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Background: Smartbands can be used to detect cigarette smoking and deliver real time smoking interventions. Brief mindfulness interventions have been found to reduce smoking. Objective: This single arm feasibility trial used a smartband to detect smoking and deliver brief mindfulness exercises. Methods: Daily smokers who were motivated to reduce their smoking wore a smartband for 60 days. For 21 days, the smartband monitored, detected and notified the user of smoking in real time. After 21 days, a 'mindful smoking' exercise was triggered by detected smoking. After 28 days, a 'RAIN' (recognize, allow, investigate, nonidentify) exercise was delivered to predicted smoking. Participants received mindfulness exercises by text message and online mindfulness training. Feasibility measures included treatment fidelity, adherence and acceptability. Results: Participants (N=155) were 54% female, 76% white non-Hispanic, and treatment starters (n=115) were analyzed. Treatment fidelity cutoffs were met, including for detecting smoking and delivering mindfulness exercises. Adherence was mixed, including moderate smartband use and low completion of mindfulness exercises. Acceptability was mixed, including high helpfulness ratings and mixed user experiences data. Retention of treatment starters was high (81.9%). Conclusions: Findings demonstrate the feasibility of using a smartband to track smoking and deliver quit smoking interventions contingent on smoking. [ABSTRACT FROM AUTHOR]

Introduction: Promoting smoking cessation is a global public health priority. E-cigarettes are increasingly being used by individuals to try quitting smoking. Identifying sources and types of information available to adults who are trying to quit, and the impact of this information during a quit attempt, is critical to augment the potential public health benefit of e-cigarettes for reducing cigarette smoking. Methods: US adults (N = 857) who reported using e-cigarettes in a recent smoking cessation attempt completed an anonymous, cross sectional, online survey. We examined sources of information and type of information received when using e-cigarettes to quit smoking and their associations with the duration of abstinence achieved. Results: The two most commonly reported information sources were friends (43.9%) and the internet (35.2%), while 14.0% received information from a healthcare provider. People received information on type of device (48.5%), flavor (46.3%), and nicotine concentration (43.6%). More people received information about gradually switching from smoking to vaping (46.7%) than abruptly switching (30.2%). Obtaining information from healthcare providers (β (SE) = 0.16 (0.08), p = 0.04), getting information about abruptly switching to e-cigarettes (β (SE) = 0.14 (0.06), p = 0.01) and what nicotine concentrations to use (β (SE) = 0.18 (0.05), p = 0.03) were associated with longer quit durations. Conclusions: Amidst the growing popularity of e-cigarettes use for quitting smoking, our results highlight common sources of information and types of information received by individuals. Few people received information from healthcare providers indicating a gap in cessation support that can be filled. Providing information about immediate switching to e-cigarettes and nicotine concentrations to use may help in increasing quit rates and duration. [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]

BackgroundThe goal of the current study was to use tree-based methods (Zhang and Singer, 2010, Recursive Partitioning and Applications, 2nd ed. Springer, New York) to identify predictors of abstinence from heavy drinking in COMBINE (Anton et al. JAMA 2006; 295:2003), the largest study of pharmacotherapy for alcoholism in the United States to date, and to validate these results in PREDICT (Mann et al. Addict Biol 2012; 18:937), a parallel study conducted in Germany.MethodsWe compared a classification tree constructed according to purely statistical criteria to a tree constructed according to a combination of statistical criteria and clinical considerations for prediction of no heavy drinking during treatment in COMBINE. We considered over 100 baseline predictors. The tree approach was compared to logistic regression. The trees and a deterministic forest identified the most important predictors of no heavy drinking for direct testing in PREDICT.ResultsThe tree built using both clinical and statistical considerations consisted of 4 splits based on consecutive days of abstinence (CDA) prior to randomization, age, family history of alcoholism, and confidence to resist drinking in response to withdrawal and urges. The tree based on statistical considerations with 4 splits also split on CDA and age but also on gamma-glutamyl transferase level and drinking goal. Deterministic forest identified CDA, age, and drinking goal as the most important predictors. Backward elimination logistic regression among the top 18 predictors identified in the deterministic forest analyses identified only age and CDA as significant main effects. Longer CDA and goal of complete abstinence were associated with better outcomes in both data sets.ConclusionsThe most reliable predictors of abstinence from heavy drinking were CDA and drinking goal. Trees provide binary decision rules and straightforward graphical representations for identification of subgroups based on response and may be easier to implement in clinical settings.

