Intra-day Activity Better Predicts Chronic Conditions
This work addresses the problem of predicting chronic conditions for users of mobile health wearables, but it is incremental as it builds on existing methods with new data.
The study tackled predicting chronic conditions by analyzing intra-day activity patterns from wearable devices, showing that using intra-day step and sleep data significantly improves classification performance for mental health and nervous system disorders, with Convolutional Neural Networks achieving top performance and multi-task learning enhancing generalization.
In this work we investigate intra-day patterns of activity on a population of 7,261 users of mobile health wearable devices and apps. We show that: (1) using intra-day step and sleep data recorded from passive trackers significantly improves classification performance on self-reported chronic conditions related to mental health and nervous system disorders, (2) Convolutional Neural Networks achieve top classification performance vs. baseline models when trained directly on multivariate time series of activity data, and (3) jointly predicting all condition classes via multi-task learning can be leveraged to extract features that generalize across data sets and achieve the highest classification performance.