Predicting clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics

Abstract

Modeling smartphone keyboard dynamics as the foundation of an early warning system (EWS) for mood instability holds potential to expand the reach of healthcare beyond the traditional clinic wall's, which may lead to better ongoing care for chronic mental illnesses such as bipolar disorder. Here, we investigate the feasibility of such a system using a real-world open-science dataset. In particular, we are interested in whether passive technology interaction patterns in real-world datasets reflect findings from more controlled research trials, and the implications for clinical care. Data from 328 people who downloaded an open-science app was analyzed using a variety of machine learning methods, including different modeling methods (random forests, gradient boosting, neural networks), different types of class rebalancing, and pre-processing techniques. The aim was to predict fluctuations in PHQ scores in the weeks before the fluctuation occurred. Various feature selection methods were also employed to identify the top features driving the predictive patterns (out of total 54 starting features). Results showed predictive accuracy around ∼90%, similar to controlled research trials, while revealing a number of interesting features (e.g. PTSD and mood instability) that suggest future research avenues. The findings from our analysis appear to indicate that real-world interaction data from smartphones can be utilized as an EWS monitoring tool for mood disorders like bipolar. We also discuss the broader applicability of ecological momentary assessment (EMA) approaches to connected systems combining different forms of pervasive technology interaction (smartphones, wearables, social robots) to track everyday health status.