Search

Aims: Theory-driven, exploratory study to: (i) identify a reward drinking phenotype in young adults; (ii) evaluate this phenotype as a predictor of naltrexone response; and (iii) examine mechanisms of naltrexone in reward drinkers., Design: Secondary analysis of a randomized controlled trial., Setting: USA., Participants: A total of 128 young adult (ages 18-25) heavy drinkers., Interventions: Naltrexone versus placebo., Measurements: Daily surveys assessed affect, urge, drinking, and context. The Drinking Motives Questionnaire was used to identify phenotypes based on reward (enhancement motives) and relief (coping motives) drinking., Findings: We identified three profiles: "Low reward/Low relief" (14.1%; low enhancement/low coping motives); "Reward drinkers" (62.2%; high enhancement/low coping motives); and "High reward/High relief" (22.7%; high enhancement/high coping motives). Among reward drinkers (versus low profile), naltrexone significantly reduced percent days drinking to intoxication (blood alcohol concentration [BAC] ≥0.08) (PDI) (d = 0.56; 95% CI [0.17, 0.96]) and percent high intensity drinking days (PHID) (8/10 drinks for women/men) (d = 0.32; 95% CI [0.01, 0.68]). Among the high reward/high relief profile drinkers (versus low profile), naltrexone reduced PHID (d = 0.69; 95% CI [0.02, 1.50]). Using profile-informed cutoffs and observed scores (for clinical applicability): (i) among cutoff-derived reward drinkers, we found a medium-to-large (d = 0.66; 95% CI [0.24, 1.16]) and small effect (d = 0.28; 95% CI [0.04, 0.72]) of naltrexone in reducing PDI and PHID, respectively; and (ii) among the cutoff-derived high reward/high relief subgroup, we found a medium-to-large effect (d = 0.63; 95% CI [0.05, 1.1]) of naltrexone in reducing PHID. Among reward drinkers (not other profiles), naltrexone reduced drinking on days a drinking event occurred by weakening the within-day association between positive affect and urges (P < 0.05)., Conclusions: Naltrexone has pronounced effects in reducing risky drinking among young adult reward drinkers (high reward/low relief) by reducing urges on days when individuals have higher positive affect and are exposed to a drinking event. Naltrexone also appears to reduce risky drinking among young adult high reward/high relief drinkers, but not via the same mechanism., (© 2021 Society for the Study of Addiction.)

Background and Aims: To estimate progression to polytobacco use (PTU) over 1 year among a sample of US youth., Design: Prospective survey with two waves 1 year apart: wave 1 (2013-14) and wave 2 (2014-15). We conducted latent transition analysis (LTA) to identify latent class transitions and examine socio-demographic predictors of transition types., Setting: United States., Participants: A total of 11, 996 people who were aged 12-17 years at wave 1., Measurements: Publicly available data were used from the Population Assessment of Tobacco and Health (PATH) study, a nationally representative sample of US civilian, non-institutionalized population aged 12 years and older. Tobacco use status was assessed and classified in terms of: never use, non-current (not in the past 30 days) and current (past 30-day) use of cigarettes, cigars, e-cigarettes, hookah and smokeless tobacco. Other nicotine products were excluded because rates of use were either too low to model (e.g. pipe) or the product was not assessed in the PATH youth sample (e.g. nicotine replacement products)., Findings: We identified three distinct patterns: class 1, non-use (wave 1 prevalence = 86%; wave 2 prevalence = 78%); class 2, ever use of cigarettes and e-cigarettes (wave 1 prevalence = 11%; wave 2 prevalence = 14%); and class 3, current PTU (wave 1 prevalence = 4%; wave 2 prevalence = 7%). Probability of progression from non-use to ever use of cigarettes and e-cigarettes was 0.06 and ever use of cigarettes and e-cigarettes to current PTU was 0.32. Non-users were more likely to transition to ever use of cigarettes and e-cigarettes if they were older (versus younger), white (versus non-white) or if their parental education level was high school or less (versus more than high school); and ever users of cigarettes and e-cigarettes to current PTU if they were older, male or white., Conclusions: US youth who had previously tried e-cigarettes and cigarettes at wave 1 (2013-14) had a 32% chance of transitioning to current use of two or more tobacco products within 1 year., (© 2019 Society for the Study of Addiction.)

