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

Abstract

Background 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 underexplored. Objective We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.

Is Social Media a New Type of Social Support? Social Media Use in Spain during the COVID-19 Pandemic: A Mixed Methods Study

“…A pandemic report found that Spain was the top third European country for increases in social network activity during the beginning of the COVID-19 pandemic [ 7 ], and our data validates these increases. Early reports found these social media increases as being harmful to mental and physical health [ 6 , 31 , 32 , 34 ], although with recent data and our data suggesting there were several benefits to these increases such as social support, it is possible that without initial qualitative data to assess the reasons and implications of the social media use increases, the negative results originally found were confounded by experiences of stress, anxiety, depression, helplessness, and loneliness that were experienced by so many during the pandemic. Thus, while in general, overuse of social media can potentially be harmful and cause lower mental health, our data provides evidence that in a time of crisis and isolation, social media use can be leveraged for health benefits and needed social support.…”

“…Further, those who reported greater well-being also reported spending less time on social networking sites, and those who engaged in more use of these sites were less happy than those who did so modestly, and had lower general and social well-being [ 6 ]. Contrarily, a study of psychiatric outpatients in Spain found that those who used social media more frequently during the lockdown period engaged in more physical activity [ 33 ], and that those who reported higher anxiety symptoms were more active in social networking apps (e.g., Facebook, Instagram, Twitter) and less active in communication apps (e.g., WhatsApp, telegram) [ 34 ].…”

Short-circuiting biology: Digital phenotypes, digital biomarkers, and shifting gazes in psychiatry

“…And in the context of the ever-increasing digitization of healthcare and everyday life, the “behavioral” data seem almost limitless. For example, in psychiatry, the concept has been used to describe how home visits—measured using a visit detection system—may be a biomarker of geriatric depression (Schutz et al, 2021), how the frequency of social media app use among outpatients may be a biomarker of a generalized anxiety disorder (Ryu et al, 2021), and how skipping the use of a breathalyzer, which is connected to a smartphone by an app, may be a biomarker for assessing alcohol use disorder (Zetterström et al, 2019).…”

Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets

“…Fauziah et al [12] developed two machine language algorithms, the random forest and xgboost in order to detect the anxiety feeling during the pandemic, where the author used 4862 records from a dataset that was collected from YouTube comments. Moreover, [13] used the Machine Learning for detecting the patients' anxiety during the Covid-19 pandemic by using data from two different types of social media apps namely a communication app as well as a social networking app. On the other hand, [14] used Facebook's dataset in order to predict the spreading of new cases of Covid-19.…”