Predicting Device Usage Patterns in Patients with Problematic Smartphone Use Through Individualized Hidden Markov Models

Problematic smartphone use (PSU) is a social issue affecting daily lives without a definitive treatment. Current PSU treatments rely on self-reported data, which can be inaccurate and lead to ineffective treatments. To address this, we propose using a hidden Markov model to objectively analyze smartphone usage patterns from log data. The model is trained on data from various patients and then individualized, allowing for meaningful interpretation and comparison of usage states. We conduct two case studies on patients attending outpatient clinics for Internet addiction due to PSU. These case studies compare the model-estimated usage state of patients with 1) a daily activity log as reported by the patients and 2) a several-month clinical history of the patients as reported by their attending psychiatrists. Through each case study, we validate the appropriateness of the definitions assigned to each usage state and the effectiveness of the state estimations performed using the proposed method. Our approach demonstrated that it is possible to objectively understand and track changes in PSU, which self-reports alone cannot achieve.