Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study

Zulueta, John; Piscitello, Andrea; Rasic, Mladen; Easter, Rebecca; Babu, Pallavi; Langenecker, Scott A.; McInnis, Melvin; Ajilore, Olusola; Nelson, Peter C.; Ryan, Kelly; Leow, Alex
Journal of Medical Internet Research

Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania.