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Introduction: Adoption of rigorous standards for reporting treatment fidelity is essential for advancing discovery, validation, and implementation of behavioral treatments. Whereas the NIH Behavior Change Consortium (BCC) developed an assessment tool to assess the quality of reporting and monitoring of treatment fidelity across health behavior change interventions, it has not yet been applied specifically to treatment fidelity in behavioral tobacco treatment trials.Aims and Methods: We conducted a scoping review of peer-reviewed, clinical trials of behavioral adult tobacco treatment interventions published in English between 2006 and 2018. Using the BCC treatment fidelity checklist, articles were coded for the presence or absence of various treatment fidelity strategies within each of 5 domains: Design, Training, Delivery, Receipt, and Enactment. Eligible articles (N = 755) were coded by two independent coders.Results: The proportion of reporting strategies varied within the fidelity domains, ranging from 5.2% to 96.3% in Design, 1.9% to 24.9% in Training, 2.6% to 32.3% in Delivery, 5.2% to 44.3% in Receipt, and 6.7% to 43.2% in Enactment. The mean proportion of adherence to treatment fidelity strategies within each domain was: Design (68%), Training (14%), Delivery (15%), Receipt (16%), and Enactment (25%). Only 11 studies achieved ≥80% reporting across >1 fidelity domain. There was no evidence for improvement in fidelity reporting across the 13-year time frame from the initial BCC publication to the present.Conclusions: These findings illustrate the lack of consistency in fidelity reporting in tobacco treatment trials and underscore the challenges faced in evaluating rigor and reproducibility, as well as interpretation and dissemination of findings. Recommendations are made for improving fidelity reporting in tobacco treatment trials.Implications: The SRNT Treatment Research Network sponsored a scoping review to summarize the current state of reporting treatment fidelity and make recommendations for best practices in reporting fidelity in tobacco treatment trials. The review identified a lack of consistency in fidelity reporting, illustrating the challenges faced in evaluating rigor, and reproducibility, as well as interpretation and dissemination of findings. [ABSTRACT FROM AUTHOR]

Significance: Increased rates of smoking cessation will be essential to maximize the population benefit of low-dose CT screening for lung cancer. The NCI's Smoking Cessation at Lung Examination (SCALE) Collaboration includes eight randomized trials, each assessing evidence-based interventions among smokers undergoing lung cancer screening (LCS). We examined predictors of trial enrollment to improve future outreach efforts for cessation interventions offered to older smokers in this and other clinical settings.Methods: We included the six SCALE trials that randomized individual participants. We assessed demographics, intervention modalities, LCS site and trial administration characteristics, and reasons for declining.Results: Of 6285 trial- and LCS-eligible individuals, 3897 (62%) declined and 2388 (38%) enrolled. In multivariable logistic regression analyses, Blacks had higher enrollment rates (OR 1.5, 95% CI 1.2,1.8) compared to Whites. Compared to "NRT Only" trials, those approached for "NRT + prescription medication" trials had higher odds of enrollment (OR 6.1, 95% CI 4.7,7.9). Regarding enrollment methods, trials using "Phone + In Person" methods had higher odds of enrollment (OR 1.6, 95% CI 1.2,1.9) compared to trials using "Phone Only" methods. Some of the reasons for declining enrollment included "too busy" (36.6%), "not ready to quit" (8.2%), "not interested in research" (7.7%), and "not interested in the intervention offered" (6.2%).Conclusion: Enrolling smokers in cessation interventions in the LCS setting is a major priority that requires multiple enrollment and intervention modalities. Barriers to enrollment provide insights that can be addressed and applied to future cessation interventions to improve implementation in LCS and other clinical settings with older smokers.Implications: We explored enrollment rates and reasons for declining across six smoking cessation trials in the lung cancer screening setting. Offering multiple accrual methods and pharmacotherapy options predicted increased enrollment across trials. Enrollment rates were also greater among Blacks compared to Whites. The findings offer practical information for the implementation of cessation trials and interventions in the lung cancer screening context and other clinical settings, regarding intervention modalities that may be most appealing to older, long-term smokers. [ABSTRACT FROM AUTHOR]

Introduction: In smoking cessation clinical trials, timeline followback (TLFB) interviews are widely used to track daily cigarette consumption. However, there are no standard tools for calculating abstinence based on TLFB data. Individual research groups have to develop their own calculation tools, which is not only time- and resource-consuming but might also lead to variability in the data processing and calculation procedures.Aims and Methods: To address these issues, we developed a novel open-source Python package named abstcal to calculate abstinence using TLFB data. This package provides data verification, duplicate and outlier detection, missing-data imputation, integration of biochemical verification data, and calculation of a variety of definitions of abstinence, including continuous, point-prevalence, and prolonged abstinence.Results: We verified the accuracy of the calculator using data derived from a clinical smoking cessation study. To improve the package's accessibility, we have made it available as a free web app.Conclusions: The abstcal package is a reliable abstinence calculator with open-source access, providing a shared validated online tool to the addiction research field. We expect that this open-source abstinence calculation tool will improve the rigor and reproducibility of smoking and addiction research by standardizing TLFB-based abstinence calculation.Implications: Abstinence calculation is an essential task in any smoking intervention study. However, there have not been standard open-source tools available to the researchers. This commentary describes a Python-based package called abstcal that can calculate abstinence from TLFB data, a common methodology to collect smoking consumption data in research settings. The package supports the calculation of point-prevalence, prolonged, and continuous abstinence. Importantly, the package has a web app interface that allows researchers to use the tool without any coding experience. This tool will facilitate smoking research by providing a standardized and easy-to-use abstinence calculation tool. [ABSTRACT FROM AUTHOR]