In order to curb increasing youth electronic cigarette (e-cig) use, the United States Food and Drug Administration (FDA) banned the sale of flavored cartridge/pod-based products in February 2020. This mixed-methods study aimed to evaluate the impact of the FDA ban on adult JUUL users. The samples of current adult JUUL users were surveyed via Amazon Mechanical Turk at three time-points n = 76 (Sample 1); n = 128 (Sample 2); n = 86 (Sample 3) before and after the FDA flavored/pod ban. The participants were asked to report the JUUL flavored pod used most often and answer questions on purchasing generic pods or refilling (Quantitative). JUUL users were then interviewed in order to explore their perceptions and behaviors related to the FDA ban of flavored cartridge/pod-based products (Qualitative; n = 16). Quantitative data analysis evaluated the differences in variables by time-point. Qualitative data were coded into themes while using the constant comparative method. We found a significant decrease in the use of mint pods (43.4%, 22.7%, 16.3%) (p < 0.01), while there was a significant increase in the use of menthol pods (6.6%, 26.6%, 37.2%) (p < 0.01). Themes that emerged from the qualitative data included switching from mint to menthol pods, refilling pods, and switching to other products that are available in the desired flavors, such as generic pods or disposable e-cigs. Future research is needed in order to evaluate the impact of these behaviors on public health.

Background: Public health concerns over the addictive potential of electronic cigarettes (e-cigs) have heightened in recent years. Brain function during e-cig use could provide an objective measure of the addictive potential of new vaping products to facilitate research; however, there are limited methods for delivering e-cig aerosols during functional magnetic resonance imaging (fMRI). The current study describes the development and feasibility testing of a prototype to deliver up to four different e-cig aerosols during fMRI. Methods: Standardized methods were used to test the devices’ air flow variability, nicotine yield, and free radical production. MRI scans were run with and without the device present to assess its safety and effects on MRI data quality. Five daily smokers were recruited to assess plasma nicotine absorption from e-liquids containing nicotine concentrations of 8, 11, 16, 24, and 36 mg/ml. Feedback was collected from participants through a semi-structured interview and computerized questionnaire to assess comfort and subjective experiences of inhaling aerosol from the device. Results: Nicotine yield captured from the aerosol produced by the device was highly correlated with the nicotine concentration of the e-liquids used (R2 = 0.965). Nicotine yield was reduced by a mean of 48% and free radical production by 17% after traveling through the device. The e-liquid containing the highest nicotine concentration tested (36 mg/ml) resulted in the highest plasma nicotine boost (6.6 ng/ml). Overall, participants reported that the device was comfortable to use and inhaling the e-cig aerosols was tolerable. The device was determined to be safe for use during fMRI and had insignificant effects on scan quality. Conclusions: With the current project, we were able to design a working prototype that safely and effectively delivers e-cig aerosols during fMRI. The device has the potential to be used to assess brain activation during e-cig use and to compare brain reactivity to varying flavors, nicotine concentrations, and other e-cig characteristics.

Context: The National Academy of Medicine endorses screening for social determinants of health (SDoH); however, questions remain on how providers and/or hospitals should address SDoH once collected. Objective: To gather patient perspectives on provider obligations when collecting SDoH data in cancer treatment settings. Study design: For this mixed-methods study, participants reviewed the Protocol for Responding to and Assessing Patients' Assets, Risk, and Experiences (PRAPARE) and completed individual qualitative interviews that were recorded, transcribed verbatim, and coded by 3 coders (Cohen's Kappa > 0.85). Population studied: Cancer patients (n=21) in the Penn State Cancer Institute catchment area. Outcome measures: Primary outcome included clinical obligations after collecting SDoH data. Results: Participants were 50% F, 86% white, with a mean age of 52.5. 33% were in current cancer treatment. Collectively, gathering SDoH was considered beneficial to cancer treatment outcomes but clinicians should acknowledge individual circumstances. They stated, "...I just think if they're going to ask the questions, they need to address concerns. Yeah. That's my biggest thing. If you're going to ask the question, then you need to acknowledge it with the patient… And don't just do it to put it in the chart and never talk about it." Resources advocated for included access to food, housing, and transportation. "…Find out what resources they would be eligible for, what can help them get back and forth to appointments, co-pay issues….because they're out there... we need to pull them and implement them." This emphasizes the need for clinicians to act on information shared by patients. Participants also noted that stress and emotional well-being are critical considerations while undergoing cancer treatment. They noted, "I would feel more comfortable if they would acknowledge my emotional status." Further, "It's been proven that stress can bring on cancer and it can make the cancers worse… If you're stressed because you don't have food or you don't have transportation or you can't pay your bills, it's hurting your treatment." Conclusions: The presence of SDoH, such as elevated levels of stress and compromised emotional well-being, reinforce the value of access to supportive care services during cancer treatment. Well-designed SDoH surveys should include questions that are both actionable and tied to resources that address a patient's most pressing social needs.