Instafit provides users with real-time evaluation of health risks based on their health patterns and rewards them for exercising.
Inspiration
Instafit was developed in response to the high volume and low information approach of the current iterations of iOS Health and Google Fit. While they provide the user with a deluge of health information to consider, they leave it to the user to interpret this technical data and establish healthy habits without knowing which stats matter and which ones don't. Instafit fills the gaps by providing users with predictive modeling of potential health risks based on this wealth of data, and it also leverages cryptocurrency as an incentive for exercising.
What it does
Instafit provides users with analytics that allow them to see their risk profiles without having to interpret the data themselves. It also provides suggestions to improve their health and cryptocurrency tokens that incentivize exercise and will allow for a gamified model of fitness.
How we built it
We leveraged Supervised Machine Learning techniques to correlate LOINC values from the InterSystems FHIR server representing observational health data with conditions corresponding to heart health. Through the use of Python, Jupyter Notebooks, Dart, and Flutter, we created a dynamic multi-platform app through which the user can understand their health and interact with their network.
Challenges we ran into
As the majority of us are beginners, we struggled on implementing an app for mobile devices using Flutter. Additionally, we struggled in getting risk assessment values based on individual health data. After many challenges, we learned about the FHIR API, which we utilized to predictively model risk assessment.
Accomplishments that we're proud of
We are proud of getting a mobile app implemented that represents the fusion of machine learning and cryptocurrency and helps users parse through health data without the noise. We are proud that our app is able to ease an individual’s stress by showing how their good habits are truly marks of health while also alerting users who may not understand the quantified impact of bad habits on their long-term prognosis. We were happy to find a useful tool in the InterSystems FHIR server for predictively modeling risk assessment with a robust electronic health data format, as we initially struggled to get risk assessment values based on an individual’s health data. The FHIR system provides many opportunities for scalability of our project, ranging from increasing our assessed parameters to personalizing health data for populations such as veterans.
What we learned
As the majority of us are beginners, we were new to working with the Flutter SDK for app development. However, we were ultimately able to leverage the Flutter technology to create a clean, effective app. We were also very new to the idea of making an app that utilizes cryptocurrency. However, in the end, we made a successfully functional app that utilizes cryptocurrency as an incentive to exercise. Therefore, we learned how to leverage the benefits of cryptocurrency and combine them with building an app to create a better product.
What's next for InstaFit
In the future, we would like to scale with regards to incentives for health, such as by fostering healthy competition between users and their friends. Additionally, the Instafit platform could allow telehealth services to be recommended or even conducted through the app, and we could utilize the user's risk profile to provide personalized recommendations for nutrition and exercise. Most interestingly, the Instafit platform in conjunction with rapidly advancing wearable tech could provide relatively low-cost Early Warning Systems for assessing the stability of hospitalized patients and other individuals at immediate risk.