A Test of Multisession Automatic Action Tendency Retraining to Reduce Alcohol Consumption Among Young Adults in the Context of a Human Laboratory Paradigm.

BACKGROUND: Young adult heavy drinking is an important public health concern. Current interventions have efficacy but with only modest effects, and thus, novel interventions are needed. In prior studies, heavy drinkers, including young adults, have demonstrated stronger automatically triggered approach tendencies to alcohol-related stimuli than lighter drinkers. Automatic action tendency retraining has been developed to correct this tendency and consequently reduce alcohol consumption. This study is the first to test multiple iterations of automatic action tendency retraining, followed by laboratory alcohol self-administration. METHODS: A total of 72 nontreatment-seeking, heavy drinking young adults ages 21 to 25 were randomized to automatic action tendency retraining or a control condition (i.e., sham training ). Of these, 69 (54% male) completed 4 iterations of retraining or the control condition over 5 days with an alcohol drinking session on Day 5. Self-administration was conducted according to a human laboratory paradigm designed to model individual differences in impaired control (i.e., difficulty adhering to limits on alcohol consumption). RESULTS: Automatic action tendency retraining was not associated with greater reduction in alcohol approach tendency or less alcohol self-administration than the control condition. The laboratory paradigm was probably sufficiently sensitive to detect an effect of an experimental manipulation given the range of self-administration behavior observed, both in terms of number of alcoholic and nonalcoholic drinks and measures of drinking topography. CONCLUSIONS: Automatic action tendency retraining was ineffective among heavy drinking young adults without motivation to change their drinking. Details of the retraining procedure may have contributed to the lack of a significant effect. Despite null primary findings, the impaired control laboratory paradigm is a valid laboratory-based measure of young adult alcohol consumption that provides the opportunity to observe drinking topography and self-administration of nonalcoholic beverages (i.e., protective behavioral strategies directly related to alcohol use)

Comparing cigarette and e-cigarette dependence and predicting frequency of smoking and e-cigarette use in dual-users of cigarettes and e-cigarettes

Introduction: The 4-item Patient-Reported Outcomes Measurement Information System Nicotine Dependence Item Bank is a psychometrically sound measure for assessing cigarette (PROMIS) and e-cigarette dependence (PROMIS-E). We evaluated whether dual-users of cigarettes and e-cigarettes self-report experiencing different levels of dependence on each product. We subsequently examined whether cigarette and e-cigarette dependence are associated with the frequency of using each product in dual-users. Methods: Dual-users completed an online survey in Summer 2017 (n = 326; 49.7% male, 85.3% White, mean age 38.17 [13.08] years). Measurement invariance of the PROMIS and PROMIS-E was evaluated. Mean differences in cigarette and e-cigarette dependence then were examined. The correlation between cigarette and e-cigarette dependence also was examined. Finally, one-way MANOVA was used to evaluate how cigarette and e-cigarette dependence relate to past-month frequency of e-cigarette use and cigarette smoking. Results: The PROMIS and the PROMIS-E were scalar measurement invariant, and, on average, dual-users reported stronger dependence on cigarettes than on e-cigarettes. Cigarette and e-cigarette dependence were related, yet distinct constructs (r = 0.35), suggesting that dual-users can discriminate between dependence on each product. Stronger cigarette dependence predicted more frequent past-month smoking and less frequent past-month vaping. Stronger e-cigarette dependence predicted more frequent past-month vaping and less frequent smoking. Conclusions: Overall, dual-users reported stronger dependence on cigarettes than on e-cigarettes. However, dependence on each product was associated with increased use of each respective product and with less frequent use of the other product. Future research using the PROMIS can evaluate how potential FDA regulations could reduce nicotine dependence across products

Assessing nicotine dependence in adolescent E-cigarette users: The 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Nicotine Dependence Item Bank for electronic cigarettes

Background: Adolescent e-cigarette use (i.e., “vaping”) likely confers risk for developing nicotine dependence. However, there have been no studies assessing e-cigarette nicotine dependence in youth. We evaluated the psychometric properties of the 4-item Patient-Reported Outcomes Measurement Information System Nicotine Dependence Item Bank for E-cigarettes (PROMIS-E) for assessing youth e-cigarette nicotine dependence and examined risk factors for experiencing stronger dependence symptoms. Methods: In 2017, 520 adolescent past-month e-cigarette users completed the PROMIS-E during a school-based survey (50.5% female, 84.8% White, 16.22[1.19] years old). Adolescents also reported on sex, grade, race, age at e-cigarette use onset, vaping frequency, nicotine e-liquid use, and past-month cigarette smoking. Analyses included conducting confirmatory factor analysis and examining the internal consistency of the PROMIS-E. Bivariate correlations and independent-samples t-tests were used to examine unadjusted relationships between e-cigarette nicotine dependence and the proposed risk factors. Regression models were run in which all potential risk factors were entered as simultaneous predictors of PROMIS-E scores. Results: The single-factor structure of the PROMIS-E was confirmed and evidenced good internal consistency. Across models, larger PROMIS-E scores were associated with being in a higher grade, initiating e-cigarette use at an earlier age, vaping more frequently, using nicotine e-liquid (and higher nicotine concentrations), and smoking cigarettes. Conclusion: Adolescent e-cigarette users reported experiencing nicotine dependence, which was assessed using the psychometrically sound PROMIS-E. Experiencing stronger nicotine dependence symptoms was associated with characteristics that previously have been shown to confer risk for frequent vaping and tobacco cigarette dependence

A randomized controlled trial of smartphone-based mindfulness training for smoking cessation: a study protocol

