FactumFit

FactumFit pairs data from fitness apps with users wellness logs to find valuable insights into how users habits effect their mental and physical health, helping them optimize their life, their way.

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Inspiration

As an avid fitness and tech enthusiast, it feels like we have more data about our habits and lifestyle than ever. And yet it has little value to us. Data flows from the tributaries of our apps and wearables into results pages and data warehouses, and we never really learn from it.

FactumFit changes that. By pairing integrated data streams with users logs about their current energy, mental clarity, mood, strength, and anxiety we are able to create valuable insights into exactly how, what a user does, effects how they feel. We empower users to optimize their lives by finding valuable correlations previously hidden.

FactumFit brings value from data, empowering users to optimize their life, thier way.

What it does

Users integrate with fitness apps, such as MyFitnessPal, Apple Health, and Garmin, to create a holistic data stream about thier habits and lifestyle (Food, exercise, etc). These are the users 'inputs'

Users then submit logs a couple times a day where they rate how they feel along several important categories, Energy, Mental Clarity, Strength, Mood, And Anxiety. These are the users 'outputs'

FactumFit finds what Inputs correlate strongest with what outputs. Along with tracking the users overall feelings overtime, we are able to tell them the top 3 inputs that have the strongest positive, and negative correlation with each of the 5 categories, basically a Do's and Don'ts for users looking to optimize.

FactumFit can also use these insights to build users custom plans to best incorporate these dsicoveries into thier day-to-day. With our Apple calendar integration, FactumFit makes it easy for users to create a plan, and follow it.

How we built it

We used react native for the front end, flask for the backend and firebase for the database. We used linear regression to find large correlations between our integrated data streams and users self-reported rating. We used ChatGPT to help write and plan fitness plans for the user.

Challenges we ran into

We ran into trouble implementing the linear regression, as atleast to start our data was fairly noisy and uncorrelated. once we found better data our causal engine was better able to discover insights.

Accomplishments that we're proud of

Apple Calendar Integration Power causal engine Building an App that all of us want to use in our day to day and that we truly believe can help people

What we learned

We learned alot about mobile developement as well as how to handle large amounts of data in python and gain insights

What's next for Factum Fit

More integrations, along with improving the Causal engine by adding other means of finding correlations. Possibly replace our linear regression with a more complex model.

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