Due to the profound health effects of tobacco smoking,¹ it is important to identify treatments that increase rates of long-term smoking abstinence. Research on pharmacotherapies for cessation is especially important because pharmacotherapies can be disseminated broadly via health care systems.

This article extends a methodological approach considered by Bolt and Johnson for the measurement and control of extreme response style (ERS) to the analysis of rating data from multiple scales. Specifically, it is shown how the simultaneous analysis of item responses across scales allows for more accurate identification of ERS, and more effective control of ERS effects on the substantive trait estimates, than when analyzing just one scale. Moreover, unlike a competing approach presented by Greenleaf, the current strategy can accommodate conditions in which the substantive traits across scales correlate, as is almost always the case in social sciences research. Simulation and real data analyses are used for illustration.

Background: A growing number of randomized controlled trials (RCTs) suggest psychological benefits associated with meditation training delivered via mobile health. However, research in this area has primarily focused on mindfulness, only one of many meditative techniques. Objective: This study aims to evaluate the efficacy of 2 versions of a self-guided, smartphone-based meditation app—the Healthy Minds Program (HMP)—which includes training in mindfulness (Awareness), along with practices designed to cultivate positive relationships (Connection) or insight into the nature of the self (Insight). Methods: A three-arm, fully remote RCT compared 8 weeks of one of 2 HMP conditions (Awareness+Connection and Awareness+Insight) with a waitlist control. Adults (≥18 years) without extensive previous meditation experience were eligible. The primary outcome was psychological distress (depression, anxiety, and stress). Secondary outcomes were social connection, empathy, compassion, self-reflection, insight, rumination, defusion, and mindfulness. Measures were completed at pretest, midtreatment, and posttest between October 2019 and April 2020. Longitudinal data were analyzed using intention-to-treat principles with maximum likelihood. Results: A total of 343 participants were randomized and 186 (54.2%) completed at least one posttest assessment. The majority (166/228, 72.8%) of those assigned to HMP conditions downloaded the app. The 2 HMP conditions did not differ from one another in terms of changes in any outcome. Relative to the waitlist control, the HMP conditions showed larger improvements in distress, social connectedness, mindfulness, and measures theoretically linked to insight training (d=–0.28 to 0.41; Ps≤.02), despite modest exposure to connection- and insight-related practice. The results were robust to some assumptions about nonrandom patterns of missing data. Improvements in distress were associated with days of use. Candidate mediators (social connection, insight, rumination, defusion, and mindfulness) and moderators (baseline rumination, defusion, and empathy) of changes in distress were identified. Conclusions: This study provides initial evidence of efficacy for the HMP app in reducing distress and improving outcomes related to well-being, including social connectedness. Future studies should attempt to increase study retention and user engagement. Trial Registration: ClinicalTrials.gov NCT04139005; https://clinicaltrials.gov/ct2/show/NCT04139005

This research examined why smokers receiving combination medication for smoking cessation are more likely to quit smoking than are those who receive either single agent (monotherapy) or placebo. Data were collected from 1,504 current smokers (58.2% women, 83.9% White; mean age = 44.67 years, SD = 11.08) participating in a cessation clinical trial who were randomized to 1 of 6 cessation pharmacotherapy conditions (placebo, nicotine patch, nicotine lozenge, bupropion, nicotine patch + nicotine lozenge, and bupropion + nicotine lozenge). Participants completed ecological momentary assessments 4 times a day, concerning 5 hypothesized mediators (negative affect, positive affect, craving, smoking expectations, and withdrawal) of pharmacotherapy effects. Medications were provided for 8-12 weeks post-quit along with 6 individual counseling sessions. Mediational paths were estimated via a novel Bayesian approach with estimation of multiple mediator models. Biochemically confirmed 8-week abstinence was the outcome variable, with the monotherapy and combination pharmacotherapy composites producing 45% (n = 689) and 54% (n = 478) abstinence rates, respectively. The univariate models suggested that the combination treatments produced higher abstinence rates than the monotherapies because of greater suppression of withdrawal, craving, and smoking expectations. However, multiple mediator models showed that the suppression of craving on the quit day produced the strongest mediational effects and could account for the mediational effects of other tested variables. Suppression of craving on the quit day significantly mediates the clinical effects of monotherapies and combination smoking pharmacotherapies, and the higher abstinence rates for combination therapy versus monotherapies appear primarily due to greater craving suppression.

Background: Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out. Objective: This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR. Methods: We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach). Results: Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects. Conclusions: MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results.

This paper presents a mixture item response tree (IRTree) model for extreme response style. Unlike traditional applications of single IRTree models, a mixture approach provides a way of representing the mixture of respondents following different underlying response processes (between individuals), as well as the uncertainty present at the individual level (within an individual). Simulation analyses reveal the potential of the mixture approach in identifying subgroups of respondents exhibiting response behavior reflective of different underlying response processes. Application to real data from the Students Like Learning Mathematics (SLM) scale of Trends in International Mathematics and Science Study (TIMSS) 2015 demonstrates the superior comparative fit of the mixture representation, as well as the consequences of applying the mixture on the estimation of content and response style traits. We argue that methodology applied to investigate response styles should attend to the inherent uncertainty of response style influence due to the likely influence of both response styles and the content trait on the selection of extreme response categories.