Background: This study is the first to examine longitudinal posttreatment outcomes of a placebo-controlled trial of varenicline for alcohol use disorder (AUD) with comorbid cigarette smoking., Methods: Participants were 131 adults (n = 39 female) seeking alcohol treatment in a randomized, double-blind, parallel group, placebo-controlled, 16-week multisite trial of varenicline combined with medical management (MM). Timeline follow-back assessments of alcohol and smoking behavior were conducted at the end of treatment (4 months), with follow-ups at 6, 9, and 12 months. Outcomes were percentage of heavy drinking days (PHDD), percent of participants with no heavy drinking days (NHDD), cotinine-confirmed prolonged smoking abstinence (PA), and good clinical outcome on either NHDD or PA., Results: Treatment improvements were maintained posttreatment. For the sample overall, PHDD or NHDD did not differ significantly by treatment condition (ps > 0.13), but varenicline produced higher rates of PA versus placebo at 4, 9, and 12 months (p < 0.05). Significant differences were observed by sex: Males had higher rates of NHDD with varenicline (28.9%) versus placebo (6.4%) at the end of treatment (p = 0.004), and these effects were maintained at 12 months (varenicline: 40.0% vs. placebo: 19.2%, p = 0.03). Higher rates of PA were seen for varenicline in both males (8.9%) and females (21.1%) versus placebo (males/females: 0%) at the end of treatment (p = 0.05), and this effect was maintained at 12 months for females (varenicline: 21.1% vs. placebo, 0.0%, p = 0.05)., Conclusions: Varenicline treatment combined with MM appears to have enduring benefits for patients with co-occurring AUD and cigarette smoking, and these effects may differ by sex., (© 2019 by the Research Society on Alcoholism.)

Aims: To assess whether individuals trying to quit smoking who have high depressive symptoms (HD), compared with low depressive symptoms (LD): (1) report more frequent stressful events (SEs), (2) are more likely to smoke after SEs, (3) experience greater acute or persistent changes in affect after an SE, and (4) are at greater risk of smoking following affective changes., Design: Smoking cessation data were analyzed using multi-level path modeling to examine the moderating effects of depressive symptoms on relations among SEs, subsequent affect, and smoking., Setting: An academic research center in Central New Jersey, USA., Participants: Seventy-one adult treatment-seeking daily smokers recruited from 2010 to 2012., Measurements: Baseline depressive symptoms [HD: Center for Epidemiological Studies Depression Scale (CES-D) ≥ 16 versus LD: CES-D < 16]; and real-time ecological momentary assessment (EMA) reports of SEs, affect, and smoking assessed during 21 days post-quit., Findings: Multi-level models indicated that HD smokers were more likely than LD smokers to report stressful events [odds ratio (OR) = 2.323, P = 0.009], but had similar post-stress acute affective changes (negative affect: b = -0.117, P = 0.137, positive affect: b = 0.020, P = 0.805). Only HD smokers reported increased negative affect (NA) (b = 0.199, P = 0.030) and decreased positive affect (PA) up to 12 hours later (b = -0.217, P = 0.021), and greater lapse risk up to 24 hours after an SE (OR = 3.213, P = 0.017). The persistence of elevated NA and suppressed PA was partially explained by increased odds of subsequent SEs among HD smokers. However, the heightened stress-lapse association over 24 hours found in HD smokers was not fully explained by sustained aversive affect or subsequent SEs., Conclusions: Depressed and non-depressed smokers trying to quit appear to experience similar acute affective changes following stress: however, depressed smokers experience higher rates of exposure to stress, longer-lasting post-stress affective disturbance and greater risk of smoking lapse 12-24 hours after a stressful event than non-depressed smokers., (© 2017 Society for the Study of Addiction.)

