Keying Into Cognition: Temporal Smoothing of Smartphone Typing Behaviors for Passive Assessment of Processing Speed and Executive Function in Individuals With Mood Disorders

Abstract Cognitive deficits commonly affect everyday life for individuals with mood disorders, even between mood episodes. Monitoring of these symptoms can pose several challenges due to the limitations of current methods, prompting the need for enhanced modalities to unobtrusively and objectively measure cognitive function and its fluctuations in individuals. This study explored the feasibility of passively assessing processing speed and executive function, traditionally measured by the trail-making test part B (TMT-B), using smartphone keyboard typing behaviors and assessed how diurnal patterns may impact cognitive function. Through a novel method of temporal smoothing of smartphone typing behaviors via graph-regularized singular value decomposition, we engineered features to capture typing regularity as a proxy for diurnal patterns and sleep. These features were added to machine learning models constructed to predict TMT-B performance and evaluated for improvement in model performance. Of the models tested, a random forest model built with the addition of typing regularity features performed the best with the lowest RMSE and MAE of 0.769 and 0.644, respectively. Our findings suggest that aspects of individuals’ cognitive function, specifically processing speed and executive function, can be estimated through their smartphone typing behaviors without the need for clinical or demographic input, and these estimates are improved with additional information capturing diurnal patterns and estimated sleep. This objective approach, passively administered in-the-wild, has the potential to supplement current methods of cognitive assessment and provide a more detailed report of cognitive fluctuations and the influence of diurnal patterns on cognitive function in individuals with mood disorders.