BACKGROUND: Tobacco use is responsible for the death of about 1 in 10 individuals worldwide. Mindfulness training has shown preliminary efficacy as a behavioral treatment for smoking cessation. Recent advances in mobile health suggest advantages to smartphone-based smoking cessation treatment including smartphone-based mindfulness training. This study evaluates the efficacy of a smartphone app-based mindfulness training program for improving smoking cessation rates at 6-months follow-up. METHODS/DESIGN: A two-group parallel-randomized clinical trial with allocation concealment will be conducted. Group assignment will be concealed from study researchers through to follow-up. The study will be conducted by smartphone and online. Daily smokers who are interested in quitting smoking and own a smartphone (n = 140) will be recruited through study advertisements posted online. After completion of a baseline survey, participants will be allocated randomly to the control or intervention group. Participants in both groups will receive a 22-day smartphone-based treatment program for smoking. Participants in the intervention group will receive mobile mindfulness training plus experience sampling. Participants in the control group will receive experience sampling-only. The primary outcome measure will be one-week point prevalence abstinence from smoking (at 6-months follow-up) assessed using carbon monoxide breath monitoring, which will be validated through smartphone-based video chat. DISCUSSION: This is the first intervention study to evaluate smartphone-based delivery of mindfulness training for smoking cessation. Such an intervention may provide treatment in-hand, in real-world contexts, to help individuals quit smoking. TRIAL REGISTRATION: Clinicaltrials.gov NCT02134509 . Registered 7 May 2014

Psychometric Evaluation of the Patient-Reported Outcomes Measurement Information System (PROMIS) Nicotine Dependence Item Bank for Use With Electronic Cigarettes

Introduction: Psychometrically sound measures of e-cigarette dependence are lacking. Methods: We modified the PROMIS Nicotine Dependence Item Banks for use with e-cigarettes and evaluated the psychometrics of the 22-, 8-, and 4-item adapted versions. Adults (1009) who reported using e-cigarettes at least weekly completed an anonymous survey in summer 2016 (50.2% male, 77.1% White, mean age 35.81 [10.71], 66.4% daily e-cigarette users, 72.6% current cigarette smokers). Psychometric analyses included confirmatory factor analysis, internal consistency, measurement invariance, examination of mean-level differences, convergent validity, and test-criterion relationships with e-cigarette use outcomes. Results: All PROMIS-E versions had confirmable, internally consistent latent structures that were scalar invariant by sex, race, e-cigarette use (nondaily/daily), e-liquid nicotine content (no/yes), and current cigarette smoking status (no/yes). Daily e-cigarette users, nicotine e-liquid users, and cigarette smokers reported being more dependent on e-cigarettes than their counterparts. All PROMIS-E versions correlated strongly with one another, evidenced convergent validity with the Penn State E-cigarette Dependence Index and time to first e-cigarette use in the morning, and evidenced test-criterion relationships with vaping frequency, e-liquid nicotine concentration, and e-cigarette quit attempts. Similar results were observed when analyses were conducted within subsamples of exclusive e-cigarette users and duals-users of cigarettes and e-cigarettes. Conclusions: Each PROMIS-E version evidenced strong psychometric properties for assessing e-cigarette dependence in adults who either use e-cigarette exclusively or who are dual-users of cigarettes and e-cigarettes. However, results indicated little benefit of the longer versions over the 4-item PROMIS-E, which provides an efficient assessment of e-cigarette dependence. Implications: The availability of the novel, psychometrically sound PROMIS-E can further research on a wide range of questions related to e-cigarette use and dependence. In addition, the overlap between the PROMIS-E and the original PROMIS that was developed for assessing nicotine dependence to cigarettes provides consistency within the field

Psychometric Evaluation of the E-cigarette Dependence Scale

Introduction: Psychometrically sound measures of e-cigarette dependence are lacking. Methods: We modified the PROMIS Item Bank v1.0-Smoking: Nicotine Dependence for All Smokers for use with e-cigarettes and evaluated the psychometrics of the 22-, 8-, and 4-item adapted versions, referred to as The E-cigarette dependence scale (EDS). Adults (1009) who reported using e-cigarettes at least weekly completed an anonymous survey in summer 2016 (50.2% male, 77.1% White, mean age 35.81 [10.71], 66.4% daily e-cigarette users, 72.6% current cigarette smokers). Psychometric analyses included confirmatory factor analysis, internal consistency, measurement invariance, examination of mean-level differences, convergent validity, and test-criterion relationships with e-cigarette use outcomes. Results: All EDS versions had confirmable, internally consistent latent structures that were scalar invariant by sex, race, e-cigarette use (nondaily/daily), e-liquid nicotine content (no/yes), and current cigarette smoking status (no/yes). Daily e-cigarette users, nicotine e-liquid users, and cigarette smokers reported being more dependent on e-cigarettes than their counterparts. All EDS versions correlated strongly with one another, evidenced convergent validity with the Penn State E-cigarette Dependence Index and time to first e-cigarette use in the morning, and evidenced test-criterion relationships with vaping frequency, e-liquid nicotine concentration, and e-cigarette quit attempts. Similar results were observed when analyses were conducted within subsamples of exclusive e-cigarette users and duals-users of cigarettes and e-cigarettes. Conclusions: Each EDS version evidenced strong psychometric properties for assessing e-cigarette dependence in adults who either use e-cigarette exclusively or who are dual-users of cigarettes and e-cigarettes. However, results indicated little benefit of the longer versions over the 4-item EDS, which provides an efficient assessment of e-cigarette dependence. Implications: The availability of the novel, psychometrically sound EDS can further research on a wide range of questions related to e-cigarette use and dependence. In addition, the overlap between the EDS and the original PROMIS that was developed for assessing nicotine dependence to cigarettes provides consistency within the field