Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data

Abstract

Abstract Depression is a leading cause of disability and is associated with suicide risk. However, a quarter of patients with major depression remain undiagnosed. Prior work has demonstrated that a smartphone user's depression level can be detected by analyzing data gathered from their smartphone's sensors or from their social media posts over a few weeks after enrollment in a user study. These studies typically utilize a prospective study design, which is burdensome as it requires participants smartphone data to be gathered for prolonged periods before their depression level can be assessed. In contrast, we present a feasibility study of our Mood Assessment Capable Framework (Moodable) that facilitates almost instantaneous mood assessment by analyzing instantaneous voice samples provided by the user as well as historical sensor data harvested (scraped) from their smartphone and recent social media posts. Our retrospective, low-burden approach means that Moodable no longer requires study participants to engage with their phone for weeks before a depression score can be inferred. Moodable has the potential to minimize user data collection burden, increase user compliance, avoid study awareness bias and offer a near instantaneous depression screening. To lay a solid foundation for Moodable, we first surveyed 202 volunteer participants about their willingness to share voice samples and various smartphone and social media data types for mental health assessment. Based on these findings, we then developed the Moodable app. Thereafter, we utilized Moodable to collect short voice samples, and a rich array of retrospectively harvested data from users' smartphones (location, browser history, call logs) and social media accounts (instagram, twitter and facebook), with appropriate permissions, of 335 volunteer participants who also responded to 9 depression related questions of the Patient Health Questionaire (PHQ-9). Moodable then used machine learning to build classification models and classify the user's depression and suicidal ideation, for users which scores where unknown to the models. Results of Moodable's screening capability are promising. In particular, for the depression classification task we achieved F1 scores (the harmonic mean of the precision and recall) of 0.766, sensitivity of 0.750, and specificity of 0.792. For the suicidal ideation task we achieved F1 scores of 0.848, sensitivity of 0.864, and specificity of 0.725. This work could significantly increase depression-screening at the population level and opens numerous avenues for further research into this newly proposed paradigm of instantaneously screening depression and suicide risk levels from voice samples and retrospective smartphone and social media data.