ObjectivePatients entering treatment for alcohol problems do not have uniform treatment goals, and a pretreatment drinking goal has a significant impact on treatment outcome. The objective of this study was to understand better how an array of individual characteristics, including factors that affect treatment, are related to treatment goals before beginning alcohol treatment in the COMBINE (Combining Medications and Behavioral Interventions) Study.MethodParticipants were alcohol-dependent individuals (N = 1,156; 357 women) recruited at 11 outpatient academic alcoholism-treatment clinics across the United States to participate in a randomized, double-blind, placebo-controlled trial that combined behavioral intervention with acamprosate and/or naltrexone. Treatment goal was coded as controlled drinking, conditional abstinence, or total abstinence. Multinomial logistic regressions assessed whether there were significant relationships between predictor variables and pretreatment goal selection.ResultsLower levels of alcohol-related consequences, lower readiness to change, higher family income, more daily drinkers in social network, and lack of prior treatment or Alcoholics Anonymous engagement predicted choice of a controlled drinking goal over a total abstinence goal. Fewer alcohol-related consequences, lower readiness to change, and more daily drinkers in-network predicted choice of a conditional abstinence goal over a total abstinence goal.ConclusionsHigher levels of functioning, lower levels of consequences, no prior involvement in treatment and Alcoholics Anonymous, and a more drinking-saturated social environment are associated with the choice of a non-abstinence goal.

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]

Randomized controlled trials (RCTs) evaluating medications for alcohol use disorder (AUD) often examine heterogeneity of treatment effects through subgroup analyses that contrast effect estimates in groups of patients across individual demographic, clinical, and study design‐related characteristics. However, these analyses are often not prespecified or adequately powered, highlighting the potential role of subgroup analyses in meta‐analysis. Here, we conducted an umbrella review (i.e., a systematic review of meta‐analyses) to determine the range and characteristics of reported subgroup analyses in meta‐analyses of AUD medications. We searched PubMed to identify meta‐analyses of RCTs evaluating medications for the management of AUD, alcohol abuse, or alcohol dependence in adults. We sought studies that measured drinking‐related outcomes; quality of life, function, and rates of mortality; adverse events; and dropout. We considered meta‐analyses that reported the results from formal subgroup analyses (comparing the summary effects across subgroup levels); summary effect estimates stratified across subgroup levels; and meta‐regression, regression, or correlation‐based subgroup analyses. We analyzed nine meta‐analyses that included 61 formal subgroup analyses (median = 6 per meta‐analysis), of which 33 (54%) were based on baseline participant‐level and 28 (46%) were based on trial‐level characteristics. Of the 58 subgroup analyses with either a p‐value from a subgroup test or a statement by the authors that the subgroup analyses were not statistically significant, eight (14%) were statistically significant at the p < 0.05 level. Twelve meta‐analyses reported the results of 102 meta‐regression analyses, of which 25 (25%) identified statistically significant predictors of the relevant outcome of interest; nine (9%) were based on baseline participant‐level and 93 (91%) were based on trial characteristics. Subgroup analyses across meta‐analyses of AUD medications often focus on study‐level characteristics, which may not be as clinically informative as subgroup analyses based on participant‐level characteristics. Opportunities exist for future meta‐analyses to standardize their subgroup methodology, focus on more clinically informative participant‐level characteristics, and use predictive approaches to account for multiple relevant variables. This umbrella review identified and summarized the range of characteristics of reported subgroup analyses across meta‐analyses of alcohol use disorder medications. The review identified 9 meta‐analyses that included 61 formal subgroup analyses and 12 meta‐analyses reporting the results of 102 meta‐regression analyses. The study findings suggest that subgroup analyses across meta‐analyses of alcohol use disorder medications often focus on study‐level characteristics, which may not be as clinically informative as subgroup analyses based on participant‐level characteristics. [ABSTRACT FROM AUTHOR]

