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Objective: Morbidity and mortality from smoking-related diseases among people living with HIV (PLWH) in the U.S. surpasses that due to HIV itself. Conventional smoking cessation treatments have not demonstrated strong efficacy among PLWH. We conducted a pilot randomized controlled trial (RCT) to evaluate a tailored smoking cessation intervention based on the minority stress model. We compared standard of care counseling (SOC) to a tailored intervention (TI) including one face-to-face counseling session incorporating cognitive behavioral therapy to build resilience, and 30 days of 2-way text messaging. Results: The primary outcome was smoking cessation. Secondary outcomes included cigarettes per day (CPD), exhaled carbon monoxide (CO), and cessation self-efficacy. A total of 25 participants were enrolled (TI:11, SOC:14), and 2 were lost to follow-up. There were no significant differences in quit rates between study groups. However, there was a significantly greater decrease in CPD in the TI versus SOC (13.5 vs. 0.0, p-value:0.036). Additionally, self-efficacy increased in both groups (TI p-value:0.012, SOC p-value:0.049) and CO decreased in both groups (TI p-value: < 0.001, SOC p-value:0.049). This intervention shows promise to support smoking cessation among PLWH. A larger study is needed to fully evaluate the efficacy of this approach. Clinical trial: Trial Registration: Retrospectively registered (10/20/2020) NCT04594109. [ABSTRACT FROM AUTHOR]

Background: With a rapidly evolving tobacco retail environment, it is increasingly necessary to understand the point-of-sale (POS) advertising environment as part of tobacco surveillance and control. Advances in machine learning and image processing suggest the ability for more efficient and nuanced data capture than previously available.Objective: The study aims to use machine learning algorithms to discover the presence of tobacco advertising in photographs of tobacco POS advertising and their location in the photograph.Methods: We first collected images of the interiors of tobacco retailers in West Virginia and the District of Columbia during 2016 and 2018. The clearest photographs were selected and used to create a training and test data set. We then used a pretrained image classification network model, Inception V3, to discover the presence of tobacco logos and a unified object detection system, You Only Look Once V3, to identify logo locations.Results: Our model was successful in identifying the presence of advertising within images, with a classification accuracy of over 75% for 8 of the 42 brands. Discovering the location of logos within a given photograph was more challenging because of the relatively small training data set, resulting in a mean average precision score of 0.72 and an intersection over union score of 0.62.Conclusions: Our research provides preliminary evidence for a novel methodological approach that tobacco researchers and other public health practitioners can apply in the collection and processing of data for tobacco or other POS surveillance efforts. The resulting surveillance information can inform policy adoption, implementation, and enforcement. Limitations notwithstanding, our analysis shows the promise of using machine learning as part of a suite of tools to understand the tobacco retail environment, make policy recommendations, and design public health interventions at the municipal or other jurisdictional scale. [ABSTRACT FROM AUTHOR]