Abstract
Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients’ routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects’ status. The best-performing pipeline achieved an AUC = 0.89 (0.72–1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.
Highlights
Mental illnesses are marked as the single largest burden of global disabilities, denoted by years lived with disabilities, whereas depressive disorder (DD) is considered the most common mental illness with an estimated share of 25–30%1
The data collection study was remotely conducted via a custom application, namely TypeOfMood, with a keyboard developed for the Android Operating System (OS) that participants installed on their own smartphone devices
The vast majority (76%) of typing-related data were captured while users typed messages on the Facebook Messenger application, while the rest were captured from typing on the Chrome mobile browser (5%), Instagram social media application (5%), WhatsApp messaging application (3%), and other applications (11%)
Summary
Mental illnesses are marked as the single largest burden of global disabilities, denoted by years lived with disabilities, whereas depressive disorder (DD) is considered the most common mental illness with an estimated share of 25–30%1. The recent approach of Cao et al.[32], involving the fusion of keystroke timing information, accelerometer data and special characters typed, yielded promising results in terms of prediction of depression scores from bipolar patients and healthy controls, yet without providing insights on interpretability. Their approach was based on a typing session level and the use of deep learning, involving each subject’s data, both in the training and evaluation phases, while requiring at least 400 valid typing for converging to accurate results.
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