Due to the COVID-19 pandemic, declared in March of 2020, there have been many lifestyle changes which have likely influenced tobacco smoking behavior. Such lifestyle changes include lockdowns, stay at home orders, reduction in social cues related to smoking, increased stress, and boredom among other things. This study utilized a cross-sectional survey which looked into these behaviors, primarily perceived risk to COVID-19, and determined if there is an association between perceived risk and education level/race. Education level is a proxy for income and material resources, therefore making it more likely that people with lower levels of education have fewer resources and higher perceived risk to negative effects of COVID-19. Additionally, people of color are often marginalized in the medical community along with being the target of heavy advertising by tobacco companies which have likely impacted risk to COVID-19 as well.

This research analyzes lesbian, gay, bisexual, transgender, and queer/ questioning (LGBTQ) students’ experiences with sex education in Arizona. This research is a grey literature review of Arizona’s previous state policies, current state sex education curricula law, and legislative proposals within the past few years. Analysis focuses on changes after the repeal of the “no promo homo” law in 2019. Through defining the differences between abstinence only and comprehensive sex education (CSE), this will provide a framework to better understand approaches to sex education. As of now, Arizona stresses abstinence-based education. Delving into LGBTQ students’ general experiences in schools provides a foundation to better understand why these students especially benefit from CSE. Since LGBTQ students are disproportionately affected by bullying and are at increased sexual health risks, it is important to address misperceptions surrounding the LGBTQ community. The purpose of this research is to push for more LGBTQ inclusive sex education curricula in Arizona.

This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as the workout becomes harder for the user, increasingly motivating music is played.