Background and Aims: Alcohol and marijuana are widely used among college students. Emotion regulation strategies have been linked to alcohol and marijuana use, but little attention has been devoted to modeling the directionality of these associations. The aims of the current study were to test whether (a) daytime use of emotion regulation strategies influences the likelihood of evening substance use and (b) evening substance use influences the likelihood of next-day use of emotion regulation strategies., Design: Longitudinal daily diary data were collected for 30 days via on-line surveys., Setting: Northeastern United States., Participants: A total of 1640 college students (mean age = 19.2 years, 54% female, 80% European American) were recruited each semester between Spring 2008 and Spring 2012., Measurements: Daily diaries assessed emotion regulation strategies (distraction, reappraisal, problem-solving, avoidance) and substance use (any drinking, heavy drinking, marijuana use, co-use of any drinking/heavy drinking and marijuana). Covariates included gender, age, race/ethnicity, fraternity/sorority involvement and baseline depression., Findings: Daytime distraction [odds ratio (OR) = 0.95], reappraisal (OR = 0.95) and problem-solving (OR = 0.94) predicted lower odds of evening marijuana use (P-values < 0.02). Evening heavy drinking (OR = 0.90) and marijuana use (OR = 0.89) predicted lower odds of next-day problem-solving, with heavy drinking also predicting higher odds (OR = 1.08) of next-day avoidance and marijuana use also predicting higher odds (OR = 1.08) of next-day reappraisal (P-values < 0.03)., Conclusions: There appear to be reciprocal relations among emotion regulation strategies and substance use: greater daytime use of distraction, reappraisal, and problem solving predicts lower evening substance use, while higher evening substance use predicts higher next-day avoidance and reappraisal but lower next-day problem-solving., (© 2016 Society for the Study of Addiction.)

Aims: Ecological Momentary Assessment (EMA) captures real-time reports in subjects' natural environments. This experiment manipulated EMA frequency to estimate effects on abstinence and peri-cessation subjective experiences., Design: In this randomized trial, subjects had an equal chance of being assigned to low-frequency (once) or high-frequency (six times) daily EMA for 4 weeks (1 week pre- and 3 weeks post-cessation). Participants completed six office visits over 5 weeks and 6- and 12-week follow-up telephone interviews., Setting: Community participants were recruited from central New Jersey, USA., Participants: One hundred and ten adult daily smokers seeking to quit smoking were included in intent-to-treat analyses of tobacco abstinence; 94 were available for secondary analyses of peri-cessation subjective ratings., Measurements: Primary outcomes were cessation (abstaining at least 24 hours within 2 weeks of attempting to quit) and prolonged abstinence (no relapse between weeks 2 and 12 post-quit). Secondary outcomes were mean levels and growth in ratings of cigarette craving, affect and quitting motivation and self-efficacy., Findings: EMA frequency was unrelated to cessation (odds ratio = 1.367, 95% confidence interval = 0.603-3.098) or prolonged abstinence (odds ratio = 1.040, 95% confidence interval = 0.453-2.388) in intent-to-treat analyses. High-frequency EMA was associated with lower craving (B = -0.544, standard error (SE) = 0.183, P = 0.004, anxiety (B = -0.424, SE = 0.170, P = 0.015), anger (B = -0.474, SE = 0.139, P = 0.001), hunger (B = -0.388, SE = 0.170, P = 0.025) and positive affect (B = -0.430, SE = 0.196, P = 0.03)., Conclusions: In smokers trying to quit, more frequent ecological momentary assessment self-monitoring results in lower craving, anxiety, anger, hunger and positive affect. It is not clear whether this translates into higher rates of smoking abstinence., (© 2015 Society for the Study of Addiction.)