MoodMunch

Project Description

MoodMunch relates to Tech for Good by promoting good health practices and potentially improving mental and physical health, thus contributing to the goal of promoting social justice through a health lens. We analyzed the dataset for the most accurate coefficients for the linear regression model, using Pandas, Numpy and Python. We created the website, using Java, Flask and SQLite. Our project has prioritized the ethical treatment of human research participants, by using a data set that informed consent is obtained and participant privacy is maintained. We also recognize the implications of whether or not the linear regression algorithm is trained on diverse and representative datasets, in order to mitigate any biases that could result from underrepresented groups. (Health Equity track and Data & Analytics overlay).

Purpose

This project primarily focuses on increasing awareness of mindful eating. Our team was motivated by the common goal to create a simple equitable health resource that has affected us and many others. Which is eating - something so simple, but if not taken seriously can have gradual consequences on mental and physical health. The future of MoodMunch involves further development to reach a wider audience and provide more effective guidance on the relationship between emotions and food(ex. eating disorders). By helping people improve their mental and physical health, MoodMunch could contribute to a better world by reducing the negative impact of emotional eating and improving overall well-being.

How it works

To navigate the website, users will first provide 2 input parameters(the time of their last meal and hunger level). The algorithm is developed by multiple linear regression using a dataset from a cited academic paper, with more than 9000 observations. Then it will output an estimate of the extent to which their mood and mental health is affected by their hunger level.