As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets<br/>identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL)<br/>among business, communications, management/training, law, and clinical analysis. The first<br/>chapter of this manuscript covers the background of clinical laboratory automation and details<br/>the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The<br/>second chapter discusses the usability and efficiency of key information technology systems of<br/>the ABCTL. The third chapter explains the role of quality control and data management within<br/>ABCTL’s use of information technology. The fourth chapter highlights the importance of data<br/>modeling and 10 best practices when responding to future public health emergencies.

In the past year, considerable misinformation about the COVID-19 pandemic has circulated on social media platforms. Faced with this pervasive issue, it is important to identify the extent to which people are able to spot misinformation on social media and ways to improve people’s accuracy in spotting misinformation. Therefore, the current study aims to investigate people’s accuracy in spotting misinformation, the effectiveness of a game-based intervention, and the role of political affiliation in spotting misinformation. In this study, 235 participants played a misinformation game in which they evaluated COVID-19-related tweets and indicated whether or not they thought each of the tweets contained misinformation. Misinformation accuracy was measured using game scores, which were based on the correct identification of misinformation. Findings revealed that participants’ beliefs about how accurate they are at spotting misinformation about COVID-19 did not predict their actual accuracy. Participants’ accuracy improved after playing the game, but democrats were more likely to improve than republicans.

In 2020, all states and territories within the United States have at least 20% obesity rates among adults, with the state of Arizona specifically being between 30-35% of adults (CDC, 2021). Being overweight and having obesity are linked to increased risk of heart disease, stroke, type 2 diabetes, high blood pressure, certain cancers, as well as other chronic conditions (NIH, 2018). The high percentage is partly due to the work environment in society, which has become increasingly sedentary with the rise of labor-saving technologies, like computers for example. As a result, sedentary jobs have increased 83% since 1950 (American Heart Association, 2018). Our proposed solution to this problem of people not getting enough exercise is Bet Fitness. Bet Fitness is a mobile app that utilizes social and financial incentives to motivate users to consistently exercise. The quintessence of Bet Fitness is to bet money on your health. You first create a group with your friends or people you want to compete with. You then put in a specified amount of money into the betting pool. Users then have to exercise for a specified amount of days for a certain period of time (let’s say for instance, three times a week for a month). Workouts can be verified only by the other members of the group, where you can either send photos in a group chat, link your fitbit/other health data, or simply have another person vouch that you worked out as proof. Anyone who fails to keep up with the bet, loses their money that they put in and it gets equally distributed to the other members of the party. According to our initial survey, this idea has generated much interest among college students.

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.