As inhabitants of earth which has a day/night cycle of 24 hours, all living things have evolved an internal clock system that keeps them in sync with the outside environment. This system is highly sensitive to sunlight which provides the strongest resetting stimulus along with strongest entraining stimulus.
Accumulated over the years, since electrification of homes, broad access to transcontinental flights, and increase in shift work is a steady disruption of this clock system due to adoption of inconsistent cycles with inadequate exposure to light or frequent shifting between cycles both of which commonly manifests as a general malaise of symptoms termed "JET LAG". There is mounting health risks of extended jet lags in heart disease, mental health, and cancers demonstrating an avenue of broad interest with positive health impacts.
What it does
While the molecular basis of these rhythms have been extensively studied and understood for years, more recently a comprehensive mathematical model of the human circadian system was generated providing unprecedented access to the inner workings and chances for intervention. We created a mobile app that leverages activity data of an individual (as a read out of the clock system) and the mathematical model to counteract negative effects of jet lag.
How we built it
We decided to start off with a stock android app in JAVA,that would provide the broadest install base and most open frameworks. We ended up using the web API for FitBit, that provided the most comprehensive data series, to generate an activity profile for the previous week. When the user would indicate an upcoming trip across time zones, the app would combine the activity information along with the time zone information that would be automatically populated and present times in the new time zone when they should seek bright light to reduce jetlag. We also used React Native to develop a cross platform app that would get both FitBit and device activity data along with additional API's to get more detailed light information.
Challenges we ran into
Unfortunately, the React Native approach didn't pan out as we hoped. Mainly, we learnt that Apple's and Google's health data is not as granular as FitBit's, limiting analysis. Several APIs (sunset & sunrise times, & light sensor) both had unusual behavior in React Native when called which slowed down development.
Accomplishments that we're proud of
We validated our hypothesis by taking a research model and turning it into an algorithm that could be implemented in the real world. The team's perseverance and dogged pursuit was also most excellent as was the pursuit to deliver Lagless to the world to make an impact. Our accomplishments in tackling a new framework like React Native was also commendable.
What we learned
We learned a lot about being hyperfocused, paring down ideas to the essential to meet the goals. We also got up close and personal with React Native. We also hope we planted seeds in peoples minds about the importance of circadian health.
What's next for Lagless
The hackathon was great in helping us lay down the foundation of moving an idea from a thought to physical form. We will continue to iterate and build the product to it's best form and also grow the offering with the multitude of ideas that arise with the algorithm.