishment (SP) and reward (SR) to marijuana use and problems via affect lability and poor control. A 6-month prospective design was used in a sample of 2,270 young-adults (64% female). The hypothesized SP × SR interaction did not predict affect lability or poor control, but did predict use likelihood at baseline. At low levels of SR, SP was associated with an increased likelihood of abstaining, which was attenuated as SR increased. SP and SR displayed positive main effects on both affect lability and poor control. Affect lability and poor control, in turn, mediated effects on the marijuana outcomes. Poor control predicted both increased marijuana use and, controlling for use level, greater intensity of problems. Affect lability predicted greater intensity of problems, but was not associated with use level. There were few prospective effects. SR consistently predicted greater marijuana use and problems. SP however, exhibited both risk and protective pathways. Results indicate that SP is associated with a decreased likelihood of marijuana use. However, once use is initiated SP is associated with increased risk of problems, in part, due to its effects on both affect and behavioral dysregulation. (PsycINFO Database Record