Wearable data has never been as affordable and easy to access as it is right now. With the increasing popularity of consumer wearables, like Fitbit and Apple Watch, as well as the growing range of more specialized sensors, such as skin patches and glucose-tracking contacts, collection of longitudinal biological data is now more doable than ever. At the same time, mechanistic, physics-based models are finding new synergy with machine learning techniques, leveraging known physiology to compensate for sparse data. Yet the limited amount of most health-related data remains an enduring problem for any kind of model building. In this review, I outline how these two frontiers of research can come together, with wearables providing massive amounts of data collected from a single individual, and combined mechanistic modeling and machine learning approaches translating this data into better models and incentives for users to share their data.
