We want to radically transform our users’ relationship with light so they sleep and feel better.
We model what matters to your internal clock.
Your circadian rhythms are about more than just when you wake up and fall asleep. Light, activity, melatonin, your personal physiology, and more: we incorporate all of these in our algorithms.
Wearable data makes our predictions more accurate and dynamic.
Your body is constantly taking cues from your environment and activity in order to shift and update your internal time. By connecting to your wearable device, we can keep up with your clock as it changes from day to day.
No wearable? No problem: Our apps work with just the data collected from the phone.
Our code learns from your data.
No one-size-fits-all model. Your app is personalized to match you. We pull the important information to describe you straight from your data.
Open science matters to us.
Check out the team’s Github pages below
Still want more? Check out some of the papers our work is based on:
Hannay, Kevin M., Victoria Booth, and Daniel B. Forger. “Macroscopic models for human circadian rhythms.” Journal of biological rhythms 34.6 (2019): 658-671.
Hannay, Kevin M., Daniel B. Forger, and Victoria Booth. “Macroscopic models for networks of coupled biological oscillators.” Science advances 4.8 (2018): e1701047.
Christensen, Samuel, et al. “Optimal adjustment of the human circadian clock in the real world.” PLOS Computational Biology 16.12 (2020): e1008445.
Cheng, Philip, et al. “Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers.” Sleep (2020).
Walch, Olivia, Amy Cochran, and Daniel B. Forger. “A global quantification of “normal” sleep schedules using smartphone data.” Science advances 2.5 (2016): e1501705.
Forger, Daniel B., Megan E. Jewett, and Richard E. Kronauer. “A simpler model of the human circadian pacemaker.” Journal of biological rhythms 14.6 (1999): 533-538.