BackgroundThis paper describes the research protocol for a randomized controlled trial of a multimodal mobile sleep intervention for heavy-drinking young adults. Young adults report the highest rates of heavy, risky alcohol consumption and are a priority population for alcohol prevention and intervention efforts. Alcohol strategies that leverage other health concerns and use technology may offer an innovative solution. Poor sleep is common among young adults and is a risk factor for developing an alcohol use disorder. Moreover, young adults are interested in information to help them sleep better, and behavioral sleep interventions address alcohol use as a standard practice. ObjectiveThe primary aim of this study is to assess the effectiveness of a 2-week multimodal mobile sleep intervention for reducing drinks consumed per week among heavy-drinking young adults. We will explore the effects on alcohol-related consequences, assessing quantitative and qualitative sleep characteristics as secondary aims. The study’s goals are to identify the optimal combination of sleep intervention components for improving drinking outcomes, the feasibility and acceptability of these components, and the potential mechanisms by which these components may promote alcohol behavior change. MethodsYoung adults (aged 18-25 years) who report recent heavy drinking will be randomly assigned to one of three conditions: mobile sleep hygiene advice (n=30), mobile sleep hygiene advice and sleep and alcohol diary self-monitoring (n=30), or mobile sleep hygiene advice, sleep and alcohol diary self-monitoring, and sleep and alcohol data feedback (n=60). For the feedback component, participants will complete two web-based sessions with a health coach during which they will receive summaries of their sleep and alcohol data, and the potential association between them along with brief advice tailored to their data. All participants will wear sleep and alcohol biosensors daily for 2 weeks for objective assessments of these outcomes. ResultsThe study was funded by the National Institutes of Health in May 2018. Recruitment began in December 2018 and will be concluded in Spring 2021. As of February 4, 2021, we have enrolled 110 participants. ConclusionsUltimately, this research could result in an efficacious, low-cost intervention with broad population reach through the use of technology. In addition, this intervention may substantially impact public health by reducing alcohol use disorder risk at a crucial developmental stage. Trial RegistrationClinicalTrials.gov NCT03658954; https://clinicaltrials.gov/ct2/show/NCT03658954 International Registered Report Identifier (IRRID)DERR1-10.2196/26557

BackgroundNovel alcohol prevention strategies are needed for heavy-drinking young adults. Sleep problems are common among young adults who drink heavily and are a risk factor for developing an alcohol use disorder (AUD). Young adults, interested in the connection between sleep and alcohol, are open to getting help with their sleep. Therefore, sleep interventions may offer an innovative solution. This study evaluates social media advertising for reaching young adults and recruiting them for a new alcohol prevention program focused on sleep. ObjectiveThis study aims to evaluate the effectiveness and cost of using Facebook, Instagram, and Snapchat advertising to reach young adults who drink heavily for a sleep intervention; characterize responders’ sleep, alcohol use, and related concerns and interests; and identify the most appealing advertising content. MethodsIn study 1, advertisements targeting young adults with sleep concerns, heavy alcohol use, or interest in participating in a sleep program ran over 3 months. Advertisements directed volunteers to a brief web-based survey to determine initial sleep program eligibility and characterize the concerns or interests that attracted them to click the advertisement. In study 2, three advertisements ran simultaneously for 2 days to enable us to compare the effectiveness of specific advertising themes. ResultsIn study 1, advertisements generated 13,638 clicks, 909 surveys, and 27 enrolled volunteers in 3 months across the social media platforms. Fees averaged US $0.27 per click, US $3.99 per completed survey, US $11.43 per volunteer meeting initial screening eligibility, and US $106.59 per study enrollee. On average, those who completed the web-based survey were 21.1 (SD 2.3) years of age, and 69.4% (631/909) were female. Most reported sleep concerns (725/909, 79.8%) and an interest in the connection between sleep and alcohol use (547/909, 60.2%), but few had drinking concerns (49/909, 5.4%). About one-third (317/909, 34.9%) were identified as being at risk for developing an AUD based on a validated alcohol screener. Among this subsample, 8.5% (27/317) met the final criteria and were enrolled in the trial. Some volunteers also referred additional volunteers by word of mouth. In study 2, advertisements targeting sleep yielded a higher response rate than advertisements targeting alcohol use (0.91% vs 0.56% click rate, respectively; P