The purpose of this study is to create and establish an efficient and cost-effective solution to decrease the effects of sedentarism in pregnant women. Our team was given a propelling question, from which we had to narrow down our scope and conduct primary and secondary research to determine our ideal customers. The design of our study intends to imitate the development of a startup where ideas are created from scratch and the final deliverable is a business model plan that shows some sort of traction. Our first major finding is that a sedentary lifestyle can be treated without major challenges in low-risk pregnancies. We determined that uncertainty and lack of concise and clear information is one of the main causes of an increased level of sedentary behavior in low-risk pregnancies. A significant driver for women to do some sort of activity or exercise stems from feeling supported, which doesn’t necessarily come from their partner or couple, but instead from other women that are going to a similar process as them. There are apps in the market that intend to serve pregnant women; however, there is not one that incorporates a social aspect to achieve their goal. In conclusion, there is opportunity in the market for a socially integrated pregnancy fitness app. The Gleam concept has been consciously developed to decrease sedentary behavior through concise, clear, and reliable information and by encouraging women through a socialization platform.

This thesis was conducted to study and analyze the fund allocation process adopted by different states in the United States to reduce the impact of the Covid-19 virus. Seven different states and their funding methodologies were compared against the case count within the state. The study also focused on development of a physical distancing index based on three significant attributes. This index was then compared to the expenditure and case counts to support decision making.
A regression model was developed to analyze and compare how different states case counts played out against the regression model and the risk index.