Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study

Abstract

Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is under-explored. This report evaluated the usage pattern of two types of social media apps (communication and social networking) from February 1 through May 3, 2020, when the COVID-19 surge in Madrid, Spain resulted in the implementation of strict lockdown measures, and short term anxiety symptoms (GAD-7) of the users at clinical follow-up. Individual-level analysis of daily social media usage showed that the increase in communication app usage from pre-lockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group, F(1, 72)=3.84, p=0.05. A machine learning (ML)-based approach combining a hidden Markov model (HMM) and logistic regression was applied to differentiate clinical anxiety (N=44) from non-clinical anxiety group (N=51), based on the longitudinal time-series data of communication and social network app usage (in seconds) as well as anxiety-associated static variables, including the presence of an essential worker in the household, worries about life instability, and health status. The ML model achieved 62.30% mean accuracy and 0.70 area under the receiver operating curve in predicting clinical anxiety group from high social network and low communication app usage. Passive-sensing of a shift in category-based social media app usage during the lockdown can predictively model clinical behaviours moderating the severity of anxiety symptoms